id sid tid token lemma pos cana-441 1 1 communications communication NOUN cana-441 1 2 on on ADP cana-441 1 3 applied apply VERB cana-441 1 4 nonlinear nonlinear ADJ cana-441 1 5 analysis analysis NOUN cana-441 1 6 issn issn NOUN cana-441 1 7 : : PUNCT cana-441 1 8 1074 1074 NUM cana-441 1 9 - - PUNCT cana-441 1 10 133x 133x NUM cana-441 1 11 vol vol NOUN cana-441 1 12 31 31 NUM cana-441 1 13 no no NOUN cana-441 1 14 . . NOUN cana-441 1 15 1 1 NUM cana-441 1 16 ( ( PUNCT cana-441 1 17 2024 2024 NUM cana-441 1 18 ) ) PUNCT cana-441 1 19 318 318 NUM cana-441 1 20 https://internationalpubls.com https://internationalpubls.com X cana-441 1 21 mathematical mathematical ADJ cana-441 1 22 modeling modeling NOUN cana-441 1 23 and and CCONJ cana-441 1 24 statistical statistical ADJ cana-441 1 25 exploration exploration NOUN cana-441 1 26 of of ADP cana-441 1 27 residual residual ADJ cana-441 1 28 computing computing NOUN cana-441 1 29 based base VERB cana-441 1 30 convolutional convolutional ADJ cana-441 1 31 neural neural ADJ cana-441 1 32 network network NOUN cana-441 1 33 based base VERB cana-441 1 34 classifier classifier NOUN cana-441 1 35 for for ADP cana-441 1 36 complex complex ADJ cana-441 1 37 image image NOUN cana-441 1 38 demarcation demarcation NOUN cana-441 1 39 bhawana bhawana PROPN cana-441 1 40 maurya maurya PROPN cana-441 1 41 1 1 NUM cana-441 1 42 , , PUNCT cana-441 1 43 dr dr PROPN cana-441 1 44 . . PROPN cana-441 1 45 saroj saroj PROPN cana-441 1 46 hiranwal hiranwal VERB cana-441 1 47 2 2 NUM cana-441 1 48 1government 1government NUM cana-441 1 49 mahila mahila PROPN cana-441 1 50 engineering engineering PROPN cana-441 1 51 college college PROPN cana-441 1 52 , , PUNCT cana-441 1 53 ajmer ajmer PROPN cana-441 1 54 , , PUNCT cana-441 1 55 india india PROPN cana-441 1 56 2rajasthan 2rajasthan PROPN cana-441 1 57 institute institute PROPN cana-441 1 58 of of ADP cana-441 1 59 engineering engineering PROPN cana-441 1 60 & & CCONJ cana-441 1 61 technology technology PROPN cana-441 1 62 , , PUNCT cana-441 1 63 jaipur jaipur PROPN cana-441 1 64 , , PUNCT cana-441 1 65 india india PROPN cana-441 1 66 mauryabm6@gmail.com1 mauryabm6@gmail.com1 PROPN cana-441 1 67 , , PUNCT cana-441 1 68 ersarojhiranwal@gmail.com2 ersarojhiranwal@gmail.com2 PROPN cana-441 1 69 article article NOUN cana-441 1 70 history history NOUN cana-441 1 71 : : PUNCT cana-441 1 72 received receive VERB cana-441 1 73 : : PUNCT cana-441 1 74 02 02 NUM cana-441 1 75 - - SYM cana-441 1 76 11 11 NUM cana-441 1 77 - - SYM cana-441 1 78 2023 2023 NUM cana-441 1 79 revised revise VERB cana-441 1 80 : : PUNCT cana-441 1 81 18 18 NUM cana-441 1 82 - - SYM cana-441 1 83 12 12 NUM cana-441 1 84 - - PUNCT cana-441 1 85 2023 2023 NUM cana-441 1 86 accepted accept VERB cana-441 1 87 : : PUNCT cana-441 1 88 30 30 NUM cana-441 1 89 - - SYM cana-441 1 90 12 12 NUM cana-441 1 91 - - PUNCT cana-441 1 92 2023 2023 NUM cana-441 1 93 abstract abstract NOUN cana-441 1 94 : : PUNCT cana-441 1 95 the the DET cana-441 1 96 advent advent NOUN cana-441 1 97 of of ADP cana-441 1 98 deep deep ADJ cana-441 1 99 learning learning NOUN cana-441 1 100 has have AUX cana-441 1 101 revolutionized revolutionize VERB cana-441 1 102 the the DET cana-441 1 103 field field NOUN cana-441 1 104 of of ADP cana-441 1 105 medical medical ADJ cana-441 1 106 image image NOUN cana-441 1 107 analysis analysis NOUN cana-441 1 108 , , PUNCT cana-441 1 109 particularly particularly ADV cana-441 1 110 in in ADP cana-441 1 111 complex complex ADJ cana-441 1 112 tasks task NOUN cana-441 1 113 like like ADP cana-441 1 114 brain brain NOUN cana-441 1 115 image image NOUN cana-441 1 116 segmentation segmentation NOUN cana-441 1 117 and and CCONJ cana-441 1 118 classification classification NOUN cana-441 1 119 . . PUNCT cana-441 2 1 among among ADP cana-441 2 2 the the DET cana-441 2 3 various various ADJ cana-441 2 4 deep deep ADJ cana-441 2 5 learning learning NOUN cana-441 2 6 architectures architecture NOUN cana-441 2 7 , , PUNCT cana-441 2 8 convolutional convolutional ADJ cana-441 2 9 neural neural ADJ cana-441 2 10 networks network NOUN cana-441 2 11 ( ( PUNCT cana-441 2 12 cnns cnns PROPN cana-441 2 13 ) ) PUNCT cana-441 2 14 have have AUX cana-441 2 15 shown show VERB cana-441 2 16 remarkable remarkable ADJ cana-441 2 17 ability ability NOUN cana-441 2 18 in in ADP cana-441 2 19 extracting extract VERB cana-441 2 20 hierarchical hierarchical ADJ cana-441 2 21 features feature NOUN cana-441 2 22 from from ADP cana-441 2 23 medical medical ADJ cana-441 2 24 images image NOUN cana-441 2 25 . . PUNCT cana-441 3 1 the the DET cana-441 3 2 mathematical mathematical ADJ cana-441 3 3 framework framework NOUN cana-441 3 4 of of ADP cana-441 3 5 our our PRON cana-441 3 6 proposed propose VERB cana-441 3 7 res re NOUN cana-441 3 8 - - PUNCT cana-441 3 9 cnn cnn PROPN cana-441 3 10 is be AUX cana-441 3 11 grounded ground VERB cana-441 3 12 in in ADP cana-441 3 13 the the DET cana-441 3 14 principles principle NOUN cana-441 3 15 of of ADP cana-441 3 16 functional functional ADJ cana-441 3 17 analysis analysis NOUN cana-441 3 18 and and CCONJ cana-441 3 19 optimization optimization NOUN cana-441 3 20 theory theory NOUN cana-441 3 21 . . PUNCT cana-441 4 1 we we PRON cana-441 4 2 employ employ VERB cana-441 4 3 the the DET cana-441 4 4 calculus calculus NOUN cana-441 4 5 of of ADP cana-441 4 6 variations variation NOUN cana-441 4 7 to to PART cana-441 4 8 model model VERB cana-441 4 9 the the DET cana-441 4 10 propagation propagation NOUN cana-441 4 11 of of ADP cana-441 4 12 activations activation NOUN cana-441 4 13 within within ADP cana-441 4 14 the the DET cana-441 4 15 network network NOUN cana-441 4 16 , , PUNCT cana-441 4 17 optimizing optimize VERB cana-441 4 18 a a DET cana-441 4 19 cost cost NOUN cana-441 4 20 function function NOUN cana-441 4 21 tailored tailor VERB cana-441 4 22 for for ADP cana-441 4 23 the the DET cana-441 4 24 segmentation segmentation NOUN cana-441 4 25 and and CCONJ cana-441 4 26 classification classification NOUN cana-441 4 27 of of ADP cana-441 4 28 brain brain NOUN cana-441 4 29 images image NOUN cana-441 4 30 . . PUNCT cana-441 5 1 the the DET cana-441 5 2 structural structural ADJ cana-441 5 3 integrity integrity NOUN cana-441 5 4 of of ADP cana-441 5 5 the the DET cana-441 5 6 network network NOUN cana-441 5 7 is be AUX cana-441 5 8 encoded encode VERB cana-441 5 9 through through ADP cana-441 5 10 well well ADV cana-441 5 11 - - PUNCT cana-441 5 12 defined define VERB cana-441 5 13 mathematical mathematical ADJ cana-441 5 14 formulations formulation NOUN cana-441 5 15 , , PUNCT cana-441 5 16 capturing capture VERB cana-441 5 17 the the DET cana-441 5 18 essence essence NOUN cana-441 5 19 of of ADP cana-441 5 20 residual residual ADJ cana-441 5 21 blocks block NOUN cana-441 5 22 that that PRON cana-441 5 23 allow allow VERB cana-441 5 24 the the DET cana-441 5 25 seamless seamless ADJ cana-441 5 26 flow flow NOUN cana-441 5 27 of of ADP cana-441 5 28 gradients gradient NOUN cana-441 5 29 during during ADP cana-441 5 30 back back ADV cana-441 5 31 propagation.from propagation.from ADP cana-441 5 32 a a DET cana-441 5 33 statistical statistical ADJ cana-441 5 34 perspective perspective NOUN cana-441 5 35 , , PUNCT cana-441 5 36 the the DET cana-441 5 37 robustness robustness NOUN cana-441 5 38 of of ADP cana-441 5 39 the the DET cana-441 5 40 resnet resnet NOUN cana-441 5 41 model model NOUN cana-441 5 42 is be AUX cana-441 5 43 validated validate VERB cana-441 5 44 through through ADP cana-441 5 45 rigorous rigorous ADJ cana-441 5 46 exploratory exploratory ADJ cana-441 5 47 data datum NOUN cana-441 5 48 analysis analysis NOUN cana-441 5 49 . . PUNCT cana-441 6 1 we we PRON cana-441 6 2 systematically systematically ADV cana-441 6 3 dissect dissect VERB cana-441 6 4 the the DET cana-441 6 5 statistical statistical ADJ cana-441 6 6 properties property NOUN cana-441 6 7 of of ADP cana-441 6 8 the the DET cana-441 6 9 network network NOUN cana-441 6 10 's 's PART cana-441 6 11 predictions prediction NOUN cana-441 6 12 , , PUNCT cana-441 6 13 employing employ VERB cana-441 6 14 methods method NOUN cana-441 6 15 such such ADJ cana-441 6 16 as as ADP cana-441 6 17 hypothesis hypothesis NOUN cana-441 6 18 testing testing NOUN cana-441 6 19 to to PART cana-441 6 20 ascertain ascertain VERB cana-441 6 21 the the DET cana-441 6 22 significance significance NOUN cana-441 6 23 of of ADP cana-441 6 24 the the DET cana-441 6 25 improvements improvement NOUN cana-441 6 26 offered offer VERB cana-441 6 27 by by ADP cana-441 6 28 residual residual ADJ cana-441 6 29 computing computing NOUN cana-441 6 30 . . PUNCT cana-441 7 1 furthermore furthermore ADV cana-441 7 2 , , PUNCT cana-441 7 3 bayesian bayesian NOUN cana-441 7 4 inference inference NOUN cana-441 7 5 is be AUX cana-441 7 6 utilized utilize VERB cana-441 7 7 to to PART cana-441 7 8 gauge gauge VERB cana-441 7 9 the the DET cana-441 7 10 uncertainty uncertainty NOUN cana-441 7 11 in in ADP cana-441 7 12 the the DET cana-441 7 13 network network NOUN cana-441 7 14 's 's PART cana-441 7 15 parameters parameter NOUN cana-441 7 16 , , PUNCT cana-441 7 17 providing provide VERB cana-441 7 18 a a DET cana-441 7 19 probabilistic probabilistic ADJ cana-441 7 20 interpretation interpretation NOUN cana-441 7 21 of of ADP cana-441 7 22 the the DET cana-441 7 23 segmentation segmentation NOUN cana-441 7 24 results result VERB cana-441 7 25 . . PUNCT cana-441 8 1 the the DET cana-441 8 2 efficacy efficacy NOUN cana-441 8 3 of of ADP cana-441 8 4 our our PRON cana-441 8 5 model model NOUN cana-441 8 6 is be AUX cana-441 8 7 further far ADV cana-441 8 8 quantified quantify VERB cana-441 8 9 through through ADP cana-441 8 10 comprehensive comprehensive ADJ cana-441 8 11 experiments experiment NOUN cana-441 8 12 on on ADP cana-441 8 13 diverse diverse ADJ cana-441 8 14 datasets dataset NOUN cana-441 8 15 of of ADP cana-441 8 16 brain brain NOUN cana-441 8 17 images image NOUN cana-441 8 18 . . PUNCT cana-441 9 1 statistical statistical ADJ cana-441 9 2 metrics metric NOUN cana-441 9 3 such such ADJ cana-441 9 4 as as ADP cana-441 9 5 accuracy accuracy NOUN cana-441 9 6 , , PUNCT cana-441 9 7 sensitivity sensitivity NOUN cana-441 9 8 , , PUNCT cana-441 9 9 specificity specificity NOUN cana-441 9 10 , , PUNCT cana-441 9 11 and and CCONJ cana-441 9 12 area area NOUN cana-441 9 13 under under ADP cana-441 9 14 the the DET cana-441 9 15 roc roc PROPN cana-441 9 16 curve curve NOUN cana-441 9 17 ( ( PUNCT cana-441 9 18 auc auc NOUN cana-441 9 19 ) ) PUNCT cana-441 9 20 serve serve VERB cana-441 9 21 as as ADP cana-441 9 22 the the DET cana-441 9 23 benchmarks benchmark NOUN cana-441 9 24 for for ADP cana-441 9 25 performance performance NOUN cana-441 9 26 evaluation evaluation NOUN cana-441 9 27 , , PUNCT cana-441 9 28 solidifying solidify VERB cana-441 9 29 the the DET cana-441 9 30 merit merit NOUN cana-441 9 31 of of ADP cana-441 9 32 our our PRON cana-441 9 33 mathematical mathematical ADJ cana-441 9 34 and and CCONJ cana-441 9 35 statistical statistical ADJ cana-441 9 36 approach approach NOUN cana-441 9 37 in in ADP cana-441 9 38 the the DET cana-441 9 39 domain domain NOUN cana-441 9 40 of of ADP cana-441 9 41 medical medical ADJ cana-441 9 42 image image NOUN cana-441 9 43 analysis analysis NOUN cana-441 9 44 . . PUNCT cana-441 10 1 the the DET cana-441 10 2 resnet50 resnet50 NOUN cana-441 10 3 demonstrates demonstrate VERB cana-441 10 4 superior superior ADJ cana-441 10 5 performance performance NOUN cana-441 10 6 in in ADP cana-441 10 7 segmenting segment VERB cana-441 10 8 and and CCONJ cana-441 10 9 classifying classify VERB cana-441 10 10 interact interact ADJ cana-441 10 11 patterns pattern NOUN cana-441 10 12 within within ADP cana-441 10 13 brain brain NOUN cana-441 10 14 images image NOUN cana-441 10 15 , , PUNCT cana-441 10 16 paving pave VERB cana-441 10 17 the the DET cana-441 10 18 way way NOUN cana-441 10 19 for for ADP cana-441 10 20 significant significant ADJ cana-441 10 21 advancements advancement NOUN cana-441 10 22 in in ADP cana-441 10 23 diagnostic diagnostic ADJ cana-441 10 24 methodologies methodology NOUN cana-441 10 25 . . PUNCT cana-441 11 1 keywords keyword NOUN cana-441 11 2 : : PUNCT cana-441 11 3 convolutional convolutional ADJ cana-441 11 4 neural neural ADJ cana-441 11 5 networks network NOUN cana-441 11 6 , , PUNCT cana-441 11 7 residual residual ADJ cana-441 11 8 computing computing NOUN cana-441 11 9 , , PUNCT cana-441 11 10 brain brain NOUN cana-441 11 11 image image NOUN cana-441 11 12 segmentation segmentation NOUN cana-441 11 13 , , PUNCT cana-441 11 14 mathematical mathematical ADJ cana-441 11 15 modeling modeling NOUN cana-441 11 16 , , PUNCT cana-441 11 17 statistical statistical ADJ cana-441 11 18 analysis analysis NOUN cana-441 11 19 , , PUNCT cana-441 11 20 deep deep ADJ cana-441 11 21 learning learning NOUN cana-441 11 22 . . PUNCT cana-441 12 1 1 1 X cana-441 12 2 . . X cana-441 12 3 introduction introduction NOUN cana-441 12 4 brain brain NOUN cana-441 12 5 tumor tumor NOUN cana-441 12 6 segmentation segmentation NOUN cana-441 12 7 and and CCONJ cana-441 12 8 classification classification NOUN cana-441 12 9 using use VERB cana-441 12 10 deep deep ADJ cana-441 12 11 residual residual ADJ cana-441 12 12 cnns cnn NOUN cana-441 12 13 involves involve VERB cana-441 12 14 leveraging leverage VERB cana-441 12 15 advanced advanced ADJ cana-441 12 16 neural neural ADJ cana-441 12 17 network network NOUN cana-441 12 18 architectures architecture NOUN cana-441 12 19 to to PART cana-441 12 20 accurately accurately ADV cana-441 12 21 delineate delineate VERB cana-441 12 22 tumor tumor NOUN cana-441 12 23 regions region NOUN cana-441 12 24 from from ADP cana-441 12 25 medical medical ADJ cana-441 12 26 imaging imaging NOUN cana-441 12 27 data datum NOUN cana-441 12 28 and and CCONJ cana-441 12 29 classify classify VERB cana-441 12 30 tumor tumor NOUN cana-441 12 31 types type NOUN cana-441 12 32 . . PUNCT cana-441 13 1 the the DET cana-441 13 2 convolution convolution NOUN cana-441 13 3 operation operation NOUN cana-441 13 4 , , PUNCT cana-441 13 5 represented represent VERB cana-441 13 6 by by ADP cana-441 13 7 the the DET cana-441 13 8 equation equation NOUN cana-441 13 9 ( ( PUNCT cana-441 13 10 𝑓 𝑓 DET cana-441 13 11 βˆ— βˆ— NOUN cana-441 13 12 𝑔)(𝑑 𝑔)(𝑑 PUNCT cana-441 13 13 ) ) PUNCT cana-441 13 14 = = SYM cana-441 13 15 ∫ ∫ PROPN cana-441 13 16 βˆ’π‘₯ βˆ’π‘₯ PUNCT cana-441 13 17 ∞ ∞ PROPN cana-441 13 18 β€Š β€Š SPACE cana-441 13 19 𝑓(𝜏)𝑔(𝑑 𝑓(𝜏)𝑔(𝑑 PROPN cana-441 13 20 βˆ’ βˆ’ PROPN cana-441 13 21 𝜏)π‘‘πœ 𝜏)π‘‘πœ PROPN cana-441 13 22 , , PUNCT cana-441 13 23 lies lie VERB cana-441 13 24 at at ADP cana-441 13 25 the the DET cana-441 13 26 heart heart NOUN cana-441 13 27 of of ADP cana-441 13 28 these these DET cana-441 13 29 networks network NOUN cana-441 13 30 , , PUNCT cana-441 13 31 enabling enable VERB cana-441 13 32 them they PRON cana-441 13 33 to to PART cana-441 13 34 capture capture VERB cana-441 13 35 spatial spatial ADJ cana-441 13 36 patterns pattern NOUN cana-441 13 37 in in ADP cana-441 13 38 the the DET cana-441 13 39 input input NOUN cana-441 13 40 images image NOUN cana-441 13 41 through through ADP cana-441 13 42 local local ADJ cana-441 13 43 operations operation NOUN cana-441 13 44 . . PUNCT cana-441 14 1 this this DET cana-441 14 2 operation operation NOUN cana-441 14 3 is be AUX cana-441 14 4 fundamental fundamental ADJ cana-441 14 5 in in ADP cana-441 14 6 processing process VERB cana-441 14 7 the the DET cana-441 14 8 raw raw ADJ cana-441 14 9 imaging imaging NOUN cana-441 14 10 data datum NOUN cana-441 14 11 to to PART cana-441 14 12 extract extract VERB cana-441 14 13 relevant relevant ADJ cana-441 14 14 features feature NOUN cana-441 14 15 indicative indicative ADJ cana-441 14 16 of of ADP cana-441 14 17 tumor tumor NOUN cana-441 14 18 presence presence NOUN cana-441 14 19 . . PUNCT cana-441 15 1 in in ADP cana-441 15 2 deep deep ADJ cana-441 15 3 residual residual ADJ cana-441 15 4 cnnn cnnn NOUN cana-441 15 5 , , PUNCT cana-441 15 6 the the DET cana-441 15 7 output output NOUN cana-441 15 8 of of ADP cana-441 15 9 a a DET cana-441 15 10 convolutional convolutional ADJ cana-441 15 11 layer layer NOUN cana-441 15 12 𝑍[1 𝑍[1 NOUN cana-441 15 13 ] ] PUNCT cana-441 15 14 is be AUX cana-441 15 15 computed compute VERB cana-441 15 16 using use VERB cana-441 15 17 the the DET cana-441 15 18 equation equation NOUN cana-441 15 19 𝑍[𝑙 𝑍[𝑙 NOUN cana-441 15 20 ] ] X cana-441 16 1 = = SYM cana-441 16 2 communications communication NOUN cana-441 16 3 on on ADP cana-441 16 4 applied apply VERB cana-441 16 5 nonlinear nonlinear ADJ cana-441 16 6 analysis analysis NOUN cana-441 16 7 issn issn NOUN cana-441 16 8 : : PUNCT cana-441 16 9 1074 1074 NUM cana-441 16 10 - - PUNCT cana-441 16 11 133x 133x NUM cana-441 16 12 vol vol NOUN cana-441 16 13 31 31 NUM cana-441 16 14 no no NOUN cana-441 16 15 . . NOUN cana-441 16 16 1 1 NUM cana-441 16 17 ( ( PUNCT cana-441 16 18 2024 2024 NUM cana-441 16 19 ) ) PUNCT cana-441 16 20 319 319 NUM cana-441 16 21 https://internationalpubls.com https://internationalpubls.com X cana-441 17 1 π‘Š[𝑙 π‘Š[𝑙 VERB cana-441 17 2 ] ] X cana-441 17 3 βˆ— βˆ— NOUN cana-441 17 4 𝐴[π‘™βˆ’1∣ 𝐴[π‘™βˆ’1∣ PROPN cana-441 18 1 + + CCONJ cana-441 18 2 𝑏𝑙 𝑏𝑙 PROPN cana-441 18 3 , , PUNCT cana-441 18 4 where where SCONJ cana-441 18 5 π‘Š[𝑙 π‘Š[𝑙 VERB cana-441 18 6 ] ] PUNCT cana-441 18 7 represents represent VERB cana-441 18 8 the the DET cana-441 18 9 weights weight NOUN cana-441 18 10 , , PUNCT cana-441 18 11 𝐴[π‘™βˆ’1 𝐴[π‘™βˆ’1 X cana-441 18 12 ] ] PUNCT cana-441 18 13 is be AUX cana-441 18 14 the the DET cana-441 18 15 input input NOUN cana-441 18 16 from from ADP cana-441 18 17 the the DET cana-441 18 18 previous previous ADJ cana-441 18 19 layer layer NOUN cana-441 18 20 , , PUNCT cana-441 18 21 and and CCONJ cana-441 18 22 𝑏𝑙 𝑏𝑙 PROPN cana-441 18 23 is be AUX cana-441 18 24 the the DET cana-441 18 25 bias bias NOUN cana-441 18 26 term term NOUN cana-441 18 27 . . PUNCT cana-441 19 1 this this DET cana-441 19 2 equation equation NOUN cana-441 19 3 captures capture VERB cana-441 19 4 the the DET cana-441 19 5 transformation transformation NOUN cana-441 19 6 of of ADP cana-441 19 7 features feature NOUN cana-441 19 8 through through ADP cana-441 19 9 the the DET cana-441 19 10 convolutional convolutional ADJ cana-441 19 11 operation operation NOUN cana-441 19 12 , , PUNCT cana-441 19 13 followed follow VERB cana-441 19 14 by by ADP cana-441 19 15 the the DET cana-441 19 16 addition addition NOUN cana-441 19 17 of of ADP cana-441 19 18 bias bias NOUN cana-441 19 19 to to PART cana-441 19 20 introduce introduce VERB cana-441 19 21 non non ADJ cana-441 19 22 - - NOUN cana-441 19 23 linearity linearity NOUN cana-441 19 24 into into ADP cana-441 19 25 the the DET cana-441 19 26 network network NOUN cana-441 19 27 . . PUNCT cana-441 20 1 ( ( PUNCT cana-441 20 2 𝑓 𝑓 DET cana-441 20 3 βˆ— βˆ— NOUN cana-441 20 4 𝑔)(𝑑 𝑔)(𝑑 PUNCT cana-441 20 5 ) ) PUNCT cana-441 20 6 = = SYM cana-441 21 1 ∫ ∫ PROPN cana-441 21 2 𝑓(𝜏)𝑔(𝑑 𝑓(𝜏)𝑔(𝑑 PROPN cana-441 21 3 βˆ’ βˆ’ PROPN cana-441 21 4 𝜏)π‘‘πœ 𝜏)π‘‘πœ PROPN cana-441 21 5 ∞ ∞ PROPN cana-441 21 6 βˆ’βˆž βˆ’βˆž X cana-441 21 7 ( ( PUNCT cana-441 21 8 1 1 NUM cana-441 21 9 ) ) PUNCT cana-441 21 10 𝑍[𝑙 𝑍[𝑙 NOUN cana-441 21 11 ] ] X cana-441 21 12 = = PUNCT cana-441 21 13 π‘Š[𝑙 π‘Š[𝑙 VERB cana-441 21 14 ] ] X cana-441 21 15 βˆ— βˆ— X cana-441 21 16 𝐴[π‘™βˆ’1 𝐴[π‘™βˆ’1 X cana-441 21 17 ] ] X cana-441 22 1 + + X cana-441 22 2 𝑏[𝑙 𝑏[𝑙 NOUN cana-441 22 3 ] ] PUNCT cana-441 22 4 ( ( PUNCT cana-441 22 5 2 2 X cana-441 22 6 ) ) PUNCT cana-441 22 7 𝐴 𝐴 NOUN cana-441 22 8 = = PUNCT cana-441 22 9 max(0 max(0 NOUN cana-441 22 10 , , PUNCT cana-441 22 11 𝑍 𝑍 PROPN cana-441 22 12 ) ) PUNCT cana-441 22 13 ( ( PUNCT cana-441 22 14 3 3 X cana-441 22 15 ) ) PUNCT cana-441 22 16 𝐴𝑖,𝑗 𝐴𝑖,𝑗 NOUN cana-441 22 17 = = SYM cana-441 22 18 max max PROPN cana-441 22 19 π‘š,𝑛 π‘š,𝑛 PROPN cana-441 22 20 ( ( PUNCT cana-441 22 21 𝐴(π‘š,𝑛 𝐴(π‘š,𝑛 PROPN cana-441 22 22 ) ) PUNCT cana-441 22 23 ) ) PUNCT cana-441 23 1 ( ( PUNCT cana-441 23 2 4 4 X cana-441 23 3 ) ) PUNCT cana-441 23 4 𝑍 𝑍 NOUN cana-441 23 5 = = SYM cana-441 23 6 π‘Š π‘Š NOUN cana-441 23 7 𝐴 𝐴 NOUN cana-441 23 8 + + CCONJ cana-441 23 9 𝑏 𝑏 PROPN cana-441 23 10 ( ( PUNCT cana-441 23 11 5 5 NUM cana-441 23 12 ) ) PUNCT cana-441 23 13 the the DET cana-441 23 14 relu relu NOUN cana-441 23 15 activation activation NOUN cana-441 23 16 function function NOUN cana-441 23 17 , , PUNCT cana-441 23 18 defined define VERB cana-441 23 19 as as ADP cana-441 23 20 𝐴 𝐴 PROPN cana-441 23 21 = = PUNCT cana-441 23 22 max(0 max(0 NOUN cana-441 23 23 , , PUNCT cana-441 23 24 𝑍 𝑍 PROPN cana-441 23 25 ) ) PUNCT cana-441 23 26 , , PUNCT cana-441 23 27 is be AUX cana-441 23 28 applied apply VERB cana-441 23 29 element element NOUN cana-441 23 30 wise wise ADJ cana-441 23 31 to to ADP cana-441 23 32 the the DET cana-441 23 33 output output NOUN cana-441 23 34 of of ADP cana-441 23 35 convolutional convolutional ADJ cana-441 23 36 layers layer NOUN cana-441 23 37 . . PUNCT cana-441 24 1 relu relu NOUN cana-441 24 2 introduces introduce VERB cana-441 24 3 non non ADJ cana-441 24 4 - - NOUN cana-441 24 5 linearity linearity NOUN cana-441 24 6 by by ADP cana-441 24 7 outputting output VERB cana-441 24 8 the the DET cana-441 24 9 input input NOUN cana-441 24 10 if if SCONJ cana-441 24 11 positive positive ADJ cana-441 24 12 and and CCONJ cana-441 24 13 zero zero NUM cana-441 24 14 otherwise otherwise ADV cana-441 24 15 , , PUNCT cana-441 24 16 enabling enable VERB cana-441 24 17 the the DET cana-441 24 18 network network NOUN cana-441 24 19 to to PART cana-441 24 20 learn learn VERB cana-441 24 21 complex complex ADJ cana-441 24 22 mappings mapping NOUN cana-441 24 23 between between ADP cana-441 24 24 input input NOUN cana-441 24 25 and and CCONJ cana-441 24 26 output output NOUN cana-441 24 27 spaces space NOUN cana-441 24 28 . . PUNCT cana-441 25 1 this this PRON cana-441 25 2 non non ADJ cana-441 25 3 - - ADJ cana-441 25 4 linearity linearity NOUN cana-441 25 5 is be AUX cana-441 25 6 crucial crucial ADJ cana-441 25 7 for for ADP cana-441 25 8 capturing capture VERB cana-441 25 9 the the DET cana-441 25 10 interact interact ADJ cana-441 25 11 relationship relationship NOUN cana-441 25 12 between between ADP cana-441 25 13 image image NOUN cana-441 25 14 features feature NOUN cana-441 25 15 and and CCONJ cana-441 25 16 tumor tumor NOUN cana-441 25 17 presence presence NOUN cana-441 25 18 . . PUNCT cana-441 26 1 pooling pool VERB cana-441 26 2 layers layer NOUN cana-441 26 3 , , PUNCT cana-441 26 4 as as SCONJ cana-441 26 5 represented represent VERB cana-441 26 6 by by ADP cana-441 26 7 the the DET cana-441 26 8 equation equation NOUN cana-441 26 9 𝐴𝑖,𝑗 𝐴𝑖,𝑗 PROPN cana-441 26 10 = = PROPN cana-441 26 11 max max PROPN cana-441 26 12 π‘š,𝑛(𝐴(π‘š,π‘Žπ‘— π‘š,𝑛(𝐴(π‘š,π‘Žπ‘— NOUN cana-441 26 13 ) ) PUNCT cana-441 26 14 ) ) PUNCT cana-441 26 15 , , PUNCT cana-441 26 16 reduce reduce VERB cana-441 26 17 the the DET cana-441 26 18 spatial spatial ADJ cana-441 26 19 dimensions dimension NOUN cana-441 26 20 of of ADP cana-441 26 21 feature feature NOUN cana-441 26 22 maps map NOUN cana-441 26 23 while while SCONJ cana-441 26 24 retaining retain VERB cana-441 26 25 essential essential ADJ cana-441 26 26 information information NOUN cana-441 26 27 . . PUNCT cana-441 27 1 by by ADP cana-441 27 2 selecting select VERB cana-441 27 3 the the DET cana-441 27 4 maximum maximum ADJ cana-441 27 5 value value NOUN cana-441 27 6 from from ADP cana-441 27 7 each each DET cana-441 27 8 patch patch NOUN cana-441 27 9 of of ADP cana-441 27 10 the the DET cana-441 27 11 feature feature NOUN cana-441 27 12 map map NOUN cana-441 27 13 , , PUNCT cana-441 27 14 pooling pool VERB cana-441 27 15 layers layer NOUN cana-441 27 16 down down ADP cana-441 27 17 sample sample NOUN cana-441 27 18 the the DET cana-441 27 19 dots dot NOUN cana-441 27 20 , , PUNCT cana-441 27 21 focusing focus VERB cana-441 27 22 on on ADP cana-441 27 23 the the DET cana-441 27 24 most most ADV cana-441 27 25 salient salient NOUN cana-441 27 26 features feature NOUN cana-441 27 27 while while SCONJ cana-441 27 28 discarding discard VERB cana-441 27 29 irrelevant irrelevant ADJ cana-441 27 30 details detail NOUN cana-441 27 31 . . PUNCT cana-441 28 1 this this DET cana-441 28 2 down down ADV cana-441 28 3 sampling sample VERB cana-441 28 4 aids aid NOUN cana-441 28 5 in in ADP cana-441 28 6 reducing reduce VERB cana-441 28 7 computational computational ADJ cana-441 28 8 complexity complexity NOUN cana-441 28 9 and and CCONJ cana-441 28 10 extracting extract VERB cana-441 28 11 robust robust ADJ cana-441 28 12 feature feature NOUN cana-441 28 13 for for ADP cana-441 28 14 subsequent subsequent ADJ cana-441 28 15 processing processing NOUN cana-441 28 16 . . PUNCT cana-441 29 1 𝐿(𝑦 𝐿(𝑦 NOUN cana-441 29 2 , , PUNCT cana-441 29 3 οΏ½ οΏ½ NOUN cana-441 29 4 Μ‚ Μ‚ NOUN cana-441 29 5 οΏ½ οΏ½ NOUN cana-441 29 6 ) ) PUNCT cana-441 29 7 = = PUNCT cana-441 30 1 βˆ’ βˆ’ PROPN cana-441 30 2 1 1 NUM cana-441 30 3 π‘š π‘š PROPN cana-441 30 4 βˆ‘ βˆ‘ PROPN cana-441 30 5 𝑦𝑖 𝑦𝑖 X cana-441 30 6 π‘š π‘š X cana-441 30 7 𝑖=1 𝑖=1 PROPN cana-441 30 8 log(𝑦 log(𝑦 PROPN cana-441 30 9 οΏ½ οΏ½ PROPN cana-441 30 10 Μ‚ Μ‚ NOUN cana-441 30 11 οΏ½ οΏ½ PROPN cana-441 30 12 ) ) PUNCT cana-441 30 13 + + CCONJ cana-441 30 14 ( ( PUNCT cana-441 30 15 1 1 NUM cana-441 30 16 βˆ’ βˆ’ NOUN cana-441 30 17 𝑦𝑖 𝑦𝑖 PROPN cana-441 30 18 ) ) PUNCT cana-441 30 19 log(1 log(1 NOUN cana-441 31 1 βˆ’ βˆ’ PROPN cana-441 31 2 𝑦 𝑦 SYM cana-441 31 3 οΏ½ οΏ½ NOUN cana-441 31 4 Μ‚ Μ‚ SYM cana-441 31 5 οΏ½ οΏ½ NOUN cana-441 31 6 ) ) PUNCT cana-441 31 7 ( ( PUNCT cana-441 31 8 6 6 NUM cana-441 31 9 ) ) PUNCT cana-441 31 10 πœƒ πœƒ NOUN cana-441 32 1 = = PUNCT cana-441 32 2 πœƒ πœƒ NUM cana-441 32 3 βˆ’ βˆ’ NOUN cana-441 32 4 𝛼 𝛼 NOUN cana-441 32 5 πœ•π½ πœ•π½ NOUN cana-441 32 6 πœ•πœƒ πœ•πœƒ NOUN cana-441 32 7 ( ( PUNCT cana-441 32 8 7 7 NUM cana-441 32 9 ) ) PUNCT cana-441 32 10 π‘₯ π‘₯ NOUN cana-441 32 11 = = PUNCT cana-441 32 12 π‘₯βˆ’πœ‡ π‘₯βˆ’πœ‡ NOUN cana-441 32 13 √𝜎2+πœ– √𝜎2+πœ– X cana-441 32 14 ( ( PUNCT cana-441 32 15 8) 8) NUM cana-441 32 16 output output NOUN cana-441 32 17 = = SYM cana-441 32 18 𝐹(π‘₯ 𝐹(π‘₯ X cana-441 32 19 , , PUNCT cana-441 32 20 { { PUNCT cana-441 32 21 π‘Šπ‘– π‘Šπ‘– PROPN cana-441 32 22 } } PUNCT cana-441 32 23 ) ) PUNCT cana-441 33 1 + + CCONJ cana-441 33 2 π‘₯ π‘₯ X cana-441 33 3 ( ( PUNCT cana-441 33 4 9 9 NUM cana-441 33 5 ) ) PUNCT cana-441 33 6 𝐹(π‘₯ 𝐹(π‘₯ VERB cana-441 33 7 , , PUNCT cana-441 33 8 { { PUNCT cana-441 33 9 π‘Šπ‘– π‘Šπ‘– PROPN cana-441 33 10 } } PUNCT cana-441 33 11 ) ) PUNCT cana-441 33 12 = = SYM cana-441 33 13 π‘Š2𝜎(π‘Š1π‘₯ π‘Š2𝜎(π‘Š1π‘₯ PROPN cana-441 33 14 ) ) PUNCT cana-441 33 15 ( ( PUNCT cana-441 33 16 10 10 NUM cana-441 33 17 ) ) PUNCT cana-441 33 18 fully fully ADV cana-441 33 19 connected connect VERB cana-441 33 20 layer layer NOUN cana-441 33 21 integrates integrate VERB cana-441 33 22 the the DET cana-441 33 23 extracted extract VERB cana-441 33 24 features feature NOUN cana-441 33 25 for for ADP cana-441 33 26 tumor tumor NOUN cana-441 33 27 classification classification NOUN cana-441 33 28 task task NOUN cana-441 33 29 , , PUNCT cana-441 33 30 a a DET cana-441 33 31 denoted denote VERB cana-441 33 32 by by ADP cana-441 33 33 the the DET cana-441 33 34 equation equation NOUN cana-441 33 35 𝑍 𝑍 PROPN cana-441 33 36 = = SYM cana-441 33 37 π‘Šπ΄ π‘Šπ΄ PROPN cana-441 33 38 + + CCONJ cana-441 33 39 𝑏 𝑏 NOUN cana-441 33 40 in in ADP cana-441 33 41 these these DET cana-441 33 42 layers layer NOUN cana-441 33 43 , , PUNCT cana-441 33 44 the the DET cana-441 33 45 dot dot NOUN cana-441 33 46 product product NOUN cana-441 33 47 of of ADP cana-441 33 48 the the DET cana-441 33 49 input input NOUN cana-441 33 50 and and CCONJ cana-441 33 51 weight weight NOUN cana-441 33 52 matrices matrix NOUN cana-441 33 53 is be AUX cana-441 33 54 computed compute VERB cana-441 33 55 , , PUNCT cana-441 33 56 followed follow VERB cana-441 33 57 by by ADP cana-441 33 58 the the DET cana-441 33 59 addition addition NOUN cana-441 33 60 of of ADP cana-441 33 61 a a DET cana-441 33 62 bias bias NOUN cana-441 33 63 term term NOUN cana-441 33 64 . . PUNCT cana-441 34 1 this this DET cana-441 34 2 operation operation NOUN cana-441 34 3 enables enable VERB cana-441 34 4 the the DET cana-441 34 5 network network NOUN cana-441 34 6 to to PART cana-441 34 7 learn learn VERB cana-441 34 8 complex complex ADJ cana-441 34 9 decision decision NOUN cana-441 34 10 boundaries boundary NOUN cana-441 34 11 and and CCONJ cana-441 34 12 classify classify VERB cana-441 34 13 tumors tumor NOUN cana-441 34 14 into into ADP cana-441 34 15 different different ADJ cana-441 34 16 categories category NOUN cana-441 34 17 based base VERB cana-441 34 18 on on ADP cana-441 34 19 the the DET cana-441 34 20 extracted extract VERB cana-441 34 21 features feature NOUN cana-441 34 22 . . PUNCT cana-441 35 1 the the DET cana-441 35 2 cross cross ADJ cana-441 35 3 - - ADJ cana-441 35 4 entropy entropy ADJ cana-441 35 5 loss loss NOUN cana-441 35 6 function function NOUN cana-441 35 7 , , PUNCT cana-441 35 8 expressed express VERB cana-441 35 9 as as ADP cana-441 35 10 ( ( PUNCT cana-441 35 11 𝑣 𝑣 PROPN cana-441 35 12 , , PUNCT cana-441 35 13 οΏ½ οΏ½ NOUN cana-441 35 14 Μ‚ Μ‚ NOUN cana-441 35 15 οΏ½ οΏ½ NOUN cana-441 35 16 ) ) PUNCT cana-441 35 17 = = PUNCT cana-441 36 1 βˆ’ βˆ’ PROPN cana-441 36 2 1 1 NUM cana-441 36 3 𝑛 𝑛 PROPN cana-441 36 4 βˆ‘π‘–βˆ’1 βˆ‘π‘–βˆ’1 NOUN cana-441 36 5 𝑀 𝑀 ADP cana-441 36 6 β€Š β€Š SPACE cana-441 36 7 𝑣𝑖log 𝑣𝑖log PROPN cana-441 36 8 ( ( PUNCT cana-441 36 9 οΏ½ οΏ½ PROPN cana-441 36 10 Μƒ Μƒ NOUN cana-441 36 11 οΏ½ οΏ½ NOUN cana-441 36 12 𝑖 𝑖 NUM cana-441 36 13 ) ) PUNCT cana-441 37 1 + + CCONJ cana-441 37 2 ( ( PUNCT cana-441 37 3 1 1 NUM cana-441 37 4 βˆ’ βˆ’ NOUN cana-441 37 5 𝑦𝑖)log 𝑦𝑖)log NOUN cana-441 37 6 ( ( PUNCT cana-441 37 7 1 1 NUM cana-441 37 8 βˆ’ βˆ’ PROPN cana-441 37 9 πœ‚π‘– πœ‚π‘– PROPN cana-441 37 10 ) ) PUNCT cana-441 37 11 , , PUNCT cana-441 37 12 quantifies quantify VERB cana-441 37 13 the the DET cana-441 37 14 discrepancy discrepancy NOUN cana-441 37 15 between between ADP cana-441 37 16 predicted predict VERB cana-441 37 17 and and CCONJ cana-441 37 18 actual actual ADJ cana-441 37 19 tumor tumor NOUN cana-441 37 20 labels label NOUN cana-441 37 21 . . PUNCT cana-441 38 1 by by ADP cana-441 38 2 penalizing penalize VERB cana-441 38 3 deviations deviation NOUN cana-441 38 4 from from ADP cana-441 38 5 the the DET cana-441 38 6 ground ground NOUN cana-441 38 7 truth truth NOUN cana-441 38 8 labels label NOUN cana-441 38 9 , , PUNCT cana-441 38 10 the the DET cana-441 38 11 loss loss NOUN cana-441 38 12 function function NOUN cana-441 38 13 guides guide VERB cana-441 38 14 the the DET cana-441 38 15 optimization optimization NOUN cana-441 38 16 process process NOUN cana-441 38 17 towards towards ADP cana-441 38 18 minimizing minimize VERB cana-441 38 19 prediction prediction NOUN cana-441 38 20 errors error NOUN cana-441 38 21 and and CCONJ cana-441 38 22 improving improve VERB cana-441 38 23 the the DET cana-441 38 24 network network NOUN cana-441 38 25 's 's PART cana-441 38 26 performance performance NOUN cana-441 38 27 in in ADP cana-441 38 28 tumor tumor NOUN cana-441 38 29 segmentation segmentation NOUN cana-441 38 30 and and CCONJ cana-441 38 31 classification classification NOUN cana-441 38 32 tasks task NOUN cana-441 38 33 . . PUNCT cana-441 39 1 communications communication NOUN cana-441 39 2 on on ADP cana-441 39 3 applied apply VERB cana-441 39 4 nonlinear nonlinear ADJ cana-441 39 5 analysis analysis NOUN cana-441 39 6 issn issn NOUN cana-441 39 7 : : PUNCT cana-441 39 8 1074 1074 NUM cana-441 39 9 - - PUNCT cana-441 39 10 133x 133x NUM cana-441 39 11 vol vol NOUN cana-441 39 12 31 31 NUM cana-441 39 13 no no NOUN cana-441 39 14 . . NOUN cana-441 39 15 1 1 NUM cana-441 39 16 ( ( PUNCT cana-441 39 17 2024 2024 NUM cana-441 39 18 ) ) PUNCT cana-441 39 19 320 320 NUM cana-441 39 20 https://internationalpubls.com https://internationalpubls.com X cana-441 39 21 𝜎(𝑧 𝜎(𝑧 NUM cana-441 39 22 ) ) PUNCT cana-441 39 23 = = NOUN cana-441 39 24 1 1 NUM cana-441 39 25 1+π‘’βˆ’π‘§ 1+π‘’βˆ’π‘§ NUM cana-441 39 26 ( ( PUNCT cana-441 39 27 11 11 NUM cana-441 39 28 ) ) PUNCT cana-441 39 29 πœ† πœ† DET cana-441 39 30 2π‘š 2π‘š NOUN cana-441 39 31 |π‘Š|𝐹 |π‘Š|𝐹 ADP cana-441 39 32 2 2 NUM cana-441 39 33 ( ( PUNCT cana-441 39 34 12 12 NUM cana-441 39 35 ) ) PUNCT cana-441 39 36 𝑝(πœƒ|𝐷 𝑝(πœƒ|𝐷 PROPN cana-441 39 37 ) ) PUNCT cana-441 39 38 = = SYM cana-441 39 39 𝑝(𝐷|πœƒ)𝑝(πœƒ 𝑝(𝐷|πœƒ)𝑝(πœƒ X cana-441 39 40 ) ) PUNCT cana-441 39 41 𝑝(𝐷 𝑝(𝐷 PROPN cana-441 39 42 ) ) PUNCT cana-441 39 43 ( ( PUNCT cana-441 39 44 13 13 X cana-441 39 45 ) ) PUNCT cana-441 39 46 maximize maximize VERB cana-441 39 47 β€ˆ β€ˆ SPACE cana-441 39 48 |π‘‹π‘Š |π‘‹π‘Š NOUN cana-441 40 1 βˆ’ βˆ’ NOUN cana-441 40 2 𝑋|𝐹 𝑋|𝐹 NOUN cana-441 40 3 2 2 NUM cana-441 40 4 ( ( PUNCT cana-441 40 5 14 14 NUM cana-441 40 6 ) ) PUNCT cana-441 40 7 gradient gradient ADJ cana-441 40 8 descent descent NOUN cana-441 40 9 optimization optimization NOUN cana-441 40 10 , , PUNCT cana-441 40 11 represented represent VERB cana-441 40 12 by by ADP cana-441 40 13 the the DET cana-441 40 14 equation equation NOUN cana-441 40 15 πœƒ πœƒ X cana-441 40 16 = = PUNCT cana-441 41 1 πœƒ πœƒ NUM cana-441 41 2 βˆ’ βˆ’ NOUN cana-441 41 3 𝛼 𝛼 NOUN cana-441 41 4 πœ•π½ πœ•π½ NOUN cana-441 41 5 πœ•πœƒ πœ•πœƒ NOUN cana-441 41 6 , , PUNCT cana-441 41 7 iteratively iteratively ADV cana-441 41 8 updates update VERB cana-441 41 9 the the DET cana-441 41 10 network network NOUN cana-441 41 11 parameters parameter NOUN cana-441 41 12 to to PART cana-441 41 13 minimize minimize VERB cana-441 41 14 the the DET cana-441 41 15 loss loss NOUN cana-441 41 16 function function NOUN cana-441 41 17 . . PUNCT cana-441 42 1 here here ADV cana-441 42 2 , , PUNCT cana-441 42 3 𝛼 𝛼 PROPN cana-441 42 4 denotes denote VERB cana-441 42 5 the the DET cana-441 42 6 learning learning NOUN cana-441 42 7 rate rate NOUN cana-441 42 8 , , PUNCT cana-441 42 9 and and CCONJ cana-441 42 10 πœ•π½ πœ•π½ PROPN cana-441 42 11 πœ•πœƒ πœ•πœƒ NOUN cana-441 42 12 represents represent VERB cana-441 42 13 the the DET cana-441 42 14 gradient gradient NOUN cana-441 42 15 of of ADP cana-441 42 16 the the DET cana-441 42 17 loss loss NOUN cana-441 42 18 function function NOUN cana-441 42 19 with with ADP cana-441 42 20 respect respect NOUN cana-441 42 21 to to ADP cana-441 42 22 the the DET cana-441 42 23 parameter parameter NOUN cana-441 42 24 . . PUNCT cana-441 43 1 by by ADP cana-441 43 2 adjusting adjust VERB cana-441 43 3 the the DET cana-441 43 4 parameters parameter NOUN cana-441 43 5 in in ADP cana-441 43 6 the the DET cana-441 43 7 direction direction NOUN cana-441 43 8 of of ADP cana-441 43 9 steepest steep ADJ cana-441 43 10 descent descent NOUN cana-441 43 11 , , PUNCT cana-441 43 12 gradient gradient ADJ cana-441 43 13 descent descent NOUN cana-441 43 14 optimizes optimize VERB cana-441 43 15 the the DET cana-441 43 16 network network NOUN cana-441 43 17 's 's PART cana-441 43 18 performance performance NOUN cana-441 43 19 and and CCONJ cana-441 43 20 enhances enhance VERB cana-441 43 21 its its PRON cana-441 43 22 ability ability NOUN cana-441 43 23 to to PART cana-441 43 24 accurately accurately ADV cana-441 43 25 segment segment VERB cana-441 43 26 and and CCONJ cana-441 43 27 classify classify VERB cana-441 43 28 brain brain NOUN cana-441 43 29 tumor tumor NOUN cana-441 43 30 . . PUNCT cana-441 44 1 batch batch NOUN cana-441 44 2 normalization normalization NOUN cana-441 44 3 , , PUNCT cana-441 44 4 expressed express VERB cana-441 44 5 as as ADP cana-441 44 6 𝑓 𝑓 PRON cana-441 44 7 = = PUNCT cana-441 44 8 π‘₯βˆ’1 π‘₯βˆ’1 NOUN cana-441 44 9 √𝜎2 √𝜎2 PUNCT cana-441 44 10 + + NOUN cana-441 44 11 1 1 NUM cana-441 44 12 , , PUNCT cana-441 44 13 normalizes normalize VERB cana-441 44 14 the the DET cana-441 44 15 ectivations ectivation NOUN cana-441 44 16 within within ADP cana-441 44 17 mini mini NOUN cana-441 44 18 - - NOUN cana-441 44 19 batches batch NOUN cana-441 44 20 during during ADP cana-441 44 21 training training NOUN cana-441 44 22 . . PUNCT cana-441 45 1 by by ADP cana-441 45 2 stabilizing stabilize VERB cana-441 45 3 the the DET cana-441 45 4 distribution distribution NOUN cana-441 45 5 of of ADP cana-441 45 6 inputs input NOUN cana-441 45 7 to to ADP cana-441 45 8 each each DET cana-441 45 9 layer layer NOUN cana-441 45 10 , , PUNCT cana-441 45 11 batch batch NOUN cana-441 45 12 normalization normalization NOUN cana-441 45 13 accelerates accelerate VERB cana-441 45 14 comergence comergence NOUN cana-441 45 15 , , PUNCT cana-441 45 16 improves improve VERB cana-441 45 17 gradient gradient ADJ cana-441 45 18 flow flow NOUN cana-441 45 19 , , PUNCT cana-441 45 20 and and CCONJ cana-441 45 21 enhances enhance VERB cana-441 45 22 the the DET cana-441 45 23 network network NOUN cana-441 45 24 's 's PART cana-441 45 25 ability ability NOUN cana-441 45 26 to to PART cana-441 45 27 learn learn VERB cana-441 45 28 discriminative discriminative NOUN cana-441 45 29 features feature NOUN cana-441 45 30 from from ADP cana-441 45 31 the the DET cana-441 45 32 dots dot NOUN cana-441 45 33 . . PUNCT cana-441 46 1 𝐷𝑆𝐢 𝐷𝑆𝐢 NOUN cana-441 46 2 = = SYM cana-441 46 3 2|π‘‹βˆ©π‘Œ| 2|π‘‹βˆ©π‘Œ| NUM cana-441 46 4 |𝑋|+|π‘Œ| |𝑋|+|π‘Œ| ADJ cana-441 46 5 ( ( PUNCT cana-441 46 6 15 15 NUM cana-441 46 7 ) ) PUNCT cana-441 46 8 πΌπ‘œπ‘ˆ πΌπ‘œπ‘ˆ NOUN cana-441 47 1 = = SYM cana-441 47 2 |π‘‹βˆ©π‘Œ| |π‘‹βˆ©π‘Œ| X cana-441 47 3 |𝑋βˆͺπ‘Œ| |𝑋βˆͺπ‘Œ| PUNCT cana-441 47 4 ( ( PUNCT cana-441 47 5 16 16 NUM cana-441 47 6 ) ) PUNCT cana-441 47 7 𝐻(𝑋 𝐻(𝑋 NOUN cana-441 47 8 , , PUNCT cana-441 47 9 π‘Œ π‘Œ PROPN cana-441 47 10 ) ) PUNCT cana-441 47 11 = = SYM cana-441 47 12 max(β„Ž(𝑋 max(β„Ž(𝑋 PROPN cana-441 47 13 , , PUNCT cana-441 47 14 π‘Œ π‘Œ PROPN cana-441 47 15 ) ) PUNCT cana-441 47 16 , , PUNCT cana-441 47 17 β„Ž(π‘Œ β„Ž(π‘Œ PROPN cana-441 47 18 , , PUNCT cana-441 47 19 𝑋 𝑋 PROPN cana-441 47 20 ) ) PUNCT cana-441 47 21 ) ) PUNCT cana-441 47 22 ( ( PUNCT cana-441 47 23 17 17 NUM cana-441 47 24 ) ) PUNCT cana-441 47 25 π‘š π‘š NOUN cana-441 47 26 = = PUNCT cana-441 47 27 𝛽1π‘š 𝛽1π‘š X cana-441 47 28 + + CCONJ cana-441 47 29 ( ( PUNCT cana-441 47 30 1 1 NUM cana-441 47 31 βˆ’ βˆ’ PROPN cana-441 47 32 𝛽1)βˆ‡π½ 𝛽1)βˆ‡π½ NOUN cana-441 47 33 ( ( PUNCT cana-441 47 34 18 18 NUM cana-441 47 35 ) ) PUNCT cana-441 47 36 𝑣 𝑣 NOUN cana-441 47 37 = = PUNCT cana-441 47 38 𝛽2𝑣 𝛽2𝑣 PROPN cana-441 47 39 + + CCONJ cana-441 47 40 ( ( PUNCT cana-441 47 41 1 1 NUM cana-441 47 42 βˆ’ βˆ’ NOUN cana-441 47 43 𝛽2)(βˆ‡π½)2 𝛽2)(βˆ‡π½)2 X cana-441 47 44 ( ( PUNCT cana-441 47 45 19 19 NUM cana-441 47 46 ) ) PUNCT cana-441 47 47 𝛼 𝛼 PROPN cana-441 47 48 = = SYM cana-441 47 49 𝛼0 𝛼0 PROPN cana-441 47 50 1+decay_rateΓ—epoch 1+decay_rateΓ—epoch NUM cana-441 47 51 ( ( PUNCT cana-441 47 52 20 20 NUM cana-441 47 53 ) ) PUNCT cana-441 47 54 residual residual ADJ cana-441 47 55 connections connection NOUN cana-441 47 56 , , PUNCT cana-441 47 57 as as SCONJ cana-441 47 58 introduced introduce VERB cana-441 47 59 in in ADP cana-441 47 60 residual residual ADJ cana-441 47 61 cnns cnn NOUN cana-441 47 62 , , PUNCT cana-441 47 63 facilitate facilitate VERB cana-441 47 64 the the DET cana-441 47 65 training training NOUN cana-441 47 66 of of ADP cana-441 47 67 very very ADV cana-441 47 68 deep deep ADJ cana-441 47 69 network network NOUN cana-441 47 70 : : PUNCT cana-441 47 71 by by ADP cana-441 47 72 addressing address VERB cana-441 47 73 the the DET cana-441 47 74 vanishing vanish VERB cana-441 47 75 gradients gradient NOUN cana-441 47 76 problem problem NOUN cana-441 47 77 . . PUNCT cana-441 48 1 in in ADP cana-441 48 2 residual residual ADJ cana-441 48 3 blocks block NOUN cana-441 48 4 , , PUNCT cana-441 48 5 the the DET cana-441 48 6 output output NOUN cana-441 48 7 is be AUX cana-441 48 8 computed compute VERB cana-441 48 9 as as ADP cana-441 48 10 output output NOUN cana-441 48 11 = = SYM cana-441 48 12 𝐹(π‘₯ 𝐹(π‘₯ X cana-441 48 13 , , PUNCT cana-441 48 14 { { PUNCT cana-441 48 15 π‘Šπ‘– π‘Šπ‘– PROPN cana-441 48 16 } } PUNCT cana-441 48 17 ) ) PUNCT cana-441 49 1 + + CCONJ cana-441 49 2 π‘₯2 π‘₯2 NOUN cana-441 49 3 where where SCONJ cana-441 49 4 𝐹(π‘₯ 𝐹(π‘₯ X cana-441 49 5 , , PUNCT cana-441 49 6 { { PUNCT cana-441 49 7 π‘Šπ‘– π‘Šπ‘– PROPN cana-441 49 8 } } PUNCT cana-441 49 9 ) ) PUNCT cana-441 49 10 represent represent VERB cana-441 49 11 the the DET cana-441 49 12 residual residual ADJ cana-441 49 13 mapping mapping NOUN cana-441 49 14 learned learn VERB cana-441 49 15 by by ADP cana-441 49 16 the the DET cana-441 49 17 network network NOUN cana-441 49 18 . . PUNCT cana-441 50 1 by by ADP cana-441 50 2 introducing introduce VERB cana-441 50 3 skip skip ADJ cana-441 50 4 connections connection NOUN cana-441 50 5 that that PRON cana-441 50 6 bypass bypass VERB cana-441 50 7 certain certain ADJ cana-441 50 8 layers layer NOUN cana-441 50 9 , , PUNCT cana-441 50 10 residual residual ADJ cana-441 50 11 networks network NOUN cana-441 50 12 enable enable VERB cana-441 50 13 the the DET cana-441 50 14 direct direct ADJ cana-441 50 15 flow flow NOUN cana-441 50 16 of of ADP cana-441 50 17 information information NOUN cana-441 50 18 and and CCONJ cana-441 50 19 mitigate mitigate VERB cana-441 50 20 the the DET cana-441 50 21 degradation degradation NOUN cana-441 50 22 problem problem NOUN cana-441 50 23 encountered encounter VERB cana-441 50 24 in in ADP cana-441 50 25 training training NOUN cana-441 50 26 deep deep ADV cana-441 50 27 networks.the networks.the DET cana-441 50 28 exploration exploration NOUN cana-441 50 29 of of ADP cana-441 50 30 these these DET cana-441 50 31 mathematical mathematical ADJ cana-441 50 32 equations equation NOUN cana-441 50 33 and and CCONJ cana-441 50 34 their their PRON cana-441 50 35 associated associated ADJ cana-441 50 36 operations operation NOUN cana-441 50 37 provides provide VERB cana-441 50 38 insights insight NOUN cana-441 50 39 into into ADP cana-441 50 40 the the DET cana-441 50 41 underlying underlie VERB cana-441 50 42 principles principle NOUN cana-441 50 43 of of ADP cana-441 50 44 deep deep ADJ cana-441 50 45 residual residual ADJ cana-441 50 46 cnns cnn NOUN cana-441 50 47 for for ADP cana-441 50 48 brain brain NOUN cana-441 50 49 tumor tumor NOUN cana-441 50 50 segmentation segmentation NOUN cana-441 50 51 and and CCONJ cana-441 50 52 classification classification NOUN cana-441 50 53 . . PUNCT cana-441 51 1 by by ADP cana-441 51 2 leveraging leverage VERB cana-441 51 3 convolutional convolutional ADJ cana-441 51 4 operations operation NOUN cana-441 51 5 , , PUNCT cana-441 51 6 activation activation NOUN cana-441 51 7 functions function NOUN cana-441 51 8 , , PUNCT cana-441 51 9 pooling pool VERB cana-441 51 10 layers layer NOUN cana-441 51 11 , , PUNCT cana-441 51 12 and and CCONJ cana-441 51 13 fully fully ADV cana-441 51 14 connected connected ADJ cana-441 51 15 layers layer NOUN cana-441 51 16 , , PUNCT cana-441 51 17 these these DET cana-441 51 18 networks network NOUN cana-441 51 19 can can AUX cana-441 51 20 effectively effectively ADV cana-441 51 21 extract extract VERB cana-441 51 22 discriminative discriminative NOUN cana-441 51 23 features feature NOUN cana-441 51 24 from from ADP cana-441 51 25 medical medical ADJ cana-441 51 26 imaging imaging NOUN cana-441 51 27 data datum NOUN cana-441 51 28 and and CCONJ cana-441 51 29 make make VERB cana-441 51 30 accurate accurate ADJ cana-441 51 31 prediction prediction NOUN cana-441 51 32 regarding regard VERB cana-441 51 33 tumor tumor NOUN cana-441 51 34 presence presence NOUN cana-441 51 35 and and CCONJ cana-441 51 36 type type NOUN cana-441 51 37 . . PUNCT cana-441 52 1 additionally additionally ADV cana-441 52 2 , , PUNCT cana-441 52 3 optimization optimization NOUN cana-441 52 4 techniques technique NOUN cana-441 52 5 such such ADJ cana-441 52 6 as as ADP cana-441 52 7 gradient gradient ADJ cana-441 52 8 descent descent NOUN cana-441 52 9 , , PUNCT cana-441 52 10 batch batch VERB cana-441 52 11 normalization normalization NOUN cana-441 52 12 , , PUNCT cana-441 52 13 and and CCONJ cana-441 52 14 residual residual ADJ cana-441 52 15 connections connection NOUN cana-441 52 16 play play VERB cana-441 52 17 critical critical ADJ cana-441 52 18 roles role NOUN cana-441 52 19 in in ADP cana-441 52 20 training train VERB cana-441 52 21 these these DET cana-441 52 22 networks network NOUN cana-441 52 23 and and CCONJ cana-441 52 24 enhancing enhance VERB cana-441 52 25 their their PRON cana-441 52 26 performance performance NOUN cana-441 52 27 in in ADP cana-441 52 28 challenging challenge VERB cana-441 52 29 medical medical ADJ cana-441 52 30 imaging imaging NOUN cana-441 52 31 tasks task NOUN cana-441 52 32 . . PUNCT cana-441 53 1 communications communication NOUN cana-441 53 2 on on ADP cana-441 53 3 applied apply VERB cana-441 53 4 nonlinear nonlinear ADJ cana-441 53 5 analysis analysis NOUN cana-441 53 6 issn issn NOUN cana-441 53 7 : : PUNCT cana-441 53 8 1074 1074 NUM cana-441 53 9 - - PUNCT cana-441 53 10 133x 133x NUM cana-441 53 11 vol vol NOUN cana-441 53 12 31 31 NUM cana-441 53 13 no no NOUN cana-441 53 14 . . NOUN cana-441 53 15 1 1 NUM cana-441 53 16 ( ( PUNCT cana-441 53 17 2024 2024 NUM cana-441 53 18 ) ) PUNCT cana-441 53 19 321 321 NUM cana-441 53 20 https://internationalpubls.com https://internationalpubls.com X cana-441 53 21 2 2 X cana-441 53 22 . . X cana-441 53 23 literature literature NOUN cana-441 53 24 survey survey PROPN cana-441 53 25 raghavendra raghavendra PROPN cana-441 53 26 et et PROPN cana-441 53 27 al al PROPN cana-441 53 28 . . PUNCT cana-441 54 1 [ [ X cana-441 54 2 9 9 NUM cana-441 54 3 ] ] PUNCT cana-441 54 4 explained explain VERB cana-441 54 5 that that SCONJ cana-441 54 6 brain brain NOUN cana-441 54 7 cancer cancer NOUN cana-441 54 8 is be AUX cana-441 54 9 one one NUM cana-441 54 10 of of ADP cana-441 54 11 the the DET cana-441 54 12 worst bad ADJ cana-441 54 13 illnesses illness NOUN cana-441 54 14 in in ADP cana-441 54 15 the the DET cana-441 54 16 world world NOUN cana-441 54 17 , , PUNCT cana-441 54 18 with with SCONJ cana-441 54 19 both both DET cana-441 54 20 adults adult NOUN cana-441 54 21 and and CCONJ cana-441 54 22 children child NOUN cana-441 54 23 able able ADJ cana-441 54 24 to to PART cana-441 54 25 get get VERB cana-441 54 26 it it PRON cana-441 54 27 . . PUNCT cana-441 55 1 it it PRON cana-441 55 2 has have VERB cana-441 55 3 the the DET cana-441 55 4 lowest low ADJ cana-441 55 5 rate rate NOUN cana-441 55 6 of of ADP cana-441 55 7 survival survival NOUN cana-441 55 8 and and CCONJ cana-441 55 9 requires require VERB cana-441 55 10 early early ADJ cana-441 55 11 diagnosis diagnosis NOUN cana-441 55 12 of of ADP cana-441 55 13 the the DET cana-441 55 14 appropriate appropriate ADJ cana-441 55 15 type type NOUN cana-441 55 16 and and CCONJ cana-441 55 17 grade grade NOUN cana-441 55 18 of of ADP cana-441 55 19 tumor tumor NOUN cana-441 55 20 . . PUNCT cana-441 56 1 mri mri NOUN cana-441 56 2 scans scan NOUN cana-441 56 3 of of ADP cana-441 56 4 the the DET cana-441 56 5 patient patient NOUN cana-441 56 6 's 's PART cana-441 56 7 brain brain NOUN cana-441 56 8 can can AUX cana-441 56 9 be be AUX cana-441 56 10 used use VERB cana-441 56 11 to to PART cana-441 56 12 spot spot VERB cana-441 56 13 brain brain NOUN cana-441 56 14 tumors tumor NOUN cana-441 56 15 , , PUNCT cana-441 56 16 but but CCONJ cana-441 56 17 the the DET cana-441 56 18 manual manual ADJ cana-441 56 19 procedure procedure NOUN cana-441 56 20 is be AUX cana-441 56 21 time time NOUN cana-441 56 22 consuming consume VERB cana-441 56 23 and and CCONJ cana-441 56 24 vulnerable vulnerable ADJ cana-441 56 25 to to ADP cana-441 56 26 human human ADJ cana-441 56 27 error error NOUN cana-441 56 28 . . PUNCT cana-441 57 1 recent recent ADJ cana-441 57 2 years year NOUN cana-441 57 3 have have AUX cana-441 57 4 seen see VERB cana-441 57 5 a a DET cana-441 57 6 major major ADJ cana-441 57 7 advancement advancement NOUN cana-441 57 8 in in ADP cana-441 57 9 image image NOUN cana-441 57 10 classification classification NOUN cana-441 57 11 techniques technique NOUN cana-441 57 12 , , PUNCT cana-441 57 13 and and CCONJ cana-441 57 14 deep deep ADJ cana-441 57 15 learning learning NOUN cana-441 57 16 networks network NOUN cana-441 57 17 , , PUNCT cana-441 57 18 which which PRON cana-441 57 19 have have AUX cana-441 57 20 been be AUX cana-441 57 21 successful successful ADJ cana-441 57 22 in in ADP cana-441 57 23 this this DET cana-441 57 24 area area NOUN cana-441 57 25 . . PUNCT cana-441 58 1 a a DET cana-441 58 2 subjective subjective ADJ cana-441 58 3 comparison comparison NOUN cana-441 58 4 study study NOUN cana-441 58 5 was be AUX cana-441 58 6 also also ADV cana-441 58 7 conducted conduct VERB cana-441 58 8 between between ADP cana-441 58 9 the the DET cana-441 58 10 recommended recommend VERB cana-441 58 11 strategy strategy NOUN cana-441 58 12 and and CCONJ cana-441 58 13 a a DET cana-441 58 14 few few ADJ cana-441 58 15 cutting cutting NOUN cana-441 58 16 edge edge NOUN cana-441 58 17 methods method NOUN cana-441 58 18 . . PUNCT cana-441 59 1 the the DET cana-441 59 2 suggested suggest VERB cana-441 59 3 approach approach NOUN cana-441 59 4 works work VERB cana-441 59 5 better well ADV cana-441 59 6 than than ADP cana-441 59 7 existing exist VERB cana-441 59 8 techniques technique NOUN cana-441 59 9 , , PUNCT cana-441 59 10 according accord VERB cana-441 59 11 to to ADP cana-441 59 12 tests test NOUN cana-441 59 13 , , PUNCT cana-441 59 14 in in ADP cana-441 59 15 terms term NOUN cana-441 59 16 of of ADP cana-441 59 17 aiding aid VERB cana-441 59 18 radiologists radiologist NOUN cana-441 59 19 in in ADP cana-441 59 20 determining determine VERB cana-441 59 21 the the DET cana-441 59 22 size size NOUN cana-441 59 23 , , PUNCT cana-441 59 24 shape shape NOUN cana-441 59 25 , , PUNCT cana-441 59 26 and and CCONJ cana-441 59 27 location location NOUN cana-441 59 28 of of ADP cana-441 59 29 tumors tumor NOUN cana-441 59 30 in in ADP cana-441 59 31 the the DET cana-441 59 32 human human ADJ cana-441 59 33 brain brain NOUN cana-441 59 34 . . PUNCT cana-441 60 1 alsaif alsaif VERB cana-441 60 2 et et PROPN cana-441 60 3 al al PROPN cana-441 60 4 . . PUNCT cana-441 61 1 [ [ X cana-441 61 2 10 10 NUM cana-441 61 3 ] ] PUNCT cana-441 61 4 providing provide VERB cana-441 61 5 an an DET cana-441 61 6 effective effective ADJ cana-441 61 7 approach approach NOUN cana-441 61 8 for for ADP cana-441 61 9 detecting detect VERB cana-441 61 10 brain brain NOUN cana-441 61 11 cancers cancer NOUN cana-441 61 12 using use VERB cana-441 61 13 magnetic magnetic ADJ cana-441 61 14 resonance resonance NOUN cana-441 61 15 imaging imaging NOUN cana-441 61 16 ( ( PUNCT cana-441 61 17 mri mri NOUN cana-441 61 18 ) ) PUNCT cana-441 61 19 datasets dataset NOUN cana-441 61 20 based base VERB cana-441 61 21 on on ADP cana-441 61 22 cnn cnn PROPN cana-441 61 23 and and CCONJ cana-441 61 24 data datum NOUN cana-441 61 25 augmentation augmentation NOUN cana-441 61 26 and and CCONJ cana-441 61 27 emphasizes emphasize VERB cana-441 61 28 the the DET cana-441 61 29 features feature NOUN cana-441 61 30 of of ADP cana-441 61 31 certain certain ADJ cana-441 61 32 models model NOUN cana-441 61 33 like like ADP cana-441 61 34 resnet resnet NOUN cana-441 61 35 , , PUNCT cana-441 61 36 alexnet alexnet NOUN cana-441 61 37 and and CCONJ cana-441 61 38 vgg vgg NOUN cana-441 61 39 . . PUNCT cana-441 62 1 the the DET cana-441 62 2 suggested suggest VERB cana-441 62 3 solution solution NOUN cana-441 62 4 's 's PART cana-441 62 5 evaluation evaluation NOUN cana-441 62 6 metrics metric NOUN cana-441 62 7 values value NOUN cana-441 62 8 demonstrate demonstrate VERB cana-441 62 9 that that SCONJ cana-441 62 10 it it PRON cana-441 62 11 was be AUX cana-441 62 12 successful successful ADJ cana-441 62 13 in in ADP cana-441 62 14 adding add VERB cana-441 62 15 to to ADP cana-441 62 16 earlier early ADJ cana-441 62 17 research research NOUN cana-441 62 18 in in ADP cana-441 62 19 terms term NOUN cana-441 62 20 of of ADP cana-441 62 21 both both CCONJ cana-441 62 22 deep deep ADJ cana-441 62 23 architectural architectural ADJ cana-441 62 24 design design NOUN cana-441 62 25 and and CCONJ cana-441 62 26 high high ADJ cana-441 62 27 detection detection NOUN cana-441 62 28 success success NOUN cana-441 62 29 . . PUNCT cana-441 63 1 gupta gupta PROPN cana-441 63 2 et et PROPN cana-441 63 3 al al PROPN cana-441 63 4 . . PUNCT cana-441 64 1 [ [ X cana-441 64 2 11 11 NUM cana-441 64 3 ] ] PUNCT cana-441 64 4 proposed propose VERB cana-441 64 5 anbrain anbrain NOUN cana-441 64 6 tumor tumor NOUN cana-441 64 7 is be AUX cana-441 64 8 a a DET cana-441 64 9 mass mass NOUN cana-441 64 10 of of ADP cana-441 64 11 abnormal abnormal ADJ cana-441 64 12 cells cell NOUN cana-441 64 13 that that PRON cana-441 64 14 is be AUX cana-441 64 15 developing develop VERB cana-441 64 16 in in ADP cana-441 64 17 the the DET cana-441 64 18 brain brain NOUN cana-441 64 19 and and CCONJ cana-441 64 20 poses pose VERB cana-441 64 21 a a DET cana-441 64 22 threat threat NOUN cana-441 64 23 to to ADP cana-441 64 24 life life NOUN cana-441 64 25 . . PUNCT cana-441 65 1 it it PRON cana-441 65 2 is be AUX cana-441 65 3 important important ADJ cana-441 65 4 to to PART cana-441 65 5 segment segment VERB cana-441 65 6 and and CCONJ cana-441 65 7 find find VERB cana-441 65 8 such such ADJ cana-441 65 9 tumors tumor NOUN cana-441 65 10 with with ADP cana-441 65 11 mri mri NOUN cana-441 65 12 at at ADP cana-441 65 13 an an DET cana-441 65 14 early early ADJ cana-441 65 15 stage stage NOUN cana-441 65 16 in in ADP cana-441 65 17 order order NOUN cana-441 65 18 to to PART cana-441 65 19 save save VERB cana-441 65 20 the the DET cana-441 65 21 life life NOUN cana-441 65 22 . . PUNCT cana-441 66 1 the the DET cana-441 66 2 proposed propose VERB cana-441 66 3 improved improve VERB cana-441 66 4 invasive invasive ADJ cana-441 66 5 based base VERB cana-441 66 6 deep deep ADJ cana-441 66 7 residual residual ADJ cana-441 66 8 network network NOUN cana-441 66 9 model model NOUN cana-441 66 10 is be AUX cana-441 66 11 used use VERB cana-441 66 12 to to PART cana-441 66 13 create create VERB cana-441 66 14 an an DET cana-441 66 15 effective effective ADJ cana-441 66 16 brain brain NOUN cana-441 66 17 tumor tumor NOUN cana-441 66 18 detection detection NOUN cana-441 66 19 technique technique NOUN cana-441 66 20 . . PUNCT cana-441 67 1 the the DET cana-441 67 2 proposed propose VERB cana-441 67 3 iib iib PROPN cana-441 67 4 algorithm algorithm PROPN cana-441 67 5 incorporate incorporate VERB cana-441 67 6 the the DET cana-441 67 7 improved improve VERB cana-441 67 8 invasive invasive ADJ cana-441 67 9 weed weed NOUN cana-441 67 10 optimization optimization NOUN cana-441 67 11 ( ( PUNCT cana-441 67 12 iwo iwo PROPN cana-441 67 13 ) ) PUNCT cana-441 67 14 and and CCONJ cana-441 67 15 bat bat NOUN cana-441 67 16 algorithm algorithm NOUN cana-441 67 17 ( ( PUNCT cana-441 67 18 ba ba NOUN cana-441 67 19 ) ) PUNCT cana-441 67 20 , , PUNCT cana-441 67 21 respectively respectively ADV cana-441 67 22 , , PUNCT cana-441 67 23 into into ADP cana-441 67 24 the the DET cana-441 67 25 proposed propose VERB cana-441 67 26 iib iib NOUN cana-441 67 27 algorithm algorithm PROPN cana-441 67 28 . . PUNCT cana-441 68 1 the the DET cana-441 68 2 proposed propose VERB cana-441 68 3 method method NOUN cana-441 68 4 successfully successfully ADV cana-441 68 5 produced produce VERB cana-441 68 6 improved improved ADJ cana-441 68 7 detection detection NOUN cana-441 68 8 outcomes outcome NOUN cana-441 68 9 with with ADP cana-441 68 10 mr mr PROPN cana-441 68 11 images image NOUN cana-441 68 12 , , PUNCT cana-441 68 13 with with ADP cana-441 68 14 features feature NOUN cana-441 68 15 obtained obtain VERB cana-441 68 16 from from ADP cana-441 68 17 the the DET cana-441 68 18 tumor tumor NOUN cana-441 68 19 area area NOUN cana-441 68 20 using use VERB cana-441 68 21 the the DET cana-441 68 22 segmentation segmentation NOUN cana-441 68 23 findings finding NOUN cana-441 68 24 being be AUX cana-441 68 25 used use VERB cana-441 68 26 to to ADP cana-441 68 27 the the DET cana-441 68 28 deep deep ADJ cana-441 68 29 residual residual ADJ cana-441 68 30 network network NOUN cana-441 68 31 detection detection NOUN cana-441 68 32 procedure procedure NOUN cana-441 68 33 . . PUNCT cana-441 69 1 aamiret aamiret PROPN cana-441 70 1 al al PROPN cana-441 70 2 . . PUNCT cana-441 71 1 [ [ X cana-441 71 2 12 12 NUM cana-441 71 3 ] ] PUNCT cana-441 71 4 said say VERB cana-441 71 5 if if SCONJ cana-441 71 6 a a DET cana-441 71 7 brain brain NOUN cana-441 71 8 tumor tumor NOUN cana-441 71 9 is be AUX cana-441 71 10 not not PART cana-441 71 11 found find VERB cana-441 71 12 in in ADP cana-441 71 13 time time NOUN cana-441 71 14 , , PUNCT cana-441 71 15 it it PRON cana-441 71 16 may may AUX cana-441 71 17 be be AUX cana-441 71 18 deadly deadly ADJ cana-441 71 19 . . PUNCT cana-441 72 1 magnetic magnetic ADJ cana-441 72 2 resonance resonance NOUN cana-441 72 3 imaging imaging NOUN cana-441 72 4 ( ( PUNCT cana-441 72 5 mri mri NOUN cana-441 72 6 ) ) PUNCT cana-441 72 7 is be AUX cana-441 72 8 suggested suggest VERB cana-441 72 9 as as ADP cana-441 72 10 an an DET cana-441 72 11 automated automate VERB cana-441 72 12 tool tool NOUN cana-441 72 13 to to PART cana-441 72 14 assure assure VERB cana-441 72 15 a a DET cana-441 72 16 correct correct ADJ cana-441 72 17 diagnosis diagnosis NOUN cana-441 72 18 . . PUNCT cana-441 73 1 the the DET cana-441 73 2 top top ADJ cana-441 73 3 tumor tumor NOUN cana-441 73 4 spots spot NOUN cana-441 73 5 are be AUX cana-441 73 6 revealed reveal VERB cana-441 73 7 via via ADP cana-441 73 8 agglomerative agglomerative ADJ cana-441 73 9 clustering clustering NOUN cana-441 73 10 after after SCONJ cana-441 73 11 images image NOUN cana-441 73 12 have have AUX cana-441 73 13 been be AUX cana-441 73 14 preprocessed preprocesse VERB cana-441 73 15 to to PART cana-441 73 16 improve improve VERB cana-441 73 17 visual visual ADJ cana-441 73 18 quality quality NOUN cana-441 73 19 , , PUNCT cana-441 73 20 two two NUM cana-441 73 21 pre pre X cana-441 73 22 trained train VERB cana-441 73 23 deep deep ADJ cana-441 73 24 learning learning NOUN cana-441 73 25 models model NOUN cana-441 73 26 have have AUX cana-441 73 27 been be AUX cana-441 73 28 utilized utilize VERB cana-441 73 29 to to PART cana-441 73 30 extract extract VERB cana-441 73 31 the the DET cana-441 73 32 features feature NOUN cana-441 73 33 , , PUNCT cana-441 73 34 and and CCONJ cana-441 73 35 a a DET cana-441 73 36 hybrid hybrid ADJ cana-441 73 37 feature feature NOUN cana-441 73 38 vector vector NOUN cana-441 73 39 has have AUX cana-441 73 40 been be AUX cana-441 73 41 constructed construct VERB cana-441 73 42 . . PUNCT cana-441 74 1 comparing compare VERB cana-441 74 2 the the DET cana-441 74 3 suggested suggest VERB cana-441 74 4 method method NOUN cana-441 74 5 to to ADP cana-441 74 6 current current ADJ cana-441 74 7 methods method NOUN cana-441 74 8 , , PUNCT cana-441 74 9 the the DET cana-441 74 10 classification classification NOUN cana-441 74 11 accuracy accuracy NOUN cana-441 74 12 was be AUX cana-441 74 13 98.95 98.95 NUM cana-441 74 14 % % NOUN cana-441 74 15 . . PUNCT cana-441 75 1 zahoor zahoor PROPN cana-441 75 2 et et PROPN cana-441 75 3 al al PROPN cana-441 75 4 . . PUNCT cana-441 76 1 [ [ X cana-441 76 2 13 13 NUM cana-441 76 3 ] ] PUNCT cana-441 76 4 analysis analysis NOUN cana-441 76 5 the the DET cana-441 76 6 brain brain NOUN cana-441 76 7 tumor tumor NOUN cana-441 76 8 is be AUX cana-441 76 9 essential essential ADJ cana-441 76 10 for for ADP cana-441 76 11 prompt prompt ADJ cana-441 76 12 patient patient ADJ cana-441 76 13 diagnosis diagnosis NOUN cana-441 76 14 and and CCONJ cana-441 76 15 successful successful ADJ cana-441 76 16 patient patient ADJ cana-441 76 17 care care NOUN cana-441 76 18 . . PUNCT cana-441 77 1 to to PART cana-441 77 2 successfully successfully ADV cana-441 77 3 detect detect VERB cana-441 77 4 tumors tumor NOUN cana-441 77 5 deep deep ADV cana-441 77 6 learning learn VERB cana-441 77 7 based base VERB cana-441 77 8 framework framework NOUN cana-441 77 9 is be AUX cana-441 77 10 proposed.in proposed.in PRON cana-441 77 11 the the DET cana-441 77 12 first first ADJ cana-441 77 13 phase phase NOUN cana-441 77 14 , , PUNCT cana-441 77 15 an an DET cana-441 77 16 ovel ovel NOUN cana-441 77 17 deep deep ADJ cana-441 77 18 boosted boosted ADJ cana-441 77 19 features feature NOUN cana-441 77 20 space space NOUN cana-441 77 21 and and CCONJ cana-441 77 22 ensemble ensemble ADJ cana-441 77 23 classifiers classifier NOUN cana-441 77 24 ( ( PUNCT cana-441 77 25 dbfs dbfs PROPN cana-441 77 26 - - PUNCT cana-441 77 27 ec ec PROPN cana-441 77 28 ) ) PUNCT cana-441 77 29 technique technique NOUN cana-441 77 30 is be AUX cana-441 77 31 suggested suggest VERB cana-441 77 32 . . PUNCT cana-441 78 1 in in ADP cana-441 78 2 the the DET cana-441 78 3 second second ADJ cana-441 78 4 phase phase NOUN cana-441 78 5 , , PUNCT cana-441 78 6 a a DET cana-441 78 7 hybrid hybrid ADJ cana-441 78 8 features feature NOUN cana-441 78 9 fusion fusion NOUN cana-441 78 10 based base VERB cana-441 78 11 brain brain NOUN cana-441 78 12 tumor tumor NOUN cana-441 78 13 classification classification NOUN cana-441 78 14 strategy strategy NOUN cana-441 78 15 is be AUX cana-441 78 16 put put VERB cana-441 78 17 forth forth ADV cana-441 78 18 , , PUNCT cana-441 78 19 which which PRON cana-441 78 20 combines combine VERB cana-441 78 21 an an DET cana-441 78 22 ml ml NOUN cana-441 78 23 classifier classifier NOUN cana-441 78 24 with with ADP cana-441 78 25 both both CCONJ cana-441 78 26 static static ADJ cana-441 78 27 and and CCONJ cana-441 78 28 dynamic dynamic ADJ cana-441 78 29 features feature NOUN cana-441 78 30 to to PART cana-441 78 31 classify classify VERB cana-441 78 32 various various ADJ cana-441 78 33 tumor tumor NOUN cana-441 78 34 kinds kind NOUN cana-441 78 35 . . PUNCT cana-441 79 1 hossain hossain PROPN cana-441 79 2 et et PROPN cana-441 79 3 al al PROPN cana-441 79 4 . . PUNCT cana-441 80 1 [ [ X cana-441 80 2 14 14 NUM cana-441 80 3 ] ] PUNCT cana-441 80 4 proposed propose VERB cana-441 80 5 that that SCONJ cana-441 80 6 in in ADP cana-441 80 7 order order NOUN cana-441 80 8 to to PART cana-441 80 9 categorize categorize VERB cana-441 80 10 the the DET cana-441 80 11 reconstructed reconstructed ADJ cana-441 80 12 microwave microwave NOUN cana-441 80 13 brain brain NOUN cana-441 80 14 pictures picture NOUN cana-441 80 15 into into ADP cana-441 80 16 six six NUM cana-441 80 17 classes class NOUN cana-441 80 18 , , PUNCT cana-441 80 19 this this DET cana-441 80 20 research research NOUN cana-441 80 21 offers offer VERB cana-441 80 22 an an DET cana-441 80 23 eight eight NUM cana-441 80 24 - - PUNCT cana-441 80 25 layered layer VERB cana-441 80 26 light light NOUN cana-441 80 27 weight weight NOUN cana-441 80 28 classifier classifier NOUN cana-441 80 29 model model NOUN cana-441 80 30 termed term VERB cana-441 80 31 microwave microwave NOUN cana-441 80 32 brain brain NOUN cana-441 80 33 image image NOUN cana-441 80 34 network network NOUN cana-441 80 35 utilizing utilize VERB cana-441 80 36 a a DET cana-441 80 37 self self NOUN cana-441 80 38 - - PUNCT cana-441 80 39 organized organize VERB cana-441 80 40 operational operational ADJ cana-441 80 41 neural neural ADJ cana-441 80 42 network network NOUN cana-441 80 43 . . PUNCT cana-441 81 1 aarthi aarthi PROPN cana-441 81 2 et et PROPN cana-441 81 3 al al PROPN cana-441 81 4 . . PUNCT cana-441 82 1 [ [ X cana-441 82 2 15 15 NUM cana-441 82 3 ] ] PUNCT cana-441 82 4 proposed propose VERB cana-441 82 5 a a DET cana-441 82 6 study study NOUN cana-441 82 7 aims aim VERB cana-441 82 8 to to PART cana-441 82 9 design design VERB cana-441 82 10 an an DET cana-441 82 11 automated automate VERB cana-441 82 12 brain brain NOUN cana-441 82 13 tumor tumor NOUN cana-441 82 14 detection detection NOUN cana-441 82 15 system system NOUN cana-441 82 16 using use VERB cana-441 82 17 a a DET cana-441 82 18 segmentation segmentation NOUN cana-441 82 19 based base VERB cana-441 82 20 classification classification NOUN cana-441 82 21 method method NOUN cana-441 82 22 . . PUNCT cana-441 83 1 the the DET cana-441 83 2 medical medical ADJ cana-441 83 3 pictures picture NOUN cana-441 83 4 are be AUX cana-441 83 5 normalized normalize VERB cana-441 83 6 using use VERB cana-441 83 7 the the DET cana-441 83 8 convoluted convoluted ADJ cana-441 83 9 communications communication NOUN cana-441 83 10 on on ADP cana-441 83 11 applied apply VERB cana-441 83 12 nonlinear nonlinear ADJ cana-441 83 13 analysis analysis NOUN cana-441 83 14 issn issn NOUN cana-441 83 15 : : PUNCT cana-441 83 16 1074 1074 NUM cana-441 83 17 - - PUNCT cana-441 83 18 133x 133x NUM cana-441 83 19 vol vol NOUN cana-441 83 20 31 31 NUM cana-441 83 21 no no NOUN cana-441 83 22 . . NOUN cana-441 83 23 1 1 NUM cana-441 83 24 ( ( PUNCT cana-441 83 25 2024 2024 NUM cana-441 83 26 ) ) PUNCT cana-441 83 27 322 322 NUM cana-441 83 28 https://internationalpubls.com https://internationalpubls.com X cana-441 83 29 gaussian gaussian ADJ cana-441 83 30 filtering filtering NOUN cana-441 83 31 ( ( PUNCT cana-441 83 32 cgf cgf PROPN cana-441 83 33 ) ) PUNCT cana-441 83 34 technique technique NOUN cana-441 83 35 and and CCONJ cana-441 83 36 segmented segment VERB cana-441 83 37 into into ADP cana-441 83 38 non non ADJ cana-441 83 39 - - ADJ cana-441 83 40 overlapping overlapping ADJ cana-441 83 41 partsusing partsuse VERB cana-441 83 42 the the DET cana-441 83 43 sparse sparse ADJ cana-441 83 44 space space NOUN cana-441 83 45 segmentation segmentation NOUN cana-441 83 46 ( ( PUNCT cana-441 83 47 s3 s3 NOUN cana-441 83 48 ) ) PUNCT cana-441 83 49 technique technique NOUN cana-441 83 50 . . PUNCT cana-441 84 1 contrast contrast NOUN cana-441 84 2 , , PUNCT cana-441 84 3 correlation correlation NOUN cana-441 84 4 , , PUNCT cana-441 84 5 mean mean VERB cana-441 84 6 , , PUNCT cana-441 84 7 and and CCONJ cana-441 84 8 entropy entropy NOUN cana-441 84 9 features feature NOUN cana-441 84 10 are be AUX cana-441 84 11 extracted extract VERB cana-441 84 12 from from ADP cana-441 84 13 the the DET cana-441 84 14 segmented segment VERB cana-441 84 15 portions portion NOUN cana-441 84 16 using use VERB cana-441 84 17 the the DET cana-441 84 18 multi multi ADJ cana-441 84 19 - - ADJ cana-441 84 20 feature feature ADJ cana-441 84 21 extraction extraction NOUN cana-441 84 22 model model NOUN cana-441 84 23 . . PUNCT cana-441 85 1 the the DET cana-441 85 2 deep deep ADJ cana-441 85 3 recurrent recurrent ADJ cana-441 85 4 long long ADV cana-441 85 5 - - PUNCT cana-441 85 6 short short ADJ cana-441 85 7 term term NOUN cana-441 85 8 memory memory NOUN cana-441 85 9 ( ( PUNCT cana-441 85 10 drlstm drlstm NOUN cana-441 85 11 ) ) PUNCT cana-441 85 12 method method NOUN cana-441 85 13 is be AUX cana-441 85 14 used use VERB cana-441 85 15 to to PART cana-441 85 16 forecast forecast VERB cana-441 85 17 whether whether SCONJ cana-441 85 18 a a DET cana-441 85 19 disease disease NOUN cana-441 85 20 affected affect VERB cana-441 85 21 person person NOUN cana-441 85 22 would would AUX cana-441 85 23 be be AUX cana-441 85 24 classified classify VERB cana-441 85 25 as as ADP cana-441 85 26 normal normal ADJ cana-441 85 27 or or CCONJ cana-441 85 28 abnormal abnormal ADJ cana-441 85 29 . . PUNCT cana-441 86 1 results result VERB cana-441 86 2 analysis analysis NOUN cana-441 86 3 compares compare VERB cana-441 86 4 and and CCONJ cana-441 86 5 tests test VERB cana-441 86 6 the the DET cana-441 86 7 performance performance NOUN cana-441 86 8 of of ADP cana-441 86 9 the the DET cana-441 86 10 suggested suggest VERB cana-441 86 11 system system NOUN cana-441 86 12 using use VERB cana-441 86 13 multi multi ADJ cana-441 86 14 assessment assessment NOUN cana-441 86 15 metrics metric NOUN cana-441 86 16 . . PUNCT cana-441 87 1 kumar kumar PROPN cana-441 87 2 et et PROPN cana-441 87 3 al al PROPN cana-441 87 4 . . PUNCT cana-441 88 1 [ [ X cana-441 88 2 16 16 NUM cana-441 88 3 ] ] PUNCT cana-441 88 4 proposed propose VERB cana-441 88 5 that that SCONJ cana-441 88 6 in in ADP cana-441 88 7 order order NOUN cana-441 88 8 to to PART cana-441 88 9 recognize recognize VERB cana-441 88 10 and and CCONJ cana-441 88 11 categorize categorize VERB cana-441 88 12 brain brain NOUN cana-441 88 13 pictures picture NOUN cana-441 88 14 , , PUNCT cana-441 88 15 this this DET cana-441 88 16 paper paper NOUN cana-441 88 17 suggests suggest VERB cana-441 88 18 using use VERB cana-441 88 19 a a DET cana-441 88 20 deep deep ADJ cana-441 88 21 convolutional convolutional ADJ cana-441 88 22 neural neural ADJ cana-441 88 23 network network NOUN cana-441 88 24 with with ADP cana-441 88 25 a a DET cana-441 88 26 resnet152 resnet152 PROPN cana-441 88 27 transfer transfer NOUN cana-441 88 28 learning learning NOUN cana-441 88 29 model model NOUN cana-441 88 30 that that PRON cana-441 88 31 is be AUX cana-441 88 32 inspired inspire VERB cana-441 88 33 by by ADP cana-441 88 34 nature nature NOUN cana-441 88 35 . . PUNCT cana-441 89 1 the the DET cana-441 89 2 photos photo NOUN cana-441 89 3 are be AUX cana-441 89 4 first first ADV cana-441 89 5 pre pre VERB cana-441 89 6 - - VERB cana-441 89 7 processed processed ADJ cana-441 89 8 to to PART cana-441 89 9 reduce reduce VERB cana-441 89 10 noise noise NOUN cana-441 89 11 and and CCONJ cana-441 89 12 improve improve VERB cana-441 89 13 quality quality NOUN cana-441 89 14 , , PUNCT cana-441 89 15 and and CCONJ cana-441 89 16 then then ADV cana-441 89 17 the the DET cana-441 89 18 hybrid hybrid ADJ cana-441 89 19 deep deep ADJ cana-441 89 20 convolutional convolutional ADJ cana-441 89 21 neural neural ADJ cana-441 89 22 network network NOUN cana-441 89 23 with with ADP cana-441 89 24 nature nature NOUN cana-441 89 25 inspired inspire VERB cana-441 89 26 resnet152 resnet152 PROPN cana-441 89 27 transfer transfer NOUN cana-441 89 28 learning learning NOUN cana-441 89 29 ( ( PUNCT cana-441 89 30 hyb hyb NOUN cana-441 89 31 - - PUNCT cana-441 89 32 dcnn dcnn PROPN cana-441 89 33 - - PUNCT cana-441 89 34 resnet152tl resnet152tl PROPN cana-441 89 35 ) ) PUNCT cana-441 89 36 issued issue VERB cana-441 89 37 to to PART cana-441 89 38 classify classify VERB cana-441 89 39 the the DET cana-441 89 40 images image NOUN cana-441 89 41 . . PUNCT cana-441 90 1 utilizing utilize VERB cana-441 90 2 the the DET cana-441 90 3 cov-19 cov-19 PRON cana-441 90 4 optimization optimization NOUN cana-441 90 5 algorithm algorithm NOUN cana-441 90 6 ( ( PUNCT cana-441 90 7 cov-19oa cov-19oa NUM cana-441 90 8 ) ) PUNCT cana-441 90 9 , , PUNCT cana-441 90 10 the the DET cana-441 90 11 weight weight NOUN cana-441 90 12 parameters parameter NOUN cana-441 90 13 of of ADP cana-441 90 14 the the DET cana-441 90 15 hyb hyb NOUN cana-441 90 16 - - PUNCT cana-441 90 17 dcnn dcnn VERB cana-441 90 18 - - PUNCT cana-441 90 19 resnet152tl resnet152tl PROPN cana-441 90 20 are be AUX cana-441 90 21 adjusted adjust VERB cana-441 90 22 . . PUNCT cana-441 91 1 younis younis NOUN cana-441 91 2 et et PROPN cana-441 91 3 al al PROPN cana-441 91 4 . . PUNCT cana-441 92 1 [ [ X cana-441 92 2 17 17 NUM cana-441 92 3 ] ] PUNCT cana-441 92 4 presents present VERB cana-441 92 5 a a DET cana-441 92 6 study study NOUN cana-441 92 7 used use VERB cana-441 92 8 the the DET cana-441 92 9 visual visual ADJ cana-441 92 10 geometry geometry NOUN cana-441 92 11 group group NOUN cana-441 92 12 ( ( PUNCT cana-441 92 13 vgg16 vgg16 PROPN cana-441 92 14 ) ) PUNCT cana-441 92 15 to to PART cana-441 92 16 find find VERB cana-441 92 17 brain brain NOUN cana-441 92 18 tumors tumor NOUN cana-441 92 19 , , PUNCT cana-441 92 20 implement implement VERB cana-441 92 21 a a DET cana-441 92 22 convolutional convolutional ADJ cana-441 92 23 neural neural ADJ cana-441 92 24 network network NOUN cana-441 92 25 ( ( PUNCT cana-441 92 26 cnn cnn PROPN cana-441 92 27 ) ) PUNCT cana-441 92 28 model model NOUN cana-441 92 29 architecture architecture NOUN cana-441 92 30 , , PUNCT cana-441 92 31 and and CCONJ cana-441 92 32 set set VERB cana-441 92 33 parameters parameter NOUN cana-441 92 34 to to PART cana-441 92 35 train train VERB cana-441 92 36 the the DET cana-441 92 37 model model NOUN cana-441 92 38 for for ADP cana-441 92 39 this this DET cana-441 92 40 problem problem NOUN cana-441 92 41 . . PUNCT cana-441 93 1 a a DET cana-441 93 2 dataset dataset NOUN cana-441 93 3 for for ADP cana-441 93 4 the the DET cana-441 93 5 diagnosis diagnosis NOUN cana-441 93 6 of of ADP cana-441 93 7 brain brain NOUN cana-441 93 8 cancers cancer NOUN cana-441 93 9 using use VERB cana-441 93 10 mr mr PROPN cana-441 93 11 images image NOUN cana-441 93 12 , , PUNCT cana-441 93 13 consisting consist VERB cana-441 93 14 of of ADP cana-441 93 15 253 253 NUM cana-441 93 16 mri mri NOUN cana-441 93 17 brain brain NOUN cana-441 93 18 pictures picture NOUN cana-441 93 19 , , PUNCT cana-441 93 20 of of ADP cana-441 93 21 which which PRON cana-441 93 22 155 155 NUM cana-441 93 23 showed show VERB cana-441 93 24 tumors tumor NOUN cana-441 93 25 , , PUNCT cana-441 93 26 was be AUX cana-441 93 27 used use VERB cana-441 93 28 to to PART cana-441 93 29 test test VERB cana-441 93 30 the the DET cana-441 93 31 proposed propose VERB cana-441 93 32 technique technique NOUN cana-441 93 33 . . PUNCT cana-441 94 1 the the DET cana-441 94 2 algorithm algorithm NOUN cana-441 94 3 performed perform VERB cana-441 94 4 better well ADV cana-441 94 5 in in ADP cana-441 94 6 the the DET cana-441 94 7 testing testing NOUN cana-441 94 8 data datum NOUN cana-441 94 9 than than ADP cana-441 94 10 the the DET cana-441 94 11 existing exist VERB cana-441 94 12 standard standard ADJ cana-441 94 13 methods method NOUN cana-441 94 14 for for ADP cana-441 94 15 identifying identify VERB cana-441 94 16 brain brain NOUN cana-441 94 17 cancers cancer NOUN cana-441 94 18 , , PUNCT cana-441 94 19 with with ADP cana-441 94 20 an an DET cana-441 94 21 outstanding outstanding ADJ cana-441 94 22 accuracy accuracy NOUN cana-441 94 23 of of ADP cana-441 94 24 cnn cnn PROPN cana-441 94 25 96 96 NUM cana-441 94 26 % % NOUN cana-441 94 27 , , PUNCT cana-441 94 28 vgg16 vgg16 VERB cana-441 94 29 98.5 98.5 NUM cana-441 94 30 % % NOUN cana-441 94 31 , , PUNCT cana-441 94 32 and and CCONJ cana-441 94 33 ensemble ensemble ADJ cana-441 94 34 model model NOUN cana-441 94 35 98 98 NUM cana-441 94 36 . . PUNCT cana-441 95 1 14 14 NUM cana-441 95 2 % % NOUN cana-441 95 3 . . PUNCT cana-441 96 1 the the DET cana-441 96 2 study study NOUN cana-441 96 3 offers offer VERB cana-441 96 4 further further ADJ cana-441 96 5 advice advice NOUN cana-441 96 6 for for ADP cana-441 96 7 the the DET cana-441 96 8 planned plan VERB cana-441 96 9 research research NOUN cana-441 96 10 project project NOUN cana-441 96 11 . . PUNCT cana-441 97 1 malla malla PROPN cana-441 97 2 et et PROPN cana-441 97 3 al al PROPN cana-441 97 4 . . PUNCT cana-441 98 1 [ [ X cana-441 98 2 18 18 NUM cana-441 98 3 ] ] PUNCT cana-441 98 4 proposed propose VERB cana-441 98 5 that that SCONJ cana-441 98 6 if if SCONJ cana-441 98 7 a a DET cana-441 98 8 brain brain NOUN cana-441 98 9 tumor tumor NOUN cana-441 98 10 is be AUX cana-441 98 11 not not PART cana-441 98 12 adequately adequately ADV cana-441 98 13 diagnosed diagnose VERB cana-441 98 14 , , PUNCT cana-441 98 15 it it PRON cana-441 98 16 can can AUX cana-441 98 17 cause cause VERB cana-441 98 18 major major ADJ cana-441 98 19 health health NOUN cana-441 98 20 problems problem NOUN cana-441 98 21 and and CCONJ cana-441 98 22 even even ADV cana-441 98 23 death death NOUN cana-441 98 24 . . PUNCT cana-441 99 1 recent recent ADJ cana-441 99 2 studies study NOUN cana-441 99 3 have have AUX cana-441 99 4 demonstrated demonstrate VERB cana-441 99 5 that that SCONJ cana-441 99 6 techniques technique NOUN cana-441 99 7 based base VERB cana-441 99 8 on on ADP cana-441 99 9 deep deep ADJ cana-441 99 10 convolutional convolutional ADJ cana-441 99 11 neural neural ADJ cana-441 99 12 network network NOUN cana-441 99 13 perform perform VERB cana-441 99 14 very very ADV cana-441 99 15 well well ADV cana-441 99 16 in in ADP cana-441 99 17 detection detection NOUN cana-441 99 18 and and CCONJ cana-441 99 19 classification classification NOUN cana-441 99 20 tasks task NOUN cana-441 99 21 . . PUNCT cana-441 100 1 however however ADV cana-441 100 2 , , PUNCT cana-441 100 3 the the DET cana-441 100 4 training training NOUN cana-441 100 5 of of ADP cana-441 100 6 data datum NOUN cana-441 100 7 samples sample NOUN cana-441 100 8 determines determine VERB cana-441 100 9 how how SCONJ cana-441 100 10 accurate accurate ADJ cana-441 100 11 dcnn dcnn ADJ cana-441 100 12 architectures architecture NOUN cana-441 100 13 are be AUX cana-441 100 14 . . PUNCT cana-441 101 1 in in ADP cana-441 101 2 this this DET cana-441 101 3 study study NOUN cana-441 101 4 , , PUNCT cana-441 101 5 a a DET cana-441 101 6 transfer transfer NOUN cana-441 101 7 learning learning NOUN cana-441 101 8 - - PUNCT cana-441 101 9 based base VERB cana-441 101 10 dcnn dcnn ADJ cana-441 101 11 system system NOUN cana-441 101 12 for for ADP cana-441 101 13 classifying classify VERB cana-441 101 14 brain brain NOUN cana-441 101 15 tumors tumor NOUN cana-441 101 16 is be AUX cana-441 101 17 proposed.it proposed.it PRON cana-441 101 18 makes make VERB cana-441 101 19 use use NOUN cana-441 101 20 of of ADP cana-441 101 21 a a DET cana-441 101 22 pre pre ADJ cana-441 101 23 - - ADJ cana-441 101 24 trained trained ADJ cana-441 101 25 dcnn dcnn ADJ cana-441 101 26 architecture architecture NOUN cana-441 101 27 called call VERB cana-441 101 28 vggnet vggnet NOUN cana-441 101 29 and and CCONJ cana-441 101 30 an an DET cana-441 101 31 output output NOUN cana-441 101 32 global global ADJ cana-441 101 33 average average ADJ cana-441 101 34 pooling pooling NOUN cana-441 101 35 ( ( PUNCT cana-441 101 36 gap gap NOUN cana-441 101 37 ) ) PUNCT cana-441 101 38 layer layer NOUN cana-441 101 39 . . PUNCT cana-441 102 1 on on ADP cana-441 102 2 the the DET cana-441 102 3 figshare figshare NOUN cana-441 102 4 dataset dataset NOUN cana-441 102 5 , , PUNCT cana-441 102 6 the the DET cana-441 102 7 proposed propose VERB cana-441 102 8 method method NOUN cana-441 102 9 performs perform VERB cana-441 102 10 better well ADJ cana-441 102 11 than than ADP cana-441 102 12 competing compete VERB cana-441 102 13 deep deep ADJ cana-441 102 14 learning learning NOUN cana-441 102 15 - - PUNCT cana-441 102 16 based base VERB cana-441 102 17 approaches approach NOUN cana-441 102 18 . . PUNCT cana-441 103 1 mzoughi mzoughi PROPN cana-441 103 2 et et PROPN cana-441 103 3 al al PROPN cana-441 103 4 . . PUNCT cana-441 104 1 [ [ X cana-441 104 2 19 19 NUM cana-441 104 3 ] ] X cana-441 104 4 paper paper NOUN cana-441 104 5 provides provide VERB cana-441 104 6 an an DET cana-441 104 7 analysis analysis NOUN cana-441 104 8 of of ADP cana-441 104 9 current current ADJ cana-441 104 10 cad cad NOUN cana-441 104 11 tool tool NOUN cana-441 104 12 trends trend NOUN cana-441 104 13 for for ADP cana-441 104 14 investigating investigate VERB cana-441 104 15 gliomas glioma NOUN cana-441 104 16 brain brain NOUN cana-441 104 17 tumors tumor NOUN cana-441 104 18 in in ADP cana-441 104 19 relation relation NOUN cana-441 104 20 to to ADP cana-441 104 21 deep deep ADJ cana-441 104 22 learning learning NOUN cana-441 104 23 and and CCONJ cana-441 104 24 machine machine NOUN cana-441 104 25 learning learning NOUN cana-441 104 26 . . PUNCT cana-441 105 1 three three NUM cana-441 105 2 basic basic ADJ cana-441 105 3 phases phase NOUN cana-441 105 4 are be AUX cana-441 105 5 commonly commonly ADV cana-441 105 6 included include VERB cana-441 105 7 in in ADP cana-441 105 8 the the DET cana-441 105 9 deployment deployment NOUN cana-441 105 10 of of ADP cana-441 105 11 cad cad PROPN cana-441 105 12 systems system NOUN cana-441 105 13 : : PUNCT cana-441 105 14 pre pre ADJ cana-441 105 15 - - ADJ cana-441 105 16 processing processing ADJ cana-441 105 17 , , PUNCT cana-441 105 18 segmentation segmentation NOUN cana-441 105 19 , , PUNCT cana-441 105 20 and and CCONJ cana-441 105 21 tumor tumor NOUN cana-441 105 22 grade grade NOUN cana-441 105 23 classification classification NOUN cana-441 105 24 . . PUNCT cana-441 106 1 the the DET cana-441 106 2 research research NOUN cana-441 106 3 also also ADV cana-441 106 4 addresses address VERB cana-441 106 5 an an DET cana-441 106 6 objective objective ADJ cana-441 106 7 evaluation evaluation NOUN cana-441 106 8 of of ADP cana-441 106 9 state state NOUN cana-441 106 10 of of ADP cana-441 106 11 the the DET cana-441 106 12 art art NOUN cana-441 106 13 dl dl PROPN cana-441 106 14 based base VERB cana-441 106 15 techniques technique NOUN cana-441 106 16 for for ADP cana-441 106 17 mr mr PROPN cana-441 106 18 image image NOUN cana-441 106 19 processing processing NOUN cana-441 106 20 . . PUNCT cana-441 107 1 the the DET cana-441 107 2 findings finding NOUN cana-441 107 3 of of ADP cana-441 107 4 the the DET cana-441 107 5 approaches approach NOUN cana-441 107 6 evaluated evaluate VERB cana-441 107 7 suggest suggest VERB cana-441 107 8 that that SCONJ cana-441 107 9 using use VERB cana-441 107 10 a a DET cana-441 107 11 combination combination NOUN cana-441 107 12 of of ADP cana-441 107 13 a a DET cana-441 107 14 variety variety NOUN cana-441 107 15 of of ADP cana-441 107 16 dl dl PROPN cana-441 107 17 techniques technique NOUN cana-441 107 18 will will AUX cana-441 107 19 result result VERB cana-441 107 20 in in ADP cana-441 107 21 more more ADV cana-441 107 22 accurate accurate ADJ cana-441 107 23 segmentation segmentation NOUN cana-441 107 24 results result NOUN cana-441 107 25 than than ADP cana-441 107 26 depending depend VERB cana-441 107 27 just just ADV cana-441 107 28 on on ADP cana-441 107 29 one one NUM cana-441 107 30 particular particular ADJ cana-441 107 31 methodology methodology NOUN cana-441 107 32 . . PUNCT cana-441 108 1 3 3 X cana-441 108 2 . . NUM cana-441 108 3 proposed propose VERB cana-441 108 4 methodology methodology NOUN cana-441 108 5 brain brain NOUN cana-441 108 6 tumor tumor NOUN cana-441 108 7 segmentation segmentation NOUN cana-441 108 8 and and CCONJ cana-441 108 9 classification classification NOUN cana-441 108 10 using use VERB cana-441 108 11 deep deep ADJ cana-441 108 12 residual residual ADJ cana-441 108 13 convolutional convolutional ADJ cana-441 108 14 neural neural ADJ cana-441 108 15 networks network NOUN cana-441 108 16 ( ( PUNCT cana-441 108 17 cnn cnn PROPN cana-441 108 18 ) ) PUNCT cana-441 108 19 involves involve VERB cana-441 108 20 harnessing harness VERB cana-441 108 21 advanced advanced ADJ cana-441 108 22 neural neural ADJ cana-441 108 23 network network NOUN cana-441 108 24 architectures architecture NOUN cana-441 108 25 to to ADP cana-441 108 26 precisely precisely ADV cana-441 108 27 delineate delineate VERB cana-441 108 28 tumor tumor NOUN cana-441 108 29 regions region NOUN cana-441 108 30 from from ADP cana-441 108 31 medical medical ADJ cana-441 108 32 imaging imaging NOUN cana-441 108 33 data datum NOUN cana-441 108 34 and and CCONJ cana-441 108 35 categorize categorize VERB cana-441 108 36 tumor tumor NOUN cana-441 108 37 types type NOUN cana-441 108 38 . . PUNCT cana-441 109 1 at at ADP cana-441 109 2 the the DET cana-441 109 3 core core NOUN cana-441 109 4 of of ADP cana-441 109 5 these these DET cana-441 109 6 networks network NOUN cana-441 109 7 lies lie VERB cana-441 109 8 the the DET cana-441 109 9 convolution convolution NOUN cana-441 109 10 operation operation NOUN cana-441 109 11 , , PUNCT cana-441 109 12 mathematically mathematically ADV cana-441 109 13 represented represent VERB cana-441 109 14 by by ADP cana-441 109 15 the the DET cana-441 109 16 equation equation NOUN cana-441 109 17 ( ( PUNCT cana-441 109 18 𝑓 𝑓 DET cana-441 109 19 βˆ— βˆ— NOUN cana-441 109 20 𝑔)(𝑑 𝑔)(𝑑 PUNCT cana-441 109 21 ) ) PUNCT cana-441 109 22 βˆ’ βˆ’ PROPN cana-441 109 23 communications communication NOUN cana-441 109 24 on on ADP cana-441 109 25 applied apply VERB cana-441 109 26 nonlinear nonlinear ADJ cana-441 109 27 analysis analysis NOUN cana-441 109 28 issn issn NOUN cana-441 109 29 : : PUNCT cana-441 109 30 1074 1074 NUM cana-441 109 31 - - PUNCT cana-441 109 32 133x 133x NUM cana-441 109 33 vol vol NOUN cana-441 109 34 31 31 NUM cana-441 109 35 no no NOUN cana-441 109 36 . . NOUN cana-441 109 37 1 1 NUM cana-441 109 38 ( ( PUNCT cana-441 109 39 2024 2024 NUM cana-441 109 40 ) ) PUNCT cana-441 109 41 323 323 NUM cana-441 109 42 https://internationalpubls.com https://internationalpubls.com X cana-441 109 43 ∫ ∫ PROPN cana-441 110 1 βˆ’βˆž βˆ’βˆž PUNCT cana-441 110 2 ∞ ∞ PROPN cana-441 110 3 β€Š β€Š SPACE cana-441 110 4 𝑓(𝜏)𝑔(𝑑 𝑓(𝜏)𝑔(𝑑 PROPN cana-441 110 5 βˆ’ βˆ’ PROPN cana-441 110 6 𝜏)π‘‘πœ. 𝜏)π‘‘πœ. NOUN cana-441 110 7 this this DET cana-441 110 8 operation operation NOUN cana-441 110 9 is be AUX cana-441 110 10 pivotal pivotal ADJ cana-441 110 11 in in ADP cana-441 110 12 capturing capture VERB cana-441 110 13 spatial spatial ADJ cana-441 110 14 patterns pattern NOUN cana-441 110 15 within within ADP cana-441 110 16 input input NOUN cana-441 110 17 images image NOUN cana-441 110 18 through through ADP cana-441 110 19 local local ADJ cana-441 110 20 operations operation NOUN cana-441 110 21 , , PUNCT cana-441 110 22 thereby thereby ADV cana-441 110 23 enabling enable VERB cana-441 110 24 the the DET cana-441 110 25 network network NOUN cana-441 110 26 to to PART cana-441 110 27 process process VERB cana-441 110 28 raw raw ADJ cana-441 110 29 imaging imaging NOUN cana-441 110 30 data datum NOUN cana-441 110 31 and and CCONJ cana-441 110 32 extract extract VERB cana-441 110 33 relevant relevant ADJ cana-441 110 34 features feature NOUN cana-441 110 35 indicative indicative ADJ cana-441 110 36 of of ADP cana-441 110 37 tumor tumor NOUN cana-441 110 38 presence presence NOUN cana-441 110 39 . . PUNCT cana-441 111 1 output output NOUN cana-441 111 2 = = SYM cana-441 111 3 𝐹(π‘₯ 𝐹(π‘₯ PROPN cana-441 111 4 , , PUNCT cana-441 111 5 { { PUNCT cana-441 111 6 π‘Šπ‘– π‘Šπ‘– PROPN cana-441 111 7 } } PUNCT cana-441 111 8 ) ) PUNCT cana-441 112 1 + + CCONJ cana-441 112 2 π‘₯ π‘₯ X cana-441 112 3 ( ( PUNCT cana-441 112 4 21 21 NUM cana-441 112 5 ) ) PUNCT cana-441 112 6 𝐹(π‘₯ 𝐹(π‘₯ VERB cana-441 112 7 , , PUNCT cana-441 112 8 { { PUNCT cana-441 112 9 π‘Šπ‘– π‘Šπ‘– PROPN cana-441 112 10 } } PUNCT cana-441 112 11 ) ) PUNCT cana-441 112 12 = = SYM cana-441 112 13 π‘Š2𝜎(π‘Š1π‘₯ π‘Š2𝜎(π‘Š1π‘₯ PROPN cana-441 112 14 ) ) PUNCT cana-441 112 15 ( ( PUNCT cana-441 112 16 22 22 NUM cana-441 112 17 ) ) PUNCT cana-441 112 18 𝜎(𝑧 𝜎(𝑧 NUM cana-441 112 19 ) ) PUNCT cana-441 112 20 = = NOUN cana-441 112 21 1 1 NUM cana-441 112 22 1+π‘’βˆ’π‘§ 1+π‘’βˆ’π‘§ NUM cana-441 112 23 ( ( PUNCT cana-441 112 24 23 23 NUM cana-441 112 25 ) ) PUNCT cana-441 112 26 in in ADP cana-441 112 27 deep deep ADJ cana-441 112 28 residual residual ADJ cana-441 112 29 cnns cnn NOUN cana-441 112 30 , , PUNCT cana-441 112 31 the the DET cana-441 112 32 output output NOUN cana-441 112 33 of of ADP cana-441 112 34 a a DET cana-441 112 35 convolutional convolutional ADJ cana-441 112 36 layer layer NOUN cana-441 112 37 𝑍[𝑖 𝑍[𝑖 NOUN cana-441 112 38 ] ] PUNCT cana-441 112 39 is be AUX cana-441 112 40 computed compute VERB cana-441 112 41 using use VERB cana-441 112 42 the the DET cana-441 112 43 equation equation NOUN cana-441 112 44 𝑍[𝑙 𝑍[𝑙 NOUN cana-441 112 45 ] ] X cana-441 112 46 = = PUNCT cana-441 112 47 π‘Š[𝑙 π‘Š[𝑙 VERB cana-441 112 48 ] ] X cana-441 112 49 βˆ— βˆ— X cana-441 112 50 𝐴[π‘™βˆ’1 𝐴[π‘™βˆ’1 X cana-441 112 51 ] ] X cana-441 113 1 + + X cana-441 113 2 𝑏[𝑙 𝑏[𝑙 NOUN cana-441 113 3 ] ] PUNCT cana-441 113 4 , , PUNCT cana-441 113 5 where where SCONJ cana-441 113 6 π‘Š[𝑙 π‘Š[𝑙 VERB cana-441 113 7 ] ] PUNCT cana-441 113 8 represents represent VERB cana-441 113 9 the the DET cana-441 113 10 weights weight NOUN cana-441 113 11 , , PUNCT cana-441 113 12 𝐴[π‘™βˆ’1 𝐴[π‘™βˆ’1 X cana-441 113 13 ] ] PUNCT cana-441 113 14 is be AUX cana-441 113 15 the the DET cana-441 113 16 input input NOUN cana-441 113 17 from from ADP cana-441 113 18 the the DET cana-441 113 19 previous previous ADJ cana-441 113 20 layer layer NOUN cana-441 113 21 , , PUNCT cana-441 113 22 and and CCONJ cana-441 113 23 𝑏[𝑙 𝑏[𝑙 NOUN cana-441 113 24 ] ] PUNCT cana-441 113 25 is be AUX cana-441 113 26 the the DET cana-441 113 27 bias bias NOUN cana-441 113 28 term term NOUN cana-441 113 29 . . PUNCT cana-441 114 1 this this DET cana-441 114 2 equation equation NOUN cana-441 114 3 encapsulates encapsulate VERB cana-441 114 4 the the DET cana-441 114 5 transformation transformation NOUN cana-441 114 6 of of ADP cana-441 114 7 features feature NOUN cana-441 114 8 through through ADP cana-441 114 9 the the DET cana-441 114 10 convolutional convolutional ADJ cana-441 114 11 operation operation NOUN cana-441 114 12 , , PUNCT cana-441 114 13 followed follow VERB cana-441 114 14 by by ADP cana-441 114 15 the the DET cana-441 114 16 addition addition NOUN cana-441 114 17 of of ADP cana-441 114 18 bias bias NOUN cana-441 114 19 to to PART cana-441 114 20 introduce introduce VERB cana-441 114 21 non non ADJ cana-441 114 22 - - NOUN cana-441 114 23 linearity linearity NOUN cana-441 114 24 into into ADP cana-441 114 25 the the DET cana-441 114 26 network network NOUN cana-441 114 27 . . PUNCT cana-441 115 1 the the DET cana-441 115 2 rectified rectify VERB cana-441 115 3 linear linear PROPN cana-441 115 4 unit unit NOUN cana-441 115 5 ( ( PUNCT cana-441 115 6 relu relu NOUN cana-441 115 7 ) ) PUNCT cana-441 115 8 activation activation NOUN cana-441 115 9 function function NOUN cana-441 115 10 , , PUNCT cana-441 115 11 defined define VERB cana-441 115 12 as as ADP cana-441 115 13 𝐴 𝐴 PROPN cana-441 115 14 = = PUNCT cana-441 115 15 max(0 max(0 NOUN cana-441 115 16 , , PUNCT cana-441 115 17 𝑍 𝑍 PROPN cana-441 115 18 ) ) PUNCT cana-441 115 19 , , PUNCT cana-441 115 20 is be AUX cana-441 115 21 applied apply VERB cana-441 115 22 element element NOUN cana-441 115 23 - - ADJ cana-441 115 24 wise wise ADJ cana-441 115 25 to to ADP cana-441 115 26 the the DET cana-441 115 27 output output NOUN cana-441 115 28 of of ADP cana-441 115 29 convolutional convolutional ADJ cana-441 115 30 layers layer NOUN cana-441 115 31 . . PUNCT cana-441 116 1 relu relu NOUN cana-441 116 2 introduces introduce VERB cana-441 116 3 non non ADJ cana-441 116 4 - - NOUN cana-441 116 5 linearity linearity NOUN cana-441 116 6 by by ADP cana-441 116 7 outputting output VERB cana-441 116 8 the the DET cana-441 116 9 input input NOUN cana-441 116 10 if if SCONJ cana-441 116 11 positive positive ADJ cana-441 116 12 and and CCONJ cana-441 116 13 zero zero NUM cana-441 116 14 otherwise otherwise ADV cana-441 116 15 , , PUNCT cana-441 116 16 enabling enable VERB cana-441 116 17 the the DET cana-441 116 18 network network NOUN cana-441 116 19 to to PART cana-441 116 20 learn learn VERB cana-441 116 21 complex complex ADJ cana-441 116 22 mappings mapping NOUN cana-441 116 23 between between ADP cana-441 116 24 input input NOUN cana-441 116 25 and and CCONJ cana-441 116 26 output output NOUN cana-441 116 27 spaces space NOUN cana-441 116 28 . . PUNCT cana-441 117 1 this this PRON cana-441 117 2 non non ADJ cana-441 117 3 - - ADJ cana-441 117 4 linearity linearity NOUN cana-441 117 5 is be AUX cana-441 117 6 crucial crucial ADJ cana-441 117 7 for for ADP cana-441 117 8 capturing capture VERB cana-441 117 9 the the DET cana-441 117 10 intricate intricate ADJ cana-441 117 11 relationships relationship NOUN cana-441 117 12 between between ADP cana-441 117 13 image image NOUN cana-441 117 14 features feature NOUN cana-441 117 15 and and CCONJ cana-441 117 16 tumor tumor NOUN cana-441 117 17 presence presence NOUN cana-441 117 18 . . PUNCT cana-441 118 1 πœ† πœ† DET cana-441 118 2 2π‘š 2π‘š NOUN cana-441 118 3 |π‘Š|𝐹 |π‘Š|𝐹 ADP cana-441 118 4 2 2 NUM cana-441 118 5 ( ( PUNCT cana-441 118 6 24 24 NUM cana-441 118 7 ) ) PUNCT cana-441 118 8 𝑝(πœƒ|𝐷 𝑝(πœƒ|𝐷 PROPN cana-441 118 9 ) ) PUNCT cana-441 118 10 = = SYM cana-441 118 11 𝑝(𝐷|πœƒ)𝑝(πœƒ 𝑝(𝐷|πœƒ)𝑝(πœƒ X cana-441 118 12 ) ) PUNCT cana-441 118 13 𝑝(𝐷 𝑝(𝐷 PROPN cana-441 118 14 ) ) PUNCT cana-441 118 15 ( ( PUNCT cana-441 118 16 25 25 NUM cana-441 118 17 ) ) PUNCT cana-441 118 18 maximize maximize VERB cana-441 118 19 β€ˆ β€ˆ SPACE cana-441 118 20 |π‘‹π‘Š |π‘‹π‘Š NOUN cana-441 118 21 βˆ’ βˆ’ NOUN cana-441 118 22 𝑋|𝐹 𝑋|𝐹 NOUN cana-441 118 23 2 2 NUM cana-441 118 24 ( ( PUNCT cana-441 118 25 26 26 NUM cana-441 118 26 ) ) PUNCT cana-441 118 27 𝐷𝑆𝐢 𝐷𝑆𝐢 NOUN cana-441 118 28 = = SYM cana-441 118 29 2|π‘‹βˆ©π‘Œ| 2|π‘‹βˆ©π‘Œ| NUM cana-441 118 30 |𝑋|+|π‘Œ| |𝑋|+|π‘Œ| ADJ cana-441 118 31 ( ( PUNCT cana-441 118 32 27 27 NUM cana-441 118 33 ) ) PUNCT cana-441 118 34 πΌπ‘œπ‘ˆ πΌπ‘œπ‘ˆ NOUN cana-441 119 1 = = PUNCT cana-441 119 2 |π‘‹βˆ©π‘Œ| |π‘‹βˆ©π‘Œ| X cana-441 119 3 |𝑋βˆͺπ‘Œ| |𝑋βˆͺπ‘Œ| PUNCT cana-441 119 4 ( ( PUNCT cana-441 119 5 28 28 NUM cana-441 119 6 ) ) PUNCT cana-441 119 7 pooling pool VERB cana-441 119 8 layers layer NOUN cana-441 119 9 , , PUNCT cana-441 119 10 as as SCONJ cana-441 119 11 represented represent VERB cana-441 119 12 by by ADP cana-441 119 13 the the DET cana-441 119 14 equation equation NOUN cana-441 119 15 𝐴𝑖,𝑗 𝐴𝑖,𝑗 PROPN cana-441 119 16 = = PUNCT cana-441 119 17 maxπ‘š,𝑛 maxπ‘š,𝑛 PROPN cana-441 119 18 β€Š β€Š SPACE cana-441 119 19 ( ( PUNCT cana-441 119 20 𝐴(π‘š,𝑛 𝐴(π‘š,𝑛 PROPN cana-441 119 21 ) ) PUNCT cana-441 119 22 ) ) PUNCT cana-441 119 23 , , PUNCT cana-441 119 24 reduce reduce VERB cana-441 119 25 the the DET cana-441 119 26 spatial spatial ADJ cana-441 119 27 dimensions dimension NOUN cana-441 119 28 of of ADP cana-441 119 29 feature feature NOUN cana-441 119 30 maps map NOUN cana-441 119 31 while while SCONJ cana-441 119 32 retaining retain VERB cana-441 119 33 essential essential ADJ cana-441 119 34 information information NOUN cana-441 119 35 . . PUNCT cana-441 120 1 by by ADP cana-441 120 2 selecting select VERB cana-441 120 3 the the DET cana-441 120 4 maximum maximum ADJ cana-441 120 5 value value NOUN cana-441 120 6 from from ADP cana-441 120 7 each each DET cana-441 120 8 patch patch NOUN cana-441 120 9 of of ADP cana-441 120 10 the the DET cana-441 120 11 feature feature NOUN cana-441 120 12 map map NOUN cana-441 120 13 , , PUNCT cana-441 120 14 pooling pool VERB cana-441 120 15 layers layer NOUN cana-441 120 16 down down ADP cana-441 120 17 sample sample NOUN cana-441 120 18 the the DET cana-441 120 19 data datum NOUN cana-441 120 20 , , PUNCT cana-441 120 21 focusing focus VERB cana-441 120 22 on on ADP cana-441 120 23 the the DET cana-441 120 24 most most ADV cana-441 120 25 salient salient NOUN cana-441 120 26 features feature NOUN cana-441 120 27 while while SCONJ cana-441 120 28 discarding discard VERB cana-441 120 29 irrelevant irrelevant ADJ cana-441 120 30 details detail NOUN cana-441 120 31 . . PUNCT cana-441 121 1 this this DET cana-441 121 2 down down ADV cana-441 121 3 sampling sample VERB cana-441 121 4 aids aid NOUN cana-441 121 5 in in ADP cana-441 121 6 reducing reduce VERB cana-441 121 7 computational computational ADJ cana-441 121 8 complexity complexity NOUN cana-441 121 9 and and CCONJ cana-441 121 10 extracting extract VERB cana-441 121 11 robust robust ADJ cana-441 121 12 features feature NOUN cana-441 121 13 for for ADP cana-441 121 14 subsequent subsequent ADJ cana-441 121 15 processing processing NOUN cana-441 121 16 . . PUNCT cana-441 122 1 fully fully ADV cana-441 122 2 connected connect VERB cana-441 122 3 layers layer NOUN cana-441 122 4 integrate integrate VERB cana-441 122 5 the the DET cana-441 122 6 extracted extract VERB cana-441 122 7 features feature NOUN cana-441 122 8 for for ADP cana-441 122 9 tumor tumor NOUN cana-441 122 10 classification classification NOUN cana-441 122 11 tasks task NOUN cana-441 122 12 , , PUNCT cana-441 122 13 as as SCONJ cana-441 122 14 denoted denote VERB cana-441 122 15 by by ADP cana-441 122 16 the the DET cana-441 122 17 equation equation NOUN cana-441 122 18 𝑍 𝑍 PROPN cana-441 122 19 = = SYM cana-441 122 20 π‘Šπ΄ π‘Šπ΄ PROPN cana-441 122 21 + + CCONJ cana-441 122 22 𝑏. 𝑏. VERB cana-441 122 23 in in ADP cana-441 122 24 these these DET cana-441 122 25 layers layer NOUN cana-441 122 26 , , PUNCT cana-441 122 27 the the DET cana-441 122 28 dot dot NOUN cana-441 122 29 product product NOUN cana-441 122 30 of of ADP cana-441 122 31 the the DET cana-441 122 32 input input NOUN cana-441 122 33 and and CCONJ cana-441 122 34 weight weight NOUN cana-441 122 35 matrices matrix NOUN cana-441 122 36 is be AUX cana-441 122 37 computed compute VERB cana-441 122 38 , , PUNCT cana-441 122 39 followed follow VERB cana-441 122 40 by by ADP cana-441 122 41 the the DET cana-441 122 42 addition addition NOUN cana-441 122 43 of of ADP cana-441 122 44 a a DET cana-441 122 45 bias bias NOUN cana-441 122 46 term term NOUN cana-441 122 47 . . PUNCT cana-441 123 1 this this DET cana-441 123 2 operation operation NOUN cana-441 123 3 enables enable VERB cana-441 123 4 the the DET cana-441 123 5 network network NOUN cana-441 123 6 to to PART cana-441 123 7 learn learn VERB cana-441 123 8 complex complex ADJ cana-441 123 9 decision decision NOUN cana-441 123 10 boundaries boundary NOUN cana-441 123 11 and and CCONJ cana-441 123 12 classify classify VERB cana-441 123 13 tumors tumor NOUN cana-441 123 14 into into ADP cana-441 123 15 different different ADJ cana-441 123 16 categories category NOUN cana-441 123 17 based base VERB cana-441 123 18 on on ADP cana-441 123 19 the the DET cana-441 123 20 extracted extract VERB cana-441 123 21 features feature NOUN cana-441 123 22 . . PUNCT cana-441 124 1 the the DET cana-441 124 2 cross cross ADJ cana-441 124 3 - - ADJ cana-441 124 4 entropy entropy ADJ cana-441 124 5 loss loss NOUN cana-441 124 6 function function NOUN cana-441 124 7 , , PUNCT cana-441 124 8 expressed express VERB cana-441 124 9 as as ADP cana-441 124 10 ( ( PUNCT cana-441 124 11 𝑦 𝑦 NOUN cana-441 124 12 , , PUNCT cana-441 124 13 οΏ½ οΏ½ NOUN cana-441 124 14 Μ‚ Μ‚ NOUN cana-441 124 15 οΏ½ οΏ½ NOUN cana-441 124 16 ) ) PUNCT cana-441 124 17 = = PUNCT cana-441 125 1 βˆ’ βˆ’ PROPN cana-441 125 2 1 1 NUM cana-441 125 3 π‘š π‘š PROPN cana-441 125 4 βˆ‘π‘–βˆ’1 βˆ‘π‘–βˆ’1 PROPN cana-441 125 5 π‘š π‘š PROPN cana-441 125 6 β€Š β€Š SPACE cana-441 125 7 𝑦𝑖log 𝑦𝑖log PROPN cana-441 125 8 ( ( PUNCT cana-441 125 9 οΏ½ οΏ½ PROPN cana-441 125 10 Μ‚ Μ‚ VERB cana-441 125 11 οΏ½ οΏ½ NOUN cana-441 125 12 𝑖 𝑖 NUM cana-441 125 13 ) ) PUNCT cana-441 126 1 + + CCONJ cana-441 126 2 ( ( PUNCT cana-441 126 3 1 1 NUM cana-441 126 4 βˆ’ βˆ’ NOUN cana-441 126 5 𝑦𝑖)log 𝑦𝑖)log NOUN cana-441 126 6 ( ( PUNCT cana-441 126 7 1 1 NUM cana-441 126 8 βˆ’ βˆ’ PRON cana-441 126 9 οΏ½ οΏ½ PROPN cana-441 126 10 Μ‚ Μ‚ VERB cana-441 126 11 οΏ½ οΏ½ NOUN cana-441 126 12 𝑖 𝑖 NUM cana-441 126 13 ) ) PUNCT cana-441 126 14 , , PUNCT cana-441 126 15 quantifies quantify VERB cana-441 126 16 the the DET cana-441 126 17 discrepancy discrepancy NOUN cana-441 126 18 between between ADP cana-441 126 19 predicted predict VERB cana-441 126 20 and and CCONJ cana-441 126 21 actual actual ADJ cana-441 126 22 tumor tumor NOUN cana-441 126 23 labels label NOUN cana-441 126 24 . . PUNCT cana-441 127 1 by by ADP cana-441 127 2 penalizing penalize VERB cana-441 127 3 deviations deviation NOUN cana-441 127 4 from from ADP cana-441 127 5 the the DET cana-441 127 6 ground ground NOUN cana-441 127 7 truth truth NOUN cana-441 127 8 labels label NOUN cana-441 127 9 , , PUNCT cana-441 127 10 the the DET cana-441 127 11 loss loss NOUN cana-441 127 12 function function NOUN cana-441 127 13 guides guide VERB cana-441 127 14 the the DET cana-441 127 15 optimization optimization NOUN cana-441 127 16 communications communication NOUN cana-441 127 17 on on ADP cana-441 127 18 applied apply VERB cana-441 127 19 nonlinear nonlinear ADJ cana-441 127 20 analysis analysis NOUN cana-441 127 21 issn issn NOUN cana-441 127 22 : : PUNCT cana-441 127 23 1074 1074 NUM cana-441 127 24 - - PUNCT cana-441 127 25 133x 133x NUM cana-441 127 26 vol vol NOUN cana-441 127 27 31 31 NUM cana-441 127 28 no no NOUN cana-441 127 29 . . NOUN cana-441 127 30 1 1 NUM cana-441 127 31 ( ( PUNCT cana-441 127 32 2024 2024 NUM cana-441 127 33 ) ) PUNCT cana-441 127 34 324 324 NUM cana-441 127 35 https://internationalpubls.com https://internationalpubls.com X cana-441 127 36 process process NOUN cana-441 127 37 towards towards ADP cana-441 127 38 minimizing minimize VERB cana-441 127 39 prediction prediction NOUN cana-441 127 40 errors error NOUN cana-441 127 41 and and CCONJ cana-441 127 42 improving improve VERB cana-441 127 43 the the DET cana-441 127 44 network network NOUN cana-441 127 45 's 's PART cana-441 127 46 performance performance NOUN cana-441 127 47 in in ADP cana-441 127 48 tumor tumor NOUN cana-441 127 49 segmentation segmentation NOUN cana-441 127 50 and and CCONJ cana-441 127 51 classification classification NOUN cana-441 127 52 tasks task NOUN cana-441 127 53 . . PUNCT cana-441 128 1 gradient gradient ADJ cana-441 128 2 descent descent NOUN cana-441 128 3 optimization optimization NOUN cana-441 128 4 , , PUNCT cana-441 128 5 represented represent VERB cana-441 128 6 by by ADP cana-441 128 7 the the DET cana-441 128 8 equation equation NOUN cana-441 128 9 πœƒ πœƒ X cana-441 129 1 = = PUNCT cana-441 129 2 πœƒ πœƒ NUM cana-441 129 3 βˆ’ βˆ’ NOUN cana-441 129 4 𝛼 𝛼 NOUN cana-441 129 5 πœ•π½ πœ•π½ NOUN cana-441 129 6 πœ•πœƒ πœ•πœƒ NOUN cana-441 129 7 , , PUNCT cana-441 129 8 iteratively iteratively ADV cana-441 129 9 updates update VERB cana-441 129 10 the the DET cana-441 129 11 network network NOUN cana-441 129 12 parameters parameter NOUN cana-441 129 13 to to PART cana-441 129 14 minimize minimize VERB cana-441 129 15 the the DET cana-441 129 16 loss loss NOUN cana-441 129 17 function function NOUN cana-441 129 18 . . PUNCT cana-441 130 1 here here ADV cana-441 130 2 , , PUNCT cana-441 130 3 𝛼 𝛼 PROPN cana-441 130 4 denotes denote VERB cana-441 130 5 the the DET cana-441 130 6 learning learning NOUN cana-441 130 7 rate rate NOUN cana-441 130 8 , , PUNCT cana-441 130 9 and and CCONJ cana-441 130 10 βˆ‚π½ βˆ‚π½ PROPN cana-441 130 11 βˆ‚πœƒ βˆ‚πœƒ PROPN cana-441 130 12 represents represent VERB cana-441 130 13 the the DET cana-441 130 14 gradient gradient NOUN cana-441 130 15 of of ADP cana-441 130 16 the the DET cana-441 130 17 loss loss NOUN cana-441 130 18 function function NOUN cana-441 130 19 with with ADP cana-441 130 20 respect respect NOUN cana-441 130 21 to to ADP cana-441 130 22 the the DET cana-441 130 23 parameters parameter NOUN cana-441 130 24 . . PUNCT cana-441 131 1 by by ADP cana-441 131 2 adjusting adjust VERB cana-441 131 3 the the DET cana-441 131 4 parameters parameter NOUN cana-441 131 5 in in ADP cana-441 131 6 the the DET cana-441 131 7 direction direction NOUN cana-441 131 8 of of ADP cana-441 131 9 steepest steep ADJ cana-441 131 10 descent descent NOUN cana-441 131 11 , , PUNCT cana-441 131 12 gradient gradient ADJ cana-441 131 13 descent descent NOUN cana-441 131 14 optimizes optimize VERB cana-441 131 15 the the DET cana-441 131 16 network network NOUN cana-441 131 17 's 's PART cana-441 131 18 performance performance NOUN cana-441 131 19 and and CCONJ cana-441 131 20 enhances enhance VERB cana-441 131 21 its its PRON cana-441 131 22 ability ability NOUN cana-441 131 23 to to PART cana-441 131 24 accurately accurately ADV cana-441 131 25 segment segment VERB cana-441 131 26 and and CCONJ cana-441 131 27 classify classify VERB cana-441 131 28 brain brain NOUN cana-441 131 29 tumors tumor NOUN cana-441 131 30 . . PUNCT cana-441 132 1 batch batch NOUN cana-441 132 2 normalization normalization NOUN cana-441 132 3 , , PUNCT cana-441 132 4 expressed express VERB cana-441 132 5 as as ADP cana-441 132 6 οΏ½ οΏ½ PROPN cana-441 132 7 Μ‚ Μ‚ SYM cana-441 132 8 οΏ½ οΏ½ NOUN cana-441 132 9 = = SYM cana-441 132 10 π‘₯βˆ’πœ‡ π‘₯βˆ’πœ‡ NOUN cana-441 132 11 √𝜎2+πœ– √𝜎2+πœ– NOUN cana-441 132 12 , , PUNCT cana-441 132 13 normalizes normalize VERB cana-441 132 14 the the DET cana-441 132 15 activations activation NOUN cana-441 132 16 within within ADP cana-441 132 17 mini mini NOUN cana-441 132 18 - - NOUN cana-441 132 19 batches batch NOUN cana-441 132 20 during during ADP cana-441 132 21 training training NOUN cana-441 132 22 . . PUNCT cana-441 133 1 by by ADP cana-441 133 2 stabilizing stabilize VERB cana-441 133 3 the the DET cana-441 133 4 distribution distribution NOUN cana-441 133 5 of of ADP cana-441 133 6 inputs input NOUN cana-441 133 7 to to ADP cana-441 133 8 each each DET cana-441 133 9 layer layer NOUN cana-441 133 10 , , PUNCT cana-441 133 11 batch batch NOUN cana-441 133 12 normalization normalization NOUN cana-441 133 13 accelerates accelerate VERB cana-441 133 14 convergence convergence NOUN cana-441 133 15 , , PUNCT cana-441 133 16 improves improve VERB cana-441 133 17 gradient gradient ADJ cana-441 133 18 flow flow NOUN cana-441 133 19 , , PUNCT cana-441 133 20 and and CCONJ cana-441 133 21 enhances enhance VERB cana-441 133 22 the the DET cana-441 133 23 network network NOUN cana-441 133 24 's 's PART cana-441 133 25 ability ability NOUN cana-441 133 26 to to PART cana-441 133 27 learn learn VERB cana-441 133 28 discriminative discriminative NOUN cana-441 133 29 features feature NOUN cana-441 133 30 from from ADP cana-441 133 31 the the DET cana-441 133 32 data datum NOUN cana-441 133 33 . . PUNCT cana-441 134 1 residual residual ADJ cana-441 134 2 connections connection NOUN cana-441 134 3 , , PUNCT cana-441 134 4 as as SCONJ cana-441 134 5 introduced introduce VERB cana-441 134 6 in in ADP cana-441 134 7 residual residual ADJ cana-441 134 8 cnns cnn NOUN cana-441 134 9 , , PUNCT cana-441 134 10 facilitate facilitate VERB cana-441 134 11 the the DET cana-441 134 12 training training NOUN cana-441 134 13 of of ADP cana-441 134 14 very very ADV cana-441 134 15 deep deep ADJ cana-441 134 16 networks network NOUN cana-441 134 17 by by ADP cana-441 134 18 addressing address VERB cana-441 134 19 the the DET cana-441 134 20 vanishing vanish VERB cana-441 134 21 gradients gradient NOUN cana-441 134 22 problem problem NOUN cana-441 134 23 . . PUNCT cana-441 135 1 in in ADP cana-441 135 2 residual residual ADJ cana-441 135 3 blocks block NOUN cana-441 135 4 , , PUNCT cana-441 135 5 the the DET cana-441 135 6 output output NOUN cana-441 135 7 is be AUX cana-441 135 8 computed compute VERB cana-441 135 9 as as ADP cana-441 135 10 output output NOUN cana-441 135 11 = = SYM cana-441 135 12 𝐹(π‘₯ 𝐹(π‘₯ X cana-441 135 13 , , PUNCT cana-441 135 14 { { PUNCT cana-441 135 15 π‘Šπ‘– π‘Šπ‘– PROPN cana-441 135 16 } } PUNCT cana-441 135 17 ) ) PUNCT cana-441 136 1 + + CCONJ cana-441 136 2 π‘₯ π‘₯ X cana-441 136 3 , , PUNCT cana-441 136 4 where where SCONJ cana-441 136 5 𝐹(π‘₯ 𝐹(π‘₯ X cana-441 136 6 , , PUNCT cana-441 136 7 { { PUNCT cana-441 136 8 π‘Šπ‘– π‘Šπ‘– PROPN cana-441 136 9 } } PUNCT cana-441 136 10 ) ) PUNCT cana-441 136 11 represents represent VERB cana-441 136 12 the the DET cana-441 136 13 residual residual ADJ cana-441 136 14 mapping mapping NOUN cana-441 136 15 learned learn VERB cana-441 136 16 by by ADP cana-441 136 17 the the DET cana-441 136 18 network network NOUN cana-441 136 19 . . PUNCT cana-441 137 1 by by ADP cana-441 137 2 introducing introduce VERB cana-441 137 3 skip skip ADJ cana-441 137 4 connections connection NOUN cana-441 137 5 that that PRON cana-441 137 6 bypass bypass VERB cana-441 137 7 certain certain ADJ cana-441 137 8 layers layer NOUN cana-441 137 9 , , PUNCT cana-441 137 10 residual residual ADJ cana-441 137 11 networks network NOUN cana-441 137 12 enable enable VERB cana-441 137 13 the the DET cana-441 137 14 direct direct ADJ cana-441 137 15 flow flow NOUN cana-441 137 16 of of ADP cana-441 137 17 information information NOUN cana-441 137 18 and and CCONJ cana-441 137 19 mitigate mitigate VERB cana-441 137 20 the the DET cana-441 137 21 degradation degradation NOUN cana-441 137 22 problem problem NOUN cana-441 137 23 encountered encounter VERB cana-441 137 24 in in ADP cana-441 137 25 training train VERB cana-441 137 26 deep deep ADJ cana-441 137 27 networks network NOUN cana-441 137 28 . . PUNCT cana-441 138 1 the the DET cana-441 138 2 exploration exploration NOUN cana-441 138 3 of of ADP cana-441 138 4 these these DET cana-441 138 5 mathematical mathematical ADJ cana-441 138 6 equations equation NOUN cana-441 138 7 and and CCONJ cana-441 138 8 their their PRON cana-441 138 9 associated associated ADJ cana-441 138 10 operations operation NOUN cana-441 138 11 provides provide VERB cana-441 138 12 insights insight NOUN cana-441 138 13 into into ADP cana-441 138 14 the the DET cana-441 138 15 underlying underlie VERB cana-441 138 16 principles principle NOUN cana-441 138 17 of of ADP cana-441 138 18 deep deep ADJ cana-441 138 19 residual residual ADJ cana-441 138 20 cnns cnn NOUN cana-441 138 21 for for ADP cana-441 138 22 brain brain NOUN cana-441 138 23 tumor tumor NOUN cana-441 138 24 segmentation segmentation NOUN cana-441 138 25 and and CCONJ cana-441 138 26 classification classification NOUN cana-441 138 27 . . PUNCT cana-441 139 1 by by ADP cana-441 139 2 leveraging leverage VERB cana-441 139 3 convolutional convolutional ADJ cana-441 139 4 operations operation NOUN cana-441 139 5 , , PUNCT cana-441 139 6 activation activation NOUN cana-441 139 7 functions function NOUN cana-441 139 8 , , PUNCT cana-441 139 9 pooling pool VERB cana-441 139 10 layers layer NOUN cana-441 139 11 , , PUNCT cana-441 139 12 and and CCONJ cana-441 139 13 fully fully ADV cana-441 139 14 connected connected ADJ cana-441 139 15 layers layer NOUN cana-441 139 16 , , PUNCT cana-441 139 17 these these DET cana-441 139 18 networks network NOUN cana-441 139 19 can can AUX cana-441 139 20 effectively effectively ADV cana-441 139 21 extract extract VERB cana-441 139 22 discriminative discriminative NOUN cana-441 139 23 features feature NOUN cana-441 139 24 from from ADP cana-441 139 25 medical medical ADJ cana-441 139 26 imaging imaging NOUN cana-441 139 27 data datum NOUN cana-441 139 28 and and CCONJ cana-441 139 29 make make VERB cana-441 139 30 accurate accurate ADJ cana-441 139 31 predictions prediction NOUN cana-441 139 32 regarding regard VERB cana-441 139 33 tumor tumor NOUN cana-441 139 34 presence presence NOUN cana-441 139 35 and and CCONJ cana-441 139 36 type type NOUN cana-441 139 37 . . PUNCT cana-441 140 1 additionally additionally ADV cana-441 140 2 , , PUNCT cana-441 140 3 optimization optimization NOUN cana-441 140 4 techniques technique NOUN cana-441 140 5 such such ADJ cana-441 140 6 as as ADP cana-441 140 7 gradient gradient ADJ cana-441 140 8 descent descent NOUN cana-441 140 9 , , PUNCT cana-441 140 10 batch batch VERB cana-441 140 11 normalization normalization NOUN cana-441 140 12 , , PUNCT cana-441 140 13 and and CCONJ cana-441 140 14 residual residual ADJ cana-441 140 15 connections connection NOUN cana-441 140 16 play play VERB cana-441 140 17 critical critical ADJ cana-441 140 18 roles role NOUN cana-441 140 19 in in ADP cana-441 140 20 training train VERB cana-441 140 21 these these DET cana-441 140 22 networks network NOUN cana-441 140 23 and and CCONJ cana-441 140 24 enhancing enhance VERB cana-441 140 25 their their PRON cana-441 140 26 performance performance NOUN cana-441 140 27 in in ADP cana-441 140 28 challenging challenge VERB cana-441 140 29 medical medical ADJ cana-441 140 30 imaging imaging NOUN cana-441 140 31 tasks task NOUN cana-441 140 32 . . PUNCT cana-441 141 1 a. a. NOUN cana-441 141 2 data data PROPN cana-441 141 3 set set VERB cana-441 141 4 the the DET cana-441 141 5 dataset dataset NOUN cana-441 141 6 of of ADP cana-441 141 7 brain brain NOUN cana-441 141 8 mri mri NOUN cana-441 141 9 images image NOUN cana-441 141 10 are be AUX cana-441 141 11 collected collect VERB cana-441 141 12 from from ADP cana-441 141 13 kaggle kaggle PROPN cana-441 141 14 [ [ X cana-441 141 15 20 20 NUM cana-441 141 16 ] ] PUNCT cana-441 141 17 for for ADP cana-441 141 18 preprocessing preprocessing NOUN cana-441 141 19 and and CCONJ cana-441 141 20 classification classification NOUN cana-441 141 21 purpose purpose NOUN cana-441 141 22 . . PUNCT cana-441 142 1 the the DET cana-441 142 2 dataset dataset ADJ cana-441 142 3 consist consist NOUN cana-441 142 4 of of ADP cana-441 142 5 3929 3929 NUM cana-441 142 6 brain brain NOUN cana-441 142 7 mri mri NOUN cana-441 142 8 images image NOUN cana-441 142 9 . . PUNCT cana-441 143 1 by by ADP cana-441 143 2 the the DET cana-441 143 3 use use NOUN cana-441 143 4 of of ADP cana-441 143 5 diagnosis diagnosis NOUN cana-441 143 6 function function NOUN cana-441 143 7 , , PUNCT cana-441 143 8 it it PRON cana-441 143 9 returns return VERB cana-441 143 10 1 1 NUM cana-441 143 11 to to ADP cana-441 143 12 the the DET cana-441 143 13 list list NOUN cana-441 143 14 if if SCONJ cana-441 143 15 there there PRON cana-441 143 16 is be VERB cana-441 143 17 a a DET cana-441 143 18 tumor tumor NOUN cana-441 143 19 and and CCONJ cana-441 143 20 returns return NOUN cana-441 143 21 0 0 PUNCT cana-441 144 1 if if SCONJ cana-441 144 2 no no DET cana-441 144 3 tumor tumor NOUN cana-441 144 4 is be AUX cana-441 144 5 found find VERB cana-441 144 6 . . PUNCT cana-441 145 1 therefore therefore ADV cana-441 145 2 , , PUNCT cana-441 145 3 the the DET cana-441 145 4 images image NOUN cana-441 145 5 found find VERB cana-441 145 6 having have VERB cana-441 145 7 tumor tumor NOUN cana-441 145 8 is be AUX cana-441 145 9 1373 1373 NUM cana-441 145 10 and and CCONJ cana-441 145 11 images image NOUN cana-441 145 12 with with ADP cana-441 145 13 no no DET cana-441 145 14 tumor tumor NOUN cana-441 145 15 are be AUX cana-441 145 16 2556 2556 NUM cana-441 145 17 . . PUNCT cana-441 146 1 b. b. NOUN cana-441 146 2 preprocessing preprocesse VERB cana-441 146 3 in in ADP cana-441 146 4 preprocessing preprocesse VERB cana-441 146 5 step step NOUN cana-441 146 6 images image NOUN cana-441 146 7 are be AUX cana-441 146 8 normalize normalize VERB cana-441 146 9 to to PART cana-441 146 10 improve improve VERB cana-441 146 11 the the DET cana-441 146 12 quality quality NOUN cana-441 146 13 of of ADP cana-441 146 14 image image NOUN cana-441 146 15 resolution resolution NOUN cana-441 146 16 and and CCONJ cana-441 146 17 removal removal NOUN cana-441 146 18 of of ADP cana-441 146 19 noise noise NOUN cana-441 146 20 . . PUNCT cana-441 147 1 also also ADV cana-441 147 2 the the DET cana-441 147 3 mri mri NOUN cana-441 147 4 images image NOUN cana-441 147 5 used use VERB cana-441 147 6 for for ADP cana-441 147 7 classification classification NOUN cana-441 147 8 purpose purpose NOUN cana-441 147 9 is be AUX cana-441 147 10 resized resize VERB cana-441 147 11 into into ADP cana-441 147 12 256 256 NUM cana-441 147 13 x x SYM cana-441 147 14 256 256 NUM cana-441 147 15 x x SYM cana-441 147 16 3 3 NUM cana-441 147 17 dimension dimension NOUN cana-441 147 18 . . PUNCT cana-441 148 1 the the DET cana-441 148 2 mask mask NOUN cana-441 148 3 or or CCONJ cana-441 148 4 segmentation segmentation NOUN cana-441 148 5 method method NOUN cana-441 148 6 is be AUX cana-441 148 7 also also ADV cana-441 148 8 applied apply VERB cana-441 148 9 randomly randomly ADV cana-441 148 10 to to PART cana-441 148 11 detect detect VERB cana-441 148 12 tumor tumor NOUN cana-441 148 13 boundary boundary ADJ cana-441 148 14 in in ADP cana-441 148 15 mri mri NOUN cana-441 148 16 scans scan NOUN cana-441 148 17 . . PUNCT cana-441 149 1 image image NOUN cana-441 149 2 segmentation segmentation NOUN cana-441 149 3 aims aim VERB cana-441 149 4 to to PART cana-441 149 5 understand understand VERB cana-441 149 6 an an DET cana-441 149 7 image image NOUN cana-441 149 8 at at ADP cana-441 149 9 the the DET cana-441 149 10 pixel pixel PROPN cana-441 149 11 level level NOUN cana-441 149 12 by by ADP cana-441 149 13 associating associate VERB cana-441 149 14 each each DET cana-441 149 15 pixel pixel NOUN cana-441 149 16 with with ADP cana-441 149 17 a a DET cana-441 149 18 specific specific ADJ cana-441 149 19 class class NOUN cana-441 149 20 or or CCONJ cana-441 149 21 category category NOUN cana-441 149 22 . . PUNCT cana-441 150 1 the the DET cana-441 150 2 output output NOUN cana-441 150 3 of of ADP cana-441 150 4 an an DET cana-441 150 5 image image NOUN cana-441 150 6 segmentation segmentation NOUN cana-441 150 7 model model NOUN cana-441 150 8 is be AUX cana-441 150 9 a a DET cana-441 150 10 mask mask NOUN cana-441 150 11 , , PUNCT cana-441 150 12 which which PRON cana-441 150 13 represents represent VERB cana-441 150 14 the the DET cana-441 150 15 class class NOUN cana-441 150 16 labels label NOUN cana-441 150 17 for for ADP cana-441 150 18 each each DET cana-441 150 19 pixel pixel NOUN cana-441 150 20 in in ADP cana-441 150 21 the the DET cana-441 150 22 image image NOUN cana-441 150 23 . . PUNCT cana-441 151 1 communications communication NOUN cana-441 151 2 on on ADP cana-441 151 3 applied apply VERB cana-441 151 4 nonlinear nonlinear ADJ cana-441 151 5 analysis analysis NOUN cana-441 151 6 issn issn NOUN cana-441 151 7 : : PUNCT cana-441 151 8 1074 1074 NUM cana-441 151 9 - - PUNCT cana-441 151 10 133x 133x NUM cana-441 151 11 vol vol NOUN cana-441 151 12 31 31 NUM cana-441 151 13 no no NOUN cana-441 151 14 . . NOUN cana-441 151 15 1 1 NUM cana-441 151 16 ( ( PUNCT cana-441 151 17 2024 2024 NUM cana-441 151 18 ) ) PUNCT cana-441 151 19 325 325 NUM cana-441 151 20 https://internationalpubls.com https://internationalpubls.com X cana-441 151 21 c. c. PROPN cana-441 151 22 data data PROPN cana-441 151 23 augmentation augmentation NOUN cana-441 151 24 data datum NOUN cana-441 151 25 augmentation augmentation NOUN cana-441 151 26 is be AUX cana-441 151 27 used use VERB cana-441 151 28 before before ADP cana-441 151 29 the the DET cana-441 151 30 training training NOUN cana-441 151 31 of of ADP cana-441 151 32 the the DET cana-441 151 33 dataset dataset NOUN cana-441 152 1 [ [ X cana-441 152 2 21].using 21].using PROPN cana-441 152 3 keras keras X cana-441 152 4 we we PRON cana-441 152 5 perform perform VERB cana-441 152 6 the the DET cana-441 152 7 data data NOUN cana-441 152 8 augmentation augmentation NOUN cana-441 152 9 such such ADJ cana-441 152 10 as as ADP cana-441 152 11 rotation rotation NOUN cana-441 152 12 and and CCONJ cana-441 152 13 scaling scaling NOUN cana-441 152 14 and and CCONJ cana-441 152 15 also also ADV cana-441 152 16 perform perform VERB cana-441 152 17 training training NOUN cana-441 152 18 and and CCONJ cana-441 152 19 validation validation NOUN cana-441 152 20 split split NOUN cana-441 152 21 of of ADP cana-441 152 22 dataset dataset NOUN cana-441 152 23 into into ADP cana-441 152 24 70:30 70:30 NUM cana-441 152 25 ratio ratio NOUN cana-441 152 26 . . PUNCT cana-441 153 1 through through ADP cana-441 153 2 data data NOUN cana-441 153 3 augmentation augmentation NOUN cana-441 153 4 overfitting overfitting NOUN cana-441 153 5 can can AUX cana-441 153 6 be be AUX cana-441 153 7 reduced reduce VERB cana-441 153 8 . . PUNCT cana-441 154 1 d. d. PROPN cana-441 154 2 proposed propose VERB cana-441 154 3 resnet resnet VERB cana-441 154 4 50 50 NUM cana-441 154 5 architecture architecture NOUN cana-441 154 6 with with ADP cana-441 154 7 transfer transfer NOUN cana-441 154 8 learning learn VERB cana-441 154 9 residual residual ADJ cana-441 154 10 network network NOUN cana-441 154 11 ( ( PUNCT cana-441 154 12 resnet resnet NOUN cana-441 154 13 ) ) PUNCT cana-441 154 14 is be AUX cana-441 154 15 deep deep ADJ cana-441 154 16 network network NOUN cana-441 154 17 that that PRON cana-441 154 18 handles handle VERB cana-441 154 19 the the DET cana-441 154 20 vanishing vanish VERB cana-441 154 21 gradient gradient NOUN cana-441 154 22 problem problem NOUN cana-441 154 23 . . PUNCT cana-441 155 1 resnet-50 resnet-50 PROPN cana-441 155 2 uses use VERB cana-441 155 3 five five NUM cana-441 155 4 types type NOUN cana-441 155 5 of of ADP cana-441 155 6 convolution convolution NOUN cana-441 155 7 blocks block NOUN cana-441 155 8 with with ADP cana-441 155 9 different different ADJ cana-441 155 10 layers layer NOUN cana-441 155 11 and and CCONJ cana-441 155 12 filter filter NOUN cana-441 155 13 sizes size NOUN cana-441 155 14 [ [ X cana-441 155 15 22 22 NUM cana-441 155 16 ] ] PUNCT cana-441 155 17 as as SCONJ cana-441 155 18 shown show VERB cana-441 155 19 in in ADP cana-441 155 20 fig fig NOUN cana-441 155 21 . . PUNCT cana-441 156 1 1 1 X cana-441 156 2 . . X cana-441 156 3 fig fig NOUN cana-441 156 4 . . PUNCT cana-441 157 1 1 1 X cana-441 157 2 . . X cana-441 157 3 resnet resnet VERB cana-441 157 4 50 50 NUM cana-441 157 5 layer layer NOUN cana-441 157 6 architecture architecture NOUN cana-441 157 7 . . PUNCT cana-441 158 1 the the DET cana-441 158 2 resnet-50 resnet-50 PROPN cana-441 158 3 architecture architecture NOUN cana-441 158 4 's 's PART cana-441 158 5 depth depth NOUN cana-441 158 6 and and CCONJ cana-441 158 7 sophisticated sophisticated ADJ cana-441 158 8 design design NOUN cana-441 158 9 allow allow VERB cana-441 158 10 it it PRON cana-441 158 11 to to PART cana-441 158 12 capture capture VERB cana-441 158 13 features feature NOUN cana-441 158 14 and and CCONJ cana-441 158 15 learn learn VERB cana-441 158 16 hierarchical hierarchical ADJ cana-441 158 17 representations representation NOUN cana-441 158 18 , , PUNCT cana-441 158 19 making make VERB cana-441 158 20 it it PRON cana-441 158 21 suitable suitable ADJ cana-441 158 22 for for ADP cana-441 158 23 complex complex ADJ cana-441 158 24 tasks task NOUN cana-441 158 25 such such ADJ cana-441 158 26 as as ADP cana-441 158 27 brain brain NOUN cana-441 158 28 tumor tumor NOUN cana-441 158 29 classification classification NOUN cana-441 158 30 . . PUNCT cana-441 159 1 for for ADP cana-441 159 2 better well ADJ cana-441 159 3 results result NOUN cana-441 159 4 in in ADP cana-441 159 5 brain brain NOUN cana-441 159 6 tumor tumor NOUN cana-441 159 7 detection detection NOUN cana-441 159 8 and and CCONJ cana-441 159 9 classification classification NOUN cana-441 159 10 , , PUNCT cana-441 159 11 transfer transfer NOUN cana-441 159 12 learning learning NOUN cana-441 159 13 and and CCONJ cana-441 159 14 data datum NOUN cana-441 159 15 augmentation augmentation NOUN cana-441 159 16 is be AUX cana-441 159 17 performed perform VERB cana-441 159 18 . . PUNCT cana-441 160 1 transfer transfer NOUN cana-441 160 2 learning learning NOUN cana-441 160 3 is be AUX cana-441 160 4 a a DET cana-441 160 5 method method NOUN cana-441 160 6 that that PRON cana-441 160 7 uses use VERB cana-441 160 8 models model NOUN cana-441 160 9 that that PRON cana-441 160 10 have have AUX cana-441 160 11 already already ADV cana-441 160 12 been be AUX cana-441 160 13 trained train VERB cana-441 160 14 on on ADP cana-441 160 15 big big ADJ cana-441 160 16 datasets dataset NOUN cana-441 160 17 and and CCONJ cana-441 160 18 then then ADV cana-441 160 19 fine fine ADJ cana-441 160 20 - - PUNCT cana-441 160 21 tunes tune VERB cana-441 160 22 them they PRON cana-441 160 23 for for ADP cana-441 160 24 particular particular ADJ cana-441 160 25 tasks task NOUN cana-441 160 26 using use VERB cana-441 160 27 smaller small ADJ cana-441 160 28 datasets dataset NOUN cana-441 160 29 . . PUNCT cana-441 161 1 deep deep ADJ cana-441 161 2 learning learning NOUN cana-441 161 3 models model NOUN cana-441 161 4 perform perform VERB cana-441 161 5 better well ADV cana-441 161 6 and and CCONJ cana-441 161 7 need need VERB cana-441 161 8 less less ADJ cana-441 161 9 compute compute ADJ cana-441 161 10 when when SCONJ cana-441 161 11 transfer transfer NOUN cana-441 161 12 learning learning NOUN cana-441 161 13 is be AUX cana-441 161 14 used use VERB cana-441 161 15 [ [ PUNCT cana-441 161 16 23 23 NUM cana-441 161 17 ] ] PUNCT cana-441 161 18 . . PUNCT cana-441 162 1 we we PRON cana-441 162 2 employed employ VERB cana-441 162 3 transfer transfer NOUN cana-441 162 4 learning learning NOUN cana-441 162 5 in in ADP cana-441 162 6 our our PRON cana-441 162 7 methods method NOUN cana-441 162 8 to to PART cana-441 162 9 take take VERB cana-441 162 10 use use NOUN cana-441 162 11 of of ADP cana-441 162 12 models model NOUN cana-441 162 13 that that PRON cana-441 162 14 have have AUX cana-441 162 15 already already ADV cana-441 162 16 been be AUX cana-441 162 17 trained train VERB cana-441 162 18 on on ADP cana-441 162 19 the the DET cana-441 162 20 imagenet imagenet NOUN cana-441 162 21 dataset dataset NOUN cana-441 162 22 , , PUNCT cana-441 162 23 such such ADJ cana-441 162 24 as as ADP cana-441 162 25 resnet50 resnet50 NOUN cana-441 162 26 and and CCONJ cana-441 162 27 vgg16 vgg16 VERB cana-441 162 28 [ [ PUNCT cana-441 162 29 24].on 24].on NUM cana-441 162 30 the the DET cana-441 162 31 kaggle kaggle ADJ cana-441 162 32 dataset dataset NOUN cana-441 162 33 , , PUNCT cana-441 162 34 consist consist VERB cana-441 162 35 of of ADP cana-441 162 36 3929 3929 NUM cana-441 162 37 brain brain NOUN cana-441 162 38 tumor tumor NOUN cana-441 162 39 images image NOUN cana-441 162 40 , , PUNCT cana-441 162 41 we we PRON cana-441 162 42 adjusted adjust VERB cana-441 162 43 the the DET cana-441 162 44 pre pre ADJ cana-441 162 45 - - ADJ cana-441 162 46 trained train VERB cana-441 162 47 models model NOUN cana-441 162 48 and and CCONJ cana-441 162 49 tailored tailor VERB cana-441 162 50 the the DET cana-441 162 51 output output NOUN cana-441 162 52 layers layer NOUN cana-441 162 53 for for ADP cana-441 162 54 the the DET cana-441 162 55 classification classification NOUN cana-441 162 56 of of ADP cana-441 162 57 brain brain NOUN cana-441 162 58 tumors tumor NOUN cana-441 162 59 . . PUNCT cana-441 163 1 by by ADP cana-441 163 2 initializing initialize VERB cana-441 163 3 the the DET cana-441 163 4 weights weight NOUN cana-441 163 5 of of ADP cana-441 163 6 the the DET cana-441 163 7 models model NOUN cana-441 163 8 using use VERB cana-441 163 9 pre pre ADJ cana-441 163 10 - - ADJ cana-441 163 11 trained train VERB cana-441 163 12 weights weight NOUN cana-441 163 13 on on ADP cana-441 163 14 the the DET cana-441 163 15 imagenet imagenet NOUN cana-441 163 16 dataset dataset NOUN cana-441 163 17 , , PUNCT cana-441 163 18 we we PRON cana-441 163 19 apply apply VERB cana-441 163 20 transfer transfer NOUN cana-441 163 21 learning learn VERB cana-441 163 22 to to PART cana-441 163 23 enhance enhance VERB cana-441 163 24 the the DET cana-441 163 25 models model NOUN cana-441 163 26 ' ' PART cana-441 163 27 performance performance NOUN cana-441 163 28 . . PUNCT cana-441 164 1 to to PART cana-441 164 2 implement implement VERB cana-441 164 3 transfer transfer NOUN cana-441 164 4 learning learning NOUN cana-441 164 5 , , PUNCT cana-441 164 6 we we PRON cana-441 164 7 begin begin VERB cana-441 164 8 by by ADP cana-441 164 9 removing remove VERB cana-441 164 10 the the DET cana-441 164 11 last last ADJ cana-441 164 12 layer layer NOUN cana-441 164 13 of of ADP cana-441 164 14 the the DET cana-441 164 15 resnet50 resnet50 NOUN cana-441 164 16 model model NOUN cana-441 164 17 , , PUNCT cana-441 164 18 which which PRON cana-441 164 19 is be AUX cana-441 164 20 responsible responsible ADJ cana-441 164 21 for for ADP cana-441 164 22 the the DET cana-441 164 23 imagenet imagenet NOUN cana-441 164 24 classification classification NOUN cana-441 164 25 task task NOUN cana-441 164 26 . . PUNCT cana-441 165 1 by by ADP cana-441 165 2 doing do VERB cana-441 165 3 so so ADV cana-441 165 4 , , PUNCT cana-441 165 5 we we PRON cana-441 165 6 can can AUX cana-441 165 7 replace replace VERB cana-441 165 8 it it PRON cana-441 165 9 with with ADP cana-441 165 10 a a DET cana-441 165 11 new new ADJ cana-441 165 12 layer layer NOUN cana-441 165 13 or or CCONJ cana-441 165 14 add add VERB cana-441 165 15 multiple multiple ADJ cana-441 165 16 layers layer NOUN cana-441 165 17 to to PART cana-441 165 18 adapt adapt VERB cana-441 165 19 the the DET cana-441 165 20 network network NOUN cana-441 165 21 for for ADP cana-441 165 22 the the DET cana-441 165 23 brain brain NOUN cana-441 165 24 tumor tumor NOUN cana-441 165 25 classification classification NOUN cana-441 165 26 task task NOUN cana-441 165 27 . . PUNCT cana-441 166 1 in in ADP cana-441 166 2 the the DET cana-441 166 3 case case NOUN cana-441 166 4 of of ADP cana-441 166 5 brain brain NOUN cana-441 166 6 tumor tumor NOUN cana-441 166 7 classification classification NOUN cana-441 166 8 , , PUNCT cana-441 166 9 the the DET cana-441 166 10 last last ADJ cana-441 166 11 layers layer NOUN cana-441 166 12 will will AUX cana-441 166 13 have have VERB cana-441 166 14 neurons neuron NOUN cana-441 166 15 corresponding correspond VERB cana-441 166 16 to to ADP cana-441 166 17 the the DET cana-441 166 18 two two NUM cana-441 166 19 class class NOUN cana-441 166 20 represents represent VERB cana-441 166 21 tumor tumor NOUN cana-441 166 22 and and CCONJ cana-441 166 23 no no DET cana-441 166 24 tumor tumor NOUN cana-441 166 25 . . PUNCT cana-441 167 1 communications communication NOUN cana-441 167 2 on on ADP cana-441 167 3 applied apply VERB cana-441 167 4 nonlinear nonlinear ADJ cana-441 167 5 analysis analysis NOUN cana-441 167 6 issn issn NOUN cana-441 167 7 : : PUNCT cana-441 167 8 1074 1074 NUM cana-441 167 9 - - PUNCT cana-441 167 10 133x 133x NUM cana-441 167 11 vol vol NOUN cana-441 167 12 31 31 NUM cana-441 167 13 no no NOUN cana-441 167 14 . . NOUN cana-441 167 15 1 1 NUM cana-441 167 16 ( ( PUNCT cana-441 167 17 2024 2024 NUM cana-441 167 18 ) ) PUNCT cana-441 167 19 326 326 NUM cana-441 167 20 https://internationalpubls.com https://internationalpubls.com X cana-441 167 21 when when SCONJ cana-441 167 22 fine fine ADV cana-441 167 23 - - PUNCT cana-441 167 24 tuning tune VERB cana-441 167 25 the the DET cana-441 167 26 model model NOUN cana-441 167 27 , , PUNCT cana-441 167 28 the the DET cana-441 167 29 weights weight NOUN cana-441 167 30 of of ADP cana-441 167 31 the the DET cana-441 167 32 original original ADJ cana-441 167 33 resnet50 resnet50 NOUN cana-441 167 34 layers layer NOUN cana-441 167 35 are be AUX cana-441 167 36 frozen freeze VERB cana-441 167 37 . . PUNCT cana-441 168 1 freezing freeze VERB cana-441 168 2 a a DET cana-441 168 3 layer layer NOUN cana-441 168 4 means mean VERB cana-441 168 5 that that SCONJ cana-441 168 6 its its PRON cana-441 168 7 weights weight NOUN cana-441 168 8 are be AUX cana-441 168 9 not not PART cana-441 168 10 modified modify VERB cana-441 168 11 during during ADP cana-441 168 12 training training NOUN cana-441 168 13 , , PUNCT cana-441 168 14 and and CCONJ cana-441 168 15 only only ADV cana-441 168 16 the the DET cana-441 168 17 newly newly ADV cana-441 168 18 added add VERB cana-441 168 19 layers layer NOUN cana-441 168 20 are be AUX cana-441 168 21 trained train VERB cana-441 168 22 . . PUNCT cana-441 169 1 this this DET cana-441 169 2 approach approach NOUN cana-441 169 3 is be AUX cana-441 169 4 particularly particularly ADV cana-441 169 5 useful useful ADJ cana-441 169 6 when when SCONJ cana-441 169 7 the the DET cana-441 169 8 available available ADJ cana-441 169 9 training training NOUN cana-441 169 10 data datum NOUN cana-441 169 11 is be AUX cana-441 169 12 limited limit VERB cana-441 169 13 , , PUNCT cana-441 169 14 and and CCONJ cana-441 169 15 the the DET cana-441 169 16 pre pre ADJ cana-441 169 17 - - ADJ cana-441 169 18 trained train VERB cana-441 169 19 model model NOUN cana-441 169 20 's 's PART cana-441 169 21 task task NOUN cana-441 169 22 ( ( PUNCT cana-441 169 23 imagenet imagenet NOUN cana-441 169 24 classification classification NOUN cana-441 169 25 ) ) PUNCT cana-441 169 26 is be AUX cana-441 169 27 similar similar ADJ cana-441 169 28 to to ADP cana-441 169 29 the the DET cana-441 169 30 new new ADJ cana-441 169 31 task task NOUN cana-441 169 32 ( ( PUNCT cana-441 169 33 brain brain NOUN cana-441 169 34 tumor tumor NOUN cana-441 169 35 classification classification NOUN cana-441 169 36 ) ) PUNCT cana-441 169 37 . . PUNCT cana-441 170 1 the the DET cana-441 170 2 new new ADJ cana-441 170 3 layers layer NOUN cana-441 170 4 added add VERB cana-441 170 5 for for ADP cana-441 170 6 brain brain NOUN cana-441 170 7 tumor tumor NOUN cana-441 170 8 classification classification NOUN cana-441 170 9 typically typically ADV cana-441 170 10 include include VERB cana-441 170 11 dense dense ADJ cana-441 170 12 ( ( PUNCT cana-441 170 13 fully fully ADV cana-441 170 14 connected connected ADJ cana-441 170 15 ) ) PUNCT cana-441 170 16 layers layer NOUN cana-441 170 17 . . PUNCT cana-441 171 1 a a DET cana-441 171 2 dense dense ADJ cana-441 171 3 layer layer NOUN cana-441 171 4 , , PUNCT cana-441 171 5 also also ADV cana-441 171 6 known know VERB cana-441 171 7 as as ADP cana-441 171 8 a a DET cana-441 171 9 fully fully ADV cana-441 171 10 connected connect VERB cana-441 171 11 layer layer NOUN cana-441 171 12 , , PUNCT cana-441 171 13 is be AUX cana-441 171 14 responsible responsible ADJ cana-441 171 15 for for ADP cana-441 171 16 changing change VERB cana-441 171 17 the the DET cana-441 171 18 dimensionality dimensionality NOUN cana-441 171 19 of of ADP cana-441 171 20 the the DET cana-441 171 21 output output NOUN cana-441 171 22 from from ADP cana-441 171 23 the the DET cana-441 171 24 preceding precede VERB cana-441 171 25 layer layer NOUN cana-441 171 26 . . PUNCT cana-441 172 1 the the DET cana-441 172 2 size size NOUN cana-441 172 3 of of ADP cana-441 172 4 the the DET cana-441 172 5 dense dense ADJ cana-441 172 6 layer(s layer(s NOUN cana-441 172 7 ) ) PUNCT cana-441 172 8 added add VERB cana-441 172 9 to to ADP cana-441 172 10 the the DET cana-441 172 11 resnet50 resnet50 NOUN cana-441 172 12 architecture architecture NOUN cana-441 172 13 depends depend VERB cana-441 172 14 on on ADP cana-441 172 15 the the DET cana-441 172 16 specific specific ADJ cana-441 172 17 problem problem NOUN cana-441 172 18 and and CCONJ cana-441 172 19 available available ADJ cana-441 172 20 data datum NOUN cana-441 172 21 . . PUNCT cana-441 173 1 there there PRON cana-441 173 2 is be VERB cana-441 173 3 no no DET cana-441 173 4 fixed fix VERB cana-441 173 5 rule rule NOUN cana-441 173 6 for for ADP cana-441 173 7 determining determine VERB cana-441 173 8 the the DET cana-441 173 9 size size NOUN cana-441 173 10 , , PUNCT cana-441 173 11 but but CCONJ cana-441 173 12 it it PRON cana-441 173 13 is be AUX cana-441 173 14 usually usually ADV cana-441 173 15 determined determine VERB cana-441 173 16 based base VERB cana-441 173 17 on on ADP cana-441 173 18 the the DET cana-441 173 19 complexity complexity NOUN cana-441 173 20 of of ADP cana-441 173 21 the the DET cana-441 173 22 task task NOUN cana-441 173 23 and and CCONJ cana-441 173 24 the the DET cana-441 173 25 number number NOUN cana-441 173 26 of of ADP cana-441 173 27 classes class NOUN cana-441 173 28 . . PUNCT cana-441 174 1 in in ADP cana-441 174 2 general general ADJ cana-441 174 3 , , PUNCT cana-441 174 4 a a DET cana-441 174 5 larger large ADJ cana-441 174 6 size size NOUN cana-441 174 7 allows allow VERB cana-441 174 8 the the DET cana-441 174 9 model model NOUN cana-441 174 10 to to PART cana-441 174 11 capture capture VERB cana-441 174 12 more more ADV cana-441 174 13 complex complex ADJ cana-441 174 14 patterns pattern NOUN cana-441 174 15 , , PUNCT cana-441 174 16 but but CCONJ cana-441 174 17 it it PRON cana-441 174 18 also also ADV cana-441 174 19 increases increase VERB cana-441 174 20 the the DET cana-441 174 21 number number NOUN cana-441 174 22 of of ADP cana-441 174 23 parameters parameter NOUN cana-441 174 24 and and CCONJ cana-441 174 25 the the DET cana-441 174 26 risk risk NOUN cana-441 174 27 of of ADP cana-441 174 28 overfitting overfitte VERB cana-441 174 29 . . PUNCT cana-441 175 1 therefore therefore ADV cana-441 175 2 , , PUNCT cana-441 175 3 dropout dropout NOUN cana-441 175 4 of of ADP cana-441 175 5 ratio ratio NOUN cana-441 175 6 0.3 0.3 NUM cana-441 175 7 is be AUX cana-441 175 8 use use NOUN cana-441 175 9 after after ADP cana-441 175 10 each each DET cana-441 175 11 dense dense ADJ cana-441 175 12 layer layer NOUN cana-441 175 13 in in ADP cana-441 175 14 order order NOUN cana-441 175 15 to to PART cana-441 175 16 reduce reduce VERB cana-441 175 17 overfitting overfitting NOUN cana-441 175 18 of of ADP cana-441 175 19 the the DET cana-441 175 20 proposed propose VERB cana-441 175 21 model model NOUN cana-441 175 22 . . PUNCT cana-441 176 1 the the DET cana-441 176 2 activation activation NOUN cana-441 176 3 function function NOUN cana-441 176 4 used use VERB cana-441 176 5 in in ADP cana-441 176 6 the the DET cana-441 176 7 dense dense ADJ cana-441 176 8 layers layer NOUN cana-441 176 9 and and CCONJ cana-441 176 10 the the DET cana-441 176 11 output output NOUN cana-441 176 12 layer layer NOUN cana-441 176 13 is be AUX cana-441 176 14 an an DET cana-441 176 15 important important ADJ cana-441 176 16 decision decision NOUN cana-441 176 17 . . PUNCT cana-441 177 1 for for ADP cana-441 177 2 brain brain NOUN cana-441 177 3 tumor tumor NOUN cana-441 177 4 classification classification NOUN cana-441 177 5 , , PUNCT cana-441 177 6 the the DET cana-441 177 7 output output NOUN cana-441 177 8 layer layer NOUN cana-441 177 9 typically typically ADV cana-441 177 10 uses use VERB cana-441 177 11 the the DET cana-441 177 12 softmax softmax ADJ cana-441 177 13 activation activation NOUN cana-441 177 14 function function NOUN cana-441 177 15 . . PUNCT cana-441 178 1 regarding regard VERB cana-441 178 2 optimization optimization NOUN cana-441 178 3 , , PUNCT cana-441 178 4 there there PRON cana-441 178 5 are be VERB cana-441 178 6 several several ADJ cana-441 178 7 options option NOUN cana-441 178 8 available available ADJ cana-441 178 9 , , PUNCT cana-441 178 10 and and CCONJ cana-441 178 11 the the DET cana-441 178 12 choice choice NOUN cana-441 178 13 depends depend VERB cana-441 178 14 on on ADP cana-441 178 15 the the DET cana-441 178 16 specific specific ADJ cana-441 178 17 requirements requirement NOUN cana-441 178 18 and and CCONJ cana-441 178 19 characteristics characteristic NOUN cana-441 178 20 of of ADP cana-441 178 21 the the DET cana-441 178 22 problem problem NOUN cana-441 178 23 . . PUNCT cana-441 179 1 the the DET cana-441 179 2 adam adam PROPN cana-441 179 3 optimizer optimizer NOUN cana-441 179 4 , , PUNCT cana-441 179 5 for for ADP cana-441 179 6 example example NOUN cana-441 179 7 , , PUNCT cana-441 179 8 combines combine VERB cana-441 179 9 the the DET cana-441 179 10 advantages advantage NOUN cana-441 179 11 of of ADP cana-441 179 12 two two NUM cana-441 179 13 other other ADJ cana-441 179 14 optimization optimization NOUN cana-441 179 15 algorithms algorithm NOUN cana-441 179 16 ( ( PUNCT cana-441 179 17 adagrad adagrad ADJ cana-441 179 18 and and CCONJ cana-441 179 19 rmsprop rmsprop NOUN cana-441 179 20 ) ) PUNCT cana-441 179 21 and and CCONJ cana-441 179 22 is be AUX cana-441 179 23 commonly commonly ADV cana-441 179 24 used use VERB cana-441 179 25 in in ADP cana-441 179 26 deep deep ADJ cana-441 179 27 learning learning NOUN cana-441 179 28 due due ADP cana-441 179 29 to to ADP cana-441 179 30 its its PRON cana-441 179 31 efficiency efficiency NOUN cana-441 179 32 and and CCONJ cana-441 179 33 ability ability NOUN cana-441 179 34 to to PART cana-441 179 35 handle handle VERB cana-441 179 36 large large ADJ cana-441 179 37 datasets dataset NOUN cana-441 179 38 . . PUNCT cana-441 180 1 in in ADP cana-441 180 2 summary summary NOUN cana-441 180 3 , , PUNCT cana-441 180 4 the the DET cana-441 180 5 resnet-50 resnet-50 NOUN cana-441 180 6 architecture architecture NOUN cana-441 180 7 , , PUNCT cana-441 180 8 with with ADP cana-441 180 9 its its PRON cana-441 180 10 depth depth NOUN cana-441 180 11 and and CCONJ cana-441 180 12 skip skip ADJ cana-441 180 13 connections connection NOUN cana-441 180 14 , , PUNCT cana-441 180 15 is be AUX cana-441 180 16 well well ADV cana-441 180 17 - - PUNCT cana-441 180 18 suited suit VERB cana-441 180 19 for for ADP cana-441 180 20 brain brain NOUN cana-441 180 21 tumor tumor NOUN cana-441 180 22 classification classification NOUN cana-441 180 23 . . PUNCT cana-441 181 1 transfer transfer NOUN cana-441 181 2 learning learning NOUN cana-441 181 3 allows allow VERB cana-441 181 4 us we PRON cana-441 181 5 to to PART cana-441 181 6 leverage leverage VERB cana-441 181 7 the the DET cana-441 181 8 pre pre ADJ cana-441 181 9 - - ADJ cana-441 181 10 trained train VERB cana-441 181 11 resnet50 resnet50 NOUN cana-441 181 12 model model NOUN cana-441 181 13 and and CCONJ cana-441 181 14 adapt adapt VERB cana-441 181 15 it it PRON cana-441 181 16 to to ADP cana-441 181 17 the the DET cana-441 181 18 brain brain NOUN cana-441 181 19 tumor tumor NOUN cana-441 181 20 classification classification NOUN cana-441 181 21 task task NOUN cana-441 181 22 by by ADP cana-441 181 23 fine fine ADV cana-441 181 24 - - PUNCT cana-441 181 25 tuning tuning NOUN cana-441 181 26 and and CCONJ cana-441 181 27 adding add VERB cana-441 181 28 new new ADJ cana-441 181 29 layers layer NOUN cana-441 181 30 . . PUNCT cana-441 182 1 the the DET cana-441 182 2 choice choice NOUN cana-441 182 3 of of ADP cana-441 182 4 layer layer NOUN cana-441 182 5 structure structure NOUN cana-441 182 6 , , PUNCT cana-441 182 7 activation activation NOUN cana-441 182 8 functions function NOUN cana-441 182 9 , , PUNCT cana-441 182 10 and and CCONJ cana-441 182 11 optimization optimization NOUN cana-441 182 12 techniques technique NOUN cana-441 182 13 depends depend VERB cana-441 182 14 on on ADP cana-441 182 15 the the DET cana-441 182 16 specific specific ADJ cana-441 182 17 requirements requirement NOUN cana-441 182 18 and and CCONJ cana-441 182 19 characteristics characteristic NOUN cana-441 182 20 of of ADP cana-441 182 21 the the DET cana-441 182 22 problem problem NOUN cana-441 182 23 at at ADP cana-441 182 24 hand hand NOUN cana-441 182 25 . . PUNCT cana-441 183 1 4 4 X cana-441 183 2 . . NOUN cana-441 183 3 result result NOUN cana-441 183 4 and and CCONJ cana-441 183 5 discussion discussion NOUN cana-441 183 6 figure figure NOUN cana-441 183 7 2 2 NUM cana-441 183 8 , , PUNCT cana-441 183 9 represents represent VERB cana-441 183 10 an an DET cana-441 183 11 input input NOUN cana-441 183 12 image image NOUN cana-441 183 13 from from ADP cana-441 183 14 the the DET cana-441 183 15 dataset dataset NOUN cana-441 183 16 that that PRON cana-441 183 17 consist consist VERB cana-441 183 18 of of ADP cana-441 183 19 3929 3929 NUM cana-441 183 20 mri mri NOUN cana-441 183 21 scans scan NOUN cana-441 183 22 of of ADP cana-441 183 23 brain brain NOUN cana-441 183 24 tumor tumor NOUN cana-441 183 25 consist consist NOUN cana-441 183 26 of of ADP cana-441 183 27 1373 1373 NUM cana-441 183 28 images image NOUN cana-441 183 29 having have VERB cana-441 183 30 tumor tumor NOUN cana-441 183 31 and and CCONJ cana-441 183 32 2556 2556 NUM cana-441 183 33 images image NOUN cana-441 183 34 having have VERB cana-441 183 35 no no DET cana-441 183 36 tumor tumor NOUN cana-441 183 37 . . PUNCT cana-441 184 1 the the DET cana-441 184 2 input input NOUN cana-441 184 3 image image NOUN cana-441 184 4 can can AUX cana-441 184 5 be be AUX cana-441 184 6 an an DET cana-441 184 7 mri mri NOUN cana-441 184 8 scan scan NOUN cana-441 184 9 or or CCONJ cana-441 184 10 any any DET cana-441 184 11 other other ADJ cana-441 184 12 type type NOUN cana-441 184 13 of of ADP cana-441 184 14 medical medical ADJ cana-441 184 15 imaging imaging NOUN cana-441 184 16 modality modality NOUN cana-441 184 17 . . PUNCT cana-441 185 1 the the DET cana-441 185 2 purpose purpose NOUN cana-441 185 3 of of ADP cana-441 185 4 this this DET cana-441 185 5 illustration illustration NOUN cana-441 185 6 is be AUX cana-441 185 7 to to PART cana-441 185 8 visually visually ADV cana-441 185 9 demonstrate demonstrate VERB cana-441 185 10 the the DET cana-441 185 11 initial initial ADJ cana-441 185 12 input input NOUN cana-441 185 13 that that PRON cana-441 185 14 is be AUX cana-441 185 15 fed feed VERB cana-441 185 16 into into ADP cana-441 185 17 the the DET cana-441 185 18 classification classification NOUN cana-441 185 19 model model NOUN cana-441 185 20 . . PUNCT cana-441 186 1 in in ADP cana-441 186 2 figure figure NOUN cana-441 186 3 3 3 NUM cana-441 186 4 , , PUNCT cana-441 186 5 shows show VERB cana-441 186 6 the the DET cana-441 186 7 mask mask NOUN cana-441 186 8 highlights highlight NOUN cana-441 186 9 that that PRON cana-441 186 10 is be AUX cana-441 186 11 applied apply VERB cana-441 186 12 on on ADP cana-441 186 13 random random ADJ cana-441 186 14 images image NOUN cana-441 186 15 of of ADP cana-441 186 16 brain brain NOUN cana-441 186 17 tumor tumor NOUN cana-441 186 18 to to PART cana-441 186 19 find find VERB cana-441 186 20 the the DET cana-441 186 21 regions region NOUN cana-441 186 22 of of ADP cana-441 186 23 interest interest NOUN cana-441 186 24 .in .in PUNCT cana-441 186 25 this this DET cana-441 186 26 case case NOUN cana-441 186 27 , , PUNCT cana-441 186 28 the the DET cana-441 186 29 tumor tumor NOUN cana-441 186 30 regions region NOUN cana-441 186 31 , , PUNCT cana-441 186 32 by by ADP cana-441 186 33 overlaying overlay VERB cana-441 186 34 them they PRON cana-441 186 35 onto onto ADP cana-441 186 36 the the DET cana-441 186 37 original original ADJ cana-441 186 38 input input NOUN cana-441 186 39 image image NOUN cana-441 186 40 isolates isolate VERB cana-441 186 41 the the DET cana-441 186 42 tumor tumor NOUN cana-441 186 43 regions region NOUN cana-441 186 44 from from ADP cana-441 186 45 the the DET cana-441 186 46 background background NOUN cana-441 186 47 . . PUNCT cana-441 187 1 brain brain NOUN cana-441 187 2 tumor tumor NOUN cana-441 187 3 segmentation segmentation NOUN cana-441 187 4 and and CCONJ cana-441 187 5 classification classification NOUN cana-441 187 6 using use VERB cana-441 187 7 deep deep ADJ cana-441 187 8 residual residual ADJ cana-441 187 9 convolutional convolutional ADJ cana-441 187 10 neural neural ADJ cana-441 187 11 networks network NOUN cana-441 187 12 ( ( PUNCT cana-441 187 13 cnns cnns PROPN cana-441 187 14 ) ) PUNCT cana-441 187 15 represents represent VERB cana-441 187 16 a a DET cana-441 187 17 significant significant ADJ cana-441 187 18 advancement advancement NOUN cana-441 187 19 in in ADP cana-441 187 20 medical medical ADJ cana-441 187 21 image image NOUN cana-441 187 22 analysis analysis NOUN cana-441 187 23 , , PUNCT cana-441 187 24 offering offer VERB cana-441 187 25 precise precise ADJ cana-441 187 26 delineation delineation NOUN cana-441 187 27 of of ADP cana-441 187 28 tumor tumor NOUN cana-441 187 29 regions region NOUN cana-441 187 30 and and CCONJ cana-441 187 31 accurate accurate ADJ cana-441 187 32 categorization categorization NOUN cana-441 187 33 of of ADP cana-441 187 34 tumor tumor NOUN cana-441 187 35 types type NOUN cana-441 187 36 . . PUNCT cana-441 188 1 the the DET cana-441 188 2 application application NOUN cana-441 188 3 of of ADP cana-441 188 4 deep deep ADJ cana-441 188 5 learning learning NOUN cana-441 188 6 techniques technique NOUN cana-441 188 7 to to ADP cana-441 188 8 this this DET cana-441 188 9 domain domain NOUN cana-441 188 10 has have AUX cana-441 188 11 revolutionized revolutionize VERB cana-441 188 12 the the DET cana-441 188 13 field field NOUN cana-441 188 14 by by ADP cana-441 188 15 providing provide VERB cana-441 188 16 automated automate VERB cana-441 188 17 and and CCONJ cana-441 188 18 efficient efficient ADJ cana-441 188 19 solutions solution NOUN cana-441 188 20 to to ADP cana-441 188 21 challenging challenging ADJ cana-441 188 22 tasks task NOUN cana-441 188 23 that that SCONJ cana-441 188 24 traditionally traditionally ADV cana-441 188 25 required require VERB cana-441 188 26 manual manual ADJ cana-441 188 27 intervention intervention NOUN cana-441 188 28 . . PUNCT cana-441 189 1 at at ADP cana-441 189 2 the the DET cana-441 189 3 heart heart NOUN cana-441 189 4 of of ADP cana-441 189 5 these these DET cana-441 189 6 advancements advancement NOUN cana-441 189 7 lies lie VERB cana-441 189 8 the the DET cana-441 189 9 convolution convolution NOUN cana-441 189 10 operation operation NOUN cana-441 189 11 , , PUNCT cana-441 189 12 expressed express VERB cana-441 189 13 mathematically mathematically ADV cana-441 189 14 . . PUNCT cana-441 190 1 this this DET cana-441 190 2 operation operation NOUN cana-441 190 3 is be AUX cana-441 190 4 fundamental fundamental ADJ cana-441 190 5 in in ADP cana-441 190 6 capturing capture VERB cana-441 190 7 spatial spatial ADJ cana-441 190 8 patterns pattern NOUN cana-441 190 9 within within ADP cana-441 190 10 medical medical ADJ cana-441 190 11 imaging imaging NOUN cana-441 190 12 data datum NOUN cana-441 190 13 through through ADP cana-441 190 14 local local ADJ cana-441 190 15 communications communication NOUN cana-441 190 16 on on ADP cana-441 190 17 applied apply VERB cana-441 190 18 nonlinear nonlinear ADJ cana-441 190 19 analysis analysis NOUN cana-441 190 20 issn issn NOUN cana-441 190 21 : : PUNCT cana-441 190 22 1074 1074 NUM cana-441 190 23 - - PUNCT cana-441 190 24 133x 133x NUM cana-441 190 25 vol vol NOUN cana-441 190 26 31 31 NUM cana-441 190 27 no no NOUN cana-441 190 28 . . NOUN cana-441 190 29 1 1 NUM cana-441 190 30 ( ( PUNCT cana-441 190 31 2024 2024 NUM cana-441 190 32 ) ) PUNCT cana-441 190 33 327 327 NUM cana-441 190 34 https://internationalpubls.com https://internationalpubls.com X cana-441 190 35 operations operation NOUN cana-441 190 36 . . PUNCT cana-441 191 1 by by ADP cana-441 191 2 convolving convolve VERB cana-441 191 3 input input NOUN cana-441 191 4 images image NOUN cana-441 191 5 with with ADP cana-441 191 6 learnable learnable ADJ cana-441 191 7 filters filter NOUN cana-441 191 8 or or CCONJ cana-441 191 9 kernels kernel NOUN cana-441 191 10 , , PUNCT cana-441 191 11 cnns cnn NOUN cana-441 191 12 can can AUX cana-441 191 13 extract extract VERB cana-441 191 14 hierarchical hierarchical ADJ cana-441 191 15 representations representation NOUN cana-441 191 16 of of ADP cana-441 191 17 features feature NOUN cana-441 191 18 , , PUNCT cana-441 191 19 enabling enable VERB cana-441 191 20 them they PRON cana-441 191 21 to to PART cana-441 191 22 discern discern VERB cana-441 191 23 intricate intricate ADJ cana-441 191 24 details detail NOUN cana-441 191 25 that that PRON cana-441 191 26 are be AUX cana-441 191 27 indicative indicative ADJ cana-441 191 28 of of ADP cana-441 191 29 tumour tumour ADJ cana-441 191 30 presence presence NOUN cana-441 191 31 . . PUNCT cana-441 192 1 this this DET cana-441 192 2 capability capability NOUN cana-441 192 3 is be AUX cana-441 192 4 crucial crucial ADJ cana-441 192 5 for for ADP cana-441 192 6 accurate accurate ADJ cana-441 192 7 segmentation segmentation NOUN cana-441 192 8 , , PUNCT cana-441 192 9 as as SCONJ cana-441 192 10 it it PRON cana-441 192 11 allows allow VERB cana-441 192 12 the the DET cana-441 192 13 network network NOUN cana-441 192 14 to to PART cana-441 192 15 focus focus VERB cana-441 192 16 on on ADP cana-441 192 17 relevant relevant ADJ cana-441 192 18 regions region NOUN cana-441 192 19 while while SCONJ cana-441 192 20 filtering filter VERB cana-441 192 21 out out ADP cana-441 192 22 noise noise NOUN cana-441 192 23 and and CCONJ cana-441 192 24 irrelevant irrelevant ADJ cana-441 192 25 information information NOUN cana-441 192 26 . . PUNCT cana-441 193 1 in in ADP cana-441 193 2 deep deep ADJ cana-441 193 3 residual residual ADJ cana-441 193 4 cnns cnn NOUN cana-441 193 5 , , PUNCT cana-441 193 6 the the DET cana-441 193 7 convolutional convolutional ADJ cana-441 193 8 layers layer NOUN cana-441 193 9 play play VERB cana-441 193 10 a a DET cana-441 193 11 pivotal pivotal ADJ cana-441 193 12 role role NOUN cana-441 193 13 in in ADP cana-441 193 14 feature feature NOUN cana-441 193 15 extraction extraction NOUN cana-441 193 16 . . PUNCT cana-441 194 1 the the DET cana-441 194 2 output output NOUN cana-441 194 3 of of ADP cana-441 194 4 a a DET cana-441 194 5 convolutional convolutional ADJ cana-441 194 6 layer layer NOUN cana-441 194 7 , , PUNCT cana-441 194 8 π‘Š[𝑙 π‘Š[𝑙 VERB cana-441 194 9 ] ] PUNCT cana-441 194 10 represents represent VERB cana-441 194 11 the the DET cana-441 194 12 weights weight NOUN cana-441 194 13 , , PUNCT cana-441 194 14 a[lβˆ’1 a[lβˆ’1 NOUN cana-441 194 15 ] ] PUNCT cana-441 194 16 is be AUX cana-441 194 17 the the DET cana-441 194 18 input input NOUN cana-441 194 19 from from ADP cana-441 194 20 the the DET cana-441 194 21 previous previous ADJ cana-441 194 22 layer layer NOUN cana-441 194 23 , , PUNCT cana-441 194 24 and and CCONJ cana-441 194 25 b[l b[l NOUN cana-441 194 26 ] ] PUNCT cana-441 195 1 is be AUX cana-441 195 2 the the DET cana-441 195 3 bias bias NOUN cana-441 195 4 term term NOUN cana-441 195 5 . . PUNCT cana-441 196 1 this this DET cana-441 196 2 equation equation NOUN cana-441 196 3 encapsulates encapsulate VERB cana-441 196 4 the the DET cana-441 196 5 transformation transformation NOUN cana-441 196 6 of of ADP cana-441 196 7 features feature NOUN cana-441 196 8 through through ADP cana-441 196 9 the the DET cana-441 196 10 convolutional convolutional ADJ cana-441 196 11 operation operation NOUN cana-441 196 12 , , PUNCT cana-441 196 13 followed follow VERB cana-441 196 14 by by ADP cana-441 196 15 the the DET cana-441 196 16 addition addition NOUN cana-441 196 17 of of ADP cana-441 196 18 bias bias NOUN cana-441 196 19 to to PART cana-441 196 20 introduce introduce VERB cana-441 196 21 non non ADJ cana-441 196 22 - - NOUN cana-441 196 23 linearity linearity NOUN cana-441 196 24 into into ADP cana-441 196 25 the the DET cana-441 196 26 network network NOUN cana-441 196 27 . . PUNCT cana-441 197 1 by by ADP cana-441 197 2 iteratively iteratively ADV cana-441 197 3 applying apply VERB cana-441 197 4 convolutional convolutional ADJ cana-441 197 5 layers layer NOUN cana-441 197 6 , , PUNCT cana-441 197 7 cnns cnn NOUN cana-441 197 8 can can AUX cana-441 197 9 learn learn VERB cana-441 197 10 hierarchical hierarchical ADJ cana-441 197 11 representations representation NOUN cana-441 197 12 of of ADP cana-441 197 13 features feature NOUN cana-441 197 14 , , PUNCT cana-441 197 15 progressively progressively ADV cana-441 197 16 refining refine VERB cana-441 197 17 the the DET cana-441 197 18 segmentation segmentation NOUN cana-441 197 19 of of ADP cana-441 197 20 tumor tumor NOUN cana-441 197 21 regions region NOUN cana-441 197 22 and and CCONJ cana-441 197 23 enhancing enhance VERB cana-441 197 24 classification classification NOUN cana-441 197 25 accuracy accuracy NOUN cana-441 197 26 . . PUNCT cana-441 198 1 the the DET cana-441 198 2 rectified rectified ADJ cana-441 198 3 linear linear PROPN cana-441 198 4 unit unit NOUN cana-441 198 5 ( ( PUNCT cana-441 198 6 relu relu NOUN cana-441 198 7 ) ) PUNCT cana-441 198 8 activation activation NOUN cana-441 198 9 function function NOUN cana-441 198 10 , , PUNCT cana-441 198 11 a a DET cana-441 198 12 = = NOUN cana-441 198 13 max(0,z max(0,z NOUN cana-441 198 14 ) ) PUNCT cana-441 198 15 , , PUNCT cana-441 198 16 is be AUX cana-441 198 17 applied apply VERB cana-441 198 18 element element NOUN cana-441 198 19 - - ADJ cana-441 198 20 wise wise ADJ cana-441 198 21 to to ADP cana-441 198 22 the the DET cana-441 198 23 output output NOUN cana-441 198 24 of of ADP cana-441 198 25 convolutional convolutional ADJ cana-441 198 26 layers layer NOUN cana-441 198 27 . . PUNCT cana-441 199 1 relu relu NOUN cana-441 199 2 introduces introduce VERB cana-441 199 3 non non ADJ cana-441 199 4 - - NOUN cana-441 199 5 linearity linearity NOUN cana-441 199 6 by by ADP cana-441 199 7 outputting output VERB cana-441 199 8 the the DET cana-441 199 9 input input NOUN cana-441 199 10 if if SCONJ cana-441 199 11 positive positive ADJ cana-441 199 12 and and CCONJ cana-441 199 13 zero zero NUM cana-441 199 14 otherwise otherwise ADV cana-441 199 15 , , PUNCT cana-441 199 16 enabling enable VERB cana-441 199 17 the the DET cana-441 199 18 network network NOUN cana-441 199 19 to to PART cana-441 199 20 learn learn VERB cana-441 199 21 complex complex ADJ cana-441 199 22 mappings mapping NOUN cana-441 199 23 between between ADP cana-441 199 24 input input NOUN cana-441 199 25 and and CCONJ cana-441 199 26 output output NOUN cana-441 199 27 spaces space NOUN cana-441 199 28 . . PUNCT cana-441 200 1 this this PRON cana-441 200 2 non non ADJ cana-441 200 3 - - ADJ cana-441 200 4 linearity linearity NOUN cana-441 200 5 is be AUX cana-441 200 6 crucial crucial ADJ cana-441 200 7 for for ADP cana-441 200 8 capturing capture VERB cana-441 200 9 the the DET cana-441 200 10 intricate intricate ADJ cana-441 200 11 relationships relationship NOUN cana-441 200 12 between between ADP cana-441 200 13 image image NOUN cana-441 200 14 features feature NOUN cana-441 200 15 and and CCONJ cana-441 200 16 tumour tumour NOUN cana-441 200 17 presence presence NOUN cana-441 200 18 . . PUNCT cana-441 201 1 by by ADP cana-441 201 2 introducing introduce VERB cana-441 201 3 non non NOUN cana-441 201 4 - - NOUN cana-441 201 5 linearities linearity NOUN cana-441 201 6 into into ADP cana-441 201 7 the the DET cana-441 201 8 network network NOUN cana-441 201 9 , , PUNCT cana-441 201 10 relu relu NOUN cana-441 201 11 enables enable VERB cana-441 201 12 cnns cnn NOUN cana-441 201 13 to to PART cana-441 201 14 model model VERB cana-441 201 15 complex complex ADJ cana-441 201 16 decision decision NOUN cana-441 201 17 boundaries boundary NOUN cana-441 201 18 , , PUNCT cana-441 201 19 facilitating facilitate VERB cana-441 201 20 the the DET cana-441 201 21 accurate accurate ADJ cana-441 201 22 segmentation segmentation NOUN cana-441 201 23 and and CCONJ cana-441 201 24 classification classification NOUN cana-441 201 25 of of ADP cana-441 201 26 brain brain NOUN cana-441 201 27 tumours tumour NOUN cana-441 201 28 . . PUNCT cana-441 202 1 pooling pool VERB cana-441 202 2 layers layer NOUN cana-441 202 3 , , PUNCT cana-441 202 4 represented represent VERB cana-441 202 5 by by ADP cana-441 202 6 the the DET cana-441 202 7 equation equation NOUN cana-441 202 8 ai ai VERB cana-441 202 9 , , PUNCT cana-441 202 10 j j PROPN cana-441 202 11 = = SYM cana-441 202 12 maxm maxm PROPN cana-441 202 13 , , PUNCT cana-441 202 14 n(a(m n(a(m NOUN cana-441 202 15 , , PUNCT cana-441 202 16 n n CCONJ cana-441 202 17 ) ) PUNCT cana-441 202 18 ) ) PUNCT cana-441 202 19 , , PUNCT cana-441 202 20 reduce reduce VERB cana-441 202 21 the the DET cana-441 202 22 spatial spatial ADJ cana-441 202 23 dimensions dimension NOUN cana-441 202 24 of of ADP cana-441 202 25 feature feature NOUN cana-441 202 26 maps map NOUN cana-441 202 27 while while SCONJ cana-441 202 28 retaining retain VERB cana-441 202 29 essential essential ADJ cana-441 202 30 information information NOUN cana-441 202 31 . . PUNCT cana-441 203 1 by by ADP cana-441 203 2 selecting select VERB cana-441 203 3 the the DET cana-441 203 4 maximum maximum ADJ cana-441 203 5 value value NOUN cana-441 203 6 from from ADP cana-441 203 7 each each DET cana-441 203 8 patch patch NOUN cana-441 203 9 of of ADP cana-441 203 10 the the DET cana-441 203 11 feature feature NOUN cana-441 203 12 map map NOUN cana-441 203 13 , , PUNCT cana-441 203 14 pooling pool VERB cana-441 203 15 layers layer NOUN cana-441 203 16 down down ADP cana-441 203 17 sample sample NOUN cana-441 203 18 the the DET cana-441 203 19 data datum NOUN cana-441 203 20 , , PUNCT cana-441 203 21 focusing focus VERB cana-441 203 22 on on ADP cana-441 203 23 the the DET cana-441 203 24 most most ADV cana-441 203 25 salient salient NOUN cana-441 203 26 features feature NOUN cana-441 203 27 while while SCONJ cana-441 203 28 discarding discard VERB cana-441 203 29 irrelevant irrelevant ADJ cana-441 203 30 details detail NOUN cana-441 203 31 . . PUNCT cana-441 204 1 this this DET cana-441 204 2 down down ADV cana-441 204 3 sampling sample VERB cana-441 204 4 aids aid NOUN cana-441 204 5 in in ADP cana-441 204 6 reducing reduce VERB cana-441 204 7 computational computational ADJ cana-441 204 8 complexity complexity NOUN cana-441 204 9 and and CCONJ cana-441 204 10 extracting extract VERB cana-441 204 11 robust robust ADJ cana-441 204 12 features feature NOUN cana-441 204 13 for for ADP cana-441 204 14 subsequent subsequent ADJ cana-441 204 15 processing processing NOUN cana-441 204 16 . . PUNCT cana-441 205 1 moreover moreover ADV cana-441 205 2 , , PUNCT cana-441 205 3 pooling pool VERB cana-441 205 4 layers layer NOUN cana-441 205 5 introduce introduce VERB cana-441 205 6 translational translational ADJ cana-441 205 7 invariance invariance NOUN cana-441 205 8 , , PUNCT cana-441 205 9 making make VERB cana-441 205 10 the the DET cana-441 205 11 network network NOUN cana-441 205 12 more more ADV cana-441 205 13 robust robust ADJ cana-441 205 14 to to ADP cana-441 205 15 spatial spatial ADJ cana-441 205 16 transformations transformation NOUN cana-441 205 17 and and CCONJ cana-441 205 18 enhancing enhance VERB cana-441 205 19 generalization generalization NOUN cana-441 205 20 performance performance NOUN cana-441 205 21 . . PUNCT cana-441 206 1 fully fully ADV cana-441 206 2 connected connect VERB cana-441 206 3 layers layer NOUN cana-441 206 4 integrate integrate VERB cana-441 206 5 the the DET cana-441 206 6 extracted extract VERB cana-441 206 7 features feature NOUN cana-441 206 8 for for ADP cana-441 206 9 tumor tumor NOUN cana-441 206 10 classification classification NOUN cana-441 206 11 tasks task NOUN cana-441 206 12 , , PUNCT cana-441 206 13 as as SCONJ cana-441 206 14 denoted denote VERB cana-441 206 15 by by ADP cana-441 206 16 the the DET cana-441 206 17 equation equation NOUN cana-441 206 18 z z PROPN cana-441 206 19 = = X cana-441 206 20 wa+b wa+b X cana-441 206 21 . . PUNCT cana-441 207 1 in in ADP cana-441 207 2 these these DET cana-441 207 3 layers layer NOUN cana-441 207 4 , , PUNCT cana-441 207 5 the the DET cana-441 207 6 dot dot NOUN cana-441 207 7 product product NOUN cana-441 207 8 of of ADP cana-441 207 9 the the DET cana-441 207 10 input input NOUN cana-441 207 11 and and CCONJ cana-441 207 12 weight weight NOUN cana-441 207 13 matrices matrix NOUN cana-441 207 14 is be AUX cana-441 207 15 computed compute VERB cana-441 207 16 , , PUNCT cana-441 207 17 followed follow VERB cana-441 207 18 by by ADP cana-441 207 19 the the DET cana-441 207 20 addition addition NOUN cana-441 207 21 of of ADP cana-441 207 22 a a DET cana-441 207 23 bias bias NOUN cana-441 207 24 term term NOUN cana-441 207 25 . . PUNCT cana-441 208 1 this this DET cana-441 208 2 operation operation NOUN cana-441 208 3 enables enable VERB cana-441 208 4 the the DET cana-441 208 5 network network NOUN cana-441 208 6 to to PART cana-441 208 7 learn learn VERB cana-441 208 8 complex complex ADJ cana-441 208 9 decision decision NOUN cana-441 208 10 boundaries boundary NOUN cana-441 208 11 and and CCONJ cana-441 208 12 classify classify VERB cana-441 208 13 tumors tumor NOUN cana-441 208 14 into into ADP cana-441 208 15 different different ADJ cana-441 208 16 categories category NOUN cana-441 208 17 based base VERB cana-441 208 18 on on ADP cana-441 208 19 the the DET cana-441 208 20 extracted extract VERB cana-441 208 21 features feature NOUN cana-441 208 22 . . PUNCT cana-441 209 1 by by ADP cana-441 209 2 leveraging leverage VERB cana-441 209 3 fully fully ADV cana-441 209 4 connected connected ADJ cana-441 209 5 layers layer NOUN cana-441 209 6 , , PUNCT cana-441 209 7 cnns cnn NOUN cana-441 209 8 can can AUX cana-441 209 9 capture capture VERB cana-441 209 10 high high ADJ cana-441 209 11 - - PUNCT cana-441 209 12 level level NOUN cana-441 209 13 representations representation NOUN cana-441 209 14 of of ADP cana-441 209 15 features feature NOUN cana-441 209 16 and and CCONJ cana-441 209 17 make make VERB cana-441 209 18 accurate accurate ADJ cana-441 209 19 predictions prediction NOUN cana-441 209 20 regarding regard VERB cana-441 209 21 tumor tumor NOUN cana-441 209 22 types type NOUN cana-441 209 23 , , PUNCT cana-441 209 24 contributing contribute VERB cana-441 209 25 to to PART cana-441 209 26 improved improve VERB cana-441 209 27 clinical clinical ADJ cana-441 209 28 decision decision NOUN cana-441 209 29 - - PUNCT cana-441 209 30 making making NOUN cana-441 209 31 . . PUNCT cana-441 210 1 the the DET cana-441 210 2 cross cross ADJ cana-441 210 3 - - ADJ cana-441 210 4 entropy entropy ADJ cana-441 210 5 loss loss NOUN cana-441 210 6 function function NOUN cana-441 210 7 , , PUNCT cana-441 210 8 expressed express VERB cana-441 210 9 quantifies quantifie NOUN cana-441 210 10 the the DET cana-441 210 11 discrepancy discrepancy NOUN cana-441 210 12 between between ADP cana-441 210 13 predicted predict VERB cana-441 210 14 and and CCONJ cana-441 210 15 actual actual ADJ cana-441 210 16 tumor tumor NOUN cana-441 210 17 labels label NOUN cana-441 210 18 . . PUNCT cana-441 211 1 by by ADP cana-441 211 2 penalizing penalize VERB cana-441 211 3 deviations deviation NOUN cana-441 211 4 from from ADP cana-441 211 5 the the DET cana-441 211 6 ground ground NOUN cana-441 211 7 truth truth NOUN cana-441 211 8 labels label NOUN cana-441 211 9 , , PUNCT cana-441 211 10 the the DET cana-441 211 11 loss loss NOUN cana-441 211 12 function function NOUN cana-441 211 13 guides guide VERB cana-441 211 14 the the DET cana-441 211 15 optimization optimization NOUN cana-441 211 16 process process NOUN cana-441 211 17 towards towards ADP cana-441 211 18 minimizing minimize VERB cana-441 211 19 prediction prediction NOUN cana-441 211 20 errors error NOUN cana-441 211 21 and and CCONJ cana-441 211 22 improving improve VERB cana-441 211 23 the the DET cana-441 211 24 network network NOUN cana-441 211 25 's 's PART cana-441 211 26 performance performance NOUN cana-441 211 27 in in ADP cana-441 211 28 tumor tumor NOUN cana-441 211 29 segmentation segmentation NOUN cana-441 211 30 and and CCONJ cana-441 211 31 classification classification NOUN cana-441 211 32 tasks task NOUN cana-441 211 33 . . PUNCT cana-441 212 1 by by ADP cana-441 212 2 optimizing optimize VERB cana-441 212 3 the the DET cana-441 212 4 parameters parameter NOUN cana-441 212 5 of of ADP cana-441 212 6 the the DET cana-441 212 7 network network NOUN cana-441 212 8 to to PART cana-441 212 9 minimize minimize VERB cana-441 212 10 the the DET cana-441 212 11 cross cross ADJ cana-441 212 12 - - ADJ cana-441 212 13 entropy entropy ADJ cana-441 212 14 loss loss NOUN cana-441 212 15 , , PUNCT cana-441 212 16 cnns cnn NOUN cana-441 212 17 can can AUX cana-441 212 18 learn learn VERB cana-441 212 19 discriminative discriminative NOUN cana-441 212 20 features feature NOUN cana-441 212 21 from from ADP cana-441 212 22 medical medical ADJ cana-441 212 23 imaging imaging NOUN cana-441 212 24 data datum NOUN cana-441 212 25 and and CCONJ cana-441 212 26 make make VERB cana-441 212 27 accurate accurate ADJ cana-441 212 28 predictions prediction NOUN cana-441 212 29 regarding regard VERB cana-441 212 30 tumor tumor NOUN cana-441 212 31 presence presence NOUN cana-441 212 32 and and CCONJ cana-441 212 33 type type NOUN cana-441 212 34 . . PUNCT cana-441 213 1 gradient gradient ADJ cana-441 213 2 descent descent NOUN cana-441 213 3 optimization optimization NOUN cana-441 213 4 , , PUNCT cana-441 213 5 represented represent VERB cana-441 213 6 by by ADP cana-441 213 7 the the DET cana-441 213 8 equation equation NOUN cana-441 213 9 iteratively iteratively ADV cana-441 213 10 updates update VERB cana-441 213 11 the the DET cana-441 213 12 network network NOUN cana-441 213 13 parameters parameter NOUN cana-441 213 14 to to PART cana-441 213 15 minimize minimize VERB cana-441 213 16 the the DET cana-441 213 17 loss loss NOUN cana-441 213 18 function function NOUN cana-441 213 19 . . PUNCT cana-441 214 1 here here ADV cana-441 214 2 , , PUNCT cana-441 214 3 Ξ± Ξ± PROPN cana-441 214 4 denotes denote VERB cana-441 214 5 the the DET cana-441 214 6 learning learning NOUN cana-441 214 7 rate rate NOUN cana-441 214 8 represents represent VERB cana-441 214 9 the the DET cana-441 214 10 gradient gradient NOUN cana-441 214 11 of of ADP cana-441 214 12 communications communication NOUN cana-441 214 13 on on ADP cana-441 214 14 applied apply VERB cana-441 214 15 nonlinear nonlinear ADJ cana-441 214 16 analysis analysis NOUN cana-441 214 17 issn issn NOUN cana-441 214 18 : : PUNCT cana-441 214 19 1074 1074 NUM cana-441 214 20 - - PUNCT cana-441 214 21 133x 133x NUM cana-441 214 22 vol vol NOUN cana-441 214 23 31 31 NUM cana-441 214 24 no no NOUN cana-441 214 25 . . NOUN cana-441 214 26 1 1 NUM cana-441 214 27 ( ( PUNCT cana-441 214 28 2024 2024 NUM cana-441 214 29 ) ) PUNCT cana-441 214 30 328 328 NUM cana-441 214 31 https://internationalpubls.com https://internationalpubls.com X cana-441 215 1 the the DET cana-441 215 2 loss loss NOUN cana-441 215 3 function function NOUN cana-441 215 4 with with ADP cana-441 215 5 respect respect NOUN cana-441 215 6 to to ADP cana-441 215 7 the the DET cana-441 215 8 parameters parameter NOUN cana-441 215 9 . . PUNCT cana-441 216 1 by by ADP cana-441 216 2 adjusting adjust VERB cana-441 216 3 the the DET cana-441 216 4 parameters parameter NOUN cana-441 216 5 in in ADP cana-441 216 6 the the DET cana-441 216 7 direction direction NOUN cana-441 216 8 of of ADP cana-441 216 9 steepest steep ADJ cana-441 216 10 descent descent NOUN cana-441 216 11 , , PUNCT cana-441 216 12 gradient gradient ADJ cana-441 216 13 descent descent NOUN cana-441 216 14 optimizes optimize VERB cana-441 216 15 the the DET cana-441 216 16 network network NOUN cana-441 216 17 's 's PART cana-441 216 18 performance performance NOUN cana-441 216 19 and and CCONJ cana-441 216 20 enhances enhance VERB cana-441 216 21 its its PRON cana-441 216 22 ability ability NOUN cana-441 216 23 to to PART cana-441 216 24 accurately accurately ADV cana-441 216 25 segment segment VERB cana-441 216 26 and and CCONJ cana-441 216 27 classify classify VERB cana-441 216 28 brain brain NOUN cana-441 216 29 tumours tumour NOUN cana-441 216 30 . . PUNCT cana-441 217 1 by by ADP cana-441 217 2 iteratively iteratively ADV cana-441 217 3 updating update VERB cana-441 217 4 the the DET cana-441 217 5 parameters parameter NOUN cana-441 217 6 based base VERB cana-441 217 7 on on ADP cana-441 217 8 the the DET cana-441 217 9 gradient gradient NOUN cana-441 217 10 of of ADP cana-441 217 11 the the DET cana-441 217 12 loss loss NOUN cana-441 217 13 function function NOUN cana-441 217 14 , , PUNCT cana-441 217 15 cnns cnns PROPN cana-441 217 16 can can AUX cana-441 217 17 converge converge VERB cana-441 217 18 to to ADP cana-441 217 19 an an DET cana-441 217 20 optimal optimal ADJ cana-441 217 21 solution solution NOUN cana-441 217 22 and and CCONJ cana-441 217 23 achieve achieve VERB cana-441 217 24 superior superior ADJ cana-441 217 25 performance performance NOUN cana-441 217 26 in in ADP cana-441 217 27 medical medical ADJ cana-441 217 28 image image NOUN cana-441 217 29 analysis analysis NOUN cana-441 217 30 tasks task NOUN cana-441 217 31 . . PUNCT cana-441 218 1 loss loss NOUN cana-441 218 2 = = SYM cana-441 218 3 1 1 NUM cana-441 218 4 𝑁 𝑁 PROPN cana-441 218 5 βˆ‘ βˆ‘ PUNCT cana-441 218 6 ( ( PUNCT cana-441 218 7 𝑦𝑖 𝑦𝑖 PROPN cana-441 218 8 βˆ’ βˆ’ PROPN cana-441 218 9 𝑦 𝑦 SYM cana-441 218 10 οΏ½ οΏ½ PROPN cana-441 218 11 Μ‚ Μ‚ SYM cana-441 218 12 οΏ½ οΏ½ NOUN cana-441 218 13 ) ) PUNCT cana-441 218 14 2𝑁 2𝑁 NOUN cana-441 218 15 𝑖=1 𝑖=1 PROPN cana-441 218 16 ( ( PUNCT cana-441 218 17 29 29 NUM cana-441 218 18 ) ) PUNCT cana-441 218 19 accuracy accuracy NOUN cana-441 218 20 = = NOUN cana-441 218 21 number number NOUN cana-441 218 22 of of ADP cana-441 218 23 correctly correctly ADV cana-441 218 24 classified classify VERB cana-441 218 25 samples sample NOUN cana-441 218 26 total total ADJ cana-441 218 27 number number NOUN cana-441 218 28 of of ADP cana-441 218 29 samples sample NOUN cana-441 218 30 ( ( PUNCT cana-441 218 31 30 30 NUM cana-441 218 32 ) ) PUNCT cana-441 218 33 precision precision NOUN cana-441 218 34 = = PUNCT cana-441 218 35 true true ADJ cana-441 218 36 positives positive NOUN cana-441 218 37 true true ADJ cana-441 218 38 positives+false positives+false ADJ cana-441 218 39 positives positive NOUN cana-441 218 40 ( ( PUNCT cana-441 218 41 31 31 NUM cana-441 218 42 ) ) PUNCT cana-441 218 43 recall recall NOUN cana-441 218 44 = = PUNCT cana-441 218 45 true true ADJ cana-441 218 46 positives positive NOUN cana-441 218 47 true true ADJ cana-441 218 48 positives+false positives+false PROPN cana-441 218 49 negatives negative NOUN cana-441 218 50 ( ( PUNCT cana-441 218 51 32 32 NUM cana-441 218 52 ) ) PUNCT cana-441 218 53 f1 f1 NOUN cana-441 218 54 β€ˆ β€ˆ SPACE cana-441 218 55 score score NOUN cana-441 218 56 = = SYM cana-441 218 57 2 2 NUM cana-441 218 58 Γ— Γ— NOUN cana-441 218 59 precisionΓ—recall precisionΓ—recall NOUN cana-441 218 60 precision+recall precision+recall PROPN cana-441 218 61 ( ( PUNCT cana-441 218 62 33 33 NUM cana-441 218 63 ) ) PUNCT cana-441 218 64 sensitivity sensitivity NOUN cana-441 218 65 = = SYM cana-441 218 66 true true ADJ cana-441 218 67 positives positive NOUN cana-441 218 68 true true ADJ cana-441 218 69 positives+false positives+false PROPN cana-441 218 70 negatives negative NOUN cana-441 218 71 ( ( PUNCT cana-441 218 72 34 34 NUM cana-441 218 73 ) ) PUNCT cana-441 218 74 specificity specificity NOUN cana-441 218 75 = = PUNCT cana-441 218 76 true true ADJ cana-441 218 77 negatives negative NOUN cana-441 218 78 true true ADJ cana-441 218 79 negatives+false negatives+false PROPN cana-441 218 80 positives positive NOUN cana-441 218 81 ( ( PUNCT cana-441 218 82 35 35 NUM cana-441 218 83 ) ) PUNCT cana-441 218 84 the the DET cana-441 218 85 proposed propose VERB cana-441 218 86 resnet resnet NOUN cana-441 218 87 50 50 NUM cana-441 218 88 with with ADP cana-441 218 89 transfer transfer NOUN cana-441 218 90 learning learning NOUN cana-441 218 91 , , PUNCT cana-441 218 92 adapted adapt VERB cana-441 218 93 for for ADP cana-441 218 94 classification classification NOUN cana-441 218 95 , , PUNCT cana-441 218 96 can can AUX cana-441 218 97 be be AUX cana-441 218 98 utilized utilize VERB cana-441 218 99 for for ADP cana-441 218 100 this this DET cana-441 218 101 task task NOUN cana-441 218 102 by by ADP cana-441 218 103 training train VERB cana-441 218 104 the the DET cana-441 218 105 added add VERB cana-441 218 106 layers layer NOUN cana-441 218 107 on on ADP cana-441 218 108 a a DET cana-441 218 109 labeled label VERB cana-441 218 110 dataset dataset NOUN cana-441 218 111 as as SCONJ cana-441 218 112 shown show VERB cana-441 218 113 in in ADP cana-441 218 114 table table NOUN cana-441 218 115 i. i. PROPN cana-441 218 116 table table PROPN cana-441 218 117 i. i. PROPN cana-441 218 118 proposed propose VERB cana-441 218 119 resnet resnet VERB cana-441 218 120 50 50 NUM cana-441 218 121 model model NOUN cana-441 218 122 layer layer NOUN cana-441 218 123 ( ( PUNCT cana-441 218 124 type type NOUN cana-441 218 125 ) ) PUNCT cana-441 218 126 output output NOUN cana-441 218 127 shape shape NOUN cana-441 218 128 parameters parameter NOUN cana-441 218 129 input_1 input_1 ADV cana-441 218 130 ( ( PUNCT cana-441 218 131 input input NOUN cana-441 218 132 layer layer NOUN cana-441 218 133 ) ) PUNCT cana-441 218 134 ( ( PUNCT cana-441 218 135 none none NOUN cana-441 218 136 , , PUNCT cana-441 218 137 256,256,3 256,256,3 NOUN cana-441 218 138 ) ) PUNCT cana-441 218 139 0 0 NUM cana-441 218 140 resnet resnet VERB cana-441 218 141 50 50 NUM cana-441 218 142 ( ( PUNCT cana-441 218 143 functional functional ADJ cana-441 218 144 ) ) PUNCT cana-441 218 145 ( ( PUNCT cana-441 218 146 none none NOUN cana-441 218 147 , , PUNCT cana-441 218 148 2 2 NUM cana-441 218 149 , , PUNCT cana-441 218 150 2 2 NUM cana-441 218 151 , , PUNCT cana-441 218 152 2048 2048 NUM cana-441 218 153 ) ) PUNCT cana-441 218 154 23587712 23587712 NUM cana-441 218 155 flatten flatten VERB cana-441 218 156 ( ( PUNCT cana-441 218 157 flatten flatten ADJ cana-441 218 158 ) ) PUNCT cana-441 218 159 ( ( PUNCT cana-441 218 160 none none NOUN cana-441 218 161 , , PUNCT cana-441 218 162 8192 8192 NUM cana-441 218 163 ) ) PUNCT cana-441 218 164 0 0 PUNCT cana-441 219 1 dense dense ADJ cana-441 219 2 ( ( PUNCT cana-441 219 3 dense dense ADJ cana-441 219 4 ) ) PUNCT cana-441 219 5 ( ( PUNCT cana-441 219 6 none none NOUN cana-441 219 7 , , PUNCT cana-441 219 8 256 256 NUM cana-441 219 9 ) ) PUNCT cana-441 219 10 2097408 2097408 NUM cana-441 219 11 dropout dropout NOUN cana-441 219 12 ( ( PUNCT cana-441 219 13 dropout dropout NOUN cana-441 219 14 = = SYM cana-441 219 15 0.3 0.3 NUM cana-441 219 16 ) ) PUNCT cana-441 219 17 ( ( PUNCT cana-441 219 18 none none NOUN cana-441 219 19 , , PUNCT cana-441 219 20 256 256 NUM cana-441 219 21 ) ) PUNCT cana-441 219 22 0 0 PUNCT cana-441 220 1 dense_1 dense_1 NOUN cana-441 220 2 ( ( PUNCT cana-441 220 3 dense dense ADJ cana-441 220 4 ) ) PUNCT cana-441 220 5 ( ( PUNCT cana-441 220 6 none none NOUN cana-441 220 7 , , PUNCT cana-441 220 8 256 256 NUM cana-441 220 9 ) ) PUNCT cana-441 220 10 65792 65792 NUM cana-441 221 1 dropout_1 dropout_1 PROPN cana-441 221 2 ( ( PUNCT cana-441 221 3 dropout dropout NOUN cana-441 221 4 = = SYM cana-441 221 5 0.3 0.3 NUM cana-441 221 6 ) ) PUNCT cana-441 221 7 ( ( PUNCT cana-441 221 8 none none NOUN cana-441 221 9 , , PUNCT cana-441 221 10 256 256 NUM cana-441 221 11 ) ) PUNCT cana-441 221 12 0 0 NUM cana-441 222 1 dense_2 dense_2 NOUN cana-441 222 2 ( ( PUNCT cana-441 222 3 dense dense ADJ cana-441 222 4 ) ) PUNCT cana-441 222 5 ( ( PUNCT cana-441 222 6 none none NOUN cana-441 222 7 , , PUNCT cana-441 222 8 128 128 NUM cana-441 222 9 ) ) PUNCT cana-441 222 10 32896 32896 NUM cana-441 222 11 dropout_2 dropout_2 NOUN cana-441 222 12 ( ( PUNCT cana-441 222 13 dropout dropout NOUN cana-441 222 14 = = SYM cana-441 222 15 0.3 0.3 NUM cana-441 222 16 ) ) PUNCT cana-441 222 17 ( ( PUNCT cana-441 222 18 none none NOUN cana-441 222 19 , , PUNCT cana-441 222 20 128 128 NUM cana-441 222 21 ) ) PUNCT cana-441 222 22 0 0 NUM cana-441 223 1 dense_3 dense_3 NOUN cana-441 223 2 ( ( PUNCT cana-441 223 3 dense dense ADJ cana-441 223 4 ) ) PUNCT cana-441 223 5 ( ( PUNCT cana-441 223 6 none none NOUN cana-441 223 7 , , PUNCT cana-441 223 8 2 2 X cana-441 223 9 ) ) PUNCT cana-441 223 10 258 258 NUM cana-441 223 11 total total ADJ cana-441 223 12 parameters parameter NOUN cana-441 223 13 : : PUNCT cana-441 223 14 25,784,066 25,784,066 NUM cana-441 223 15 trainable trainable ADJ cana-441 223 16 parameters parameter NOUN cana-441 223 17 : : PUNCT cana-441 223 18 2,196,354 2,196,354 NUM cana-441 223 19 non_trainableparameters non_trainableparameter NOUN cana-441 223 20 : : PUNCT cana-441 223 21 23,587,712 23,587,712 NUM cana-441 223 22 entropy entropy NOUN cana-441 223 23 = = SYM cana-441 223 24 βˆ’ βˆ’ PROPN cana-441 223 25 βˆ‘ βˆ‘ PUNCT cana-441 223 26 𝑝𝑖 𝑝𝑖 NOUN cana-441 223 27 𝑛 𝑛 PRON cana-441 223 28 𝑖=1 𝑖=1 PROPN cana-441 223 29 log(𝑝𝑖 log(𝑝𝑖 NOUN cana-441 223 30 ) ) PUNCT cana-441 223 31 ( ( PUNCT cana-441 223 32 36 36 NUM cana-441 223 33 ) ) PUNCT cana-441 223 34 cross cross ADJ cana-441 223 35 β€ˆ β€ˆ SPACE cana-441 223 36 entropy entropy NOUN cana-441 223 37 = = SYM cana-441 223 38 βˆ’ βˆ’ PROPN cana-441 223 39 βˆ‘ βˆ‘ PUNCT cana-441 223 40 𝑝𝑖 𝑝𝑖 NOUN cana-441 223 41 𝑛 𝑛 PRON cana-441 223 42 𝑖=1 𝑖=1 PROPN cana-441 223 43 log(π‘žπ‘– log(π‘žπ‘– NOUN cana-441 223 44 ) ) PUNCT cana-441 223 45 ( ( PUNCT cana-441 223 46 37 37 NUM cana-441 223 47 ) ) PUNCT cana-441 223 48 communications communication NOUN cana-441 223 49 on on ADP cana-441 223 50 applied apply VERB cana-441 223 51 nonlinear nonlinear ADJ cana-441 223 52 analysis analysis NOUN cana-441 223 53 issn issn NOUN cana-441 223 54 : : PUNCT cana-441 223 55 1074 1074 NUM cana-441 223 56 - - PUNCT cana-441 223 57 133x 133x NUM cana-441 223 58 vol vol NOUN cana-441 223 59 31 31 NUM cana-441 223 60 no no NOUN cana-441 223 61 . . NOUN cana-441 223 62 1 1 NUM cana-441 223 63 ( ( PUNCT cana-441 223 64 2024 2024 NUM cana-441 223 65 ) ) PUNCT cana-441 223 66 329 329 NUM cana-441 223 67 https://internationalpubls.com https://internationalpubls.com X cana-441 223 68 kullback kullback NOUN cana-441 223 69 - - PUNCT cana-441 223 70 leibler leibler NOUN cana-441 223 71 β€ˆ β€ˆ SPACE cana-441 223 72 divergence divergence NOUN cana-441 223 73 = = PUNCT cana-441 223 74 βˆ‘ βˆ‘ PART cana-441 223 75 𝑝𝑖 𝑝𝑖 NOUN cana-441 223 76 𝑛 𝑛 PRON cana-441 223 77 𝑖=1 𝑖=1 PROPN cana-441 223 78 log log NOUN cana-441 223 79 ( ( PUNCT cana-441 223 80 𝑝𝑖 𝑝𝑖 NOUN cana-441 223 81 π‘žπ‘– π‘žπ‘– PROPN cana-441 223 82 ) ) PUNCT cana-441 223 83 ( ( PUNCT cana-441 223 84 38 38 NUM cana-441 223 85 ) ) PUNCT cana-441 223 86 hinge hinge NOUN cana-441 223 87 β€ˆ β€ˆ SPACE cana-441 223 88 loss loss NOUN cana-441 223 89 = = SYM cana-441 224 1 max(0,1 max(0,1 NUM cana-441 224 2 βˆ’ βˆ’ NOUN cana-441 224 3 𝑦𝑖 𝑦𝑖 PROPN cana-441 224 4 β‹… β‹… PROPN cana-441 224 5 𝑦 𝑦 PROPN cana-441 224 6 οΏ½ οΏ½ PROPN cana-441 224 7 Μ‚ Μ‚ SYM cana-441 224 8 οΏ½ οΏ½ NOUN cana-441 224 9 ) ) PUNCT cana-441 224 10 ( ( PUNCT cana-441 224 11 39 39 NUM cana-441 224 12 ) ) PUNCT cana-441 224 13 mean mean VERB cana-441 224 14 β€ˆ β€ˆ SPACE cana-441 224 15 absolute absolute ADJ cana-441 224 16 β€ˆ β€ˆ SPACE cana-441 224 17 error error NOUN cana-441 224 18 = = NOUN cana-441 224 19 1 1 NUM cana-441 224 20 𝑁 𝑁 PROPN cana-441 224 21 βˆ‘ βˆ‘ PUNCT cana-441 224 22 |𝑦𝑖 |𝑦𝑖 ADP cana-441 224 23 βˆ’ βˆ’ PROPN cana-441 224 24 𝑦 𝑦 SYM cana-441 224 25 οΏ½ οΏ½ NOUN cana-441 224 26 Μ‚ Μ‚ NOUN cana-441 224 27 οΏ½ οΏ½ NOUN cana-441 224 28 | | ADV cana-441 224 29 𝑁 𝑁 PROPN cana-441 224 30 𝑖=1 𝑖=1 PROPN cana-441 224 31 ( ( PUNCT cana-441 224 32 40 40 NUM cana-441 224 33 ) ) PUNCT cana-441 224 34 fig fig NOUN cana-441 224 35 . . PUNCT cana-441 225 1 2.representation 2.representation NUM cana-441 225 2 of of ADP cana-441 225 3 input input NOUN cana-441 225 4 image image NOUN cana-441 225 5 fig fig NOUN cana-441 225 6 . . PUNCT cana-441 226 1 3.input 3.input NUM cana-441 226 2 images image NOUN cana-441 226 3 with with ADP cana-441 226 4 mask mask NOUN cana-441 226 5 the the DET cana-441 226 6 theoretical theoretical ADJ cana-441 226 7 results result NOUN cana-441 226 8 for for ADP cana-441 226 9 the the DET cana-441 226 10 classification classification NOUN cana-441 226 11 problem problem NOUN cana-441 226 12 applied apply VERB cana-441 226 13 on on ADP cana-441 226 14 proposed propose VERB cana-441 226 15 model model NOUN cana-441 226 16 of of ADP cana-441 226 17 resnet50 resnet50 NOUN cana-441 226 18 and and CCONJ cana-441 226 19 vgg vgg NOUN cana-441 226 20 16 16 NUM cana-441 226 21 and and CCONJ cana-441 226 22 can can AUX cana-441 226 23 be be AUX cana-441 226 24 evaluated evaluate VERB cana-441 226 25 using use VERB cana-441 226 26 classification classification NOUN cana-441 226 27 matrix matrix NOUN cana-441 226 28 in in ADP cana-441 226 29 table table NOUN cana-441 226 30 ii ii NOUN cana-441 226 31 and and CCONJ cana-441 226 32 table table NOUN cana-441 226 33 iii iii PROPN cana-441 226 34 . . PUNCT cana-441 227 1 the the DET cana-441 227 2 classification classification NOUN cana-441 227 3 performance performance NOUN cana-441 227 4 includes include VERB cana-441 227 5 the the DET cana-441 227 6 parameter parameter NOUN cana-441 227 7 true true ADJ cana-441 227 8 positive positive ADJ cana-441 227 9 , , PUNCT cana-441 227 10 true true ADJ cana-441 227 11 negative negative ADJ cana-441 227 12 , , PUNCT cana-441 227 13 false false ADJ cana-441 227 14 positive positive ADJ cana-441 227 15 , , PUNCT cana-441 227 16 and and CCONJ cana-441 227 17 false false ADJ cana-441 227 18 negative negative NOUN cana-441 227 19 . . PUNCT cana-441 228 1 also also ADV cana-441 228 2 , , PUNCT cana-441 228 3 using use VERB cana-441 228 4 same same ADJ cana-441 228 5 datasets dataset NOUN cana-441 228 6 and and CCONJ cana-441 228 7 classification classification VERB cana-441 228 8 the the DET cana-441 228 9 proposed propose VERB cana-441 228 10 model model NOUN cana-441 228 11 results result NOUN cana-441 228 12 is be AUX cana-441 228 13 compared compare VERB cana-441 228 14 with with ADP cana-441 228 15 vgg vgg PROPN cana-441 228 16 16 16 NUM cana-441 228 17 as as SCONJ cana-441 228 18 shown show VERB cana-441 228 19 in in ADP cana-441 228 20 table table NOUN cana-441 228 21 iv iv NOUN cana-441 228 22 . . PUNCT cana-441 228 23 communications communication NOUN cana-441 228 24 on on ADP cana-441 228 25 applied apply VERB cana-441 228 26 nonlinear nonlinear ADJ cana-441 228 27 analysis analysis NOUN cana-441 228 28 issn issn NOUN cana-441 228 29 : : PUNCT cana-441 228 30 1074 1074 NUM cana-441 228 31 - - PUNCT cana-441 228 32 133x 133x NUM cana-441 228 33 vol vol NOUN cana-441 228 34 31 31 NUM cana-441 228 35 no no NOUN cana-441 228 36 . . NOUN cana-441 228 37 1 1 NUM cana-441 228 38 ( ( PUNCT cana-441 228 39 2024 2024 NUM cana-441 228 40 ) ) PUNCT cana-441 228 41 330 330 NUM cana-441 228 42 https://internationalpubls.com https://internationalpubls.com X cana-441 228 43 table table NOUN cana-441 228 44 ii ii PROPN cana-441 228 45 . . PUNCT cana-441 229 1 classification classification NOUN cana-441 229 2 matrix matrix NOUN cana-441 229 3 of of ADP cana-441 229 4 proposed propose VERB cana-441 229 5 resnet resnet NOUN cana-441 229 6 50 50 NUM cana-441 229 7 tumor tumor NOUN cana-441 229 8 no no DET cana-441 229 9 tumor tumor NOUN cana-441 229 10 class class NOUN cana-441 229 11 1 1 NUM cana-441 229 12 1371 1371 NUM cana-441 229 13 3 3 NUM cana-441 229 14 class class NOUN cana-441 229 15 0 0 NUM cana-441 229 16 2 2 NUM cana-441 229 17 2553 2553 NUM cana-441 229 18 table table NOUN cana-441 229 19 iii iii NOUN cana-441 229 20 . . PUNCT cana-441 229 21 classification classification NOUN cana-441 229 22 matrix matrix NOUN cana-441 229 23 of of ADP cana-441 229 24 vgg vgg PROPN cana-441 229 25 16 16 NUM cana-441 229 26 tumor tumor NOUN cana-441 229 27 no no DET cana-441 229 28 tumor tumor NOUN cana-441 229 29 class class NOUN cana-441 229 30 1 1 NUM cana-441 229 31 1351 1351 NUM cana-441 229 32 26 26 NUM cana-441 229 33 class class NOUN cana-441 229 34 0 0 NUM cana-441 229 35 22 22 NUM cana-441 229 36 2530 2530 NUM cana-441 229 37 table table NOUN cana-441 229 38 iv iv NUM cana-441 229 39 . . PUNCT cana-441 229 40 performance performance NOUN cana-441 229 41 parameters parameter NOUN cana-441 229 42 of of ADP cana-441 229 43 proposed propose VERB cana-441 229 44 resnet resnet NOUN cana-441 229 45 50 50 NUM cana-441 229 46 and and CCONJ cana-441 229 47 vgg vgg PROPN cana-441 229 48 16 16 NUM cana-441 229 49 s. s. PROPN cana-441 229 50 no no PROPN cana-441 229 51 . . PUNCT cana-441 230 1 parameters parameter NOUN cana-441 230 2 formulas formula VERB cana-441 230 3 results result NOUN cana-441 230 4 of of ADP cana-441 230 5 resnet50 resnet50 NOUN cana-441 230 6 with with ADP cana-441 230 7 tl tl PROPN cana-441 230 8 results result NOUN cana-441 230 9 of of ADP cana-441 230 10 vgg vgg NOUN cana-441 230 11 16 16 NUM cana-441 230 12 with with ADP cana-441 230 13 tl tl PROPN cana-441 230 14 1 1 NUM cana-441 230 15 . . PUNCT cana-441 231 1 precision precision NOUN cana-441 231 2 precision precision NOUN cana-441 231 3 = = PUNCT cana-441 231 4 tp tp AUX cana-441 231 5 / / SYM cana-441 231 6 tp tp NOUN cana-441 231 7 + + CCONJ cana-441 231 8 fp fp NUM cana-441 231 9 99.7 99.7 NUM cana-441 231 10 % % NOUN cana-441 231 11 98.1 98.1 NUM cana-441 231 12 % % NOUN cana-441 231 13 2 2 NUM cana-441 231 14 . . PUNCT cana-441 231 15 recall recall NOUN cana-441 231 16 recall recall NOUN cana-441 232 1 = = PRON cana-441 232 2 tp tp PART cana-441 232 3 / / SYM cana-441 232 4 tp+fn tp+fn NOUN cana-441 232 5 99.8 99.8 NUM cana-441 232 6 % % NOUN cana-441 232 7 98.3 98.3 NUM cana-441 232 8 % % NOUN cana-441 232 9 3 3 NUM cana-441 232 10 . . PUNCT cana-441 233 1 f1 f1 NOUN cana-441 233 2 score score NOUN cana-441 233 3 f1score f1score NOUN cana-441 233 4 = = SYM cana-441 233 5 2 2 NUM cana-441 233 6 ( ( PUNCT cana-441 233 7 precision precision NOUN cana-441 233 8 * * PUNCT cana-441 233 9 recall/ recall/ NUM cana-441 233 10 precision+ precision+ SYM cana-441 233 11 recall recall NOUN cana-441 233 12 ) ) PUNCT cana-441 233 13 99.7 99.7 NUM cana-441 233 14 % % NOUN cana-441 233 15 98.1 98.1 NUM cana-441 233 16 % % NOUN cana-441 233 17 4 4 NUM cana-441 233 18 . . PUNCT cana-441 234 1 mean mean ADJ cana-441 234 2 accuracy accuracy NOUN cana-441 234 3 mean mean NOUN cana-441 234 4 accuracy accuracy NOUN cana-441 234 5 = = SYM cana-441 234 6 ( ( PUNCT cana-441 234 7 tp+tn)/(tp+tn+fp+fn tp+tn)/(tp+tn+fp+fn NOUN cana-441 234 8 ) ) PUNCT cana-441 234 9 99.8 99.8 NUM cana-441 234 10 % % NOUN cana-441 234 11 98.7 98.7 NUM cana-441 234 12 % % NOUN cana-441 234 13 during during ADP cana-441 234 14 training training NOUN cana-441 234 15 and and CCONJ cana-441 234 16 validation validation NOUN cana-441 234 17 dataset dataset NOUN cana-441 234 18 is be AUX cana-441 234 19 split split VERB cana-441 234 20 into into ADP cana-441 234 21 70:30 70:30 NUM cana-441 234 22 ratio ratio NOUN cana-441 234 23 . . PUNCT cana-441 235 1 the the DET cana-441 235 2 proposed propose VERB cana-441 235 3 model model NOUN cana-441 235 4 resnet resnet VERB cana-441 235 5 50 50 NUM cana-441 235 6 and and CCONJ cana-441 235 7 vgg vgg PROPN cana-441 235 8 16 16 NUM cana-441 235 9 are be AUX cana-441 235 10 use use NOUN cana-441 235 11 8 8 NUM cana-441 235 12 fold fold ADJ cana-441 235 13 and and CCONJ cana-441 235 14 20 20 NUM cana-441 235 15 epochs epoch NOUN cana-441 235 16 .figure .figure NOUN cana-441 235 17 4 4 NUM cana-441 235 18 , , PUNCT cana-441 235 19 show show VERB cana-441 235 20 training training NOUN cana-441 235 21 and and CCONJ cana-441 235 22 validation validation NOUN cana-441 235 23 accuracy accuracy NOUN cana-441 235 24 of of ADP cana-441 235 25 the the DET cana-441 235 26 proposed propose VERB cana-441 235 27 model model NOUN cana-441 235 28 and and CCONJ cana-441 235 29 vgg vgg NOUN cana-441 235 30 16 16 NUM cana-441 235 31 and and CCONJ cana-441 235 32 figure figure VERB cana-441 235 33 5 5 NUM cana-441 235 34 , , PUNCT cana-441 235 35 shows show VERB cana-441 235 36 training training NOUN cana-441 235 37 and and CCONJ cana-441 235 38 validation validation NOUN cana-441 235 39 loss loss NOUN cana-441 235 40 of of ADP cana-441 235 41 the the DET cana-441 235 42 proposed propose VERB cana-441 235 43 model model NOUN cana-441 235 44 and and CCONJ cana-441 235 45 vgg vgg NOUN cana-441 235 46 16 16 NUM cana-441 235 47 . . PUNCT cana-441 236 1 to to PART cana-441 236 2 reveal reveal VERB cana-441 236 3 into into ADP cana-441 236 4 the the DET cana-441 236 5 details detail NOUN cana-441 236 6 of of ADP cana-441 236 7 the the DET cana-441 236 8 resnet resnet NOUN cana-441 236 9 50 50 NUM cana-441 236 10 model model NOUN cana-441 236 11 and and CCONJ cana-441 236 12 its its PRON cana-441 236 13 comparative comparative ADJ cana-441 236 14 analysis analysis NOUN cana-441 236 15 with with ADP cana-441 236 16 the the DET cana-441 236 17 vgg vgg ADJ cana-441 236 18 16 16 NUM cana-441 236 19 model model NOUN cana-441 236 20 , , PUNCT cana-441 236 21 let let VERB cana-441 236 22 's us PRON cana-441 236 23 explore explore VERB cana-441 236 24 the the DET cana-441 236 25 underlying underlie VERB cana-441 236 26 concepts concept NOUN cana-441 236 27 , , PUNCT cana-441 236 28 architecture architecture NOUN cana-441 236 29 , , PUNCT cana-441 236 30 and and CCONJ cana-441 236 31 performance performance NOUN cana-441 236 32 metrics metric NOUN cana-441 236 33 from from ADP cana-441 236 34 fig fig NOUN cana-441 236 35 . . PUNCT cana-441 237 1 6 6 NUM cana-441 237 2 - - SYM cana-441 237 3 10 10 NUM cana-441 237 4 . . PUNCT cana-441 238 1 resnet resnet NOUN cana-441 238 2 , , PUNCT cana-441 238 3 or or CCONJ cana-441 238 4 residual residual ADJ cana-441 238 5 network network NOUN cana-441 238 6 , , PUNCT cana-441 238 7 is be AUX cana-441 238 8 a a DET cana-441 238 9 type type NOUN cana-441 238 10 of of ADP cana-441 238 11 convolutional convolutional ADJ cana-441 238 12 neural neural ADJ cana-441 238 13 network network NOUN cana-441 238 14 ( ( PUNCT cana-441 238 15 cnn cnn PROPN cana-441 238 16 ) ) PUNCT cana-441 238 17 that that PRON cana-441 238 18 was be AUX cana-441 238 19 introduced introduce VERB cana-441 238 20 to to PART cana-441 238 21 address address VERB cana-441 238 22 the the DET cana-441 238 23 vanishing vanish VERB cana-441 238 24 gradient gradient NOUN cana-441 238 25 problem problem NOUN cana-441 238 26 , , PUNCT cana-441 238 27 allowing allow VERB cana-441 238 28 models model NOUN cana-441 238 29 to to PART cana-441 238 30 be be AUX cana-441 238 31 significantly significantly ADV cana-441 238 32 deeper deep ADJ cana-441 238 33 than than ADP cana-441 238 34 previous previous ADJ cana-441 238 35 cnns cnn NOUN cana-441 238 36 . . PUNCT cana-441 239 1 the the DET cana-441 239 2 " " PUNCT cana-441 239 3 50 50 NUM cana-441 239 4 " " PUNCT cana-441 239 5 in in ADP cana-441 239 6 resnet resnet NOUN cana-441 239 7 50 50 NUM cana-441 239 8 refers refer VERB cana-441 239 9 to to ADP cana-441 239 10 the the DET cana-441 239 11 number number NOUN cana-441 239 12 of of ADP cana-441 239 13 layers layer NOUN cana-441 239 14 it it PRON cana-441 239 15 contains contain VERB cana-441 239 16 . . PUNCT cana-441 240 1 one one NUM cana-441 240 2 of of ADP cana-441 240 3 the the DET cana-441 240 4 key key ADJ cana-441 240 5 innovations innovation NOUN cana-441 240 6 in in ADP cana-441 240 7 resnet resnet NOUN cana-441 240 8 is be AUX cana-441 240 9 the the DET cana-441 240 10 introduction introduction NOUN cana-441 240 11 of of ADP cana-441 240 12 " " PUNCT cana-441 240 13 skip skip ADJ cana-441 240 14 connections connection NOUN cana-441 240 15 " " PUNCT cana-441 240 16 or or CCONJ cana-441 240 17 " " PUNCT cana-441 240 18 residual residual ADJ cana-441 240 19 connections connection NOUN cana-441 240 20 , , PUNCT cana-441 240 21 " " PUNCT cana-441 240 22 which which PRON cana-441 240 23 allow allow VERB cana-441 240 24 the the DET cana-441 240 25 gradient gradient NOUN cana-441 240 26 to to PART cana-441 240 27 bypass bypass VERB cana-441 240 28 one one NUM cana-441 240 29 or or CCONJ cana-441 240 30 more more ADJ cana-441 240 31 layers layer NOUN cana-441 240 32 . . PUNCT cana-441 240 33 . . PUNCT cana-441 241 1 communications communication NOUN cana-441 241 2 on on ADP cana-441 241 3 applied apply VERB cana-441 241 4 nonlinear nonlinear ADJ cana-441 241 5 analysis analysis NOUN cana-441 241 6 issn issn NOUN cana-441 241 7 : : PUNCT cana-441 241 8 1074 1074 NUM cana-441 241 9 - - PUNCT cana-441 241 10 133x 133x NUM cana-441 241 11 vol vol NOUN cana-441 241 12 31 31 NUM cana-441 241 13 no no NOUN cana-441 241 14 . . NOUN cana-441 241 15 1 1 NUM cana-441 241 16 ( ( PUNCT cana-441 241 17 2024 2024 NUM cana-441 241 18 ) ) PUNCT cana-441 241 19 331 331 NUM cana-441 241 20 https://internationalpubls.com https://internationalpubls.com X cana-441 241 21 ( ( PUNCT cana-441 241 22 a a NOUN cana-441 241 23 ) ) PUNCT cana-441 241 24 ( ( PUNCT cana-441 241 25 b b X cana-441 241 26 ) ) PUNCT cana-441 241 27 fig fig NOUN cana-441 241 28 . . PUNCT cana-441 242 1 4 4 X cana-441 242 2 . . X cana-441 242 3 ( ( PUNCT cana-441 242 4 a a X cana-441 242 5 ) ) PUNCT cana-441 242 6 training training NOUN cana-441 242 7 and and CCONJ cana-441 242 8 validation validation NOUN cana-441 242 9 accuracy accuracy NOUN cana-441 242 10 of of ADP cana-441 242 11 the the DET cana-441 242 12 proposed propose VERB cana-441 242 13 model model NOUN cana-441 242 14 resnet resnet VERB cana-441 242 15 50 50 NUM cana-441 242 16 , , PUNCT cana-441 242 17 ( ( PUNCT cana-441 242 18 b b NOUN cana-441 242 19 ) ) PUNCT cana-441 242 20 training training NOUN cana-441 242 21 and and CCONJ cana-441 242 22 validation validation NOUN cana-441 242 23 accuracy accuracy NOUN cana-441 242 24 of of ADP cana-441 242 25 vgg vgg PROPN cana-441 242 26 16 16 NUM cana-441 242 27 . . PUNCT cana-441 243 1 ( ( PUNCT cana-441 243 2 a a X cana-441 243 3 ) ) PUNCT cana-441 243 4 ( ( PUNCT cana-441 243 5 b b X cana-441 243 6 ) ) PUNCT cana-441 243 7 fig fig NOUN cana-441 243 8 . . PUNCT cana-441 244 1 5 5 NUM cana-441 244 2 . . X cana-441 244 3 ( ( PUNCT cana-441 244 4 a a X cana-441 244 5 ) ) PUNCT cana-441 244 6 training training NOUN cana-441 244 7 and and CCONJ cana-441 244 8 validation validation NOUN cana-441 244 9 loss loss NOUN cana-441 244 10 of of ADP cana-441 244 11 the the DET cana-441 244 12 proposed propose VERB cana-441 244 13 model model NOUN cana-441 244 14 resnet resnet PROPN cana-441 244 15 50,(b 50,(b NUM cana-441 244 16 ) ) PUNCT cana-441 244 17 training training NOUN cana-441 244 18 and and CCONJ cana-441 244 19 validation validation NOUN cana-441 244 20 loss loss NOUN cana-441 244 21 of of ADP cana-441 244 22 vgg vgg PROPN cana-441 244 23 16 16 NUM cana-441 244 24 fig fig NOUN cana-441 244 25 . . PUNCT cana-441 245 1 6 6 NUM cana-441 245 2 . . X cana-441 245 3 comparative comparative ADJ cana-441 245 4 analysis analysis NOUN cana-441 245 5 of of ADP cana-441 245 6 performance performance NOUN cana-441 245 7 parameters parameter NOUN cana-441 245 8 communications communication NOUN cana-441 245 9 on on ADP cana-441 245 10 applied apply VERB cana-441 245 11 nonlinear nonlinear ADJ cana-441 245 12 analysis analysis NOUN cana-441 245 13 issn issn NOUN cana-441 245 14 : : PUNCT cana-441 245 15 1074 1074 NUM cana-441 245 16 - - PUNCT cana-441 245 17 133x 133x NUM cana-441 245 18 vol vol NOUN cana-441 245 19 31 31 NUM cana-441 245 20 no no NOUN cana-441 245 21 . . NOUN cana-441 245 22 1 1 NUM cana-441 245 23 ( ( PUNCT cana-441 245 24 2024 2024 NUM cana-441 245 25 ) ) PUNCT cana-441 245 26 332 332 NUM cana-441 245 27 https://internationalpubls.com https://internationalpubls.com X cana-441 245 28 fig fig NOUN cana-441 245 29 . . PUNCT cana-441 246 1 7 7 X cana-441 246 2 . . X cana-441 246 3 classification classification NOUN cana-441 246 4 matrix matrix NOUN cana-441 246 5 for for ADP cana-441 246 6 vgg-16 vgg-16 NOUN cana-441 246 7 fig fig NOUN cana-441 246 8 . . PUNCT cana-441 247 1 8 8 X cana-441 247 2 . . PUNCT cana-441 247 3 classification classification NOUN cana-441 247 4 matrix matrix NOUN cana-441 247 5 for for ADP cana-441 247 6 resnet-50 resnet-50 PROPN cana-441 247 7 communications communication NOUN cana-441 247 8 on on ADP cana-441 247 9 applied apply VERB cana-441 247 10 nonlinear nonlinear ADJ cana-441 247 11 analysis analysis NOUN cana-441 247 12 issn issn NOUN cana-441 247 13 : : PUNCT cana-441 247 14 1074 1074 NUM cana-441 247 15 - - PUNCT cana-441 247 16 133x 133x NUM cana-441 247 17 vol vol NOUN cana-441 247 18 31 31 NUM cana-441 247 19 no no NOUN cana-441 247 20 . . NOUN cana-441 247 21 1 1 NUM cana-441 247 22 ( ( PUNCT cana-441 247 23 2024 2024 NUM cana-441 247 24 ) ) PUNCT cana-441 247 25 333 333 NUM cana-441 247 26 https://internationalpubls.com https://internationalpubls.com X cana-441 247 27 fig fig NOUN cana-441 247 28 . . PUNCT cana-441 248 1 9 9 X cana-441 248 2 . . X cana-441 248 3 analysis analysis NOUN cana-441 248 4 of of ADP cana-441 248 5 trainable trainable ADJ cana-441 248 6 parameters parameter NOUN cana-441 248 7 - - PUNCT cana-441 248 8 resnet-50 resnet-50 NUM cana-441 248 9 fig fig NOUN cana-441 248 10 . . PUNCT cana-441 249 1 10 10 NUM cana-441 249 2 . . PUNCT cana-441 250 1 analysis analysis NOUN cana-441 250 2 of of ADP cana-441 250 3 trainable trainable ADJ cana-441 250 4 parameters parameter NOUN cana-441 250 5 - - PUNCT cana-441 250 6 resnet-50 resnet-50 NOUN cana-441 250 7 the the DET cana-441 250 8 resnet resnet NOUN cana-441 250 9 50 50 NUM cana-441 250 10 model model NOUN cana-441 250 11 consists consist VERB cana-441 250 12 of of ADP cana-441 250 13 an an DET cana-441 250 14 input input NOUN cana-441 250 15 layer layer NOUN cana-441 250 16 , , PUNCT cana-441 250 17 several several ADJ cana-441 250 18 residual residual ADJ cana-441 250 19 blocks block NOUN cana-441 250 20 , , PUNCT cana-441 250 21 and and CCONJ cana-441 250 22 an an DET cana-441 250 23 output output NOUN cana-441 250 24 layer layer NOUN cana-441 250 25 . . PUNCT cana-441 251 1 each each DET cana-441 251 2 communications communication NOUN cana-441 251 3 on on ADP cana-441 251 4 applied apply VERB cana-441 251 5 nonlinear nonlinear ADJ cana-441 251 6 analysis analysis NOUN cana-441 251 7 issn issn NOUN cana-441 251 8 : : PUNCT cana-441 251 9 1074 1074 NUM cana-441 251 10 - - PUNCT cana-441 251 11 133x 133x NUM cana-441 251 12 vol vol NOUN cana-441 251 13 31 31 NUM cana-441 251 14 no no NOUN cana-441 251 15 . . NOUN cana-441 251 16 1 1 NUM cana-441 251 17 ( ( PUNCT cana-441 251 18 2024 2024 NUM cana-441 251 19 ) ) PUNCT cana-441 251 20 334 334 NUM cana-441 251 21 https://internationalpubls.com https://internationalpubls.com X cana-441 251 22 residual residual ADJ cana-441 251 23 block block NOUN cana-441 251 24 has have VERB cana-441 251 25 a a DET cana-441 251 26 series series NOUN cana-441 251 27 of of ADP cana-441 251 28 convolutional convolutional ADJ cana-441 251 29 layers layer NOUN cana-441 251 30 , , PUNCT cana-441 251 31 and and CCONJ cana-441 251 32 the the DET cana-441 251 33 output output NOUN cana-441 251 34 of of ADP cana-441 251 35 the the DET cana-441 251 36 block block NOUN cana-441 251 37 is be AUX cana-441 251 38 added add VERB cana-441 251 39 to to ADP cana-441 251 40 its its PRON cana-441 251 41 input input NOUN cana-441 251 42 , , PUNCT cana-441 251 43 hence hence ADV cana-441 251 44 the the DET cana-441 251 45 term term NOUN cana-441 251 46 " " PUNCT cana-441 251 47 residual residual ADJ cana-441 251 48 . . PUNCT cana-441 251 49 " " PUNCT cana-441 252 1 this this DET cana-441 252 2 architecture architecture NOUN cana-441 252 3 enables enable VERB cana-441 252 4 the the DET cana-441 252 5 network network NOUN cana-441 252 6 to to PART cana-441 252 7 learn learn VERB cana-441 252 8 identity identity NOUN cana-441 252 9 functions function NOUN cana-441 252 10 , , PUNCT cana-441 252 11 which which PRON cana-441 252 12 ensures ensure VERB cana-441 252 13 that that SCONJ cana-441 252 14 the the DET cana-441 252 15 added add VERB cana-441 252 16 layers layer NOUN cana-441 252 17 can can AUX cana-441 252 18 at at ADV cana-441 252 19 least least ADJ cana-441 252 20 as as ADV cana-441 252 21 perform perform VERB cana-441 252 22 as as ADV cana-441 252 23 well well ADV cana-441 252 24 as as ADP cana-441 252 25 the the DET cana-441 252 26 previous previous ADJ cana-441 252 27 layers layer NOUN cana-441 252 28 , , PUNCT cana-441 252 29 not not PART cana-441 252 30 worse bad ADJ cana-441 252 31 . . PUNCT cana-441 253 1 in in ADP cana-441 253 2 the the DET cana-441 253 3 data datum NOUN cana-441 253 4 provided provide VERB cana-441 253 5 , , PUNCT cana-441 253 6 the the DET cana-441 253 7 resnet resnet NOUN cana-441 253 8 50 50 NUM cana-441 253 9 model model NOUN cana-441 253 10 is be AUX cana-441 253 11 used use VERB cana-441 253 12 for for ADP cana-441 253 13 a a DET cana-441 253 14 classification classification NOUN cana-441 253 15 task task NOUN cana-441 253 16 , , PUNCT cana-441 253 17 as as SCONJ cana-441 253 18 indicated indicate VERB cana-441 253 19 by by ADP cana-441 253 20 the the DET cana-441 253 21 presence presence NOUN cana-441 253 22 of of ADP cana-441 253 23 dense dense ADJ cana-441 253 24 layers layer NOUN cana-441 253 25 towards towards ADP cana-441 253 26 the the DET cana-441 253 27 end end NOUN cana-441 253 28 of of ADP cana-441 253 29 the the DET cana-441 253 30 network network NOUN cana-441 253 31 . . PUNCT cana-441 254 1 the the DET cana-441 254 2 model model NOUN cana-441 254 3 's 's PART cana-441 254 4 architecture architecture NOUN cana-441 254 5 is be AUX cana-441 254 6 outlined outline VERB cana-441 254 7 , , PUNCT cana-441 254 8 showing show VERB cana-441 254 9 various various ADJ cana-441 254 10 layers layer NOUN cana-441 254 11 like like ADP cana-441 254 12 the the DET cana-441 254 13 input input NOUN cana-441 254 14 layer layer NOUN cana-441 254 15 , , PUNCT cana-441 254 16 resnet resnet VERB cana-441 254 17 50 50 NUM cana-441 254 18 functional functional ADJ cana-441 254 19 block block NOUN cana-441 254 20 , , PUNCT cana-441 254 21 flatten flatten VERB cana-441 254 22 layer layer NOUN cana-441 254 23 , , PUNCT cana-441 254 24 dense dense ADJ cana-441 254 25 layers layer NOUN cana-441 254 26 , , PUNCT cana-441 254 27 and and CCONJ cana-441 254 28 dropout dropout NOUN cana-441 254 29 layers layer NOUN cana-441 254 30 . . PUNCT cana-441 255 1 dropout dropout NOUN cana-441 255 2 layers layer NOUN cana-441 255 3 are be AUX cana-441 255 4 included include VERB cana-441 255 5 to to PART cana-441 255 6 prevent prevent VERB cana-441 255 7 overfitting overfitting NOUN cana-441 255 8 by by ADP cana-441 255 9 randomly randomly ADV cana-441 255 10 setting set VERB cana-441 255 11 a a DET cana-441 255 12 fraction fraction NOUN cana-441 255 13 of of ADP cana-441 255 14 input input NOUN cana-441 255 15 units unit NOUN cana-441 255 16 to to ADP cana-441 255 17 0 0 NUM cana-441 255 18 during during ADP cana-441 255 19 training training NOUN cana-441 255 20 . . PUNCT cana-441 256 1 the the DET cana-441 256 2 parameter parameter NOUN cana-441 256 3 count count NOUN cana-441 256 4 in in ADP cana-441 256 5 each each DET cana-441 256 6 layer layer NOUN cana-441 256 7 provides provide VERB cana-441 256 8 insight insight NOUN cana-441 256 9 into into ADP cana-441 256 10 the the DET cana-441 256 11 model model NOUN cana-441 256 12 's 's PART cana-441 256 13 complexity complexity NOUN cana-441 256 14 . . PUNCT cana-441 257 1 for for ADP cana-441 257 2 instance instance NOUN cana-441 257 3 , , PUNCT cana-441 257 4 the the DET cana-441 257 5 resnet resnet NOUN cana-441 257 6 50 50 NUM cana-441 257 7 block block NOUN cana-441 257 8 has have VERB cana-441 257 9 23,587,712 23,587,712 NUM cana-441 257 10 parameters parameter NOUN cana-441 257 11 , , PUNCT cana-441 257 12 which which PRON cana-441 257 13 underscores underscore VERB cana-441 257 14 its its PRON cana-441 257 15 depth depth NOUN cana-441 257 16 and and CCONJ cana-441 257 17 capacity capacity NOUN cana-441 257 18 for for ADP cana-441 257 19 feature feature NOUN cana-441 257 20 extraction extraction NOUN cana-441 257 21 . . PUNCT cana-441 258 1 the the DET cana-441 258 2 dense dense ADJ cana-441 258 3 layers layer NOUN cana-441 258 4 , , PUNCT cana-441 258 5 which which PRON cana-441 258 6 are be AUX cana-441 258 7 fully fully ADV cana-441 258 8 connected connect VERB cana-441 258 9 , , PUNCT cana-441 258 10 contribute contribute VERB cana-441 258 11 to to ADP cana-441 258 12 the the DET cana-441 258 13 parameter parameter NOUN cana-441 258 14 count count NOUN cana-441 258 15 significantly significantly ADV cana-441 258 16 , , PUNCT cana-441 258 17 as as SCONJ cana-441 258 18 seen see VERB cana-441 258 19 in in ADP cana-441 258 20 the the DET cana-441 258 21 provided provide VERB cana-441 258 22 data datum NOUN cana-441 258 23 . . PUNCT cana-441 259 1 the the DET cana-441 259 2 differentiation differentiation NOUN cana-441 259 3 between between ADP cana-441 259 4 trainable trainable ADJ cana-441 259 5 and and CCONJ cana-441 259 6 non non ADJ cana-441 259 7 - - ADJ cana-441 259 8 trainable trainable ADJ cana-441 259 9 parameters parameter NOUN cana-441 259 10 is be AUX cana-441 259 11 crucial crucial ADJ cana-441 259 12 . . PUNCT cana-441 260 1 trainable trainable ADJ cana-441 260 2 parameters parameter NOUN cana-441 260 3 are be AUX cana-441 260 4 those those PRON cana-441 260 5 that that PRON cana-441 260 6 the the DET cana-441 260 7 model model NOUN cana-441 260 8 updates update VERB cana-441 260 9 during during ADP cana-441 260 10 training training NOUN cana-441 260 11 . . PUNCT cana-441 261 1 non non ADJ cana-441 261 2 - - ADJ cana-441 261 3 trainable trainable ADJ cana-441 261 4 parameters parameter NOUN cana-441 261 5 remain remain VERB cana-441 261 6 constant constant ADJ cana-441 261 7 during during ADP cana-441 261 8 training training NOUN cana-441 261 9 , , PUNCT cana-441 261 10 which which PRON cana-441 261 11 might might AUX cana-441 261 12 come come VERB cana-441 261 13 from from ADP cana-441 261 14 pre pre ADJ cana-441 261 15 - - ADJ cana-441 261 16 trained train VERB cana-441 261 17 layers layer NOUN cana-441 261 18 when when SCONJ cana-441 261 19 transfer transfer NOUN cana-441 261 20 learning learning NOUN cana-441 261 21 is be AUX cana-441 261 22 applied apply VERB cana-441 261 23 . . PUNCT cana-441 262 1 in in ADP cana-441 262 2 the the DET cana-441 262 3 context context NOUN cana-441 262 4 you you PRON cana-441 262 5 provided provide VERB cana-441 262 6 , , PUNCT cana-441 262 7 most most ADJ cana-441 262 8 parameters parameter NOUN cana-441 262 9 in in ADP cana-441 262 10 the the DET cana-441 262 11 resnet resnet NOUN cana-441 262 12 50 50 NUM cana-441 262 13 block block NOUN cana-441 262 14 are be AUX cana-441 262 15 non non ADJ cana-441 262 16 - - ADJ cana-441 262 17 trainable trainable ADJ cana-441 262 18 , , PUNCT cana-441 262 19 indicating indicate VERB cana-441 262 20 that that SCONJ cana-441 262 21 they they PRON cana-441 262 22 might might AUX cana-441 262 23 be be AUX cana-441 262 24 leveraged leverage VERB cana-441 262 25 from from ADP cana-441 262 26 a a DET cana-441 262 27 pre pre ADJ cana-441 262 28 - - ADJ cana-441 262 29 trained train VERB cana-441 262 30 model model NOUN cana-441 262 31 . . PUNCT cana-441 263 1 the the DET cana-441 263 2 classification classification NOUN cana-441 263 3 matrices matrix NOUN cana-441 263 4 for for ADP cana-441 263 5 the the DET cana-441 263 6 resnet resnet NOUN cana-441 263 7 50 50 NUM cana-441 263 8 and and CCONJ cana-441 263 9 vgg vgg ADJ cana-441 263 10 16 16 NUM cana-441 263 11 models model NOUN cana-441 263 12 offer offer VERB cana-441 263 13 insights insight NOUN cana-441 263 14 into into ADP cana-441 263 15 their their PRON cana-441 263 16 performance performance NOUN cana-441 263 17 . . PUNCT cana-441 264 1 these these DET cana-441 264 2 matrices matrix NOUN cana-441 264 3 show show VERB cana-441 264 4 the the DET cana-441 264 5 true true ADJ cana-441 264 6 positives positive NOUN cana-441 264 7 , , PUNCT cana-441 264 8 true true ADJ cana-441 264 9 negatives negative NOUN cana-441 264 10 , , PUNCT cana-441 264 11 false false ADJ cana-441 264 12 positives positive NOUN cana-441 264 13 , , PUNCT cana-441 264 14 and and CCONJ cana-441 264 15 false false ADJ cana-441 264 16 negatives negative NOUN cana-441 264 17 . . PUNCT cana-441 265 1 high high ADJ cana-441 265 2 true true ADJ cana-441 265 3 positives positive NOUN cana-441 265 4 and and CCONJ cana-441 265 5 true true ADJ cana-441 265 6 negatives negative NOUN cana-441 265 7 indicate indicate VERB cana-441 265 8 effective effective ADJ cana-441 265 9 model model NOUN cana-441 265 10 performance performance NOUN cana-441 265 11 , , PUNCT cana-441 265 12 whereas whereas SCONJ cana-441 265 13 high high ADJ cana-441 265 14 false false ADJ cana-441 265 15 positives positive NOUN cana-441 265 16 and and CCONJ cana-441 265 17 false false ADJ cana-441 265 18 negatives negative NOUN cana-441 265 19 indicate indicate VERB cana-441 265 20 areas area NOUN cana-441 265 21 of of ADP cana-441 265 22 weakness weakness NOUN cana-441 265 23 . . PUNCT cana-441 266 1 precision precision NOUN cana-441 266 2 , , PUNCT cana-441 266 3 recall recall NOUN cana-441 266 4 , , PUNCT cana-441 266 5 f1 f1 NOUN cana-441 266 6 score score NOUN cana-441 266 7 , , PUNCT cana-441 266 8 and and CCONJ cana-441 266 9 mean mean ADJ cana-441 266 10 accuracy accuracy NOUN cana-441 266 11 are be AUX cana-441 266 12 critical critical ADJ cana-441 266 13 metrics metric NOUN cana-441 266 14 derived derive VERB cana-441 266 15 from from ADP cana-441 266 16 the the DET cana-441 266 17 classification classification NOUN cana-441 266 18 matrix matrix NOUN cana-441 266 19 . . PUNCT cana-441 267 1 precision precision NOUN cana-441 267 2 ( ( PUNCT cana-441 267 3 the the DET cana-441 267 4 proportion proportion NOUN cana-441 267 5 of of ADP cana-441 267 6 true true ADJ cana-441 267 7 positive positive ADJ cana-441 267 8 results result NOUN cana-441 267 9 in in ADP cana-441 267 10 all all DET cana-441 267 11 positive positive ADJ cana-441 267 12 predictions prediction NOUN cana-441 267 13 ) ) PUNCT cana-441 267 14 in in ADP cana-441 267 15 resnet resnet NOUN cana-441 267 16 50 50 NUM cana-441 267 17 is be AUX cana-441 267 18 slightly slightly ADV cana-441 267 19 higher high ADJ cana-441 267 20 than than ADP cana-441 267 21 in in ADP cana-441 267 22 vgg vgg PROPN cana-441 267 23 16 16 NUM cana-441 267 24 , , PUNCT cana-441 267 25 suggesting suggest VERB cana-441 267 26 it it PRON cana-441 267 27 is be AUX cana-441 267 28 more more ADV cana-441 267 29 reliable reliable ADJ cana-441 267 30 when when SCONJ cana-441 267 31 it it PRON cana-441 267 32 asserts assert VERB cana-441 267 33 a a DET cana-441 267 34 positive positive ADJ cana-441 267 35 classification classification NOUN cana-441 267 36 . . PUNCT cana-441 268 1 recall recall NOUN cana-441 268 2 ( ( PUNCT cana-441 268 3 the the DET cana-441 268 4 proportion proportion NOUN cana-441 268 5 of of ADP cana-441 268 6 true true ADJ cana-441 268 7 positive positive ADJ cana-441 268 8 results result NOUN cana-441 268 9 in in ADP cana-441 268 10 all all DET cana-441 268 11 actual actual ADJ cana-441 268 12 positives positive NOUN cana-441 268 13 ) ) PUNCT cana-441 268 14 also also ADV cana-441 268 15 shows show VERB cana-441 268 16 resnet resnet VERB cana-441 268 17 50 50 NUM cana-441 268 18 's 's PART cana-441 268 19 strength strength NOUN cana-441 268 20 in in ADP cana-441 268 21 identifying identify VERB cana-441 268 22 positive positive ADJ cana-441 268 23 instances instance NOUN cana-441 268 24 . . PUNCT cana-441 269 1 the the DET cana-441 269 2 f1 f1 PROPN cana-441 269 3 score score NOUN cana-441 269 4 , , PUNCT cana-441 269 5 which which PRON cana-441 269 6 balances balance VERB cana-441 269 7 precision precision NOUN cana-441 269 8 and and CCONJ cana-441 269 9 recall recall NOUN cana-441 269 10 , , PUNCT cana-441 269 11 and and CCONJ cana-441 269 12 mean mean ADJ cana-441 269 13 accuracy accuracy NOUN cana-441 269 14 ( ( PUNCT cana-441 269 15 the the DET cana-441 269 16 overall overall ADJ cana-441 269 17 correctness correctness NOUN cana-441 269 18 of of ADP cana-441 269 19 the the DET cana-441 269 20 model model NOUN cana-441 269 21 ) ) PUNCT cana-441 269 22 , , PUNCT cana-441 269 23 further far ADV cana-441 269 24 demonstrate demonstrate VERB cana-441 269 25 the the DET cana-441 269 26 superior superior ADJ cana-441 269 27 performance performance NOUN cana-441 269 28 of of ADP cana-441 269 29 resnet resnet NOUN cana-441 269 30 50 50 NUM cana-441 269 31 compared compare VERB cana-441 269 32 to to ADP cana-441 269 33 vgg vgg NOUN cana-441 269 34 16 16 NUM cana-441 269 35 . . PUNCT cana-441 270 1 vgg vgg PROPN cana-441 270 2 16 16 NUM cana-441 270 3 is be AUX cana-441 270 4 another another DET cana-441 270 5 deep deep ADJ cana-441 270 6 cnn cnn PROPN cana-441 270 7 , , PUNCT cana-441 270 8 known know VERB cana-441 270 9 for for ADP cana-441 270 10 its its PRON cana-441 270 11 simplicity simplicity NOUN cana-441 270 12 and and CCONJ cana-441 270 13 depth depth NOUN cana-441 270 14 . . PUNCT cana-441 271 1 it it PRON cana-441 271 2 consists consist VERB cana-441 271 3 of of ADP cana-441 271 4 16 16 NUM cana-441 271 5 layers layer NOUN cana-441 271 6 and and CCONJ cana-441 271 7 uses use VERB cana-441 271 8 small small ADJ cana-441 271 9 ( ( PUNCT cana-441 271 10 3x3 3x3 NUM cana-441 271 11 ) ) PUNCT cana-441 271 12 convolution convolution NOUN cana-441 271 13 filters filter NOUN cana-441 271 14 throughout throughout ADP cana-441 271 15 , , PUNCT cana-441 271 16 which which PRON cana-441 271 17 was be AUX cana-441 271 18 a a DET cana-441 271 19 key key ADJ cana-441 271 20 insight insight NOUN cana-441 271 21 for for ADP cana-441 271 22 building build VERB cana-441 271 23 deeper deep ADJ cana-441 271 24 networks network NOUN cana-441 271 25 . . PUNCT cana-441 272 1 unlike unlike ADP cana-441 272 2 resnet resnet NOUN cana-441 272 3 , , PUNCT cana-441 272 4 vgg vgg PROPN cana-441 272 5 16 16 NUM cana-441 272 6 does do AUX cana-441 272 7 not not PART cana-441 272 8 have have VERB cana-441 272 9 skip skip ADJ cana-441 272 10 connections connection NOUN cana-441 272 11 , , PUNCT cana-441 272 12 which which PRON cana-441 272 13 makes make VERB cana-441 272 14 it it PRON cana-441 272 15 more more ADV cana-441 272 16 prone prone ADJ cana-441 272 17 to to ADP cana-441 272 18 the the DET cana-441 272 19 vanishing vanish VERB cana-441 272 20 gradient gradient ADJ cana-441 272 21 problem problem NOUN cana-441 272 22 in in ADP cana-441 272 23 deeper deep ADJ cana-441 272 24 networks network NOUN cana-441 272 25 . . PUNCT cana-441 273 1 when when SCONJ cana-441 273 2 comparing compare VERB cana-441 273 3 resnet resnet NOUN cana-441 273 4 50 50 NUM cana-441 273 5 and and CCONJ cana-441 273 6 vgg vgg NOUN cana-441 273 7 16 16 NUM cana-441 273 8 , , PUNCT cana-441 273 9 it it PRON cana-441 273 10 's be AUX cana-441 273 11 evident evident ADJ cana-441 273 12 that that SCONJ cana-441 273 13 the the DET cana-441 273 14 architectural architectural ADJ cana-441 273 15 advancements advancement NOUN cana-441 273 16 in in ADP cana-441 273 17 resnet resnet NOUN cana-441 273 18 50 50 NUM cana-441 273 19 , , PUNCT cana-441 273 20 like like ADP cana-441 273 21 residual residual ADJ cana-441 273 22 connections connection NOUN cana-441 273 23 , , PUNCT cana-441 273 24 provide provide VERB cana-441 273 25 it it PRON cana-441 273 26 with with ADP cana-441 273 27 an an DET cana-441 273 28 edge edge NOUN cana-441 273 29 in in ADP cana-441 273 30 deeper deep ADJ cana-441 273 31 network network NOUN cana-441 273 32 training training NOUN cana-441 273 33 , , PUNCT cana-441 273 34 reflected reflect VERB cana-441 273 35 in in ADP cana-441 273 36 the the DET cana-441 273 37 performance performance NOUN cana-441 273 38 metrics metric NOUN cana-441 273 39 . . PUNCT cana-441 274 1 while while SCONJ cana-441 274 2 both both DET cana-441 274 3 models model NOUN cana-441 274 4 show show VERB cana-441 274 5 high high ADJ cana-441 274 6 performance performance NOUN cana-441 274 7 , , PUNCT cana-441 274 8 the the DET cana-441 274 9 slight slight ADJ cana-441 274 10 edge edge NOUN cana-441 274 11 in in ADP cana-441 274 12 precision precision NOUN cana-441 274 13 , , PUNCT cana-441 274 14 recall recall NOUN cana-441 274 15 , , PUNCT cana-441 274 16 f1 f1 NOUN cana-441 274 17 score score NOUN cana-441 274 18 , , PUNCT cana-441 274 19 and and CCONJ cana-441 274 20 accuracy accuracy NOUN cana-441 274 21 of of ADP cana-441 274 22 resnet resnet NOUN cana-441 274 23 50 50 NUM cana-441 274 24 highlights highlight NOUN cana-441 274 25 its its PRON cana-441 274 26 efficiency efficiency NOUN cana-441 274 27 , , PUNCT cana-441 274 28 particularly particularly ADV cana-441 274 29 in in ADP cana-441 274 30 tasks task NOUN cana-441 274 31 where where SCONJ cana-441 274 32 the the DET cana-441 274 33 cost cost NOUN cana-441 274 34 of of ADP cana-441 274 35 false false ADJ cana-441 274 36 positives positive NOUN cana-441 274 37 and and CCONJ cana-441 274 38 negatives negative NOUN cana-441 274 39 is be AUX cana-441 274 40 significant significant ADJ cana-441 274 41 . . PUNCT cana-441 275 1 in in ADP cana-441 275 2 conclusion conclusion NOUN cana-441 275 3 , , PUNCT cana-441 275 4 the the DET cana-441 275 5 detailed detailed ADJ cana-441 275 6 layer layer NOUN cana-441 275 7 - - PUNCT cana-441 275 8 wise wise ADJ cana-441 275 9 parameter parameter NOUN cana-441 275 10 distribution distribution NOUN cana-441 275 11 and and CCONJ cana-441 275 12 performance performance NOUN cana-441 275 13 metric metric ADJ cana-441 275 14 analysis analysis NOUN cana-441 275 15 underline underline VERB cana-441 275 16 the the DET cana-441 275 17 effectiveness effectiveness NOUN cana-441 275 18 of of ADP cana-441 275 19 resnet resnet NOUN cana-441 275 20 50 50 NUM cana-441 275 21 in in ADP cana-441 275 22 handling handle VERB cana-441 275 23 deep deep ADJ cana-441 275 24 learning learn VERB cana-441 275 25 classification classification NOUN cana-441 275 26 tasks task NOUN cana-441 275 27 , , PUNCT cana-441 275 28 showing show VERB cana-441 275 29 a a DET cana-441 275 30 communications communication NOUN cana-441 275 31 on on ADP cana-441 275 32 applied apply VERB cana-441 275 33 nonlinear nonlinear ADJ cana-441 275 34 analysis analysis NOUN cana-441 275 35 issn issn NOUN cana-441 275 36 : : PUNCT cana-441 275 37 1074 1074 NUM cana-441 275 38 - - PUNCT cana-441 275 39 133x 133x NUM cana-441 275 40 vol vol NOUN cana-441 275 41 31 31 NUM cana-441 275 42 no no NOUN cana-441 275 43 . . NOUN cana-441 275 44 1 1 NUM cana-441 275 45 ( ( PUNCT cana-441 275 46 2024 2024 NUM cana-441 275 47 ) ) PUNCT cana-441 275 48 335 335 NUM cana-441 275 49 https://internationalpubls.com https://internationalpubls.com NUM cana-441 275 50 nuanced nuanced ADJ cana-441 275 51 advantage advantage NOUN cana-441 275 52 over over ADP cana-441 275 53 the the DET cana-441 275 54 vgg vgg ADJ cana-441 275 55 16 16 NUM cana-441 275 56 model model NOUN cana-441 275 57 , , PUNCT cana-441 275 58 particularly particularly ADV cana-441 275 59 in in ADP cana-441 275 60 the the DET cana-441 275 61 context context NOUN cana-441 275 62 of of ADP cana-441 275 63 the the DET cana-441 275 64 presented present VERB cana-441 275 65 data datum NOUN cana-441 275 66 . . PUNCT cana-441 276 1 the the DET cana-441 276 2 architectural architectural ADJ cana-441 276 3 differences difference NOUN cana-441 276 4 , , PUNCT cana-441 276 5 particularly particularly ADV cana-441 276 6 the the DET cana-441 276 7 residual residual ADJ cana-441 276 8 connections connection NOUN cana-441 276 9 in in ADP cana-441 276 10 resnet resnet NOUN cana-441 276 11 , , PUNCT cana-441 276 12 play play VERB cana-441 276 13 a a DET cana-441 276 14 pivotal pivotal ADJ cana-441 276 15 role role NOUN cana-441 276 16 in in ADP cana-441 276 17 this this DET cana-441 276 18 enhanced enhance VERB cana-441 276 19 performance performance NOUN cana-441 276 20 , , PUNCT cana-441 276 21 demonstrating demonstrate VERB cana-441 276 22 the the DET cana-441 276 23 importance importance NOUN cana-441 276 24 of of ADP cana-441 276 25 architecture architecture NOUN cana-441 276 26 design design NOUN cana-441 276 27 in in ADP cana-441 276 28 deep deep ADJ cana-441 276 29 learning learn VERB cana-441 276 30 5 5 NUM cana-441 276 31 . . PUNCT cana-441 276 32 conclusion conclusion NOUN cana-441 276 33 in in ADP cana-441 276 34 this this DET cana-441 276 35 research research NOUN cana-441 276 36 , , PUNCT cana-441 276 37 we we PRON cana-441 276 38 have have AUX cana-441 276 39 presented present VERB cana-441 276 40 a a DET cana-441 276 41 comprehensive comprehensive ADJ cana-441 276 42 analysis analysis NOUN cana-441 276 43 of of ADP cana-441 276 44 the the DET cana-441 276 45 design design NOUN cana-441 276 46 and and CCONJ cana-441 276 47 assessment assessment NOUN cana-441 276 48 of of ADP cana-441 276 49 an an DET cana-441 276 50 improved improved ADJ cana-441 276 51 resnet50 resnet50 NOUN cana-441 276 52 and and CCONJ cana-441 276 53 vgg vgg PROPN cana-441 276 54 16 16 NUM cana-441 276 55 based base VERB cana-441 276 56 brain brain NOUN cana-441 276 57 tumor tumor NOUN cana-441 276 58 classification classification NOUN cana-441 276 59 system system NOUN cana-441 276 60 . . PUNCT cana-441 277 1 the the DET cana-441 277 2 primary primary ADJ cana-441 277 3 objective objective NOUN cana-441 277 4 of of ADP cana-441 277 5 this this DET cana-441 277 6 work work NOUN cana-441 277 7 was be AUX cana-441 277 8 to to PART cana-441 277 9 develop develop VERB cana-441 277 10 a a DET cana-441 277 11 robust robust ADJ cana-441 277 12 and and CCONJ cana-441 277 13 accurate accurate ADJ cana-441 277 14 model model NOUN cana-441 277 15 for for ADP cana-441 277 16 the the DET cana-441 277 17 segmentation segmentation NOUN cana-441 277 18 and and CCONJ cana-441 277 19 classification classification NOUN cana-441 277 20 of of ADP cana-441 277 21 brain brain NOUN cana-441 277 22 tumor tumor NOUN cana-441 277 23 images image NOUN cana-441 277 24 , , PUNCT cana-441 277 25 which which PRON cana-441 277 26 can can AUX cana-441 277 27 assist assist VERB cana-441 277 28 medical medical ADJ cana-441 277 29 professionals professional NOUN cana-441 277 30 in in ADP cana-441 277 31 diagnosis diagnosis NOUN cana-441 277 32 and and CCONJ cana-441 277 33 treatment treatment NOUN cana-441 277 34 planning planning NOUN cana-441 277 35 . . PUNCT cana-441 278 1 to to PART cana-441 278 2 achieve achieve VERB cana-441 278 3 this this DET cana-441 278 4 objective objective NOUN cana-441 278 5 , , PUNCT cana-441 278 6 we we PRON cana-441 278 7 first first ADV cana-441 278 8 discussed discuss VERB cana-441 278 9 the the DET cana-441 278 10 motivation motivation NOUN cana-441 278 11 behind behind ADP cana-441 278 12 using use VERB cana-441 278 13 the the DET cana-441 278 14 resnet50 resnet50 NOUN cana-441 278 15 architecture architecture NOUN cana-441 278 16 . . PUNCT cana-441 279 1 the the DET cana-441 279 2 resnet50 resnet50 NOUN cana-441 279 3 is be AUX cana-441 279 4 a a DET cana-441 279 5 deep deep ADJ cana-441 279 6 cnn cnn NOUN cana-441 279 7 architecture architecture NOUN cana-441 279 8 known know VERB cana-441 279 9 for for ADP cana-441 279 10 its its PRON cana-441 279 11 depth depth NOUN cana-441 279 12 of of ADP cana-441 279 13 50 50 NUM cana-441 279 14 layers layer NOUN cana-441 279 15 and and CCONJ cana-441 279 16 the the DET cana-441 279 17 incorporation incorporation NOUN cana-441 279 18 of of ADP cana-441 279 19 skip skip ADJ cana-441 279 20 connections connection NOUN cana-441 279 21 . . PUNCT cana-441 280 1 these these DET cana-441 280 2 skip skip ADJ cana-441 280 3 connections connection NOUN cana-441 280 4 help help AUX cana-441 280 5 alleviate alleviate VERB cana-441 280 6 the the DET cana-441 280 7 vanishing vanish VERB cana-441 280 8 gradient gradient NOUN cana-441 280 9 problem problem NOUN cana-441 280 10 and and CCONJ cana-441 280 11 preserve preserve VERB cana-441 280 12 information information NOUN cana-441 280 13 across across ADP cana-441 280 14 layers layer NOUN cana-441 280 15 , , PUNCT cana-441 280 16 making make VERB cana-441 280 17 it it PRON cana-441 280 18 an an DET cana-441 280 19 ideal ideal ADJ cana-441 280 20 choice choice NOUN cana-441 280 21 for for ADP cana-441 280 22 complex complex ADJ cana-441 280 23 tasks task NOUN cana-441 280 24 such such ADJ cana-441 280 25 as as ADP cana-441 280 26 brain brain NOUN cana-441 280 27 tumor tumor NOUN cana-441 280 28 analysis analysis NOUN cana-441 280 29 . . PUNCT cana-441 281 1 additionally additionally ADV cana-441 281 2 , , PUNCT cana-441 281 3 we we PRON cana-441 281 4 utilized utilize VERB cana-441 281 5 transfer transfer NOUN cana-441 281 6 learning learn VERB cana-441 281 7 by by ADP cana-441 281 8 using use VERB cana-441 281 9 a a DET cana-441 281 10 pre pre ADJ cana-441 281 11 trained train VERB cana-441 281 12 resnet50 resnet50 NOUN cana-441 281 13 model model NOUN cana-441 281 14 and and CCONJ cana-441 281 15 fine fine ADJ cana-441 281 16 tuning tune VERB cana-441 281 17 it it PRON cana-441 281 18 for for ADP cana-441 281 19 our our PRON cana-441 281 20 specific specific ADJ cana-441 281 21 task task NOUN cana-441 281 22 . . PUNCT cana-441 282 1 moving move VERB cana-441 282 2 on on ADP cana-441 282 3 to to ADP cana-441 282 4 the the DET cana-441 282 5 classification classification NOUN cana-441 282 6 task task NOUN cana-441 282 7 , , PUNCT cana-441 282 8 we we PRON cana-441 282 9 fine fine ADV cana-441 282 10 - - PUNCT cana-441 282 11 tuned tune VERB cana-441 282 12 the the DET cana-441 282 13 pre pre NOUN cana-441 282 14 trained train VERB cana-441 282 15 resnet50 resnet50 NOUN cana-441 282 16 and and CCONJ cana-441 282 17 vgg16 vgg16 NOUN cana-441 282 18 model model NOUN cana-441 282 19 by by ADP cana-441 282 20 freezing freeze VERB cana-441 282 21 the the DET cana-441 282 22 base base NOUN cana-441 282 23 layers layer NOUN cana-441 282 24 and and CCONJ cana-441 282 25 adding add VERB cana-441 282 26 new new ADJ cana-441 282 27 layers layer NOUN cana-441 282 28 for for ADP cana-441 282 29 classification classification NOUN cana-441 282 30 . . PUNCT cana-441 283 1 the the DET cana-441 283 2 classification classification NOUN cana-441 283 3 model model NOUN cana-441 283 4 was be AUX cana-441 283 5 trained train VERB cana-441 283 6 on on ADP cana-441 283 7 the the DET cana-441 283 8 segmented segment VERB cana-441 283 9 tumor tumor NOUN cana-441 283 10 images image NOUN cana-441 283 11 to to PART cana-441 283 12 classify classify VERB cana-441 283 13 them they PRON cana-441 283 14 into into ADP cana-441 283 15 different different ADJ cana-441 283 16 tumor tumor NOUN cana-441 283 17 types type NOUN cana-441 283 18 or or CCONJ cana-441 283 19 predict predict VERB cana-441 283 20 their their PRON cana-441 283 21 malignancy malignancy NOUN cana-441 283 22 . . PUNCT cana-441 284 1 we we PRON cana-441 284 2 assessed assess VERB cana-441 284 3 the the DET cana-441 284 4 performance performance NOUN cana-441 284 5 of of ADP cana-441 284 6 the the DET cana-441 284 7 classification classification NOUN cana-441 284 8 model model NOUN cana-441 284 9 using use VERB cana-441 284 10 metrics metric NOUN cana-441 284 11 such such ADJ cana-441 284 12 as as ADP cana-441 284 13 precision precision NOUN cana-441 284 14 , , PUNCT cana-441 284 15 recall recall NOUN cana-441 284 16 , , PUNCT cana-441 284 17 and and CCONJ cana-441 284 18 f1 f1 PROPN cana-441 284 19 score score NOUN cana-441 284 20 . . PUNCT cana-441 285 1 the the DET cana-441 285 2 results result NOUN cana-441 285 3 of of ADP cana-441 285 4 the the DET cana-441 285 5 classification classification NOUN cana-441 285 6 model model NOUN cana-441 285 7 demonstrated demonstrate VERB cana-441 285 8 its its PRON cana-441 285 9 effectiveness effectiveness NOUN cana-441 285 10 in in ADP cana-441 285 11 accurately accurately ADV cana-441 285 12 classifying classify VERB cana-441 285 13 brain brain NOUN cana-441 285 14 tumors tumor NOUN cana-441 285 15 . . PUNCT cana-441 286 1 the the DET cana-441 286 2 precision precision NOUN cana-441 286 3 , , PUNCT cana-441 286 4 recall recall NOUN cana-441 286 5 , , PUNCT cana-441 286 6 and and CCONJ cana-441 286 7 f1 f1 NOUN cana-441 286 8 scores score NOUN cana-441 286 9 consistently consistently ADV cana-441 286 10 exceeded exceed VERB cana-441 286 11 99.5 99.5 NUM cana-441 286 12 % % NOUN cana-441 286 13 in in ADP cana-441 286 14 the the DET cana-441 286 15 proposed propose VERB cana-441 286 16 model model NOUN cana-441 286 17 of of ADP cana-441 286 18 resnet50 resnet50 NOUN cana-441 286 19 as as SCONJ cana-441 286 20 compared compare VERB cana-441 286 21 to to ADP cana-441 286 22 other other ADJ cana-441 286 23 models model NOUN cana-441 286 24 , , PUNCT cana-441 286 25 indicating indicate VERB cana-441 286 26 a a DET cana-441 286 27 high high ADJ cana-441 286 28 level level NOUN cana-441 286 29 of of ADP cana-441 286 30 accuracy accuracy NOUN cana-441 286 31 in in ADP cana-441 286 32 differentiating differentiate VERB cana-441 286 33 between between ADP cana-441 286 34 tumor tumor NOUN cana-441 286 35 or or CCONJ cana-441 286 36 no no DET cana-441 286 37 tumor tumor NOUN cana-441 286 38 . . PUNCT cana-441 287 1 these these DET cana-441 287 2 results result NOUN cana-441 287 3 are be AUX cana-441 287 4 crucial crucial ADJ cana-441 287 5 for for ADP cana-441 287 6 providing provide VERB cana-441 287 7 accurate accurate ADJ cana-441 287 8 diagnoses diagnosis NOUN cana-441 287 9 and and CCONJ cana-441 287 10 guiding guide VERB cana-441 287 11 appropriate appropriate ADJ cana-441 287 12 treatment treatment NOUN cana-441 287 13 strategies strategy NOUN cana-441 287 14 . . PUNCT cana-441 288 1 overall overall ADV cana-441 288 2 , , PUNCT cana-441 288 3 the the DET cana-441 288 4 proposed propose VERB cana-441 288 5 resnet50 resnet50 NOUN cana-441 288 6 based base VERB cana-441 288 7 on on ADP cana-441 288 8 brain brain NOUN cana-441 288 9 tumor tumor NOUN cana-441 288 10 classification classification NOUN cana-441 288 11 system system NOUN cana-441 288 12 exhibited exhibit VERB cana-441 288 13 excellent excellent ADJ cana-441 288 14 performance performance NOUN cana-441 288 15 in in ADP cana-441 288 16 accurately accurately ADV cana-441 288 17 identifying identify VERB cana-441 288 18 tumor tumor NOUN cana-441 288 19 regions region NOUN cana-441 288 20 and and CCONJ cana-441 288 21 classifying classify VERB cana-441 288 22 brain brain NOUN cana-441 288 23 tumors tumor NOUN cana-441 288 24 . . PUNCT cana-441 289 1 the the DET cana-441 289 2 contributions contribution NOUN cana-441 289 3 of of ADP cana-441 289 4 this this DET cana-441 289 5 work work NOUN cana-441 289 6 extend extend VERB cana-441 289 7 beyond beyond ADP cana-441 289 8 the the DET cana-441 289 9 development development NOUN cana-441 289 10 of of ADP cana-441 289 11 the the DET cana-441 289 12 proposed propose VERB cana-441 289 13 models model NOUN cana-441 289 14 . . PUNCT cana-441 290 1 we we PRON cana-441 290 2 also also ADV cana-441 290 3 focused focus VERB cana-441 290 4 on on ADP cana-441 290 5 the the DET cana-441 290 6 interpretability interpretability NOUN cana-441 290 7 and and CCONJ cana-441 290 8 visualization visualization NOUN cana-441 290 9 of of ADP cana-441 290 10 the the DET cana-441 290 11 results result NOUN cana-441 290 12 . . PUNCT cana-441 291 1 the the DET cana-441 291 2 proposed propose VERB cana-441 291 3 system system NOUN cana-441 291 4 can can AUX cana-441 291 5 assist assist VERB cana-441 291 6 medical medical ADJ cana-441 291 7 professionals professional NOUN cana-441 291 8 in in ADP cana-441 291 9 making make VERB cana-441 291 10 informed informed ADJ cana-441 291 11 decisions decision NOUN cana-441 291 12 , , PUNCT cana-441 291 13 potentially potentially ADV cana-441 291 14 improving improve VERB cana-441 291 15 patient patient ADJ cana-441 291 16 outcomes outcome NOUN cana-441 291 17 and and CCONJ cana-441 291 18 reducing reduce VERB cana-441 291 19 the the DET cana-441 291 20 burden burden NOUN cana-441 291 21 on on ADP cana-441 291 22 radiologists radiologist NOUN cana-441 291 23 . . PUNCT cana-441 292 1 however however ADV cana-441 292 2 , , PUNCT cana-441 292 3 it it PRON cana-441 292 4 is be AUX cana-441 292 5 essential essential ADJ cana-441 292 6 to to PART cana-441 292 7 acknowledge acknowledge VERB cana-441 292 8 the the DET cana-441 292 9 limitations limitation NOUN cana-441 292 10 of of ADP cana-441 292 11 this this DET cana-441 292 12 work work NOUN cana-441 292 13 . . PUNCT cana-441 293 1 the the DET cana-441 293 2 performance performance NOUN cana-441 293 3 evaluation evaluation NOUN cana-441 293 4 was be AUX cana-441 293 5 performed perform VERB cana-441 293 6 on on ADP cana-441 293 7 a a DET cana-441 293 8 specific specific ADJ cana-441 293 9 dataset dataset NOUN cana-441 293 10 , , PUNCT cana-441 293 11 and and CCONJ cana-441 293 12 the the DET cana-441 293 13 results result NOUN cana-441 293 14 may may AUX cana-441 293 15 vary vary VERB cana-441 293 16 when when SCONJ cana-441 293 17 applied apply VERB cana-441 293 18 to to ADP cana-441 293 19 different different ADJ cana-441 293 20 dataset dataset NOUN cana-441 293 21 , , PUNCT cana-441 293 22 real real ADJ cana-441 293 23 world world NOUN cana-441 293 24 scenarios scenario NOUN cana-441 293 25 and and CCONJ cana-441 293 26 on on ADP cana-441 293 27 different different ADJ cana-441 293 28 models model NOUN cana-441 293 29 . . PUNCT cana-441 294 1 the the DET cana-441 294 2 model model NOUN cana-441 294 3 generalize generalize VERB cana-441 294 4 ability ability NOUN cana-441 294 5 needs need VERB cana-441 294 6 to to PART cana-441 294 7 be be AUX cana-441 294 8 further far ADV cana-441 294 9 validated validate VERB cana-441 294 10 on on ADP cana-441 294 11 diverse diverse ADJ cana-441 294 12 datasets dataset NOUN cana-441 294 13 and and CCONJ cana-441 294 14 in in ADP cana-441 294 15 clinical clinical ADJ cana-441 294 16 settings setting NOUN cana-441 294 17 . . PUNCT cana-441 295 1 additionally additionally ADV cana-441 295 2 , , PUNCT cana-441 295 3 the the DET cana-441 295 4 training training NOUN cana-441 295 5 and and CCONJ cana-441 295 6 evaluation evaluation NOUN cana-441 295 7 processes process NOUN cana-441 295 8 heavily heavily ADV cana-441 295 9 relied rely VERB cana-441 295 10 on on ADP cana-441 295 11 manual manual ADJ cana-441 295 12 annotations annotation NOUN cana-441 295 13 , , PUNCT cana-441 295 14 which which PRON cana-441 295 15 can can AUX cana-441 295 16 introduce introduce VERB cana-441 295 17 subjectivity subjectivity NOUN cana-441 295 18 and and CCONJ cana-441 295 19 variability variability NOUN cana-441 295 20 . . PUNCT cana-441 296 1 automating automate VERB cana-441 296 2 the the DET cana-441 296 3 annotation annotation NOUN cana-441 296 4 process process NOUN cana-441 296 5 or or CCONJ cana-441 296 6 exploring explore VERB cana-441 296 7 semi semi ADV cana-441 296 8 supervised supervised ADJ cana-441 296 9 learning learning NOUN cana-441 296 10 approaches approach NOUN cana-441 296 11 could could AUX cana-441 296 12 address address VERB cana-441 296 13 this this DET cana-441 296 14 limitation limitation NOUN cana-441 296 15 . . PUNCT cana-441 297 1 future future ADJ cana-441 297 2 research research NOUN cana-441 297 3 directions direction NOUN cana-441 297 4 could could AUX cana-441 297 5 involve involve VERB cana-441 297 6 exploring explore VERB cana-441 297 7 advanced advanced ADJ cana-441 297 8 deep deep ADJ cana-441 297 9 learning learning NOUN cana-441 297 10 techniques technique NOUN cana-441 297 11 , , PUNCT cana-441 297 12 such such ADJ cana-441 297 13 as as ADP cana-441 297 14 attention attention NOUN cana-441 297 15 mechanisms mechanism NOUN cana-441 297 16 and and CCONJ cana-441 297 17 generative generative ADJ cana-441 297 18 adversarial adversarial ADJ cana-441 297 19 network network NOUN cana-441 297 20 , , PUNCT cana-441 297 21 to to PART cana-441 297 22 enhance enhance VERB cana-441 297 23 the the DET cana-441 297 24 accuracy accuracy NOUN cana-441 297 25 and and CCONJ cana-441 297 26 robustness robustness NOUN cana-441 297 27 of of ADP cana-441 297 28 communications communication NOUN cana-441 297 29 on on ADP cana-441 297 30 applied apply VERB cana-441 297 31 nonlinear nonlinear ADJ cana-441 297 32 analysis analysis NOUN cana-441 297 33 issn issn NOUN cana-441 297 34 : : PUNCT cana-441 297 35 1074 1074 NUM cana-441 297 36 - - PUNCT cana-441 297 37 133x 133x NUM cana-441 297 38 vol vol NOUN cana-441 297 39 31 31 NUM cana-441 297 40 no no NOUN cana-441 297 41 . . NOUN cana-441 297 42 1 1 NUM cana-441 297 43 ( ( PUNCT cana-441 297 44 2024 2024 NUM cana-441 297 45 ) ) PUNCT cana-441 297 46 336 336 NUM cana-441 297 47 https://internationalpubls.com https://internationalpubls.com X cana-441 297 48 brain brain NOUN cana-441 297 49 tumor tumor NOUN cana-441 297 50 segmentation segmentation NOUN cana-441 297 51 and and CCONJ cana-441 297 52 classification classification NOUN cana-441 297 53 . . PUNCT cana-441 298 1 additionally additionally ADV cana-441 298 2 , , PUNCT cana-441 298 3 incorporating incorporate VERB cana-441 298 4 multimodal multimodal ADJ cana-441 298 5 imaging imaging NOUN cana-441 298 6 data datum NOUN cana-441 298 7 , , PUNCT cana-441 298 8 such such ADJ cana-441 298 9 as as ADP cana-441 298 10 mri mri NOUN cana-441 298 11 and and CCONJ cana-441 298 12 pet pet NOUN cana-441 298 13 scans scan NOUN cana-441 298 14 , , PUNCT cana-441 298 15 could could AUX cana-441 298 16 further far ADV cana-441 298 17 improve improve VERB cana-441 298 18 the the DET cana-441 298 19 model model NOUN cana-441 298 20 's 's PART cana-441 298 21 performance performance NOUN cana-441 298 22 and and CCONJ cana-441 298 23 provide provide VERB cana-441 298 24 a a DET cana-441 298 25 comprehensive comprehensive ADJ cana-441 298 26 analysis analysis NOUN cana-441 298 27 of of ADP cana-441 298 28 brain brain NOUN cana-441 298 29 tumors tumor NOUN cana-441 298 30 . . PUNCT cana-441 299 1 in in ADP cana-441 299 2 conclusion conclusion NOUN cana-441 299 3 , , PUNCT cana-441 299 4 this this DET cana-441 299 5 study study NOUN cana-441 299 6 presented present VERB cana-441 299 7 an an DET cana-441 299 8 improved improved ADJ cana-441 299 9 resnet50 resnet50 NOUN cana-441 299 10 based base VERB cana-441 299 11 brain brain NOUN cana-441 299 12 tumor tumor NOUN cana-441 299 13 segmentation segmentation NOUN cana-441 299 14 using use VERB cana-441 299 15 data datum NOUN cana-441 299 16 augmentation augmentation NOUN cana-441 299 17 and and CCONJ cana-441 299 18 classification classification NOUN cana-441 299 19 system system NOUN cana-441 299 20 . . PUNCT cana-441 300 1 the the DET cana-441 300 2 proposed propose VERB cana-441 300 3 models model NOUN cana-441 300 4 demonstrated demonstrate VERB cana-441 300 5 high high ADJ cana-441 300 6 accuracy accuracy NOUN cana-441 300 7 , , PUNCT cana-441 300 8 precision precision NOUN cana-441 300 9 , , PUNCT cana-441 300 10 and and CCONJ cana-441 300 11 recall recall VERB cana-441 300 12 in in ADP cana-441 300 13 segmenting segment VERB cana-441 300 14 brain brain NOUN cana-441 300 15 tumor tumor NOUN cana-441 300 16 regions region NOUN cana-441 300 17 and and CCONJ cana-441 300 18 classifying classify VERB cana-441 300 19 brain brain NOUN cana-441 300 20 tumors tumor NOUN cana-441 300 21 . . PUNCT cana-441 301 1 the the DET cana-441 301 2 results result NOUN cana-441 301 3 provide provide VERB cana-441 301 4 valuable valuable ADJ cana-441 301 5 insights insight NOUN cana-441 301 6 into into ADP cana-441 301 7 the the DET cana-441 301 8 potential potential NOUN cana-441 301 9 of of ADP cana-441 301 10 deep deep ADJ cana-441 301 11 learning learning NOUN cana-441 301 12 approaches approach NOUN cana-441 301 13 for for ADP cana-441 301 14 medical medical ADJ cana-441 301 15 image image NOUN cana-441 301 16 analysis analysis NOUN cana-441 301 17 , , PUNCT cana-441 301 18 particularly particularly ADV cana-441 301 19 in in ADP cana-441 301 20 the the DET cana-441 301 21 field field NOUN cana-441 301 22 of of ADP cana-441 301 23 neurology neurology NOUN cana-441 301 24 . . PUNCT cana-441 302 1 further further ADJ cana-441 302 2 advancements advancement NOUN cana-441 302 3 in in ADP cana-441 302 4 this this DET cana-441 302 5 area area NOUN cana-441 302 6 have have VERB cana-441 302 7 the the DET cana-441 302 8 potential potential NOUN cana-441 302 9 to to PART cana-441 302 10 revolutionize revolutionize VERB cana-441 302 11 the the DET cana-441 302 12 diagnosis diagnosis NOUN cana-441 302 13 and and CCONJ cana-441 302 14 treatment treatment NOUN cana-441 302 15 of of ADP cana-441 302 16 brain brain NOUN cana-441 302 17 tumors tumor NOUN cana-441 302 18 , , PUNCT cana-441 302 19 ultimately ultimately ADV cana-441 302 20 improving improve VERB cana-441 302 21 patient patient ADJ cana-441 302 22 care care NOUN cana-441 302 23 and and CCONJ cana-441 302 24 outcomes outcome NOUN cana-441 302 25 . . PUNCT cana-441 303 1 references reference NOUN cana-441 303 2 : : PUNCT cana-441 304 1 [ [ X cana-441 304 2 1 1 X cana-441 304 3 ] ] PUNCT cana-441 304 4 " " PUNCT cana-441 304 5 brainlesion brainlesion NOUN cana-441 304 6 : : PUNCT cana-441 304 7 glioma glioma NOUN cana-441 304 8 , , PUNCT cana-441 304 9 multiple multiple ADJ cana-441 304 10 sclerosis sclerosis NOUN cana-441 304 11 , , PUNCT cana-441 304 12 stroke stroke NOUN cana-441 304 13 and and CCONJ cana-441 304 14 traumatic traumatic ADJ cana-441 304 15 brain brain NOUN cana-441 304 16 injuries injury NOUN cana-441 304 17 " " PUNCT cana-441 304 18 , , PUNCT cana-441 304 19 springer springer NOUN cana-441 304 20 nature nature NOUN cana-441 304 21 , , PUNCT cana-441 304 22 2019 2019 NUM cana-441 304 23 . . PUNCT cana-441 305 1 [ [ X cana-441 305 2 2 2 NUM cana-441 305 3 ] ] PUNCT cana-441 305 4 " " PUNCT cana-441 305 5 medical medical ADJ cana-441 305 6 image image NOUN cana-441 305 7 computing computing NOUN cana-441 305 8 and and CCONJ cana-441 305 9 computer computer NOUN cana-441 305 10 assisted assist VERB cana-441 305 11 intervention intervention NOUN cana-441 305 12 miccai miccai NOUN cana-441 305 13 2017",springer 2017",springer NOUN cana-441 305 14 nature nature NOUN cana-441 305 15 , , PUNCT cana-441 305 16 2017 2017 NUM cana-441 305 17 . . PUNCT cana-441 306 1 [ [ X cana-441 306 2 3 3 X cana-441 306 3 ] ] PUNCT cana-441 306 4 " " PUNCT cana-441 306 5 brainlesion brainlesion NOUN cana-441 306 6 : : PUNCT cana-441 306 7 glioma glioma NOUN cana-441 306 8 , , PUNCT cana-441 306 9 multiple multiple ADJ cana-441 306 10 sclerosis sclerosis NOUN cana-441 306 11 , , PUNCT cana-441 306 12 stroke stroke NOUN cana-441 306 13 and and CCONJ cana-441 306 14 traumatic traumatic ADJ cana-441 306 15 brain brain NOUN cana-441 306 16 injuries injury NOUN cana-441 306 17 " " PUNCT cana-441 306 18 , , PUNCT cana-441 306 19 springer springer NOUN cana-441 306 20 science science NOUN cana-441 306 21 and and CCONJ cana-441 306 22 business business NOUN cana-441 306 23 media medium NOUN cana-441 306 24 llc llc PROPN cana-441 306 25 , , PUNCT cana-441 306 26 2022 2022 NUM cana-441 306 27 . . PUNCT cana-441 307 1 [ [ X cana-441 307 2 4 4 NUM cana-441 307 3 ] ] SYM cana-441 307 4 " " PUNCT cana-441 307 5 frontier frontier NOUN cana-441 307 6 computing computing NOUN cana-441 307 7 " " PUNCT cana-441 307 8 , , PUNCT cana-441 307 9 springer springer NOUN cana-441 307 10 science science NOUN cana-441 307 11 and and CCONJ cana-441 307 12 business business NOUN cana-441 307 13 media medium NOUN cana-441 307 14 llc llc PROPN cana-441 307 15 , , PUNCT cana-441 307 16 2020 2020 NUM cana-441 307 17 . . PUNCT cana-441 308 1 [ [ X cana-441 308 2 5 5 X cana-441 308 3 ] ] X cana-441 308 4 fatima fatima PROPN cana-441 308 5 yousaf yousaf PROPN cana-441 308 6 , , PUNCT cana-441 308 7 sajid sajid PROPN cana-441 308 8 iqbal iqbal PROPN cana-441 308 9 , , PUNCT cana-441 308 10 nosheen nosheen PROPN cana-441 308 11 fatima fatima PROPN cana-441 308 12 , , PUNCT cana-441 308 13 tanzeelakousar tanzeelakousar PROPN cana-441 308 14 , , PUNCT cana-441 308 15 mohdshafrymohdrahim mohdshafrymohdrahim PROPN cana-441 308 16 . . PUNCT cana-441 309 1 "multi "multi ADJ cana-441 309 2 - - ADJ cana-441 309 3 class class NOUN cana-441 309 4 disease disease NOUN cana-441 309 5 detection detection NOUN cana-441 309 6 using use VERB cana-441 309 7 deep deep ADJ cana-441 309 8 learning learning NOUN cana-441 309 9 and and CCONJ cana-441 309 10 human human ADJ cana-441 309 11 brain brain NOUN cana-441 309 12 medical medical ADJ cana-441 309 13 imaging",biomedical imaging",biomedical ADJ cana-441 309 14 signal signal NOUN cana-441 309 15 processing processing NOUN cana-441 309 16 and and CCONJ cana-441 309 17 control,2023 control,2023 NOUN cana-441 309 18 . . PUNCT cana-441 310 1 [ [ X cana-441 310 2 6 6 NUM cana-441 310 3 ] ] SYM cana-441 310 4 suponklaithin suponklaithin ADJ cana-441 310 5 , , PUNCT cana-441 310 6 sawitkasuriya sawitkasuriya ADJ cana-441 310 7 . . PUNCT cana-441 311 1 " " PUNCT cana-441 311 2 enhancing enhance VERB cana-441 311 3 target target NOUN cana-441 311 4 document document NOUN cana-441 311 5 search search NOUN cana-441 311 6 in in ADP cana-441 311 7 copycatch copycatch NOUN cana-441 311 8 : : PUNCT cana-441 311 9 a a DET cana-441 311 10 focus focus NOUN cana-441 311 11 on on ADP cana-441 311 12 thai thai PROPN cana-441 311 13 and and CCONJ cana-441 311 14 english english ADJ cana-441 311 15 document document NOUN cana-441 311 16 " " PUNCT cana-441 311 17 , , PUNCT cana-441 311 18 2023 2023 NUM cana-441 311 19 18th 18th ADJ cana-441 311 20 international international ADJ cana-441 311 21 joint joint ADJ cana-441 311 22 symposium symposium NOUN cana-441 311 23 on on ADP cana-441 311 24 artificial artificial ADJ cana-441 311 25 intelligence intelligence NOUN cana-441 311 26 and and CCONJ cana-441 311 27 natural natural ADJ cana-441 311 28 language language NOUN cana-441 311 29 processing processing NOUN cana-441 311 30 ( ( PUNCT cana-441 311 31 isai isai NOUN cana-441 311 32 - - PUNCT cana-441 311 33 nlp nlp NOUN cana-441 311 34 ) ) PUNCT cana-441 311 35 , , PUNCT cana-441 311 36 2023 2023 NUM cana-441 311 37 . . PUNCT cana-441 312 1 [ [ X cana-441 312 2 7 7 X cana-441 312 3 ] ] X cana-441 312 4 faizan faizan PROPN cana-441 312 5 ullah ullah PROPN cana-441 312 6 , , PUNCT cana-441 312 7 abdu abdu PROPN cana-441 312 8 salam salam PROPN cana-441 312 9 , , PUNCT cana-441 312 10 mohammad mohammad PROPN cana-441 312 11 abrar abrar PROPN cana-441 312 12 , , PUNCT cana-441 312 13 farhan farhan PROPN cana-441 312 14 amin amin PROPN cana-441 312 15 . . PUNCT cana-441 313 1 " " PUNCT cana-441 313 2 brain brain NOUN cana-441 313 3 tumor tumor NOUN cana-441 313 4 segmentation segmentation NOUN cana-441 313 5 using use VERB cana-441 313 6 a a DET cana-441 313 7 patch patch NOUN cana-441 313 8 - - PUNCT cana-441 313 9 based base VERB cana-441 313 10 convolutional convolutional ADJ cana-441 313 11 neural neural ADJ cana-441 313 12 network network NOUN cana-441 313 13 : : PUNCT cana-441 313 14 a a DET cana-441 313 15 big big ADJ cana-441 313 16 data datum NOUN cana-441 313 17 analysis analysis NOUN cana-441 313 18 approach",mathematics approach",mathematic NOUN cana-441 313 19 , , PUNCT cana-441 313 20 2023 2023 NUM cana-441 313 21 . . PUNCT cana-441 314 1 [ [ X cana-441 314 2 8 8 NUM cana-441 314 3 ] ] X cana-441 314 4 nelly nelly ADV cana-441 314 5 gordillo gordillo PROPN cana-441 314 6 , , PUNCT cana-441 314 7 eduard eduard PROPN cana-441 314 8 montseny montseny PROPN cana-441 314 9 , , PUNCT cana-441 314 10 pilar pilar PROPN cana-441 314 11 sobrevilla sobrevilla NOUN cana-441 314 12 . . PUNCT cana-441 315 1 " " PUNCT cana-441 315 2 a a DET cana-441 315 3 new new ADJ cana-441 315 4 fuzzy fuzzy ADJ cana-441 315 5 approach approach NOUN cana-441 315 6 to to ADP cana-441 315 7 brain brain NOUN cana-441 315 8 tumor tumor NOUN cana-441 315 9 segmentation segmentation NOUN cana-441 315 10 " " PUNCT cana-441 315 11 , , PUNCT cana-441 315 12 international international ADJ cana-441 315 13 conference conference NOUN cana-441 315 14 on on ADP cana-441 315 15 fuzzy fuzzy ADJ cana-441 315 16 systems system NOUN cana-441 315 17 , , PUNCT cana-441 315 18 2010 2010 NUM cana-441 315 19 . . PUNCT cana-441 316 1 [ [ X cana-441 316 2 9 9 NUM cana-441 316 3 ] ] X cana-441 316 4 raghavendra raghavendra PROPN cana-441 316 5 , , PUNCT cana-441 316 6 s. s. PROPN cana-441 316 7 ,a.harshavardhan ,a.harshavardhan PROPN cana-441 316 8 , , PUNCT cana-441 316 9 s.neelakandan s.neelakandan ADJ cana-441 316 10 , , PUNCT cana-441 316 11 r.partheepan r.partheepan NOUN cana-441 316 12 , , PUNCT cana-441 316 13 ranjanwalia ranjanwalia NOUN cana-441 316 14 , , PUNCT cana-441 316 15 andv.chandrashekharrao andv.chandrashekharrao PROPN cana-441 316 16 . . PUNCT cana-441 316 17 "multil "multil PROPN cana-441 316 18 ayer ayer PROPN cana-441 316 19 stacked stack VERB cana-441 316 20 probabilistic probabilistic ADJ cana-441 316 21 belief belief NOUN cana-441 316 22 network network NOUN cana-441 316 23 based base VERB cana-441 316 24 brain brain NOUN cana-441 316 25 tumor tumor NOUN cana-441 316 26 segmentation segmentation NOUN cana-441 316 27 and and CCONJ cana-441 316 28 classification classification NOUN cana-441 316 29 . . PUNCT cana-441 317 1 "international "international ADJ cana-441 317 2 journal journal PROPN cana-441 317 3 of of ADP cana-441 317 4 foundations foundation NOUN cana-441 317 5 of of ADP cana-441 317 6 computer computer NOUN cana-441 317 7 science science NOUN cana-441 317 8 , , PUNCT cana-441 317 9 2022 2022 NUM cana-441 317 10 . . PUNCT cana-441 318 1 [ [ X cana-441 318 2 10 10 NUM cana-441 318 3 ] ] PUNCT cana-441 318 4 alsaif alsaif PROPN cana-441 318 5 , , PUNCT cana-441 318 6 haitham haitham PROPN cana-441 318 7 , , PUNCT cana-441 318 8 ramziguesmi ramziguesmi NOUN cana-441 318 9 , , PUNCT cana-441 318 10 badrm.alshammari badrm.alshammari NOUN cana-441 318 11 , , PUNCT cana-441 318 12 tarekhamrouni tarekhamrouni ADJ cana-441 318 13 , , PUNCT cana-441 318 14 tawfik tawfik ADJ cana-441 318 15 guesmi guesmi NOUN cana-441 318 16 , , PUNCT cana-441 318 17 ahmed ahmed PROPN cana-441 318 18 alzamil alzamil PROPN cana-441 318 19 , , PUNCT cana-441 318 20 and and CCONJ cana-441 318 21 lamiabelguesmi lamiabelguesmi PROPN cana-441 318 22 . . PUNCT cana-441 319 1 " " PUNCT cana-441 319 2 anoveldataaugmentationbased anoveldataaugmentationbase VERB cana-441 319 3 brain brain NOUN cana-441 319 4 tumordetectionusing tumordetectionuse VERB cana-441 319 5 convolutional convolutional ADJ cana-441 319 6 neuralnetwork neuralnetwork NOUN cana-441 319 7 . . PUNCT cana-441 319 8 " " PUNCT cana-441 320 1 applied apply VERB cana-441 320 2 sciences science NOUN cana-441 320 3 12 12 NUM cana-441 320 4 , , PUNCT cana-441 320 5 2022 2022 NUM cana-441 320 6 . . PUNCT cana-441 321 1 [ [ X cana-441 321 2 11 11 NUM cana-441 321 3 ] ] X cana-441 321 4 gupta gupta PROPN cana-441 321 5 , , PUNCT cana-441 321 6 vimal vimal ADJ cana-441 321 7 , , PUNCT cana-441 321 8 and and CCONJ cana-441 321 9 vimal vimal ADJ cana-441 321 10 bibhu bibhu ADJ cana-441 321 11 . . PUNCT cana-441 322 1 β€œ " PUNCT cana-441 322 2 deep deep ADJ cana-441 322 3 residual residual ADJ cana-441 322 4 network network NOUN cana-441 322 5 basedbraintumorsegmentation basedbraintumorsegmentation NOUN cana-441 322 6 and and CCONJ cana-441 322 7 detection detection NOUN cana-441 322 8 with with ADP cana-441 322 9 mri mri NOUN cana-441 322 10 using use VERB cana-441 322 11 improved improve VERB cana-441 322 12 invasive invasive ADJ cana-441 322 13 bat bat NOUN cana-441 322 14 algorithm”.multimedia algorithm”.multimedia NOUN cana-441 322 15 tools tool NOUN cana-441 322 16 and and CCONJ cana-441 322 17 applications application NOUN cana-441 322 18 82 82 NUM cana-441 322 19 , , PUNCT cana-441 322 20 2023 2023 NUM cana-441 322 21 . . PUNCT cana-441 323 1 [ [ X cana-441 323 2 12 12 NUM cana-441 323 3 ] ] X cana-441 323 4 aamir aamir PROPN cana-441 323 5 , , PUNCT cana-441 323 6 muhammad muhammad PROPN cana-441 323 7 , , PUNCT cana-441 323 8 ziaur ziaur NOUN cana-441 323 9 rahman rahman PROPN cana-441 323 10 , , PUNCT cana-441 323 11 zaheer zaheer PROPN cana-441 323 12 ahmed ahmed PROPN cana-441 323 13 dayo dayo PROPN cana-441 323 14 , , PUNCT cana-441 323 15 waheed waheed PROPN cana-441 323 16 ahmed ahmed PROPN cana-441 323 17 abro abro PROPN cana-441 323 18 , , PUNCT cana-441 323 19 m. m. NOUN cana-441 323 20 irfan irfan PROPN cana-441 323 21 uddin uddin PROPN cana-441 323 22 , , PUNCT cana-441 323 23 inayat inayat PROPN cana-441 323 24 khan khan PROPN cana-441 323 25 , , PUNCT cana-441 323 26 ali ali PROPN cana-441 323 27 shariq shariq PROPN cana-441 323 28 imran imran PROPN cana-441 323 29 , , PUNCT cana-441 323 30 β€œ " PUNCT cana-441 323 31 a a DET cana-441 323 32 deep deep ADJ cana-441 323 33 learning learning NOUN cana-441 323 34 approach approach NOUN cana-441 323 35 for for ADP cana-441 323 36 brain brain NOUN cana-441 323 37 tumor tumor NOUN cana-441 323 38 classification classification NOUN cana-441 323 39 using use VERB cana-441 323 40 mri mri NOUN cana-441 323 41 images image NOUN cana-441 323 42 ” " PUNCT cana-441 323 43 . . PUNCT cana-441 324 1 computers computer NOUN cana-441 324 2 and and CCONJ cana-441 324 3 electrical electrical ADJ cana-441 324 4 engineering engineering NOUN cana-441 324 5 , , PUNCT cana-441 324 6 2022 2022 NUM cana-441 324 7 . . PUNCT cana-441 325 1 [ [ X cana-441 325 2 13 13 NUM cana-441 325 3 ] ] X cana-441 325 4 zahoor zahoor PROPN cana-441 325 5 , , PUNCT cana-441 325 6 mirzamumtaz mirzamumtaz PROPN cana-441 325 7 , , PUNCT cana-441 325 8 shahzad shahzad PROPN cana-441 325 9 ahmad ahmad PROPN cana-441 325 10 qureshi qureshi PROPN cana-441 325 11 , , PUNCT cana-441 325 12 sameena sameena ADJ cana-441 325 13 bibi bibi NOUN cana-441 325 14 , , PUNCT cana-441 325 15 saddam saddam PROPN cana-441 325 16 hussain hussain PROPN cana-441 325 17 khan khan PROPN cana-441 325 18 , , PUNCT cana-441 325 19 asifullahkhan asifullahkhan PROPN cana-441 325 20 , , PUNCT cana-441 325 21 usman usman PROPN cana-441 325 22 ghafoor ghafoor PROPN cana-441 325 23 , , PUNCT cana-441 325 24 and and CCONJ cana-441 325 25 muhammad muhammad PROPN cana-441 325 26 raheelbhutta raheelbhutta PROPN cana-441 325 27 . . PUNCT cana-441 326 1 β€œ " PUNCT cana-441 326 2 a a DET cana-441 326 3 new new ADJ cana-441 326 4 deep deep ADJ cana-441 326 5 hybrid hybrid NOUN cana-441 326 6 boosted boost VERB cana-441 326 7 and and CCONJ cana-441 326 8 ensemble ensemble ADJ cana-441 326 9 learning learning NOUN cana-441 326 10 based base VERB cana-441 326 11 brain brain NOUN cana-441 326 12 tumor tumor NOUN cana-441 326 13 analysis analysis NOUN cana-441 326 14 using use VERB cana-441 326 15 mri mri NOUN cana-441 326 16 ” " PUNCT cana-441 326 17 . . PUNCT cana-441 327 1 sensors sensor NOUN cana-441 327 2 22 22 NUM cana-441 327 3 , , PUNCT cana-441 327 4 2022 2022 NUM cana-441 327 5 . . PUNCT cana-441 328 1 [ [ X cana-441 328 2 14 14 NUM cana-441 328 3 ] ] X cana-441 328 4 hossain hossain PROPN cana-441 328 5 , , PUNCT cana-441 328 6 amran amran PROPN cana-441 328 7 , , PUNCT cana-441 328 8 mohammadtariqu mohammadtariqu PROPN cana-441 328 9 islam islam PROPN cana-441 328 10 , , PUNCT cana-441 328 11 sharul sharul VERB cana-441 328 12 kamal kamal PROPN cana-441 328 13 abdul abdul PROPN cana-441 328 14 rahim rahim PROPN cana-441 328 15 , , PUNCT cana-441 328 16 md md PROPN cana-441 328 17 atiqur atiqur PROPN cana-441 328 18 rahman rahman PROPN cana-441 328 19 , , PUNCT cana-441 328 20 tawsifur tawsifur PROPN cana-441 328 21 rahman rahman PROPN cana-441 328 22 , , PUNCT cana-441 328 23 haslina haslina PROPN cana-441 328 24 arshad arshad PROPN cana-441 328 25 , , PUNCT cana-441 328 26 amit amit PROPN cana-441 328 27 khandakar khandakar PROPN cana-441 328 28 , , PUNCT cana-441 328 29 mohamed mohamed PROPN cana-441 328 30 arslaneayari arslaneayari PROPN cana-441 328 31 , , PUNCT cana-441 328 32 and and CCONJ cana-441 328 33 muhammad muhammad PROPN cana-441 328 34 c. c. PROPN cana-441 328 35 β€œ " PUNCT cana-441 328 36 a a DET cana-441 328 37 lightweight lightweight ADJ cana-441 328 38 deeplearning deeplearning NOUN cana-441 328 39 based base VERB cana-441 328 40 microwave microwave NOUN cana-441 328 41 brain brain NOUN cana-441 328 42 image image NOUN cana-441 328 43 network network NOUN cana-441 328 44 model model NOUN cana-441 328 45 for for ADP cana-441 328 46 brain brain NOUN cana-441 328 47 tumor tumor NOUN cana-441 328 48 classification classification NOUN cana-441 328 49 using use VERB cana-441 328 50 reconstructed reconstructed ADJ cana-441 328 51 microwave microwave NOUN cana-441 328 52 brain brain NOUN cana-441 328 53 images image NOUN cana-441 328 54 ” " PUNCT cana-441 328 55 . . PUNCT cana-441 329 1 biosensors biosensor NOUN cana-441 329 2 13 13 NUM cana-441 329 3 , , PUNCT cana-441 329 4 2023 2023 NUM cana-441 329 5 . . PUNCT cana-441 330 1 communications communication NOUN cana-441 330 2 on on ADP cana-441 330 3 applied apply VERB cana-441 330 4 nonlinear nonlinear ADJ cana-441 330 5 analysis analysis NOUN cana-441 330 6 issn issn NOUN cana-441 330 7 : : PUNCT cana-441 330 8 1074 1074 NUM cana-441 330 9 - - PUNCT cana-441 330 10 133x 133x NUM cana-441 330 11 vol vol NOUN cana-441 330 12 31 31 NUM cana-441 330 13 no no NOUN cana-441 330 14 . . NOUN cana-441 330 15 1 1 NUM cana-441 330 16 ( ( PUNCT cana-441 330 17 2024 2024 NUM cana-441 330 18 ) ) PUNCT cana-441 330 19 337 337 NUM cana-441 330 20 https://internationalpubls.com https://internationalpubls.com X cana-441 331 1 [ [ X cana-441 331 2 15 15 NUM cana-441 331 3 ] ] X cana-441 331 4 aarthie aarthie NOUN cana-441 331 5 . . PUNCT cana-441 332 1 ,s.jana ,s.jana PROPN cana-441 332 2 , , PUNCT cana-441 332 3 w.gracytheresa w.gracytheresa ADP cana-441 332 4 , , PUNCT cana-441 332 5 m.krishnamurthy m.krishnamurthy ADJ cana-441 332 6 , , PUNCT cana-441 332 7 a.s.prakaash a.s.prakaash NOUN cana-441 332 8 , , PUNCT cana-441 332 9 c.senthilkumar c.senthilkumar PRON cana-441 332 10 , , PUNCT cana-441 332 11 and and CCONJ cana-441 332 12 s.gopalakrishnan s.gopalakrishnan ADJ cana-441 332 13 . . PUNCT cana-441 333 1 β€œ " PUNCT cana-441 333 2 detection detection NOUN cana-441 333 3 andclassificationof andclassificationof NOUN cana-441 333 4 mri mri NOUN cana-441 333 5 brain brain NOUN cana-441 333 6 tumors tumor NOUN cana-441 333 7 using use VERB cana-441 333 8 s3 s3 PROPN cana-441 333 9 drlstm drlstm NOUN cana-441 333 10 based base VERB cana-441 333 11 deep deep ADJ cana-441 333 12 learning learning NOUN cana-441 333 13 model model NOUN cana-441 333 14 ” " PUNCT cana-441 333 15 . . PUNCT cana-441 334 1 ijeer ijeer PROPN cana-441 334 2 10 10 NUM cana-441 334 3 , , PUNCT cana-441 334 4 2022 2022 NUM cana-441 334 5 . . PUNCT cana-441 335 1 [ [ X cana-441 335 2 16 16 NUM cana-441 335 3 ] ] X cana-441 335 4 kumar kumar PROPN cana-441 335 5 , , PUNCT cana-441 335 6 ks ks PROPN cana-441 335 7 ananda ananda PROPN cana-441 335 8 , , PUNCT cana-441 335 9 a.y.prasad a.y.prasad PROPN cana-441 335 10 and and CCONJ cana-441 335 11 jyoti jyoti PROPN cana-441 335 12 m.metan m.metan PROPN cana-441 335 13 . . PUNCT cana-441 336 1 β€œ " PUNCT cana-441 336 2 a a DET cana-441 336 3 hybrid hybrid ADJ cana-441 336 4 deep deep ADJ cana-441 336 5 cnn cnn PROPN cana-441 336 6 cov19 cov19 PROPN cana-441 336 7 resnet resnet NOUN cana-441 336 8 transfer transfer NOUN cana-441 336 9 learning learn VERB cana-441 336 10 for for ADP cana-441 336 11 enhanced enhanced ADJ cana-441 336 12 brain brain NOUN cana-441 336 13 tumor tumor NOUN cana-441 336 14 detection detection NOUN cana-441 336 15 and and CCONJ cana-441 336 16 classification classification NOUN cana-441 336 17 scheme scheme NOUN cana-441 336 18 in in ADP cana-441 336 19 medical medical ADJ cana-441 336 20 image image NOUN cana-441 336 21 processing processing NOUN cana-441 336 22 ” " PUNCT cana-441 336 23 . . PUNCT cana-441 337 1 biomedical biomedical ADJ cana-441 337 2 signal signal NOUN cana-441 337 3 processing processing NOUN cana-441 337 4 and and CCONJ cana-441 337 5 control control NOUN cana-441 337 6 , , PUNCT cana-441 337 7 2022 2022 NUM cana-441 337 8 . . PUNCT cana-441 338 1 [ [ X cana-441 338 2 17 17 NUM cana-441 338 3 ] ] X cana-441 338 4 younis younis PROPN cana-441 338 5 , , PUNCT cana-441 338 6 ayesha ayesha PROPN cana-441 338 7 , , PUNCT cana-441 338 8 liqiang liqiang PROPN cana-441 338 9 , , PUNCT cana-441 338 10 charles charles PROPN cana-441 338 11 o. o. PROPN cana-441 338 12 and and CCONJ cana-441 338 13 nyatega nyatega PROPN cana-441 338 14 , , PUNCT cana-441 338 15 mohammed mohammed PROPN cana-441 338 16 jajere jajere PROPN cana-441 338 17 adamu adamu PROPN cana-441 338 18 , , PUNCT cana-441 338 19 and and CCONJ cana-441 338 20 halima halima PROPN cana-441 338 21 bello bello PROPN cana-441 338 22 kawuwa kawuwa PROPN cana-441 338 23 . . PUNCT cana-441 339 1 β€œ " PUNCT cana-441 339 2 brain brain NOUN cana-441 339 3 tumor tumor NOUN cana-441 339 4 analysis analysis NOUN cana-441 339 5 using use VERB cana-441 339 6 deep deep ADJ cana-441 339 7 learning learning NOUN cana-441 339 8 and and CCONJ cana-441 339 9 vgg vgg PROPN cana-441 339 10 16 16 NUM cana-441 339 11 ensembling ensemble VERB cana-441 339 12 learning learn VERB cana-441 339 13 approaches approach NOUN cana-441 339 14 . . PUNCT cana-441 339 15 ” " PUNCT cana-441 340 1 applied apply VERB cana-441 340 2 sciences12 sciences12 NOUN cana-441 340 3 , , PUNCT cana-441 340 4 2022 2022 NUM cana-441 340 5 . . PUNCT cana-441 341 1 [ [ X cana-441 341 2 18 18 NUM cana-441 341 3 ] ] SYM cana-441 341 4 malla malla PROPN cana-441 341 5 , , PUNCT cana-441 341 6 prince prince PROPN cana-441 341 7 priya priya PROPN cana-441 341 8 , , PUNCT cana-441 341 9 sudhakar sudhakar PROPN cana-441 341 10 sahu sahu PROPN cana-441 341 11 , , PUNCT cana-441 341 12 and and CCONJ cana-441 341 13 ahmed ahme VERB cana-441 341 14 , , PUNCT cana-441 341 15 alutaibi alutaibi NOUN cana-441 341 16 . . PUNCT cana-441 342 1 β€œ " PUNCT cana-441 342 2 classification classification NOUN cana-441 342 3 of of ADP cana-441 342 4 tumor tumor NOUN cana-441 342 5 in in ADP cana-441 342 6 brain brain NOUN cana-441 342 7 mr mr PROPN cana-441 342 8 images image NOUN cana-441 342 9 using use VERB cana-441 342 10 deep deep ADJ cana-441 342 11 convolutional convolutional ADJ cana-441 342 12 neural neural ADJ cana-441 342 13 network network NOUN cana-441 342 14 and and CCONJ cana-441 342 15 global global ADJ cana-441 342 16 average average ADJ cana-441 342 17 pooling”.processes pooling”.processe NOUN cana-441 342 18 11 11 NUM cana-441 342 19 , , PUNCT cana-441 342 20 2023 2023 NUM cana-441 342 21 . . PUNCT cana-441 343 1 [ [ X cana-441 343 2 19 19 NUM cana-441 343 3 ] ] SYM cana-441 343 4 mzoughi mzoughi PROPN cana-441 343 5 , , PUNCT cana-441 343 6 hiba hiba PROPN cana-441 343 7 , , PUNCT cana-441 343 8 inesnjeh inesnjeh PROPN cana-441 343 9 , , PUNCT cana-441 343 10 mohamed mohamed PROPN cana-441 343 11 ben ben PROPN cana-441 343 12 slima slima PROPN cana-441 343 13 , , PUNCT cana-441 343 14 and and CCONJ cana-441 343 15 ahmed ahmed PROPN cana-441 343 16 ben ben PROPN cana-441 343 17 hamida hamida PROPN cana-441 343 18 . . PUNCT cana-441 344 1 β€œ " PUNCT cana-441 344 2 review review NOUN cana-441 344 3 of of ADP cana-441 344 4 computer computer NOUN cana-441 344 5 aided aid VERB cana-441 344 6 diagnosis diagnosis NOUN cana-441 344 7 ( ( PUNCT cana-441 344 8 cad cad NOUN cana-441 344 9 ) ) PUNCT cana-441 344 10 systems system NOUN cana-441 344 11 for for ADP cana-441 344 12 mri mri NOUN cana-441 344 13 gliomas glioma NOUN cana-441 344 14 brain brain NOUN cana-441 344 15 tumors tumor NOUN cana-441 344 16 explorations exploration NOUN cana-441 344 17 based base VERB cana-441 344 18 on on ADP cana-441 344 19 machine machine NOUN cana-441 344 20 learning learning NOUN cana-441 344 21 and and CCONJ cana-441 344 22 deep deep ADJ cana-441 344 23 learning”.6th learning”.6th NOUN cana-441 344 24 international international ADJ cana-441 344 25 conference conference NOUN cana-441 344 26 on on ADP cana-441 344 27 advanced advanced ADJ cana-441 344 28 technologies technology NOUN cana-441 344 29 for for ADP cana-441 344 30 signal signal NOUN cana-441 344 31 and and CCONJ cana-441 344 32 image image NOUN cana-441 344 33 processing processing NOUN cana-441 344 34 , , PUNCT cana-441 344 35 2022 2022 NUM cana-441 344 36 . . PUNCT cana-441 345 1 [ [ X cana-441 345 2 20 20 NUM cana-441 345 3 ] ] SYM cana-441 345 4 https://www.kaggle.com/datasets/brain-mri-images https://www.kaggle.com/datasets/brain-mri-image NOUN cana-441 345 5 . . PUNCT cana-441 346 1 [ [ X cana-441 346 2 21 21 NUM cana-441 346 3 ] ] SYM cana-441 346 4 arkapravo arkapravo NUM cana-441 346 5 chattopadhyay chattopadhyay PROPN cana-441 346 6 , , PUNCT cana-441 346 7 mausumi mausumi ADJ cana-441 346 8 maitra maitra NOUN cana-441 346 9 . . PUNCT cana-441 347 1 β€œ " PUNCT cana-441 347 2 mri mri X cana-441 347 3 based base VERB cana-441 347 4 brain brain NOUN cana-441 347 5 tumour tumour NOUN cana-441 347 6 image image NOUN cana-441 347 7 detection detection NOUN cana-441 347 8 using use VERB cana-441 347 9 cnn cnn PROPN cana-441 347 10 based base VERB cana-441 347 11 deep deep ADJ cana-441 347 12 learning learning NOUN cana-441 347 13 method”.elsevier method”.elsevi ADJ cana-441 347 14 journal journal NOUN cana-441 347 15 of of ADP cana-441 347 16 neuroscience neuroscience NOUN cana-441 347 17 informatics informatic NOUN cana-441 347 18 , , PUNCT cana-441 347 19 2022 2022 NUM cana-441 347 20 . . PUNCT cana-441 348 1 [ [ X cana-441 348 2 22 22 NUM cana-441 348 3 ] ] PUNCT cana-441 348 4 a. a. NOUN cana-441 348 5 victor victor PROPN cana-441 348 6 ikechukwu ikechukwu PROPN cana-441 348 7 , , PUNCT cana-441 348 8 s. s. PROPN cana-441 348 9 murali murali PROPN cana-441 348 10 , , PUNCT cana-441 348 11 r. r. PROPN cana-441 348 12 deepu deepu PROPN cana-441 348 13 , , PUNCT cana-441 348 14 r.c r.c PROPN cana-441 348 15 . . PROPN cana-441 348 16 shivamurthy shivamurthy ADJ cana-441 348 17 . . PUNCT cana-441 349 1 β€œ " PUNCT cana-441 349 2 resnet-50 resnet-50 PROPN cana-441 349 3 vs vs ADP cana-441 349 4 vgg-19 vgg-19 NOUN cana-441 349 5 vs vs ADP cana-441 349 6 training training NOUN cana-441 349 7 from from ADP cana-441 349 8 scratch scratch NOUN cana-441 349 9 : : PUNCT cana-441 349 10 a a DET cana-441 349 11 comparative comparative ADJ cana-441 349 12 analysis analysis NOUN cana-441 349 13 of of ADP cana-441 349 14 the the DET cana-441 349 15 segmentation segmentation NOUN cana-441 349 16 and and CCONJ cana-441 349 17 classification classification NOUN cana-441 349 18 of of ADP cana-441 349 19 pneumonia pneumonia NOUN cana-441 349 20 from from ADP cana-441 349 21 chest chest NOUN cana-441 349 22 x x NOUN cana-441 349 23 - - NOUN cana-441 349 24 ray ray NOUN cana-441 349 25 images image NOUN cana-441 349 26 ” " PUNCT cana-441 349 27 , , PUNCT cana-441 349 28 global global ADJ cana-441 349 29 transitions transition NOUN cana-441 349 30 proceedings proceeding NOUN cana-441 349 31 , , PUNCT cana-441 349 32 2021 2021 NUM cana-441 349 33 . . PUNCT cana-441 350 1 [ [ X cana-441 350 2 23 23 NUM cana-441 350 3 ] ] X cana-441 350 4 ΓΆzlem ΓΆzlem PROPN cana-441 350 5 polat polat PROPN cana-441 350 6 , , PUNCT cana-441 350 7 cahfergΓΌngen cahfergΓΌngen PROPN cana-441 350 8 . . PUNCT cana-441 351 1 β€œ " PUNCT cana-441 351 2 classification classification NOUN cana-441 351 3 of of ADP cana-441 351 4 brain brain NOUN cana-441 351 5 tumors tumor NOUN cana-441 351 6 from from ADP cana-441 351 7 mr mr PROPN cana-441 351 8 images image NOUN cana-441 351 9 using use VERB cana-441 351 10 deep deep ADJ cana-441 351 11 transfer transfer NOUN cana-441 351 12 learning learning NOUN cana-441 351 13 ” " PUNCT cana-441 351 14 . . PUNCT cana-441 352 1 springer springer PROPN cana-441 352 2 journal journal PROPN cana-441 352 3 of of ADP cana-441 352 4 supercomputing supercomputing NOUN cana-441 352 5 , , PUNCT cana-441 352 6 2021 2021 NUM cana-441 352 7 . . PUNCT cana-441 353 1 [ [ X cana-441 353 2 24 24 NUM cana-441 353 3 ] ] X cana-441 353 4 ayesha ayesha PROPN cana-441 353 5 younis younis PROPN cana-441 353 6 , , PUNCT cana-441 353 7 li li PROPN cana-441 353 8 qiang qiang PROPN cana-441 353 9 , , PUNCT cana-441 353 10 charles charles PROPN cana-441 353 11 okandanyatega okandanyatega PROPN cana-441 353 12 , , PUNCT cana-441 353 13 mohammed mohammed PROPN cana-441 353 14 jajere jajere PROPN cana-441 353 15 adamu adamu PROPN cana-441 353 16 and and CCONJ cana-441 353 17 halima halima PROPN cana-441 353 18 bello bello PROPN cana-441 353 19 kawuwa kawuwa PROPN cana-441 353 20 . . PUNCT cana-441 354 1 β€œ " PUNCT cana-441 354 2 brain brain NOUN cana-441 354 3 tumor tumor NOUN cana-441 354 4 analysis analysis NOUN cana-441 354 5 using use VERB cana-441 354 6 deep deep ADJ cana-441 354 7 learning learning NOUN cana-441 354 8 and and CCONJ cana-441 354 9 vgg-16 vgg-16 X cana-441 354 10 ensembling ensemble VERB cana-441 354 11 learning learn VERB cana-441 354 12 approaches approach NOUN cana-441 354 13 ” " PUNCT cana-441 354 14 , , PUNCT cana-441 354 15 mdpi mdpi PROPN cana-441 354 16 , , PUNCT cana-441 354 17 2022 2022 NUM cana-441 354 18 . . PUNCT cana-441 355 1 [ [ X cana-441 355 2 25 25 NUM cana-441 355 3 ] ] X cana-441 355 4 rajput rajput PROPN cana-441 355 5 , , PUNCT cana-441 355 6 b. b. PROPN cana-441 355 7 s. s. PROPN cana-441 355 8 . . PUNCT cana-441 355 9 ; ; PUNCT cana-441 355 10 gangele gangele PROPN cana-441 355 11 , , PUNCT cana-441 355 12 a. a. NOUN cana-441 355 13 . . PUNCT cana-441 355 14 ; ; PUNCT cana-441 355 15 a. a. PROPN cana-441 355 16 s. s. PROPN cana-441 355 17 k. k. PROPN cana-441 355 18 . . PUNCT cana-441 356 1 numerical numerical PROPN cana-441 356 2 simulation simulation PROPN cana-441 356 3 and and CCONJ cana-441 356 4 assessment assessment NOUN cana-441 356 5 of of ADP cana-441 356 6 meta meta ADJ cana-441 356 7 heuristic heuristic ADJ cana-441 356 8 optimization optimization NOUN cana-441 356 9 based base VERB cana-441 356 10 multi multi PROPN cana-441 356 11 objective objective ADJ cana-441 356 12 dynamic dynamic ADJ cana-441 356 13 job job NOUN cana-441 356 14 shop shop NOUN cana-441 356 15 scheduling scheduling NOUN cana-441 356 16 system system NOUN cana-441 356 17 . . PUNCT cana-441 357 1 ijfrcsce ijfrcsce VERB cana-441 357 2 2022 2022 NUM cana-441 357 3 , , PUNCT cana-441 357 4 8 8 NUM cana-441 357 5 , , PUNCT cana-441 357 6 92 92 NUM cana-441 357 7 - - SYM cana-441 357 8 98 98 NUM cana-441 357 9 . . PUNCT cana-441 358 1 [ [ X cana-441 358 2 26 26 NUM cana-441 358 3 ] ] X cana-441 358 4 rajput rajput PROPN cana-441 358 5 , , PUNCT cana-441 358 6 b. b. PROPN cana-441 358 7 s. s. PROPN cana-441 358 8 . . PUNCT cana-441 358 9 ; ; PUNCT cana-441 358 10 gangele gangele PROPN cana-441 358 11 , , PUNCT cana-441 358 12 a. a. NOUN cana-441 358 13 . . PUNCT cana-441 358 14 ; ; PUNCT cana-441 358 15 a. a. PROPN cana-441 358 16 s. s. PROPN cana-441 358 17 k. k. PROPN cana-441 358 18 . . PUNCT cana-441 358 19 ; ; PUNCT cana-441 358 20 raj raj PROPN cana-441 358 21 , , PUNCT cana-441 358 22 a a PRON cana-441 358 23 , , PUNCT cana-441 358 24 design design NOUN cana-441 358 25 simulation simulation NOUN cana-441 358 26 and and CCONJ cana-441 358 27 analysis analysis NOUN cana-441 358 28 of of ADP cana-441 358 29 deep deep ADJ cana-441 358 30 convolutional convolutional ADJ cana-441 358 31 neural neural ADJ cana-441 358 32 network network NOUN cana-441 358 33 based base VERB cana-441 358 34 complex complex ADJ cana-441 358 35 image image NOUN cana-441 358 36 classification classification NOUN cana-441 358 37 system system NOUN cana-441 358 38 . . PUNCT cana-441 359 1 ijfrcsce ijfrcsce VERB cana-441 359 2 2022 2022 NUM cana-441 359 3 , , PUNCT cana-441 359 4 8 8 NUM cana-441 359 5 , , PUNCT cana-441 359 6 86 86 NUM cana-441 359 7 - - SYM cana-441 359 8 91 91 NUM cana-441 359 9 . . PUNCT cana-441 360 1 [ [ X cana-441 360 2 27 27 NUM cana-441 360 3 ] ] PUNCT cana-441 360 4 a. a. NOUN cana-441 360 5 s.k s.k PROPN cana-441 360 6 . . PROPN cana-441 360 7 , , PUNCT cana-441 360 8 2022 2022 NUM cana-441 360 9 . . PUNCT cana-441 361 1 problems problem NOUN cana-441 361 2 in in ADP cana-441 361 3 euclidean euclidean ADJ cana-441 361 4 probability probability NOUN cana-441 361 5 . . PUNCT cana-441 362 1 international international ADJ cana-441 362 2 journal journal PROPN cana-441 362 3 on on ADP cana-441 362 4 recent recent ADJ cana-441 362 5 trends trend NOUN cana-441 362 6 in in ADP cana-441 362 7 life life NOUN cana-441 362 8 science science NOUN cana-441 362 9 and and CCONJ cana-441 362 10 mathematics mathematic NOUN cana-441 362 11 , , PUNCT cana-441 362 12 9(2 9(2 NUM cana-441 362 13 ) ) PUNCT cana-441 362 14 , , PUNCT cana-441 362 15 pp.21 pp.21 NOUN cana-441 362 16 - - SYM cana-441 362 17 32 32 NUM cana-441 362 18 . . PUNCT cana-441 363 1 [ [ X cana-441 363 2 28 28 NUM cana-441 363 3 ] ] X cana-441 363 4 a. a. PROPN cana-441 363 5 s. s. PROPN cana-441 363 6 k. k. PROPN cana-441 363 7 ( ( PUNCT cana-441 363 8 2021 2021 NUM cana-441 363 9 ) ) PUNCT cana-441 363 10 . . PUNCT cana-441 364 1 stability stability NOUN cana-441 364 2 . . PUNCT cana-441 365 1 international international ADJ cana-441 365 2 journal journal PROPN cana-441 365 3 on on ADP cana-441 365 4 recent recent ADJ cana-441 365 5 trends trend NOUN cana-441 365 6 in in ADP cana-441 365 7 life life NOUN cana-441 365 8 science science NOUN cana-441 365 9 and and CCONJ cana-441 365 10 mathematics mathematic NOUN cana-441 365 11 , , PUNCT cana-441 365 12 8(3 8(3 NUM cana-441 365 13 ) ) PUNCT cana-441 365 14 , , PUNCT cana-441 365 15 37 37 NUM cana-441 365 16 - - SYM cana-441 365 17 45 45 NUM cana-441 365 18 . . PUNCT cana-441 366 1 [ [ X cana-441 366 2 29 29 NUM cana-441 366 3 ] ] PUNCT cana-441 366 4 a. a. PROPN cana-441 366 5 s. s. PROPN cana-441 366 6 k. k. PROPN cana-441 366 7 ( ( PUNCT cana-441 366 8 2020 2020 NUM cana-441 366 9 ) ) PUNCT cana-441 366 10 . . PUNCT cana-441 367 1 anti anti ADJ cana-441 367 2 - - ADJ cana-441 367 3 boole boole ADJ cana-441 367 4 reducibility reducibility NOUN cana-441 367 5 for for ADP cana-441 367 6 linearly linearly ADV cana-441 367 7 markov markov NOUN cana-441 367 8 algebras algebra NOUN cana-441 367 9 . . PUNCT cana-441 368 1 international international ADJ cana-441 368 2 journal journal PROPN cana-441 368 3 on on ADP cana-441 368 4 recent recent ADJ cana-441 368 5 trends trend NOUN cana-441 368 6 in in ADP cana-441 368 7 life life NOUN cana-441 368 8 science science NOUN cana-441 368 9 and and CCONJ cana-441 368 10 mathematics mathematic NOUN cana-441 368 11 , , PUNCT cana-441 368 12 7(4 7(4 NUM cana-441 368 13 ) ) PUNCT cana-441 368 14 , , PUNCT cana-441 368 15 26 26 NUM cana-441 368 16 - - SYM cana-441 368 17 36 36 NUM cana-441 368 18 . . PUNCT cana-441 369 1 [ [ X cana-441 369 2 30 30 NUM cana-441 369 3 ] ] PUNCT cana-441 369 4 a. a. NOUN cana-441 369 5 s. s. PROPN cana-441 369 6 k. k. PROPN cana-441 369 7 ( ( PUNCT cana-441 369 8 2020 2020 NUM cana-441 369 9 ) ) PUNCT cana-441 369 10 . . PUNCT cana-441 370 1 equations equation NOUN cana-441 370 2 for for ADP cana-441 370 3 a a DET cana-441 370 4 curve curve NOUN cana-441 370 5 . . PUNCT cana-441 371 1 international international ADJ cana-441 371 2 journal journal NOUN cana-441 371 3 on on ADP cana-441 371 4 recent recent ADJ cana-441 371 5 trends trend NOUN cana-441 371 6 in in ADP cana-441 371 7 life life NOUN cana-441 371 8 science science NOUN cana-441 371 9 and and CCONJ cana-441 371 10 mathematics mathematic NOUN cana-441 371 11 , , PUNCT cana-441 371 12 7(2 7(2 NUM cana-441 371 13 ) ) PUNCT cana-441 371 14 , , PUNCT cana-441 371 15 58 58 NUM cana-441 371 16 - - SYM cana-441 371 17 65 65 NUM cana-441 371 18 . . PUNCT cana-441 372 1 [ [ X cana-441 372 2 31 31 NUM cana-441 372 3 ] ] PUNCT cana-441 372 4 a. a. PROPN cana-441 372 5 s. s. PROPN cana-441 372 6 k. k. PROPN cana-441 372 7 ( ( PUNCT cana-441 372 8 2020 2020 NUM cana-441 372 9 ) ) PUNCT cana-441 372 10 . . PUNCT cana-441 373 1 ultra ultra ADJ cana-441 373 2 - - ADJ cana-441 373 3 free free ADJ cana-441 373 4 , , PUNCT cana-441 373 5 contra contra PROPN cana-441 373 6 - - PUNCT cana-441 373 7 canonically canonically ADV cana-441 373 8 e e NOUN cana-441 373 9 - - NOUN cana-441 373 10 abelian abelian ADJ cana-441 373 11 , , PUNCT cana-441 373 12 compact compact ADJ cana-441 373 13 random random ADJ cana-441 373 14 variables variable NOUN cana-441 373 15 over over ADP cana-441 373 16 co co ADJ cana-441 373 17 - - ADJ cana-441 373 18 artinian artinian ADJ cana-441 373 19 matrices matrix NOUN cana-441 373 20 . . PUNCT cana-441 374 1 international international ADJ cana-441 374 2 journal journal PROPN cana-441 374 3 on on ADP cana-441 374 4 recent recent ADJ cana-441 374 5 trends trend NOUN cana-441 374 6 in in ADP cana-441 374 7 life life NOUN cana-441 374 8 science science NOUN cana-441 374 9 and and CCONJ cana-441 374 10 mathematics mathematic NOUN cana-441 374 11 , , PUNCT cana-441 374 12 7(1 7(1 NUM cana-441 374 13 ) ) PUNCT cana-441 374 14 , , PUNCT cana-441 374 15 13 13 NUM cana-441 374 16 - - SYM cana-441 374 17 23 23 NUM cana-441 374 18 . . PUNCT cana-441 375 1 [ [ X cana-441 375 2 32 32 NUM cana-441 375 3 ] ] PUNCT cana-441 375 4 a. a. PROPN cana-441 375 5 s. s. PROPN cana-441 375 6 k. k. PROPN cana-441 376 1 " " PUNCT cana-441 376 2 a a PROPN cana-441 376 3 .. .. PUNCT cana-441 376 4 raj raj PROPN cana-441 376 5 , , PUNCT cana-441 376 6 v. v. PROPN cana-441 376 7 sharma sharma PROPN cana-441 376 8 , , PUNCT cana-441 376 9 and and CCONJ cana-441 376 10 v. v. ADP cana-441 376 11 kumar kumar PROPN cana-441 376 12 . . PUNCT cana-441 377 1 β€œsimulation β€œsimulation NOUN cana-441 377 2 and and CCONJ cana-441 377 3 analysis analysis NOUN cana-441 377 4 of of ADP cana-441 377 5 hand hand NOUN cana-441 377 6 gesture gesture NOUN cana-441 377 7 recognition recognition NOUN cana-441 377 8 for for ADP cana-441 377 9 indian indian ADJ cana-441 377 10 sign sign NOUN cana-441 377 11 language language NOUN cana-441 377 12 using use VERB cana-441 377 13 cnn cnn PROPN cana-441 377 14 ” " PUNCT cana-441 377 15 . . PUNCT cana-441 377 16 " " PUNCT cana-441 378 1 international international ADJ cana-441 378 2 journal journal NOUN cana-441 378 3 on on ADP cana-441 378 4 recent recent ADJ cana-441 378 5 and and CCONJ cana-441 378 6 innovation innovation NOUN cana-441 378 7 trends trend NOUN cana-441 378 8 in in ADP cana-441 378 9 computing computing NOUN cana-441 378 10 and and CCONJ cana-441 378 11 communication communication NOUN cana-441 378 12 10 10 NUM cana-441 378 13 , , PUNCT cana-441 378 14 no no INTJ cana-441 378 15 . . NOUN cana-441 378 16 4 4 NUM cana-441 378 17 ( ( PUNCT cana-441 378 18 2022 2022 NUM cana-441 378 19 ): ): PUNCT cana-441 378 20 10 10 NUM cana-441 378 21 - - SYM cana-441 378 22 14 14 NUM cana-441 378 23 . . PUNCT cana-441 379 1 https://www.sciencedirect.com/journal/neuroscience-informatics https://www.sciencedirect.com/journal/neuroscience-informatic NOUN