id	sid	tid	token	lemma	pos
cana-5719	1	1	communications	communication	NOUN
cana-5719	1	2	on	on	ADP
cana-5719	1	3	applied	apply	VERB
cana-5719	1	4	nonlinear	nonlinear	ADJ
cana-5719	1	5	analysis	analysis	NOUN
cana-5719	1	6	issn	issn	NOUN
cana-5719	1	7	:	:	PUNCT
cana-5719	1	8	1074	1074	NUM
cana-5719	1	9	-	-	PUNCT
cana-5719	1	10	133x	133x	NUM
cana-5719	1	11	vol	vol	VERB
cana-5719	1	12	32	32	NUM
cana-5719	1	13	no	no	NOUN
cana-5719	1	14	.	.	PUNCT
cana-5719	2	1	10s	10	NOUN
cana-5719	2	2	(	(	PUNCT
cana-5719	2	3	2025	2025	NUM
cana-5719	2	4	)	)	PUNCT
cana-5719	2	5	2780	2780	NUM
cana-5719	2	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5719	2	7	enhancing	enhance	VERB
cana-5719	2	8	brain	brain	NOUN
cana-5719	2	9	tumor	tumor	NOUN
cana-5719	2	10	diagnosis	diagnosis	NOUN
cana-5719	2	11	with	with	ADP
cana-5719	2	12	optimized	optimize	VERB
cana-5719	2	13	deep	deep	ADJ
cana-5719	2	14	convolutional	convolutional	ADJ
cana-5719	2	15	transfer	transfer	NOUN
cana-5719	2	16	learning	learning	NOUN
cana-5719	2	17	models	model	NOUN
cana-5719	2	18	jeevan	jeevan	PROPN
cana-5719	2	19	kumar1	kumar1	PROPN
cana-5719	2	20	,	,	PUNCT
cana-5719	2	21	vijay	vijay	NOUN
cana-5719	2	22	pandey2	pandey2	PROPN
cana-5719	2	23	,	,	PUNCT
cana-5719	2	24	rajesh	rajesh	PROPN
cana-5719	2	25	kumar	kumar	PROPN
cana-5719	2	26	tiwari3	tiwari3	PROPN
cana-5719	2	27	department	department	PROPN
cana-5719	2	28	of	of	ADP
cana-5719	2	29	cse1	cse1	PROPN
cana-5719	2	30	,	,	PUNCT
cana-5719	2	31	3	3	NUM
cana-5719	2	32	,	,	PUNCT
cana-5719	2	33	department	department	NOUN
cana-5719	2	34	of	of	ADP
cana-5719	2	35	me2	me2	PROPN
cana-5719	2	36	jut	jut	PROPN
cana-5719	2	37	,	,	PUNCT
cana-5719	2	38	ranchi	ranchi	PROPN
cana-5719	2	39	,	,	PUNCT
cana-5719	2	40	jharkhand	jharkhand	PROPN
cana-5719	2	41	,	,	PUNCT
cana-5719	2	42	india1	india1	PROPN
cana-5719	2	43	,	,	PUNCT
cana-5719	2	44	bit	bit	NOUN
cana-5719	2	45	sindri	sindri	PROPN
cana-5719	2	46	,	,	PUNCT
cana-5719	2	47	jharkhand	jharkhand	PROPN
cana-5719	2	48	,	,	PUNCT
cana-5719	2	49	india2	india2	PROPN
cana-5719	2	50	,	,	PUNCT
cana-5719	2	51	rvscet	rvscet	PROPN
cana-5719	2	52	jamshedpur	jamshedpur	PROPN
cana-5719	2	53	,	,	PUNCT
cana-5719	2	54	jharkhand	jharkhand	PROPN
cana-5719	2	55	,	,	PUNCT
cana-5719	2	56	india3	india3	PROPN
cana-5719	2	57	jeevancse01@gmail.com1	jeevancse01@gmail.com1	PROPN
cana-5719	2	58	,	,	PUNCT
cana-5719	2	59	vpandey.me@bitsindri.ac.in2	vpandey.me@bitsindri.ac.in2	PROPN
cana-5719	2	60	,	,	PUNCT
cana-5719	2	61	rajeshkrtiwari@yahoo.com3	rajeshkrtiwari@yahoo.com3	NOUN
cana-5719	2	62	article	article	NOUN
cana-5719	2	63	history	history	NOUN
cana-5719	2	64	:	:	PUNCT
cana-5719	2	65	received	receive	VERB
cana-5719	2	66	:	:	PUNCT
cana-5719	2	67	12	12	NUM
cana-5719	2	68	-	-	SYM
cana-5719	2	69	01	01	NUM
cana-5719	2	70	-	-	PUNCT
cana-5719	2	71	2025	2025	NUM
cana-5719	2	72	revised	revise	VERB
cana-5719	2	73	:	:	PUNCT
cana-5719	2	74	15	15	NUM
cana-5719	2	75	-	-	NUM
cana-5719	2	76	02	02	NUM
cana-5719	2	77	-	-	PUNCT
cana-5719	2	78	2025	2025	NUM
cana-5719	2	79	accepted	accept	VERB
cana-5719	2	80	:	:	PUNCT
cana-5719	2	81	01	01	NUM
cana-5719	2	82	-	-	SYM
cana-5719	2	83	03	03	NUM
cana-5719	2	84	-	-	PUNCT
cana-5719	2	85	2025	2025	NUM
cana-5719	2	86	abstract	abstract	NOUN
cana-5719	2	87	:	:	PUNCT
cana-5719	3	1	brain	brain	NOUN
cana-5719	3	2	tumors	tumor	NOUN
cana-5719	3	3	represent	represent	VERB
cana-5719	3	4	a	a	DET
cana-5719	3	5	critical	critical	ADJ
cana-5719	3	6	and	and	CCONJ
cana-5719	3	7	aggressive	aggressive	ADJ
cana-5719	3	8	class	class	NOUN
cana-5719	3	9	of	of	ADP
cana-5719	3	10	neurological	neurological	ADJ
cana-5719	3	11	disorders	disorder	NOUN
cana-5719	3	12	,	,	PUNCT
cana-5719	3	13	often	often	ADV
cana-5719	3	14	leading	lead	VERB
cana-5719	3	15	to	to	ADP
cana-5719	3	16	significantly	significantly	ADV
cana-5719	3	17	reduced	reduce	VERB
cana-5719	3	18	life	life	NOUN
cana-5719	3	19	expectancy	expectancy	NOUN
cana-5719	3	20	,	,	PUNCT
cana-5719	3	21	particularly	particularly	ADV
cana-5719	3	22	in	in	ADP
cana-5719	3	23	their	their	PRON
cana-5719	3	24	advanced	advanced	ADJ
cana-5719	3	25	stages	stage	NOUN
cana-5719	3	26	.	.	PUNCT
cana-5719	4	1	this	this	DET
cana-5719	4	2	study	study	NOUN
cana-5719	4	3	introduces	introduce	VERB
cana-5719	4	4	an	an	DET
cana-5719	4	5	enhanced	enhanced	ADJ
cana-5719	4	6	diagnostic	diagnostic	ADJ
cana-5719	4	7	approach	approach	NOUN
cana-5719	4	8	leveraging	leverage	VERB
cana-5719	4	9	optimized	optimize	VERB
cana-5719	4	10	deep	deep	ADJ
cana-5719	4	11	convolutional	convolutional	ADJ
cana-5719	4	12	neural	neural	ADJ
cana-5719	4	13	networks	network	NOUN
cana-5719	4	14	(	(	PUNCT
cana-5719	4	15	cnns	cnns	PROPN
cana-5719	4	16	)	)	PUNCT
cana-5719	4	17	integrated	integrate	VERB
cana-5719	4	18	with	with	ADP
cana-5719	4	19	transfer	transfer	NOUN
cana-5719	4	20	learning	learn	VERB
cana-5719	4	21	to	to	PART
cana-5719	4	22	improve	improve	VERB
cana-5719	4	23	the	the	DET
cana-5719	4	24	accuracy	accuracy	NOUN
cana-5719	4	25	and	and	CCONJ
cana-5719	4	26	efficiency	efficiency	NOUN
cana-5719	4	27	of	of	ADP
cana-5719	4	28	brain	brain	NOUN
cana-5719	4	29	tumor	tumor	NOUN
cana-5719	4	30	classification	classification	NOUN
cana-5719	4	31	.	.	PUNCT
cana-5719	5	1	specifically	specifically	ADV
cana-5719	5	2	,	,	PUNCT
cana-5719	5	3	we	we	PRON
cana-5719	5	4	employ	employ	VERB
cana-5719	5	5	a	a	DET
cana-5719	5	6	fine	fine	ADV
cana-5719	5	7	-	-	PUNCT
cana-5719	5	8	tuned	tune	VERB
cana-5719	5	9	mobilenetv2	mobilenetv2	PROPN
cana-5719	5	10	model	model	NOUN
cana-5719	5	11	to	to	PART
cana-5719	5	12	categorize	categorize	VERB
cana-5719	5	13	brain	brain	NOUN
cana-5719	5	14	mri	mri	NOUN
cana-5719	5	15	images	image	NOUN
cana-5719	5	16	into	into	ADP
cana-5719	5	17	four	four	NUM
cana-5719	5	18	classes	class	NOUN
cana-5719	5	19	:	:	PUNCT
cana-5719	5	20	gliomas	glioma	NOUN
cana-5719	5	21	,	,	PUNCT
cana-5719	5	22	meningiomas	meningioma	NOUN
cana-5719	5	23	,	,	PUNCT
cana-5719	5	24	pituitary	pituitary	ADJ
cana-5719	5	25	tumors	tumor	NOUN
cana-5719	5	26	,	,	PUNCT
cana-5719	5	27	and	and	CCONJ
cana-5719	5	28	non	non	ADJ
cana-5719	5	29	-	-	ADJ
cana-5719	5	30	tumorous	tumorous	ADJ
cana-5719	5	31	conditions	condition	NOUN
cana-5719	5	32	.	.	PUNCT
cana-5719	6	1	the	the	DET
cana-5719	6	2	proposed	propose	VERB
cana-5719	6	3	method	method	NOUN
cana-5719	6	4	achieves	achieve	VERB
cana-5719	6	5	a	a	DET
cana-5719	6	6	high	high	ADJ
cana-5719	6	7	classification	classification	NOUN
cana-5719	6	8	accuracy	accuracy	NOUN
cana-5719	6	9	of	of	ADP
cana-5719	6	10	98.33	98.33	NUM
cana-5719	6	11	%	%	NOUN
cana-5719	6	12	,	,	PUNCT
cana-5719	6	13	with	with	ADP
cana-5719	6	14	a	a	DET
cana-5719	6	15	precision	precision	NOUN
cana-5719	6	16	of	of	ADP
cana-5719	6	17	0.98	0.98	NUM
cana-5719	6	18	,	,	PUNCT
cana-5719	6	19	recall	recall	NOUN
cana-5719	6	20	of	of	ADP
cana-5719	6	21	0.97	0.97	NUM
cana-5719	6	22	,	,	PUNCT
cana-5719	6	23	and	and	CCONJ
cana-5719	6	24	specificity	specificity	NOUN
cana-5719	6	25	of	of	ADP
cana-5719	6	26	0.99	0.99	NUM
cana-5719	6	27	,	,	PUNCT
cana-5719	6	28	under	under	ADP
cana-5719	6	29	an	an	DET
cana-5719	6	30	80:20	80:20	NUM
cana-5719	6	31	train	train	NOUN
cana-5719	6	32	-	-	PUNCT
cana-5719	6	33	test	test	NOUN
cana-5719	6	34	split	split	NOUN
cana-5719	6	35	.	.	PUNCT
cana-5719	7	1	notably	notably	ADV
cana-5719	7	2	,	,	PUNCT
cana-5719	7	3	our	our	PRON
cana-5719	7	4	approach	approach	NOUN
cana-5719	7	5	maintains	maintain	VERB
cana-5719	7	6	low	low	ADJ
cana-5719	7	7	computational	computational	ADJ
cana-5719	7	8	complexity	complexity	NOUN
cana-5719	7	9	while	while	SCONJ
cana-5719	7	10	delivering	deliver	VERB
cana-5719	7	11	superior	superior	ADJ
cana-5719	7	12	performance	performance	NOUN
cana-5719	7	13	compared	compare	VERB
cana-5719	7	14	to	to	ADP
cana-5719	7	15	existing	exist	VERB
cana-5719	7	16	models	model	NOUN
cana-5719	7	17	.	.	PUNCT
cana-5719	8	1	these	these	DET
cana-5719	8	2	results	result	NOUN
cana-5719	8	3	highlight	highlight	VERB
cana-5719	8	4	the	the	DET
cana-5719	8	5	potential	potential	NOUN
cana-5719	8	6	of	of	ADP
cana-5719	8	7	our	our	PRON
cana-5719	8	8	optimized	optimize	VERB
cana-5719	8	9	transfer	transfer	NOUN
cana-5719	8	10	learning	learning	NOUN
cana-5719	8	11	framework	framework	NOUN
cana-5719	8	12	as	as	ADP
cana-5719	8	13	a	a	DET
cana-5719	8	14	reliable	reliable	ADJ
cana-5719	8	15	and	and	CCONJ
cana-5719	8	16	time	time	NOUN
cana-5719	8	17	-	-	PUNCT
cana-5719	8	18	efficient	efficient	ADJ
cana-5719	8	19	tool	tool	NOUN
cana-5719	8	20	for	for	ADP
cana-5719	8	21	aiding	aid	VERB
cana-5719	8	22	clinical	clinical	ADJ
cana-5719	8	23	decision	decision	NOUN
cana-5719	8	24	-	-	PUNCT
cana-5719	8	25	making	making	NOUN
cana-5719	8	26	in	in	ADP
cana-5719	8	27	brain	brain	NOUN
cana-5719	8	28	tumor	tumor	NOUN
cana-5719	8	29	diagnosis	diagnosis	NOUN
cana-5719	8	30	.	.	PUNCT
cana-5719	9	1	keywords	keyword	NOUN
cana-5719	9	2	:	:	PUNCT
cana-5719	9	3	deep	deep	ADJ
cana-5719	9	4	learning	learning	NOUN
cana-5719	9	5	,	,	PUNCT
cana-5719	9	6	brain	brain	NOUN
cana-5719	9	7	tumor	tumor	NOUN
cana-5719	9	8	,	,	PUNCT
cana-5719	9	9	convolutional	convolutional	ADJ
cana-5719	9	10	neural	neural	ADJ
cana-5719	9	11	network	network	NOUN
cana-5719	9	12	,	,	PUNCT
cana-5719	9	13	transfer	transfer	NOUN
cana-5719	9	14	learning	learning	NOUN
cana-5719	9	15	,	,	PUNCT
cana-5719	9	16	data	datum	NOUN
cana-5719	9	17	training	training	NOUN
cana-5719	9	18	,	,	PUNCT
cana-5719	9	19	data	datum	NOUN
cana-5719	9	20	testing	testing	NOUN
cana-5719	9	21	.	.	PUNCT
cana-5719	10	1	1	1	X
cana-5719	10	2	.	.	X
cana-5719	10	3	introduction	introduction	NOUN
cana-5719	10	4	an	an	DET
cana-5719	10	5	abnormal	abnormal	ADJ
cana-5719	10	6	mass	mass	NOUN
cana-5719	10	7	of	of	ADP
cana-5719	10	8	cells	cell	NOUN
cana-5719	10	9	caused	cause	VERB
cana-5719	10	10	by	by	ADP
cana-5719	10	11	the	the	DET
cana-5719	10	12	unchecked	unchecked	ADJ
cana-5719	10	13	proliferation	proliferation	NOUN
cana-5719	10	14	of	of	ADP
cana-5719	10	15	brain	brain	NOUN
cana-5719	10	16	cells	cell	NOUN
cana-5719	10	17	is	be	AUX
cana-5719	10	18	called	call	VERB
cana-5719	10	19	a	a	DET
cana-5719	10	20	brain	brain	NOUN
cana-5719	10	21	tumour	tumour	NOUN
cana-5719	10	22	.	.	PUNCT
cana-5719	11	1	low	low	ADJ
cana-5719	11	2	-	-	PUNCT
cana-5719	11	3	grade	grade	NOUN
cana-5719	11	4	and	and	CCONJ
cana-5719	11	5	high	high	ADJ
cana-5719	11	6	-	-	PUNCT
cana-5719	11	7	grade	grade	NOUN
cana-5719	11	8	are	be	AUX
cana-5719	11	9	the	the	DET
cana-5719	11	10	two	two	NUM
cana-5719	11	11	primary	primary	ADJ
cana-5719	11	12	categories	category	NOUN
cana-5719	11	13	into	into	ADP
cana-5719	11	14	which	which	PRON
cana-5719	11	15	these	these	DET
cana-5719	11	16	tumours	tumour	NOUN
cana-5719	11	17	are	be	AUX
cana-5719	11	18	typically	typically	ADV
cana-5719	11	19	divided	divide	VERB
cana-5719	11	20	.	.	PUNCT
cana-5719	12	1	benign	benign	ADJ
cana-5719	12	2	tumours	tumour	NOUN
cana-5719	12	3	,	,	PUNCT
cana-5719	12	4	another	another	DET
cana-5719	12	5	name	name	NOUN
cana-5719	12	6	for	for	ADP
cana-5719	12	7	low	low	ADJ
cana-5719	12	8	-	-	PUNCT
cana-5719	12	9	grade	grade	NOUN
cana-5719	12	10	tumours	tumour	NOUN
cana-5719	12	11	,	,	PUNCT
cana-5719	12	12	are	be	AUX
cana-5719	12	13	non	non	ADJ
cana-5719	12	14	-	-	ADJ
cana-5719	12	15	invasive	invasive	ADJ
cana-5719	12	16	and	and	CCONJ
cana-5719	12	17	noncancerous	noncancerous	ADJ
cana-5719	12	18	,	,	PUNCT
cana-5719	12	19	which	which	PRON
cana-5719	12	20	means	mean	VERB
cana-5719	12	21	they	they	PRON
cana-5719	12	22	do	do	AUX
cana-5719	12	23	n't	not	PART
cana-5719	12	24	spread	spread	VERB
cana-5719	12	25	to	to	ADP
cana-5719	12	26	other	other	ADJ
cana-5719	12	27	areas	area	NOUN
cana-5719	12	28	of	of	ADP
cana-5719	12	29	the	the	DET
cana-5719	12	30	brain	brain	NOUN
cana-5719	12	31	.	.	PUNCT
cana-5719	13	1	on	on	ADP
cana-5719	13	2	the	the	DET
cana-5719	13	3	other	other	ADJ
cana-5719	13	4	hand	hand	NOUN
cana-5719	13	5	,	,	PUNCT
cana-5719	13	6	high	high	ADJ
cana-5719	13	7	-	-	PUNCT
cana-5719	13	8	grade	grade	NOUN
cana-5719	13	9	tumours	tumour	NOUN
cana-5719	13	10	,	,	PUNCT
cana-5719	13	11	often	often	ADV
cana-5719	13	12	known	know	VERB
cana-5719	13	13	as	as	ADP
cana-5719	13	14	malignant	malignant	ADJ
cana-5719	13	15	tumours	tumours	NOUN
cana-5719	13	16	,	,	PUNCT
cana-5719	13	17	are	be	AUX
cana-5719	13	18	malignant	malignant	ADJ
cana-5719	13	19	and	and	CCONJ
cana-5719	13	20	have	have	VERB
cana-5719	13	21	the	the	DET
cana-5719	13	22	ability	ability	NOUN
cana-5719	13	23	to	to	PART
cana-5719	13	24	aggressively	aggressively	ADV
cana-5719	13	25	penetrate	penetrate	VERB
cana-5719	13	26	nearby	nearby	ADJ
cana-5719	13	27	brain	brain	NOUN
cana-5719	13	28	tissue	tissue	NOUN
cana-5719	13	29	.	.	PUNCT
cana-5719	14	1	malignant	malignant	ADJ
cana-5719	14	2	tumours	tumour	NOUN
cana-5719	14	3	proliferate	proliferate	VERB
cana-5719	14	4	quickly	quickly	ADV
cana-5719	14	5	,	,	PUNCT
cana-5719	14	6	encroaching	encroach	VERB
cana-5719	14	7	on	on	ADP
cana-5719	14	8	neighbouring	neighbouring	ADJ
cana-5719	14	9	tissues	tissue	NOUN
cana-5719	14	10	without	without	ADP
cana-5719	14	11	end	end	NOUN
cana-5719	14	12	and	and	CCONJ
cana-5719	14	13	frequently	frequently	ADV
cana-5719	14	14	metastasising	metastasise	VERB
cana-5719	14	15	to	to	ADP
cana-5719	14	16	distant	distant	ADJ
cana-5719	14	17	parts	part	NOUN
cana-5719	14	18	of	of	ADP
cana-5719	14	19	the	the	DET
cana-5719	14	20	body	body	NOUN
cana-5719	14	21	.	.	PUNCT
cana-5719	15	1	they	they	PRON
cana-5719	15	2	are	be	AUX
cana-5719	15	3	potentially	potentially	ADV
cana-5719	15	4	fatal	fatal	ADJ
cana-5719	15	5	due	due	ADP
cana-5719	15	6	to	to	ADP
cana-5719	15	7	their	their	PRON
cana-5719	15	8	aggressive	aggressive	ADJ
cana-5719	15	9	nature	nature	NOUN
cana-5719	15	10	,	,	PUNCT
cana-5719	15	11	which	which	PRON
cana-5719	15	12	makes	make	VERB
cana-5719	15	13	them	they	PRON
cana-5719	15	14	life	life	NOUN
cana-5719	15	15	-	-	PUNCT
cana-5719	15	16	threatening	threaten	VERB
cana-5719	15	17	if	if	SCONJ
cana-5719	15	18	left	leave	VERB
cana-5719	15	19	untreated	untreated	ADJ
cana-5719	15	20	.	.	PUNCT
cana-5719	16	1	deep	deep	ADJ
cana-5719	16	2	learning	learning	NOUN
cana-5719	16	3	(	(	PUNCT
cana-5719	16	4	dl	dl	INTJ
cana-5719	16	5	)	)	PUNCT
cana-5719	16	6	is	be	AUX
cana-5719	16	7	a	a	DET
cana-5719	16	8	subfield	subfield	NOUN
cana-5719	16	9	of	of	ADP
cana-5719	16	10	machine	machine	NOUN
cana-5719	16	11	learning	learning	NOUN
cana-5719	16	12	that	that	PRON
cana-5719	16	13	trains	train	VERB
cana-5719	16	14	computers	computer	NOUN
cana-5719	16	15	to	to	PART
cana-5719	16	16	make	make	VERB
cana-5719	16	17	predictions	prediction	NOUN
cana-5719	16	18	and	and	CCONJ
cana-5719	16	19	draw	draw	VERB
cana-5719	16	20	conclusions	conclusion	NOUN
cana-5719	16	21	by	by	ADP
cana-5719	16	22	using	use	VERB
cana-5719	16	23	data	datum	NOUN
cana-5719	16	24	representations	representation	NOUN
cana-5719	16	25	.	.	PUNCT
cana-5719	17	1	medical	medical	ADJ
cana-5719	17	2	picture	picture	NOUN
cana-5719	17	3	categorisation	categorisation	NOUN
cana-5719	17	4	is	be	AUX
cana-5719	17	5	one	one	NUM
cana-5719	17	6	of	of	ADP
cana-5719	17	7	the	the	DET
cana-5719	17	8	many	many	ADJ
cana-5719	17	9	applications	application	NOUN
cana-5719	17	10	that	that	PRON
cana-5719	17	11	use	use	VERB
cana-5719	17	12	deep	deep	ADJ
cana-5719	17	13	learning	learning	NOUN
cana-5719	17	14	(	(	PUNCT
cana-5719	17	15	dl	dl	PROPN
cana-5719	17	16	)	)	PUNCT
cana-5719	17	17	,	,	PUNCT
cana-5719	17	18	a	a	DET
cana-5719	17	19	crucial	crucial	ADJ
cana-5719	17	20	tool	tool	NOUN
cana-5719	17	21	in	in	ADP
cana-5719	17	22	artificial	artificial	ADJ
cana-5719	17	23	intelligence	intelligence	NOUN
cana-5719	17	24	.	.	PUNCT
cana-5719	18	1	a	a	DET
cana-5719	18	2	dataset	dataset	NOUN
cana-5719	18	3	size	size	NOUN
cana-5719	18	4	at	at	ADP
cana-5719	18	5	least	least	ADJ
cana-5719	18	6	ten	ten	NUM
cana-5719	18	7	times	time	NOUN
cana-5719	18	8	bigger	big	ADJ
cana-5719	18	9	than	than	ADP
cana-5719	18	10	the	the	DET
cana-5719	18	11	number	number	NOUN
cana-5719	18	12	of	of	ADP
cana-5719	18	13	degrees	degree	NOUN
cana-5719	18	14	of	of	ADP
cana-5719	18	15	freedom	freedom	NOUN
cana-5719	18	16	is	be	AUX
cana-5719	18	17	usually	usually	ADV
cana-5719	18	18	required	require	VERB
cana-5719	18	19	to	to	PART
cana-5719	18	20	attain	attain	VERB
cana-5719	18	21	excellent	excellent	ADJ
cana-5719	18	22	accuracy	accuracy	NOUN
cana-5719	18	23	,	,	PUNCT
cana-5719	18	24	despite	despite	SCONJ
cana-5719	18	25	its	its	PRON
cana-5719	18	26	impressive	impressive	ADJ
cana-5719	18	27	success	success	NOUN
cana-5719	18	28	in	in	ADP
cana-5719	18	29	a	a	DET
cana-5719	18	30	variety	variety	NOUN
cana-5719	18	31	of	of	ADP
cana-5719	18	32	areas	area	NOUN
cana-5719	18	33	[	[	X
cana-5719	18	34	1	1	NUM
cana-5719	18	35	]	]	PUNCT
cana-5719	18	36	.	.	PUNCT
cana-5719	19	1	as	as	ADP
cana-5719	19	2	a	a	DET
cana-5719	19	3	deep	deep	ADJ
cana-5719	19	4	learning	learning	NOUN
cana-5719	19	5	method	method	NOUN
cana-5719	19	6	,	,	PUNCT
cana-5719	19	7	transfer	transfer	NOUN
cana-5719	19	8	learning	learning	NOUN
cana-5719	19	9	entails	entail	VERB
cana-5719	19	10	first	first	ADJ
cana-5719	19	11	training	train	VERB
cana-5719	19	12	a	a	DET
cana-5719	19	13	network	network	NOUN
cana-5719	19	14	on	on	ADP
cana-5719	19	15	a	a	DET
cana-5719	19	16	big	big	ADJ
cana-5719	19	17	dataset	dataset	NOUN
cana-5719	19	18	and	and	CCONJ
cana-5719	19	19	then	then	ADV
cana-5719	19	20	using	use	VERB
cana-5719	19	21	what	what	PRON
cana-5719	19	22	it	it	PRON
cana-5719	19	23	has	have	AUX
cana-5719	19	24	learnt	learn	VERB
cana-5719	19	25	on	on	ADP
cana-5719	19	26	a	a	DET
cana-5719	19	27	smaller	small	ADJ
cana-5719	19	28	dataset	dataset	NOUN
cana-5719	19	29	[	[	X
cana-5719	19	30	2	2	NUM
cana-5719	19	31	]	]	PUNCT
cana-5719	19	32	.	.	PUNCT
cana-5719	20	1	fine	fine	ADJ
cana-5719	20	2	-	-	PUNCT
cana-5719	20	3	tuning	tuning	NOUN
cana-5719	20	4	and	and	CCONJ
cana-5719	20	5	cnn	cnn	NOUN
cana-5719	20	6	layer	layer	NOUN
cana-5719	20	7	freezing	freezing	NOUN
cana-5719	20	8	are	be	AUX
cana-5719	20	9	the	the	DET
cana-5719	20	10	two	two	NUM
cana-5719	20	11	main	main	ADJ
cana-5719	20	12	techniques	technique	NOUN
cana-5719	20	13	for	for	ADP
cana-5719	20	14	transfer	transfer	NOUN
cana-5719	20	15	learning	learning	NOUN
cana-5719	20	16	.	.	PUNCT
cana-5719	21	1	by	by	ADP
cana-5719	21	2	applying	apply	VERB
cana-5719	21	3	the	the	DET
cana-5719	21	4	pretrained	pretraine	VERB
cana-5719	21	5	cnn	cnn	PROPN
cana-5719	21	6	to	to	ADP
cana-5719	21	7	the	the	DET
cana-5719	21	8	target	target	NOUN
cana-5719	21	9	dataset	dataset	VERB
cana-5719	21	10	,	,	PUNCT
cana-5719	21	11	the	the	DET
cana-5719	21	12	fine	fine	ADV
cana-5719	21	13	-	-	PUNCT
cana-5719	21	14	tuning	tune	VERB
cana-5719	21	15	strategy	strategy	NOUN
cana-5719	21	16	permits	permit	VERB
cana-5719	21	17	backpropagation	backpropagation	NOUN
cana-5719	21	18	mailto	mailto	NOUN
cana-5719	21	19	:	:	PUNCT
cana-5719	21	20	jeevancse01@gmail.com1	jeevancse01@gmail.com1	PROPN
cana-5719	21	21	mailto:vpandey.me@bitsindri.ac.in	mailto:vpandey.me@bitsindri.ac.in	PROPN
cana-5719	21	22	mailto:rajeshkrtiwari@yahoo.com	mailto:rajeshkrtiwari@yahoo.com	PROPN
cana-5719	21	23	communications	communication	NOUN
cana-5719	21	24	on	on	ADP
cana-5719	21	25	applied	apply	VERB
cana-5719	21	26	nonlinear	nonlinear	ADJ
cana-5719	21	27	analysis	analysis	NOUN
cana-5719	21	28	issn	issn	NOUN
cana-5719	21	29	:	:	PUNCT
cana-5719	21	30	1074	1074	NUM
cana-5719	21	31	-	-	PUNCT
cana-5719	21	32	133x	133x	NUM
cana-5719	21	33	vol	vol	VERB
cana-5719	21	34	32	32	NUM
cana-5719	21	35	no	no	NOUN
cana-5719	21	36	.	.	PUNCT
cana-5719	22	1	10s	10	NOUN
cana-5719	22	2	(	(	PUNCT
cana-5719	22	3	2025	2025	NUM
cana-5719	22	4	)	)	PUNCT
cana-5719	22	5	2781	2781	NUM
cana-5719	22	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5719	22	7	through	through	ADP
cana-5719	22	8	the	the	DET
cana-5719	22	9	network	network	NOUN
cana-5719	22	10	while	while	SCONJ
cana-5719	22	11	preserving	preserve	VERB
cana-5719	22	12	a	a	DET
cana-5719	22	13	portion	portion	NOUN
cana-5719	22	14	of	of	ADP
cana-5719	22	15	the	the	DET
cana-5719	22	16	initial	initial	ADJ
cana-5719	22	17	layers	layer	NOUN
cana-5719	22	18	.	.	PUNCT
cana-5719	23	1	the	the	DET
cana-5719	23	2	target	target	NOUN
cana-5719	23	3	dataset	dataset	NOUN
cana-5719	23	4	is	be	AUX
cana-5719	23	5	then	then	ADV
cana-5719	23	6	categorised	categorise	VERB
cana-5719	23	7	by	by	ADP
cana-5719	23	8	the	the	DET
cana-5719	23	9	last	last	ADJ
cana-5719	23	10	completely	completely	ADV
cana-5719	23	11	linked	link	VERB
cana-5719	23	12	layer	layer	NOUN
cana-5719	23	13	.	.	PUNCT
cana-5719	24	1	1.1	1.1	NUM
cana-5719	24	2	motivation	motivation	NOUN
cana-5719	24	3	for	for	ADP
cana-5719	24	4	the	the	DET
cana-5719	24	5	work	work	NOUN
cana-5719	24	6	the	the	DET
cana-5719	24	7	primary	primary	ADJ
cana-5719	24	8	motivation	motivation	NOUN
cana-5719	24	9	for	for	ADP
cana-5719	24	10	this	this	DET
cana-5719	24	11	research	research	NOUN
cana-5719	24	12	is	be	AUX
cana-5719	24	13	to	to	PART
cana-5719	24	14	develop	develop	VERB
cana-5719	24	15	a	a	DET
cana-5719	24	16	prediction	prediction	NOUN
cana-5719	24	17	model	model	NOUN
cana-5719	24	18	for	for	ADP
cana-5719	24	19	detecting	detect	VERB
cana-5719	24	20	brain	brain	NOUN
cana-5719	24	21	tumor	tumor	NOUN
cana-5719	24	22	occurrence	occurrence	NOUN
cana-5719	24	23	.	.	PUNCT
cana-5719	25	1	additionally	additionally	ADV
cana-5719	25	2	,	,	PUNCT
cana-5719	25	3	the	the	DET
cana-5719	25	4	research	research	NOUN
cana-5719	25	5	aims	aim	VERB
cana-5719	25	6	to	to	PART
cana-5719	25	7	identify	identify	VERB
cana-5719	25	8	the	the	DET
cana-5719	25	9	most	most	ADV
cana-5719	25	10	effective	effective	ADJ
cana-5719	25	11	classification	classification	NOUN
cana-5719	25	12	algorithm	algorithm	NOUN
cana-5719	25	13	for	for	ADP
cana-5719	25	14	determining	determine	VERB
cana-5719	25	15	the	the	DET
cana-5719	25	16	likelihood	likelihood	NOUN
cana-5719	25	17	of	of	ADP
cana-5719	25	18	brain	brain	NOUN
cana-5719	25	19	tumor	tumor	NOUN
cana-5719	25	20	disease	disease	NOUN
cana-5719	25	21	in	in	ADP
cana-5719	25	22	patients	patient	NOUN
cana-5719	25	23	.	.	PUNCT
cana-5719	26	1	a	a	DET
cana-5719	26	2	comparative	comparative	ADJ
cana-5719	26	3	study	study	NOUN
cana-5719	26	4	is	be	AUX
cana-5719	26	5	conducted	conduct	VERB
cana-5719	26	6	by	by	ADP
cana-5719	26	7	analyzing	analyze	VERB
cana-5719	26	8	deep	deep	ADJ
cana-5719	26	9	learning	learning	NOUN
cana-5719	26	10	algorithms	algorithm	NOUN
cana-5719	26	11	across	across	ADP
cana-5719	26	12	various	various	ADJ
cana-5719	26	13	evaluation	evaluation	NOUN
cana-5719	26	14	levels	level	NOUN
cana-5719	26	15	.	.	PUNCT
cana-5719	27	1	these	these	DET
cana-5719	27	2	algorithms	algorithm	NOUN
cana-5719	27	3	are	be	AUX
cana-5719	27	4	assessed	assess	VERB
cana-5719	27	5	using	use	VERB
cana-5719	27	6	different	different	ADJ
cana-5719	27	7	evaluation	evaluation	NOUN
cana-5719	27	8	strategies	strategy	NOUN
cana-5719	27	9	to	to	PART
cana-5719	27	10	ensure	ensure	VERB
cana-5719	27	11	thorough	thorough	ADJ
cana-5719	27	12	analysis	analysis	NOUN
cana-5719	27	13	.	.	PUNCT
cana-5719	28	1	the	the	DET
cana-5719	28	2	findings	finding	NOUN
cana-5719	28	3	will	will	AUX
cana-5719	28	4	help	help	VERB
cana-5719	28	5	researchers	researcher	NOUN
cana-5719	28	6	and	and	CCONJ
cana-5719	28	7	medical	medical	ADJ
cana-5719	28	8	practitioners	practitioner	NOUN
cana-5719	28	9	establish	establish	VERB
cana-5719	28	10	a	a	DET
cana-5719	28	11	more	more	ADV
cana-5719	28	12	accurate	accurate	ADJ
cana-5719	28	13	and	and	CCONJ
cana-5719	28	14	reliable	reliable	ADJ
cana-5719	28	15	method	method	NOUN
cana-5719	28	16	for	for	ADP
cana-5719	28	17	brain	brain	NOUN
cana-5719	28	18	tumor	tumor	NOUN
cana-5719	28	19	diagnosis	diagnosis	NOUN
cana-5719	28	20	.	.	PUNCT
cana-5719	29	1	1.2	1.2	NUM
cana-5719	29	2	major	major	ADJ
cana-5719	29	3	contribution	contribution	NOUN
cana-5719	29	4	the	the	DET
cana-5719	29	5	main	main	ADJ
cana-5719	29	6	difficulty	difficulty	NOUN
cana-5719	29	7	in	in	ADP
cana-5719	29	8	brain	brain	NOUN
cana-5719	29	9	tumour	tumour	NOUN
cana-5719	29	10	diagnosis	diagnosis	NOUN
cana-5719	29	11	is	be	AUX
cana-5719	29	12	early	early	ADJ
cana-5719	29	13	identification	identification	NOUN
cana-5719	29	14	.	.	PUNCT
cana-5719	30	1	although	although	SCONJ
cana-5719	30	2	there	there	PRON
cana-5719	30	3	are	be	VERB
cana-5719	30	4	tools	tool	NOUN
cana-5719	30	5	for	for	ADP
cana-5719	30	6	forecasting	forecast	VERB
cana-5719	30	7	the	the	DET
cana-5719	30	8	likelihood	likelihood	NOUN
cana-5719	30	9	of	of	ADP
cana-5719	30	10	brain	brain	NOUN
cana-5719	30	11	tumour	tumour	NOUN
cana-5719	30	12	disease	disease	NOUN
cana-5719	30	13	,	,	PUNCT
cana-5719	30	14	they	they	PRON
cana-5719	30	15	are	be	AUX
cana-5719	30	16	frequently	frequently	ADV
cana-5719	30	17	either	either	CCONJ
cana-5719	30	18	ineffective	ineffective	ADJ
cana-5719	30	19	at	at	ADP
cana-5719	30	20	precisely	precisely	ADV
cana-5719	30	21	estimating	estimate	VERB
cana-5719	30	22	the	the	DET
cana-5719	30	23	disease	disease	NOUN
cana-5719	30	24	's	's	PART
cana-5719	30	25	risk	risk	NOUN
cana-5719	30	26	or	or	CCONJ
cana-5719	30	27	excessively	excessively	ADV
cana-5719	30	28	costly	costly	ADJ
cana-5719	30	29	.	.	PUNCT
cana-5719	31	1	early	early	ADJ
cana-5719	31	2	identification	identification	NOUN
cana-5719	31	3	can	can	AUX
cana-5719	31	4	minimize	minimize	VERB
cana-5719	31	5	complications	complication	NOUN
cana-5719	31	6	and	and	CCONJ
cana-5719	31	7	drastically	drastically	ADV
cana-5719	31	8	lower	low	ADJ
cana-5719	31	9	death	death	NOUN
cana-5719	31	10	rates	rate	NOUN
cana-5719	31	11	.	.	PUNCT
cana-5719	32	1	dl	dl	PROPN
cana-5719	32	2	algorithms	algorithms	PROPN
cana-5719	32	3	can	can	AUX
cana-5719	32	4	be	be	AUX
cana-5719	32	5	used	use	VERB
cana-5719	32	6	to	to	PART
cana-5719	32	7	analyse	analyse	VERB
cana-5719	32	8	the	the	DET
cana-5719	32	9	large	large	ADJ
cana-5719	32	10	volumes	volume	NOUN
cana-5719	32	11	of	of	ADP
cana-5719	32	12	data	datum	NOUN
cana-5719	32	13	that	that	PRON
cana-5719	32	14	are	be	AUX
cana-5719	32	15	currently	currently	ADV
cana-5719	32	16	available	available	ADJ
cana-5719	32	17	and	and	CCONJ
cana-5719	32	18	find	find	VERB
cana-5719	32	19	hidden	hidden	ADJ
cana-5719	32	20	patterns	pattern	NOUN
cana-5719	32	21	,	,	PUNCT
cana-5719	32	22	which	which	PRON
cana-5719	32	23	will	will	AUX
cana-5719	32	24	help	help	VERB
cana-5719	32	25	with	with	ADP
cana-5719	32	26	the	the	DET
cana-5719	32	27	faster	fast	ADJ
cana-5719	32	28	and	and	CCONJ
cana-5719	32	29	more	more	ADV
cana-5719	32	30	accurate	accurate	ADJ
cana-5719	32	31	identification	identification	NOUN
cana-5719	32	32	of	of	ADP
cana-5719	32	33	brain	brain	NOUN
cana-5719	32	34	tumours	tumour	NOUN
cana-5719	32	35	.	.	PUNCT
cana-5719	33	1	the	the	DET
cana-5719	33	2	following	follow	VERB
cana-5719	33	3	are	be	AUX
cana-5719	33	4	the	the	DET
cana-5719	33	5	primary	primary	ADJ
cana-5719	33	6	contributions	contribution	NOUN
cana-5719	33	7	of	of	ADP
cana-5719	33	8	our	our	PRON
cana-5719	33	9	proposed	propose	VERB
cana-5719	33	10	research	research	NOUN
cana-5719	33	11	:	:	PUNCT
cana-5719	33	12	•	•	NOUN
cana-5719	33	13	for	for	ADP
cana-5719	33	14	the	the	DET
cana-5719	33	15	classification	classification	NOUN
cana-5719	33	16	and	and	CCONJ
cana-5719	33	17	automated	automate	VERB
cana-5719	33	18	detection	detection	NOUN
cana-5719	33	19	of	of	ADP
cana-5719	33	20	brain	brain	NOUN
cana-5719	33	21	tumours	tumour	NOUN
cana-5719	33	22	,	,	PUNCT
cana-5719	33	23	a	a	DET
cana-5719	33	24	unique	unique	ADJ
cana-5719	33	25	and	and	CCONJ
cana-5719	33	26	reliable	reliable	ADJ
cana-5719	33	27	deep	deep	ADJ
cana-5719	33	28	learning	learning	NOUN
cana-5719	33	29	method	method	NOUN
cana-5719	33	30	combining	combine	VERB
cana-5719	33	31	transfer	transfer	NOUN
cana-5719	33	32	learning	learning	NOUN
cana-5719	33	33	is	be	AUX
cana-5719	33	34	described	describe	VERB
cana-5719	33	35	.	.	PUNCT
cana-5719	34	1	significant	significant	ADJ
cana-5719	34	2	and	and	CCONJ
cana-5719	34	3	rich	rich	ADJ
cana-5719	34	4	features	feature	NOUN
cana-5719	34	5	are	be	AUX
cana-5719	34	6	successfully	successfully	ADV
cana-5719	34	7	extracted	extract	VERB
cana-5719	34	8	from	from	ADP
cana-5719	34	9	the	the	DET
cana-5719	34	10	kaggle	kaggle	ADJ
cana-5719	34	11	dataset	dataset	NOUN
cana-5719	34	12	using	use	VERB
cana-5719	34	13	this	this	DET
cana-5719	34	14	strategy	strategy	NOUN
cana-5719	34	15	.	.	PUNCT
cana-5719	35	1	•	•	NUM
cana-5719	35	2	examining	examine	VERB
cana-5719	35	3	the	the	DET
cana-5719	35	4	mobilenet	mobilenet	NOUN
cana-5719	35	5	v2	v2	NOUN
cana-5719	35	6	architecture	architecture	NOUN
cana-5719	35	7	with	with	ADP
cana-5719	35	8	transfer	transfer	NOUN
cana-5719	35	9	learning	learn	VERB
cana-5719	35	10	techniques	technique	NOUN
cana-5719	35	11	on	on	ADP
cana-5719	35	12	a	a	DET
cana-5719	35	13	target	target	NOUN
cana-5719	35	14	dataset	dataset	VERB
cana-5719	35	15	comprised	comprise	VERB
cana-5719	35	16	of	of	ADP
cana-5719	35	17	mri	mri	NOUN
cana-5719	35	18	pictures	picture	NOUN
cana-5719	35	19	of	of	ADP
cana-5719	35	20	brain	brain	NOUN
cana-5719	35	21	tumours	tumour	NOUN
cana-5719	35	22	.	.	PUNCT
cana-5719	36	1	•	•	NUM
cana-5719	36	2	applying	apply	VERB
cana-5719	36	3	several	several	ADJ
cana-5719	36	4	frozen	frozen	ADJ
cana-5719	36	5	layers	layer	NOUN
cana-5719	36	6	from	from	ADP
cana-5719	36	7	a	a	DET
cana-5719	36	8	pretrained	pretraine	VERB
cana-5719	36	9	model	model	NOUN
cana-5719	36	10	to	to	ADP
cana-5719	36	11	the	the	DET
cana-5719	36	12	deep	deep	ADJ
cana-5719	36	13	learning	learning	NOUN
cana-5719	36	14	model	model	NOUN
cana-5719	36	15	after	after	SCONJ
cana-5719	36	16	they	they	PRON
cana-5719	36	17	have	have	AUX
cana-5719	36	18	been	be	AUX
cana-5719	36	19	passed	pass	VERB
cana-5719	36	20	through	through	ADP
cana-5719	36	21	brain	brain	NOUN
cana-5719	36	22	tumour	tumour	NOUN
cana-5719	36	23	classification	classification	NOUN
cana-5719	36	24	and	and	CCONJ
cana-5719	36	25	detection	detection	NOUN
cana-5719	36	26	performance	performance	NOUN
cana-5719	36	27	is	be	AUX
cana-5719	36	28	assessed	assess	VERB
cana-5719	36	29	through	through	ADP
cana-5719	36	30	a	a	DET
cana-5719	36	31	comparative	comparative	ADJ
cana-5719	36	32	analysis	analysis	NOUN
cana-5719	36	33	based	base	VERB
cana-5719	36	34	on	on	ADP
cana-5719	36	35	multiple	multiple	ADJ
cana-5719	36	36	cnn	cnn	PROPN
cana-5719	36	37	architectural	architectural	NOUN
cana-5719	36	38	.	.	PUNCT
cana-5719	37	1	the	the	DET
cana-5719	37	2	layout	layout	NOUN
cana-5719	37	3	of	of	ADP
cana-5719	37	4	this	this	DET
cana-5719	37	5	paper	paper	NOUN
cana-5719	37	6	is	be	AUX
cana-5719	37	7	as	as	SCONJ
cana-5719	37	8	follows	follow	VERB
cana-5719	37	9	:	:	PUNCT
cana-5719	37	10	the	the	DET
cana-5719	37	11	relevant	relevant	ADJ
cana-5719	37	12	work	work	NOUN
cana-5719	37	13	is	be	AUX
cana-5719	37	14	presented	present	VERB
cana-5719	37	15	in	in	ADP
cana-5719	37	16	section	section	NOUN
cana-5719	37	17	2	2	NUM
cana-5719	37	18	.	.	PUNCT
cana-5719	38	1	the	the	DET
cana-5719	38	2	materials	material	NOUN
cana-5719	38	3	and	and	CCONJ
cana-5719	38	4	techniques	technique	NOUN
cana-5719	38	5	are	be	AUX
cana-5719	38	6	described	describe	VERB
cana-5719	38	7	in	in	ADP
cana-5719	38	8	depth	depth	NOUN
cana-5719	38	9	in	in	ADP
cana-5719	38	10	section	section	NOUN
cana-5719	38	11	3	3	NUM
cana-5719	38	12	.	.	PUNCT
cana-5719	38	13	section	section	NOUN
cana-5719	38	14	4	4	NUM
cana-5719	38	15	describes	describe	VERB
cana-5719	38	16	the	the	DET
cana-5719	38	17	experimental	experimental	ADJ
cana-5719	38	18	setup	setup	NOUN
cana-5719	38	19	.	.	PUNCT
cana-5719	39	1	a	a	DET
cana-5719	39	2	comparative	comparative	ADJ
cana-5719	39	3	analysis	analysis	NOUN
cana-5719	39	4	,	,	PUNCT
cana-5719	39	5	results	result	NOUN
cana-5719	39	6	,	,	PUNCT
cana-5719	39	7	and	and	CCONJ
cana-5719	39	8	discussion	discussion	NOUN
cana-5719	39	9	are	be	AUX
cana-5719	39	10	presented	present	VERB
cana-5719	39	11	in	in	ADP
cana-5719	39	12	section	section	NOUN
cana-5719	39	13	5	5	NUM
cana-5719	39	14	.	.	PUNCT
cana-5719	40	1	lastly	lastly	ADV
cana-5719	40	2	,	,	PUNCT
cana-5719	40	3	the	the	DET
cana-5719	40	4	conclusion	conclusion	NOUN
cana-5719	40	5	provides	provide	VERB
cana-5719	40	6	a	a	DET
cana-5719	40	7	review	review	NOUN
cana-5719	40	8	of	of	ADP
cana-5719	40	9	findings	finding	NOUN
cana-5719	40	10	and	and	CCONJ
cana-5719	40	11	suggestions	suggestion	NOUN
cana-5719	40	12	for	for	ADP
cana-5719	40	13	further	further	ADJ
cana-5719	40	14	research	research	NOUN
cana-5719	40	15	.	.	PUNCT
cana-5719	41	1	2	2	X
cana-5719	41	2	.	.	X
cana-5719	41	3	related	relate	VERB
cana-5719	41	4	work	work	NOUN
cana-5719	41	5	this	this	DET
cana-5719	41	6	section	section	NOUN
cana-5719	41	7	summarizes	summarize	VERB
cana-5719	41	8	earlier	early	ADJ
cana-5719	41	9	research	research	NOUN
cana-5719	41	10	on	on	ADP
cana-5719	41	11	the	the	DET
cana-5719	41	12	classification	classification	NOUN
cana-5719	41	13	of	of	ADP
cana-5719	41	14	brain	brain	NOUN
cana-5719	41	15	tumours	tumour	NOUN
cana-5719	41	16	.	.	PUNCT
cana-5719	42	1	the	the	DET
cana-5719	42	2	automatic	automatic	ADJ
cana-5719	42	3	segmentation	segmentation	NOUN
cana-5719	42	4	of	of	ADP
cana-5719	42	5	brain	brain	NOUN
cana-5719	42	6	tumour	tumour	NOUN
cana-5719	42	7	regions	region	NOUN
cana-5719	42	8	in	in	ADP
cana-5719	42	9	mri	mri	NOUN
cana-5719	42	10	images	image	NOUN
cana-5719	42	11	has	have	AUX
cana-5719	42	12	received	receive	VERB
cana-5719	42	13	a	a	DET
cana-5719	42	14	lot	lot	NOUN
cana-5719	42	15	of	of	ADP
cana-5719	42	16	attention	attention	NOUN
cana-5719	42	17	,	,	PUNCT
cana-5719	42	18	according	accord	VERB
cana-5719	42	19	to	to	ADP
cana-5719	42	20	a	a	DET
cana-5719	42	21	review	review	NOUN
cana-5719	42	22	of	of	ADP
cana-5719	42	23	previous	previous	ADJ
cana-5719	42	24	medical	medical	ADJ
cana-5719	42	25	imaging	imaging	NOUN
cana-5719	42	26	studies	study	NOUN
cana-5719	42	27	.	.	PUNCT
cana-5719	43	1	using	use	VERB
cana-5719	43	2	mri	mri	NOUN
cana-5719	43	3	imaging	imaging	NOUN
cana-5719	43	4	techniques	technique	NOUN
cana-5719	43	5	,	,	PUNCT
cana-5719	43	6	a	a	DET
cana-5719	43	7	number	number	NOUN
cana-5719	43	8	of	of	ADP
cana-5719	43	9	researchers	researcher	NOUN
cana-5719	43	10	have	have	AUX
cana-5719	43	11	recently	recently	ADV
cana-5719	43	12	proposed	propose	VERB
cana-5719	43	13	several	several	ADJ
cana-5719	43	14	approaches	approach	NOUN
cana-5719	43	15	for	for	ADP
cana-5719	43	16	identifying	identify	VERB
cana-5719	43	17	and	and	CCONJ
cana-5719	43	18	categorizing	categorize	VERB
cana-5719	43	19	brain	brain	NOUN
cana-5719	43	20	tumours	tumour	NOUN
cana-5719	43	21	.	.	PUNCT
cana-5719	44	1	the	the	DET
cana-5719	44	2	related	relate	VERB
cana-5719	44	3	works	work	NOUN
cana-5719	44	4	are	be	AUX
cana-5719	44	5	shown	show	VERB
cana-5719	44	6	in	in	ADP
cana-5719	44	7	table	table	NOUN
cana-5719	44	8	1	1	NUM
cana-5719	44	9	.	.	PUNCT
cana-5719	45	1	communications	communication	NOUN
cana-5719	45	2	on	on	ADP
cana-5719	45	3	applied	apply	VERB
cana-5719	45	4	nonlinear	nonlinear	ADJ
cana-5719	45	5	analysis	analysis	NOUN
cana-5719	45	6	issn	issn	NOUN
cana-5719	45	7	:	:	PUNCT
cana-5719	45	8	1074	1074	NUM
cana-5719	45	9	-	-	PUNCT
cana-5719	45	10	133x	133x	NUM
cana-5719	45	11	vol	vol	VERB
cana-5719	45	12	32	32	NUM
cana-5719	45	13	no	no	NOUN
cana-5719	45	14	.	.	PUNCT
cana-5719	46	1	10s	10	NOUN
cana-5719	46	2	(	(	PUNCT
cana-5719	46	3	2025	2025	NUM
cana-5719	46	4	)	)	PUNCT
cana-5719	46	5	2782	2782	NUM
cana-5719	47	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-5719	47	2	table	table	NOUN
cana-5719	47	3	1	1	NUM
cana-5719	47	4	.	.	PUNCT
cana-5719	47	5	related	relate	VERB
cana-5719	47	6	works	work	NOUN
cana-5719	47	7	author	author	NOUN
cana-5719	47	8	&	&	CCONJ
cana-5719	47	9	reference	reference	PROPN
cana-5719	47	10	year	year	NOUN
cana-5719	47	11	description	description	NOUN
cana-5719	47	12	bauer	bauer	PROPN
cana-5719	47	13	s	s	PART
cana-5719	47	14	et	et	PROPN
cana-5719	47	15	al	al	PROPN
cana-5719	48	1	[	[	X
cana-5719	48	2	3	3	NUM
cana-5719	48	3	]	]	PUNCT
cana-5719	48	4	2011	2011	NUM
cana-5719	48	5	to	to	PART
cana-5719	48	6	simulate	simulate	VERB
cana-5719	48	7	tumour	tumour	NOUN
cana-5719	48	8	growth	growth	NOUN
cana-5719	48	9	,	,	PUNCT
cana-5719	48	10	discrete	discrete	ADJ
cana-5719	48	11	and	and	CCONJ
cana-5719	48	12	continuous	continuous	ADJ
cana-5719	48	13	approaches	approach	NOUN
cana-5719	48	14	are	be	AUX
cana-5719	48	15	used	use	VERB
cana-5719	48	16	together	together	ADV
cana-5719	48	17	.	.	PUNCT
cana-5719	49	1	liu	liu	PROPN
cana-5719	49	2	j	j	PROPN
cana-5719	49	3	et	et	PROPN
cana-5719	49	4	al	al	PROPN
cana-5719	50	1	[	[	X
cana-5719	50	2	4	4	NUM
cana-5719	50	3	]	]	PUNCT
cana-5719	50	4	2014	2014	NUM
cana-5719	50	5	dl	dl	NOUN
cana-5719	50	6	models	model	NOUN
cana-5719	50	7	are	be	AUX
cana-5719	50	8	used	use	VERB
cana-5719	50	9	to	to	PART
cana-5719	50	10	segment	segment	VERB
cana-5719	50	11	tumour	tumour	NOUN
cana-5719	50	12	locations	location	NOUN
cana-5719	50	13	from	from	ADP
cana-5719	50	14	mri	mri	NOUN
cana-5719	50	15	images	image	NOUN
cana-5719	50	16	in	in	ADP
cana-5719	50	17	a	a	DET
cana-5719	50	18	cnn	cnn	PROPN
cana-5719	50	19	-	-	PUNCT
cana-5719	50	20	based	base	VERB
cana-5719	50	21	multi	multi	ADJ
cana-5719	50	22	-	-	ADJ
cana-5719	50	23	grade	grade	ADJ
cana-5719	50	24	brain	brain	NOUN
cana-5719	50	25	tumour	tumour	NOUN
cana-5719	50	26	classification	classification	NOUN
cana-5719	50	27	.	.	PUNCT
cana-5719	51	1	menze	menze	NOUN
cana-5719	51	2	,	,	PUNCT
cana-5719	51	3	b	b	PROPN
cana-5719	51	4	et	et	NOUN
cana-5719	51	5	al	al	PROPN
cana-5719	52	1	[	[	X
cana-5719	52	2	5	5	NUM
cana-5719	52	3	]	]	SYM
cana-5719	52	4	2015	2015	NUM
cana-5719	52	5	in	in	ADP
cana-5719	52	6	order	order	NOUN
cana-5719	52	7	to	to	PART
cana-5719	52	8	outperform	outperform	VERB
cana-5719	52	9	the	the	DET
cana-5719	52	10	current	current	ADJ
cana-5719	52	11	techniques	technique	NOUN
cana-5719	52	12	,	,	PUNCT
cana-5719	52	13	a	a	DET
cana-5719	52	14	variety	variety	NOUN
cana-5719	52	15	of	of	ADP
cana-5719	52	16	segmentation	segmentation	NOUN
cana-5719	52	17	algorithms	algorithm	NOUN
cana-5719	52	18	have	have	AUX
cana-5719	52	19	been	be	AUX
cana-5719	52	20	combined	combine	VERB
cana-5719	52	21	.	.	PUNCT
cana-5719	53	1	mohsen	mohsen	PROPN
cana-5719	53	2	h	h	PROPN
cana-5719	53	3	et	et	PROPN
cana-5719	53	4	al	al	PROPN
cana-5719	53	5	.	.	PUNCT
cana-5719	54	1	[	[	X
cana-5719	54	2	6	6	NUM
cana-5719	54	3	]	]	SYM
cana-5719	54	4	2017	2017	NUM
cana-5719	54	5	a	a	DET
cana-5719	54	6	dnn	dnn	PROPN
cana-5719	54	7	is	be	AUX
cana-5719	54	8	used	use	VERB
cana-5719	54	9	to	to	PART
cana-5719	54	10	classify	classify	VERB
cana-5719	54	11	brain	brain	NOUN
cana-5719	54	12	tumours	tumour	NOUN
cana-5719	54	13	,	,	PUNCT
cana-5719	54	14	providing	provide	VERB
cana-5719	54	15	a	a	DET
cana-5719	54	16	high	high	ADJ
cana-5719	54	17	degree	degree	NOUN
cana-5719	54	18	of	of	ADP
cana-5719	54	19	precision	precision	NOUN
cana-5719	54	20	.	.	PUNCT
cana-5719	55	1	bakhtyar	bakhtyar	PROPN
cana-5719	55	2	ahmed	ahmed	PROPN
cana-5719	55	3	mohammed	mohammed	PROPN
cana-5719	55	4	et	et	PROPN
cana-5719	55	5	al	al	PROPN
cana-5719	55	6	.	.	PUNCT
cana-5719	56	1	[	[	X
cana-5719	56	2	7	7	NUM
cana-5719	56	3	]	]	SYM
cana-5719	56	4	2021	2021	NUM
cana-5719	56	5	brain	brain	NOUN
cana-5719	56	6	tumour	tumour	NOUN
cana-5719	56	7	diagnosis	diagnosis	NOUN
cana-5719	56	8	is	be	AUX
cana-5719	56	9	accomplished	accomplish	VERB
cana-5719	56	10	by	by	ADP
cana-5719	56	11	a	a	DET
cana-5719	56	12	methodical	methodical	ADJ
cana-5719	56	13	cnn	cnn	PROPN
cana-5719	56	14	-	-	PUNCT
cana-5719	56	15	based	base	VERB
cana-5719	56	16	technique	technique	NOUN
cana-5719	56	17	,	,	PUNCT
cana-5719	56	18	with	with	ADP
cana-5719	56	19	accuracy	accuracy	NOUN
cana-5719	56	20	,	,	PUNCT
cana-5719	56	21	sensitivity	sensitivity	NOUN
cana-5719	56	22	,	,	PUNCT
cana-5719	56	23	and	and	CCONJ
cana-5719	56	24	error	error	NOUN
cana-5719	56	25	rates	rate	NOUN
cana-5719	56	26	assessed	assess	VERB
cana-5719	56	27	.	.	PUNCT
cana-5719	57	1	emrah	emrah	VERB
cana-5719	57	2	irmak	irmak	PROPN
cana-5719	57	3	et	et	PROPN
cana-5719	57	4	al	al	PROPN
cana-5719	57	5	.	.	PUNCT
cana-5719	58	1	[	[	X
cana-5719	58	2	8	8	NUM
cana-5719	58	3	]	]	SYM
cana-5719	58	4	2021	2021	NUM
cana-5719	58	5	in	in	ADP
cana-5719	58	6	order	order	NOUN
cana-5719	58	7	to	to	PART
cana-5719	58	8	facilitate	facilitate	VERB
cana-5719	58	9	early	early	ADJ
cana-5719	58	10	identification	identification	NOUN
cana-5719	58	11	,	,	PUNCT
cana-5719	58	12	this	this	DET
cana-5719	58	13	study	study	NOUN
cana-5719	58	14	aims	aim	VERB
cana-5719	58	15	to	to	PART
cana-5719	58	16	categorise	categorise	VERB
cana-5719	58	17	brain	brain	NOUN
cana-5719	58	18	tumours	tumour	NOUN
cana-5719	58	19	into	into	ADP
cana-5719	58	20	several	several	ADJ
cana-5719	58	21	groups	group	NOUN
cana-5719	58	22	.	.	PUNCT
cana-5719	59	1	md	md	PROPN
cana-5719	59	2	.	.	PROPN
cana-5719	59	3	saikat	saikat	PROPN
cana-5719	59	4	islam	islam	PROPN
cana-5719	59	5	khan	khan	PROPN
cana-5719	59	6	et	et	PROPN
cana-5719	59	7	al	al	PROPN
cana-5719	59	8	.	.	PUNCT
cana-5719	60	1	[	[	X
cana-5719	60	2	9	9	NUM
cana-5719	60	3	]	]	SYM
cana-5719	60	4	2022	2022	NUM
cana-5719	60	5	brain	brain	NOUN
cana-5719	60	6	tumours	tumour	NOUN
cana-5719	60	7	classified	classify	VERB
cana-5719	60	8	as	as	ADP
cana-5719	60	9	binary	binary	ADJ
cana-5719	60	10	or	or	CCONJ
cana-5719	60	11	multiclass	multiclass	NOUN
cana-5719	60	12	can	can	AUX
cana-5719	60	13	be	be	AUX
cana-5719	60	14	identified	identify	VERB
cana-5719	60	15	using	use	VERB
cana-5719	60	16	deep	deep	ADJ
cana-5719	60	17	learning	learning	NOUN
cana-5719	60	18	algorithms	algorithm	NOUN
cana-5719	60	19	.	.	PUNCT
cana-5719	61	1	s.	s.	PROPN
cana-5719	61	2	shanthi	shanthi	PROPN
cana-5719	61	3	et	et	PROPN
cana-5719	61	4	al	al	PROPN
cana-5719	61	5	.	.	PUNCT
cana-5719	62	1	[	[	X
cana-5719	62	2	10	10	NUM
cana-5719	62	3	]	]	SYM
cana-5719	62	4	2022	2022	NUM
cana-5719	62	5	the	the	DET
cana-5719	62	6	two	two	NUM
cana-5719	62	7	stages	stage	NOUN
cana-5719	62	8	of	of	ADP
cana-5719	62	9	the	the	DET
cana-5719	62	10	suggested	suggest	VERB
cana-5719	62	11	method	method	NOUN
cana-5719	62	12	are	be	AUX
cana-5719	62	13	brain	brain	NOUN
cana-5719	62	14	tumour	tumour	NOUN
cana-5719	62	15	categorization	categorization	NOUN
cana-5719	62	16	and	and	CCONJ
cana-5719	62	17	pre	pre	ADJ
cana-5719	62	18	-	-	ADJ
cana-5719	62	19	processing	processing	ADJ
cana-5719	62	20	.	.	PUNCT
cana-5719	63	1	shtwai	shtwai	PROPN
cana-5719	63	2	alsubai	alsubai	VERB
cana-5719	63	3	et	et	PROPN
cana-5719	63	4	al	al	PROPN
cana-5719	63	5	.	.	PUNCT
cana-5719	64	1	[	[	X
cana-5719	64	2	11	11	NUM
cana-5719	64	3	]	]	SYM
cana-5719	64	4	2022	2022	NUM
cana-5719	64	5	presents	present	VERB
cana-5719	64	6	a	a	DET
cana-5719	64	7	hybrid	hybrid	ADJ
cana-5719	64	8	cnn	cnn	NOUN
cana-5719	64	9	-	-	NOUN
cana-5719	64	10	lstm	lstm	PROPN
cana-5719	64	11	for	for	ADP
cana-5719	64	12	mri	mri	NOUN
cana-5719	64	13	-	-	PUNCT
cana-5719	64	14	based	base	VERB
cana-5719	64	15	brain	brain	NOUN
cana-5719	64	16	tumour	tumour	NOUN
cana-5719	64	17	classification	classification	NOUN
cana-5719	64	18	and	and	CCONJ
cana-5719	64	19	prediction	prediction	NOUN
cana-5719	64	20	.	.	PUNCT
cana-5719	65	1	alok	alok	PROPN
cana-5719	65	2	sarkar	sarkar	PROPN
cana-5719	65	3	et	et	PROPN
cana-5719	65	4	al	al	PROPN
cana-5719	65	5	.	.	PUNCT
cana-5719	66	1	[	[	X
cana-5719	66	2	12	12	NUM
cana-5719	66	3	]	]	PUNCT
cana-5719	66	4	2023	2023	NUM
cana-5719	66	5	a	a	DET
cana-5719	66	6	novel	novel	ADJ
cana-5719	66	7	and	and	CCONJ
cana-5719	66	8	efficient	efficient	ADJ
cana-5719	66	9	method	method	NOUN
cana-5719	66	10	based	base	VERB
cana-5719	66	11	on	on	ADP
cana-5719	66	12	the	the	DET
cana-5719	66	13	alexnet	alexnet	ADJ
cana-5719	66	14	cnn	cnn	NOUN
cana-5719	66	15	architecture	architecture	NOUN
cana-5719	66	16	for	for	ADP
cana-5719	66	17	identifying	identify	VERB
cana-5719	66	18	brain	brain	NOUN
cana-5719	66	19	tumours	tumour	NOUN
cana-5719	66	20	from	from	ADP
cana-5719	66	21	mris	mris	PROPN
cana-5719	66	22	.	.	PUNCT
cana-5719	67	1	md	md	PROPN
cana-5719	67	2	.	.	PUNCT
cana-5719	68	1	alamin	alamin	PROPN
cana-5719	68	2	talukder	talukder	PROPN
cana-5719	68	3	et	et	PROPN
cana-5719	68	4	al	al	PROPN
cana-5719	68	5	.	.	PUNCT
cana-5719	69	1	[	[	X
cana-5719	69	2	13	13	NUM
cana-5719	69	3	]	]	SYM
cana-5719	69	4	2023	2023	NUM
cana-5719	69	5	brain	brain	NOUN
cana-5719	69	6	tumour	tumour	NOUN
cana-5719	69	7	classification	classification	NOUN
cana-5719	69	8	made	make	VERB
cana-5719	69	9	possible	possible	ADJ
cana-5719	69	10	by	by	ADP
cana-5719	69	11	a	a	DET
cana-5719	69	12	revolutionary	revolutionary	ADJ
cana-5719	69	13	dp	dp	NOUN
cana-5719	69	14	method	method	NOUN
cana-5719	69	15	that	that	PRON
cana-5719	69	16	makes	make	VERB
cana-5719	69	17	use	use	NOUN
cana-5719	69	18	of	of	ADP
cana-5719	69	19	transfer	transfer	NOUN
cana-5719	69	20	learning	learning	NOUN
cana-5719	69	21	.	.	PUNCT
cana-5719	70	1	saeed	saeed	PROPN
cana-5719	70	2	mohsen	mohsen	PROPN
cana-5719	70	3	et	et	PROPN
cana-5719	70	4	al	al	PROPN
cana-5719	70	5	.	.	PUNCT
cana-5719	71	1	[	[	X
cana-5719	71	2	14	14	NUM
cana-5719	71	3	]	]	SYM
cana-5719	71	4	2023	2023	NUM
cana-5719	71	5	to	to	PART
cana-5719	71	6	differentiate	differentiate	VERB
cana-5719	71	7	between	between	ADP
cana-5719	71	8	gliomas	glioma	NOUN
cana-5719	71	9	and	and	CCONJ
cana-5719	71	10	pituitary	pituitary	ADJ
cana-5719	71	11	tumours	tumour	NOUN
cana-5719	71	12	,	,	PUNCT
cana-5719	71	13	two	two	NUM
cana-5719	71	14	types	type	NOUN
cana-5719	71	15	of	of	ADP
cana-5719	71	16	brain	brain	NOUN
cana-5719	71	17	tumours	tumour	NOUN
cana-5719	71	18	,	,	PUNCT
cana-5719	71	19	resnext10_32×8d	resnext10_32×8d	PROPN
cana-5719	71	20	and	and	CCONJ
cana-5719	71	21	vgg19	vgg19	PROPN
cana-5719	71	22	are	be	AUX
cana-5719	71	23	utilised	utilise	VERB
cana-5719	71	24	.	.	PUNCT
cana-5719	72	1	tahia	tahia	PROPN
cana-5719	72	2	tazin	tazin	VERB
cana-5719	72	3	et	et	PROPN
cana-5719	72	4	al	al	PROPN
cana-5719	72	5	.	.	PUNCT
cana-5719	73	1	[	[	X
cana-5719	73	2	15	15	NUM
cana-5719	73	3	]	]	PUNCT
cana-5719	73	4	2023	2023	NUM
cana-5719	73	5	the	the	DET
cana-5719	73	6	investigation	investigation	NOUN
cana-5719	73	7	of	of	ADP
cana-5719	73	8	cnns	cnn	NOUN
cana-5719	73	9	for	for	ADP
cana-5719	73	10	the	the	DET
cana-5719	73	11	identification	identification	NOUN
cana-5719	73	12	of	of	ADP
cana-5719	73	13	brain	brain	NOUN
cana-5719	73	14	tumours	tumour	NOUN
cana-5719	73	15	from	from	ADP
cana-5719	73	16	x	x	ADJ
cana-5719	73	17	-	-	NOUN
cana-5719	73	18	ray	ray	NOUN
cana-5719	73	19	imaging	imaging	NOUN
cana-5719	73	20	is	be	AUX
cana-5719	73	21	covered	cover	VERB
cana-5719	73	22	in	in	ADP
cana-5719	73	23	this	this	DET
cana-5719	73	24	publication	publication	NOUN
cana-5719	73	25	.	.	PUNCT
cana-5719	74	1	farjana	farjana	PROPN
cana-5719	74	2	parvin	parvin	PROPN
cana-5719	74	3	et	et	PROPN
cana-5719	74	4	al	al	PROPN
cana-5719	74	5	.	.	PUNCT
cana-5719	75	1	[	[	X
cana-5719	75	2	16	16	NUM
cana-5719	75	3	]	]	SYM
cana-5719	75	4	2023	2023	NUM
cana-5719	75	5	it	it	PRON
cana-5719	75	6	extracts	extract	VERB
cana-5719	75	7	both	both	DET
cana-5719	75	8	local	local	ADJ
cana-5719	75	9	and	and	CCONJ
cana-5719	75	10	global	global	ADJ
cana-5719	75	11	information	information	NOUN
cana-5719	75	12	from	from	ADP
cana-5719	75	13	brain	brain	NOUN
cana-5719	75	14	mri	mri	NOUN
cana-5719	75	15	images	image	NOUN
cana-5719	75	16	using	use	VERB
cana-5719	75	17	two	two	NUM
cana-5719	75	18	parallel	parallel	ADJ
cana-5719	75	19	networks	network	NOUN
cana-5719	75	20	..	..	PUNCT
cana-5719	75	21	https://ieeexplore.ieee.org/author/37088545666	https://ieeexplore.ieee.org/author/37088545666	PROPN
cana-5719	75	22	communications	communication	NOUN
cana-5719	75	23	on	on	ADP
cana-5719	75	24	applied	apply	VERB
cana-5719	75	25	nonlinear	nonlinear	ADJ
cana-5719	75	26	analysis	analysis	NOUN
cana-5719	75	27	issn	issn	NOUN
cana-5719	75	28	:	:	PUNCT
cana-5719	75	29	1074	1074	NUM
cana-5719	75	30	-	-	PUNCT
cana-5719	75	31	133x	133x	NUM
cana-5719	75	32	vol	vol	VERB
cana-5719	75	33	32	32	NUM
cana-5719	75	34	no	no	NOUN
cana-5719	75	35	.	.	PUNCT
cana-5719	76	1	10s	10	NOUN
cana-5719	76	2	(	(	PUNCT
cana-5719	76	3	2025	2025	NUM
cana-5719	76	4	)	)	PUNCT
cana-5719	76	5	2783	2783	NUM
cana-5719	76	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5719	77	1	shweta	shweta	PROPN
cana-5719	77	2	suryawanshi	suryawanshi	PROPN
cana-5719	77	3	et	et	PROPN
cana-5719	77	4	al	al	PROPN
cana-5719	77	5	.	.	PUNCT
cana-5719	78	1	[	[	X
cana-5719	78	2	17	17	NUM
cana-5719	78	3	]	]	SYM
cana-5719	78	4	2024	2024	NUM
cana-5719	78	5	the	the	DET
cana-5719	78	6	suggested	suggest	VERB
cana-5719	78	7	approach	approach	NOUN
cana-5719	78	8	makes	make	VERB
cana-5719	78	9	use	use	NOUN
cana-5719	78	10	of	of	ADP
cana-5719	78	11	deep	deep	ADJ
cana-5719	78	12	learning	learning	NOUN
cana-5719	78	13	's	's	PART
cana-5719	78	14	feature	feature	NOUN
cana-5719	78	15	extraction	extraction	NOUN
cana-5719	78	16	capabilities	capability	NOUN
cana-5719	78	17	as	as	ADV
cana-5719	78	18	well	well	ADV
cana-5719	78	19	as	as	ADP
cana-5719	78	20	support	support	NOUN
cana-5719	78	21	vector	vector	NOUN
cana-5719	78	22	machines	machine	NOUN
cana-5719	78	23	'	'	PART
cana-5719	78	24	(	(	PUNCT
cana-5719	78	25	svm	svm	ADJ
cana-5719	78	26	)	)	PUNCT
cana-5719	78	27	classification	classification	NOUN
cana-5719	78	28	flexibility	flexibility	NOUN
cana-5719	78	29	.	.	PUNCT
cana-5719	79	1	daniel	daniel	PROPN
cana-5719	79	2	reyes	reyes	PROPN
cana-5719	79	3	et	et	PROPN
cana-5719	79	4	al	al	PROPN
cana-5719	79	5	.	.	PUNCT
cana-5719	80	1	[	[	X
cana-5719	80	2	18	18	NUM
cana-5719	80	3	]	]	SYM
cana-5719	80	4	2024	2024	NUM
cana-5719	80	5	the	the	DET
cana-5719	80	6	objective	objective	NOUN
cana-5719	80	7	of	of	ADP
cana-5719	80	8	this	this	DET
cana-5719	80	9	work	work	NOUN
cana-5719	80	10	is	be	AUX
cana-5719	80	11	to	to	PART
cana-5719	80	12	assess	assess	VERB
cana-5719	80	13	cnns	cnns	PROPN
cana-5719	80	14	'	'	PART
cana-5719	80	15	efficacy	efficacy	NOUN
cana-5719	80	16	in	in	ADP
cana-5719	80	17	brain	brain	NOUN
cana-5719	80	18	tumour	tumour	NOUN
cana-5719	80	19	classification	classification	NOUN
cana-5719	80	20	.	.	PUNCT
cana-5719	81	1	3	3	X
cana-5719	81	2	.	.	NUM
cana-5719	81	3	proposed	propose	VERB
cana-5719	81	4	method	method	NOUN
cana-5719	81	5	and	and	CCONJ
cana-5719	81	6	materials	material	NOUN
cana-5719	81	7	this	this	DET
cana-5719	81	8	section	section	NOUN
cana-5719	81	9	presents	present	VERB
cana-5719	81	10	different	different	ADJ
cana-5719	81	11	deep	deep	ADJ
cana-5719	81	12	cnn	cnn	PROPN
cana-5719	81	13	topologies	topology	NOUN
cana-5719	81	14	for	for	ADP
cana-5719	81	15	our	our	PRON
cana-5719	81	16	suggested	suggest	VERB
cana-5719	81	17	framework	framework	NOUN
cana-5719	81	18	,	,	PUNCT
cana-5719	81	19	which	which	PRON
cana-5719	81	20	uses	use	VERB
cana-5719	81	21	the	the	DET
cana-5719	81	22	kaggle	kaggle	ADJ
cana-5719	81	23	brain	brain	NOUN
cana-5719	81	24	tumour	tumour	NOUN
cana-5719	81	25	dataset	dataset	NOUN
cana-5719	81	26	[	[	X
cana-5719	81	27	19	19	NUM
cana-5719	81	28	]	]	PUNCT
cana-5719	81	29	for	for	ADP
cana-5719	81	30	brain	brain	NOUN
cana-5719	81	31	tumour	tumour	NOUN
cana-5719	81	32	detection	detection	NOUN
cana-5719	81	33	and	and	CCONJ
cana-5719	81	34	classification	classification	NOUN
cana-5719	81	35	.	.	PUNCT
cana-5719	82	1	we	we	PRON
cana-5719	82	2	evaluate	evaluate	VERB
cana-5719	82	3	and	and	CCONJ
cana-5719	82	4	investigate	investigate	VERB
cana-5719	82	5	well	well	ADV
cana-5719	82	6	-	-	PUNCT
cana-5719	82	7	known	know	VERB
cana-5719	82	8	cnn	cnn	NOUN
cana-5719	82	9	designs	design	NOUN
cana-5719	82	10	,	,	PUNCT
cana-5719	82	11	including	include	VERB
cana-5719	82	12	mobilenet	mobilenet	NOUN
cana-5719	82	13	v2	v2	PROPN
cana-5719	82	14	,	,	PUNCT
cana-5719	82	15	using	use	VERB
cana-5719	82	16	augmented	augment	VERB
cana-5719	82	17	mri	mri	NOUN
cana-5719	82	18	slices	slice	NOUN
cana-5719	82	19	from	from	ADP
cana-5719	82	20	the	the	DET
cana-5719	82	21	dataset	dataset	NOUN
cana-5719	82	22	of	of	ADP
cana-5719	82	23	brain	brain	NOUN
cana-5719	82	24	tumours	tumours	ADJ
cana-5719	82	25	.	.	PUNCT
cana-5719	83	1	to	to	PART
cana-5719	83	2	extract	extract	VERB
cana-5719	83	3	rich	rich	ADJ
cana-5719	83	4	,	,	PUNCT
cana-5719	83	5	visually	visually	ADV
cana-5719	83	6	discriminative	discriminative	VERB
cana-5719	83	7	features	feature	NOUN
cana-5719	83	8	,	,	PUNCT
cana-5719	83	9	transfer	transfer	NOUN
cana-5719	83	10	learning	learning	NOUN
cana-5719	83	11	techniques	technique	NOUN
cana-5719	83	12	are	be	AUX
cana-5719	83	13	applied	apply	VERB
cana-5719	83	14	to	to	ADP
cana-5719	83	15	these	these	DET
cana-5719	83	16	pretrained	pretraine	VERB
cana-5719	83	17	cnn	cnn	PROPN
cana-5719	83	18	models	model	NOUN
cana-5719	83	19	.	.	PUNCT
cana-5719	84	1	ultimately	ultimately	ADV
cana-5719	84	2	,	,	PUNCT
cana-5719	84	3	a	a	DET
cana-5719	84	4	log	log	NOUN
cana-5719	84	5	-	-	PUNCT
cana-5719	84	6	based	base	VERB
cana-5719	84	7	softmax	softmax	NOUN
cana-5719	84	8	layer	layer	NOUN
cana-5719	84	9	handles	handle	VERB
cana-5719	84	10	classification	classification	NOUN
cana-5719	84	11	.	.	PUNCT
cana-5719	85	1	the	the	DET
cana-5719	85	2	ensuing	ensue	VERB
cana-5719	85	3	subsections	subsection	NOUN
cana-5719	85	4	go	go	VERB
cana-5719	85	5	over	over	ADP
cana-5719	85	6	the	the	DET
cana-5719	85	7	main	main	ADJ
cana-5719	85	8	elements	element	NOUN
cana-5719	85	9	of	of	ADP
cana-5719	85	10	the	the	DET
cana-5719	85	11	suggested	suggest	VERB
cana-5719	85	12	framework	framework	NOUN
cana-5719	85	13	.	.	PUNCT
cana-5719	86	1	we	we	PRON
cana-5719	86	2	also	also	ADV
cana-5719	86	3	describe	describe	VERB
cana-5719	86	4	in	in	ADP
cana-5719	86	5	detail	detail	NOUN
cana-5719	86	6	the	the	DET
cana-5719	86	7	measurement	measurement	NOUN
cana-5719	86	8	matrices	matrix	NOUN
cana-5719	86	9	that	that	PRON
cana-5719	86	10	are	be	AUX
cana-5719	86	11	utilised	utilise	VERB
cana-5719	86	12	to	to	PART
cana-5719	86	13	assess	assess	VERB
cana-5719	86	14	our	our	PRON
cana-5719	86	15	proposed	propose	VERB
cana-5719	86	16	system	system	NOUN
cana-5719	86	17	's	's	PART
cana-5719	86	18	performance	performance	NOUN
cana-5719	86	19	.	.	PUNCT
cana-5719	87	1	3.1	3.1	NUM
cana-5719	87	2	dataset	dataset	NOUN
cana-5719	87	3	we	we	PRON
cana-5719	87	4	employed	employ	VERB
cana-5719	87	5	different	different	ADJ
cana-5719	87	6	cnn	cnn	PROPN
cana-5719	87	7	architectures	architecture	NOUN
cana-5719	87	8	to	to	PART
cana-5719	87	9	assess	assess	VERB
cana-5719	87	10	and	and	CCONJ
cana-5719	87	11	analyse	analyse	VERB
cana-5719	87	12	our	our	PRON
cana-5719	87	13	suggested	suggest	VERB
cana-5719	87	14	framework	framework	NOUN
cana-5719	87	15	using	use	VERB
cana-5719	87	16	a	a	DET
cana-5719	87	17	publicly	publicly	ADV
cana-5719	87	18	accessible	accessible	ADJ
cana-5719	87	19	brain	brain	NOUN
cana-5719	87	20	tumour	tumour	NOUN
cana-5719	87	21	dataset	dataset	NOUN
cana-5719	87	22	from	from	ADP
cana-5719	87	23	kaggle	kaggle	PROPN
cana-5719	87	24	.	.	PUNCT
cana-5719	88	1	in	in	ADP
cana-5719	88	2	2017	2017	NUM
cana-5719	88	3	,	,	PUNCT
cana-5719	88	4	cheng	cheng	PROPN
cana-5719	88	5	created	create	VERB
cana-5719	88	6	this	this	DET
cana-5719	88	7	dataset	dataset	NOUN
cana-5719	88	8	,	,	PUNCT
cana-5719	88	9	which	which	PRON
cana-5719	88	10	includes	include	VERB
cana-5719	88	11	2,400	2,400	NUM
cana-5719	88	12	brain	brain	NOUN
cana-5719	88	13	mri	mri	NOUN
cana-5719	88	14	slices	slice	NOUN
cana-5719	88	15	from	from	ADP
cana-5719	88	16	250	250	NUM
cana-5719	88	17	different	different	ADJ
cana-5719	88	18	patients	patient	NOUN
cana-5719	88	19	.	.	PUNCT
cana-5719	89	1	the	the	DET
cana-5719	89	2	information	information	NOUN
cana-5719	89	3	is	be	AUX
cana-5719	89	4	divided	divide	VERB
cana-5719	89	5	into	into	ADP
cana-5719	89	6	two	two	NUM
cana-5719	89	7	groups	group	NOUN
cana-5719	89	8	:	:	PUNCT
cana-5719	89	9	tumours	tumour	NOUN
cana-5719	89	10	and	and	CCONJ
cana-5719	89	11	non	non	NOUN
cana-5719	89	12	-	-	NOUN
cana-5719	89	13	tumors	tumor	NOUN
cana-5719	89	14	.	.	PUNCT
cana-5719	90	1	in	in	ADP
cana-5719	90	2	our	our	PRON
cana-5719	90	3	research	research	NOUN
cana-5719	90	4	,	,	PUNCT
cana-5719	90	5	we	we	PRON
cana-5719	90	6	only	only	ADV
cana-5719	90	7	employ	employ	VERB
cana-5719	90	8	image	image	NOUN
cana-5719	90	9	data	datum	NOUN
cana-5719	90	10	,	,	PUNCT
cana-5719	90	11	splitting	split	VERB
cana-5719	90	12	it	it	PRON
cana-5719	90	13	into	into	ADP
cana-5719	90	14	training	training	NOUN
cana-5719	90	15	,	,	PUNCT
cana-5719	90	16	validation	validation	NOUN
cana-5719	90	17	,	,	PUNCT
cana-5719	90	18	and	and	CCONJ
cana-5719	90	19	test	test	NOUN
cana-5719	90	20	sets	set	NOUN
cana-5719	90	21	,	,	PUNCT
cana-5719	90	22	and	and	CCONJ
cana-5719	90	23	all	all	DET
cana-5719	90	24	cnn	cnn	PROPN
cana-5719	90	25	models	model	NOUN
cana-5719	90	26	take	take	VERB
cana-5719	90	27	images	image	NOUN
cana-5719	90	28	as	as	ADP
cana-5719	90	29	input	input	NOUN
cana-5719	90	30	.	.	PUNCT
cana-5719	91	1	a	a	DET
cana-5719	91	2	bar	bar	NOUN
cana-5719	91	3	graph	graph	NOUN
cana-5719	91	4	showing	show	VERB
cana-5719	91	5	the	the	DET
cana-5719	91	6	types	type	NOUN
cana-5719	91	7	of	of	ADP
cana-5719	91	8	tumors	tumor	NOUN
cana-5719	91	9	is	be	AUX
cana-5719	91	10	shown	show	VERB
cana-5719	91	11	in	in	ADP
cana-5719	91	12	figure	figure	NOUN
cana-5719	91	13	1	1	NUM
cana-5719	91	14	.	.	PUNCT
cana-5719	92	1	fig.1	fig.1	PROPN
cana-5719	92	2	.	.	PUNCT
cana-5719	92	3	types	type	NOUN
cana-5719	92	4	of	of	ADP
cana-5719	92	5	tumors	tumor	NOUN
cana-5719	92	6	a	a	DET
cana-5719	92	7	grid	grid	NOUN
cana-5719	92	8	of	of	ADP
cana-5719	92	9	mri	mri	NOUN
cana-5719	92	10	scans	scan	NOUN
cana-5719	92	11	tumour	tumour	NOUN
cana-5719	92	12	groups	group	NOUN
cana-5719	92	13	is	be	AUX
cana-5719	92	14	shown	show	VERB
cana-5719	92	15	in	in	ADP
cana-5719	92	16	figure	figure	NOUN
cana-5719	92	17	2	2	NUM
cana-5719	92	18	.	.	PUNCT
cana-5719	93	1	the	the	DET
cana-5719	93	2	grid	grid	NOUN
cana-5719	93	3	consists	consist	VERB
cana-5719	93	4	of	of	ADP
cana-5719	93	5	variety	variety	NOUN
cana-5719	93	6	of	of	ADP
cana-5719	93	7	mri	mri	NOUN
cana-5719	93	8	images	image	NOUN
cana-5719	93	9	that	that	PRON
cana-5719	93	10	highlight	highlight	VERB
cana-5719	93	11	different	different	ADJ
cana-5719	93	12	regions	region	NOUN
cana-5719	93	13	of	of	ADP
cana-5719	93	14	the	the	DET
cana-5719	93	15	brain	brain	NOUN
cana-5719	93	16	scans	scan	NOUN
cana-5719	93	17	.	.	PUNCT
cana-5719	94	1	communications	communication	NOUN
cana-5719	94	2	on	on	ADP
cana-5719	94	3	applied	apply	VERB
cana-5719	94	4	nonlinear	nonlinear	ADJ
cana-5719	94	5	analysis	analysis	NOUN
cana-5719	94	6	issn	issn	NOUN
cana-5719	94	7	:	:	PUNCT
cana-5719	94	8	1074	1074	NUM
cana-5719	94	9	-	-	PUNCT
cana-5719	94	10	133x	133x	NUM
cana-5719	94	11	vol	vol	VERB
cana-5719	94	12	32	32	NUM
cana-5719	94	13	no	no	NOUN
cana-5719	94	14	.	.	PUNCT
cana-5719	95	1	10s	10	NOUN
cana-5719	95	2	(	(	PUNCT
cana-5719	95	3	2025	2025	NUM
cana-5719	95	4	)	)	PUNCT
cana-5719	95	5	2784	2784	NUM
cana-5719	95	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5719	96	1	fig.2	fig.2	PROPN
cana-5719	96	2	.	.	PUNCT
cana-5719	96	3	mri	mri	PROPN
cana-5719	96	4	scan	scan	PROPN
cana-5719	96	5	brain	brain	PROPN
cana-5719	96	6	tumour	tumour	PRON
cana-5719	96	7	3.2	3.2	NUM
cana-5719	96	8	proposed	propose	VERB
cana-5719	96	9	cnn	cnn	PROPN
cana-5719	96	10	-	-	PUNCT
cana-5719	96	11	based	base	VERB
cana-5719	96	12	architectures	architecture	NOUN
cana-5719	96	13	a	a	DET
cana-5719	96	14	cnn	cnn	PROPN
cana-5719	96	15	is	be	AUX
cana-5719	96	16	made	make	VERB
cana-5719	96	17	to	to	PART
cana-5719	96	18	automatically	automatically	ADV
cana-5719	96	19	identify	identify	VERB
cana-5719	96	20	important	important	ADJ
cana-5719	96	21	visual	visual	ADJ
cana-5719	96	22	patterns	pattern	NOUN
cana-5719	96	23	from	from	ADP
cana-5719	96	24	unprocessed	unprocessed	ADJ
cana-5719	96	25	picture	picture	NOUN
cana-5719	96	26	pixels	pixel	NOUN
cana-5719	96	27	with	with	ADP
cana-5719	96	28	very	very	ADV
cana-5719	96	29	little	little	ADJ
cana-5719	96	30	preparation	preparation	NOUN
cana-5719	96	31	.	.	PUNCT
cana-5719	97	1	to	to	PART
cana-5719	97	2	attain	attain	VERB
cana-5719	97	3	greater	great	ADJ
cana-5719	97	4	accuracy	accuracy	NOUN
cana-5719	97	5	on	on	ADP
cana-5719	97	6	image	image	NOUN
cana-5719	97	7	datasets	dataset	NOUN
cana-5719	97	8	,	,	PUNCT
cana-5719	97	9	the	the	DET
cana-5719	97	10	suggested	suggest	VERB
cana-5719	97	11	deep	deep	PROPN
cana-5719	97	12	cnn	cnn	PROPN
cana-5719	97	13	architectures	architecture	NOUN
cana-5719	97	14	add	add	VERB
cana-5719	97	15	more	more	ADV
cana-5719	97	16	convolutional	convolutional	ADJ
cana-5719	97	17	layers	layer	NOUN
cana-5719	97	18	and	and	CCONJ
cana-5719	97	19	increase	increase	VERB
cana-5719	97	20	in	in	ADP
cana-5719	97	21	complexity	complexity	NOUN
cana-5719	97	22	.	.	PUNCT
cana-5719	98	1	these	these	DET
cana-5719	98	2	architectures	architecture	NOUN
cana-5719	98	3	,	,	PUNCT
cana-5719	98	4	when	when	SCONJ
cana-5719	98	5	combined	combine	VERB
cana-5719	98	6	with	with	ADP
cana-5719	98	7	transfer	transfer	NOUN
cana-5719	98	8	learning	learning	NOUN
cana-5719	98	9	strategies	strategy	NOUN
cana-5719	98	10	like	like	ADP
cana-5719	98	11	freezing	freeze	VERB
cana-5719	98	12	layers	layer	NOUN
cana-5719	98	13	and	and	CCONJ
cana-5719	98	14	fine	fine	ADV
cana-5719	98	15	-	-	PUNCT
cana-5719	98	16	tuning	tuning	NOUN
cana-5719	98	17	,	,	PUNCT
cana-5719	98	18	have	have	AUX
cana-5719	98	19	greatly	greatly	ADV
cana-5719	98	20	enhanced	enhance	VERB
cana-5719	98	21	image	image	NOUN
cana-5719	98	22	classification	classification	NOUN
cana-5719	98	23	performance	performance	NOUN
cana-5719	98	24	.	.	PUNCT
cana-5719	99	1	in	in	ADP
cana-5719	99	2	this	this	DET
cana-5719	99	3	work	work	NOUN
cana-5719	99	4	,	,	PUNCT
cana-5719	99	5	we	we	PRON
cana-5719	99	6	use	use	VERB
cana-5719	99	7	mri	mri	NOUN
cana-5719	99	8	scans	scan	NOUN
cana-5719	99	9	from	from	ADP
cana-5719	99	10	the	the	DET
cana-5719	99	11	kaggle	kaggle	ADJ
cana-5719	99	12	brain	brain	NOUN
cana-5719	99	13	tumour	tumour	NOUN
cana-5719	99	14	dataset	dataset	NOUN
cana-5719	99	15	to	to	PART
cana-5719	99	16	investigate	investigate	VERB
cana-5719	99	17	and	and	CCONJ
cana-5719	99	18	use	use	VERB
cana-5719	99	19	the	the	DET
cana-5719	99	20	cnn	cnn	NOUN
cana-5719	99	21	architecture	architecture	NOUN
cana-5719	99	22	with	with	ADP
cana-5719	99	23	mobilenet	mobilenet	NOUN
cana-5719	99	24	v2	v2	PROPN
cana-5719	99	25	for	for	ADP
cana-5719	99	26	the	the	DET
cana-5719	99	27	classification	classification	NOUN
cana-5719	99	28	and	and	CCONJ
cana-5719	99	29	identification	identification	NOUN
cana-5719	99	30	of	of	ADP
cana-5719	99	31	brain	brain	NOUN
cana-5719	99	32	tumours	tumour	NOUN
cana-5719	99	33	.	.	PUNCT
cana-5719	100	1	the	the	DET
cana-5719	100	2	figure	figure	NOUN
cana-5719	100	3	3	3	NUM
cana-5719	100	4	represents	represent	VERB
cana-5719	100	5	a	a	DET
cana-5719	100	6	deep	deep	ADJ
cana-5719	100	7	learning	learning	NOUN
cana-5719	100	8	-	-	PUNCT
cana-5719	100	9	based	base	VERB
cana-5719	100	10	tumor	tumor	NOUN
cana-5719	100	11	classification	classification	NOUN
cana-5719	100	12	framework	framework	NOUN
cana-5719	100	13	using	use	VERB
cana-5719	100	14	transfer	transfer	NOUN
cana-5719	100	15	learning	learning	NOUN
cana-5719	100	16	.	.	PUNCT
cana-5719	101	1	the	the	DET
cana-5719	101	2	model	model	NOUN
cana-5719	101	3	receives	receive	VERB
cana-5719	101	4	tumor	tumor	NOUN
cana-5719	101	5	and	and	CCONJ
cana-5719	101	6	non	non	ADJ
cana-5719	101	7	-	-	ADJ
cana-5719	101	8	tumor	tumor	ADJ
cana-5719	101	9	images	image	NOUN
cana-5719	101	10	of	of	ADP
cana-5719	101	11	dimension	dimension	NOUN
cana-5719	101	12	224×224×3	224×224×3	PROPN
cana-5719	101	13	(	(	PUNCT
cana-5719	101	14	rgb	rgb	PROPN
cana-5719	101	15	images	image	NOUN
cana-5719	101	16	)	)	PUNCT
cana-5719	101	17	.	.	PUNCT
cana-5719	102	1	the	the	DET
cana-5719	102	2	images	image	NOUN
cana-5719	102	3	are	be	AUX
cana-5719	102	4	preprocessed	preprocesse	VERB
cana-5719	102	5	and	and	CCONJ
cana-5719	102	6	input	input	NOUN
cana-5719	102	7	to	to	ADP
cana-5719	102	8	a	a	DET
cana-5719	102	9	pre	pre	ADJ
cana-5719	102	10	-	-	ADJ
cana-5719	102	11	trained	train	VERB
cana-5719	102	12	mobilenetv2	mobilenetv2	PROPN
cana-5719	102	13	model	model	NOUN
cana-5719	102	14	.	.	PUNCT
cana-5719	103	1	the	the	DET
cana-5719	103	2	imagenet	imagenet	NOUN
cana-5719	103	3	-	-	PUNCT
cana-5719	103	4	trained	train	VERB
cana-5719	103	5	weights	weight	NOUN
cana-5719	103	6	are	be	AUX
cana-5719	103	7	utilized	utilize	VERB
cana-5719	103	8	in	in	ADP
cana-5719	103	9	transferring	transfer	VERB
cana-5719	103	10	learning	learn	VERB
cana-5719	103	11	to	to	ADP
cana-5719	103	12	this	this	DET
cana-5719	103	13	model	model	NOUN
cana-5719	103	14	for	for	ADP
cana-5719	103	15	extracting	extract	VERB
cana-5719	103	16	features	feature	NOUN
cana-5719	103	17	.	.	PUNCT
cana-5719	104	1	feature	feature	NOUN
cana-5719	104	2	maps	map	NOUN
cana-5719	104	3	are	be	AUX
cana-5719	104	4	extracted	extract	VERB
cana-5719	104	5	using	use	VERB
cana-5719	104	6	the	the	DET
cana-5719	104	7	base	base	NOUN
cana-5719	104	8	model	model	NOUN
cana-5719	104	9	.	.	PUNCT
cana-5719	105	1	global	global	ADJ
cana-5719	105	2	average	average	ADJ
cana-5719	105	3	pooling	pooling	NOUN
cana-5719	105	4	(	(	PUNCT
cana-5719	105	5	gap	gap	NOUN
cana-5719	105	6	2d	2d	NOUN
cana-5719	105	7	)	)	PUNCT
cana-5719	105	8	layer	layer	NOUN
cana-5719	105	9	is	be	AUX
cana-5719	105	10	used	use	VERB
cana-5719	105	11	to	to	ADP
cana-5719	105	12	down	down	ADP
cana-5719	105	13	-	-	PUNCT
cana-5719	105	14	sample	sample	NOUN
cana-5719	105	15	and	and	CCONJ
cana-5719	105	16	produce	produce	VERB
cana-5719	105	17	a	a	DET
cana-5719	105	18	feature	feature	NOUN
cana-5719	105	19	vector	vector	NOUN
cana-5719	105	20	.	.	PUNCT
cana-5719	106	1	the	the	DET
cana-5719	106	2	resulting	result	VERB
cana-5719	106	3	feature	feature	NOUN
cana-5719	106	4	vector	vector	NOUN
cana-5719	106	5	goes	go	VERB
cana-5719	106	6	through	through	ADP
cana-5719	106	7	several	several	ADJ
cana-5719	106	8	dense	dense	ADJ
cana-5719	106	9	layers	layer	NOUN
cana-5719	106	10	(	(	PUNCT
cana-5719	106	11	dls	dls	PROPN
cana-5719	106	12	):	):	PUNCT
cana-5719	106	13	dl-1	dl-1	NOUN
cana-5719	106	14	:	:	SYM
cana-5719	106	15	512	512	NUM
cana-5719	106	16	units	unit	NOUN
cana-5719	106	17	,	,	PUNCT
cana-5719	106	18	dl-2	dl-2	X
cana-5719	106	19	:	:	PUNCT
cana-5719	106	20	128	128	NUM
cana-5719	106	21	units	unit	NOUN
cana-5719	106	22	,	,	PUNCT
cana-5719	106	23	dl-3	dl-3	ADV
cana-5719	106	24	:	:	PUNCT
cana-5719	106	25	64	64	NUM
cana-5719	106	26	units	unit	NOUN
cana-5719	106	27	,	,	PUNCT
cana-5719	106	28	dl-4	dl-4	ADP
cana-5719	106	29	:	:	PUNCT
cana-5719	106	30	32	32	NUM
cana-5719	106	31	units	unit	NOUN
cana-5719	106	32	.	.	PUNCT
cana-5719	107	1	the	the	DET
cana-5719	107	2	output	output	NOUN
cana-5719	107	3	is	be	AUX
cana-5719	107	4	passed	pass	VERB
cana-5719	107	5	through	through	ADP
cana-5719	107	6	a	a	DET
cana-5719	107	7	softmax	softmax	NOUN
cana-5719	107	8	classifier	classifier	NOUN
cana-5719	107	9	for	for	ADP
cana-5719	107	10	final	final	ADJ
cana-5719	107	11	classification	classification	NOUN
cana-5719	107	12	.	.	PUNCT
cana-5719	108	1	the	the	DET
cana-5719	108	2	classifier	classifier	NOUN
cana-5719	108	3	classified	classify	VERB
cana-5719	108	4	tumor	tumor	NOUN
cana-5719	108	5	such	such	ADJ
cana-5719	108	6	as	as	ADP
cana-5719	108	7	gliomas	glioma	NOUN
cana-5719	108	8	,	,	PUNCT
cana-5719	108	9	meningiomas	meningioma	NOUN
cana-5719	108	10	,	,	PUNCT
cana-5719	108	11	or	or	CCONJ
cana-5719	108	12	pituitary	pituitary	ADJ
cana-5719	108	13	tumors	tumor	NOUN
cana-5719	108	14	and	and	CCONJ
cana-5719	108	15	non	non	ADJ
cana-5719	108	16	-	-	NOUN
cana-5719	108	17	tumor	tumor	NOUN
cana-5719	108	18	.	.	PUNCT
cana-5719	109	1	this	this	DET
cana-5719	109	2	model	model	NOUN
cana-5719	109	3	leverages	leverage	NOUN
cana-5719	109	4	transfer	transfer	NOUN
cana-5719	109	5	learning	learn	VERB
cana-5719	109	6	to	to	PART
cana-5719	109	7	enhance	enhance	VERB
cana-5719	109	8	efficiency	efficiency	NOUN
cana-5719	109	9	by	by	ADP
cana-5719	109	10	using	use	VERB
cana-5719	109	11	a	a	DET
cana-5719	109	12	pre	pre	ADJ
cana-5719	109	13	-	-	ADJ
cana-5719	109	14	trained	train	VERB
cana-5719	109	15	network	network	NOUN
cana-5719	109	16	,	,	PUNCT
cana-5719	109	17	reducing	reduce	VERB
cana-5719	109	18	training	training	NOUN
cana-5719	109	19	time	time	NOUN
cana-5719	109	20	while	while	SCONJ
cana-5719	109	21	improving	improve	VERB
cana-5719	109	22	accuracy	accuracy	NOUN
cana-5719	109	23	in	in	ADP
cana-5719	109	24	medical	medical	ADJ
cana-5719	109	25	image	image	NOUN
cana-5719	109	26	classification	classification	NOUN
cana-5719	109	27	.	.	PUNCT
cana-5719	110	1	3.3	3.3	NUM
cana-5719	110	2	mobile	mobile	ADJ
cana-5719	110	3	net	net	NOUN
cana-5719	110	4	v2	v2	NOUN
cana-5719	110	5	in	in	ADP
cana-5719	110	6	order	order	NOUN
cana-5719	110	7	to	to	PART
cana-5719	110	8	improve	improve	VERB
cana-5719	110	9	its	its	PRON
cana-5719	110	10	performance	performance	NOUN
cana-5719	110	11	and	and	CCONJ
cana-5719	110	12	efficiency	efficiency	NOUN
cana-5719	110	13	in	in	ADP
cana-5719	110	14	image	image	NOUN
cana-5719	110	15	classification	classification	NOUN
cana-5719	110	16	task	task	NOUN
cana-5719	110	17	,	,	PUNCT
cana-5719	110	18	mobilenetv2	mobilenetv2	PROPN
cana-5719	110	19	incorporates	incorporate	VERB
cana-5719	110	20	a	a	DET
cana-5719	110	21	number	number	NOUN
cana-5719	110	22	of	of	ADP
cana-5719	110	23	critical	critical	ADJ
cana-5719	110	24	improvements	improvement	NOUN
cana-5719	110	25	.	.	PUNCT
cana-5719	111	1	these	these	DET
cana-5719	111	2	consist	consist	NOUN
cana-5719	111	3	of	of	ADP
cana-5719	111	4	squeeze	squeeze	NOUN
cana-5719	111	5	-	-	PUNCT
cana-5719	111	6	and	and	CCONJ
cana-5719	111	7	-	-	PUNCT
cana-5719	111	8	excitation	excitation	NOUN
cana-5719	111	9	(	(	PUNCT
cana-5719	111	10	se	se	ADJ
cana-5719	111	11	)	)	PUNCT
cana-5719	111	12	blocks	block	NOUN
cana-5719	111	13	,	,	PUNCT
cana-5719	111	14	bottleneck	bottleneck	NOUN
cana-5719	111	15	design	design	NOUN
cana-5719	111	16	,	,	PUNCT
cana-5719	111	17	linear	linear	ADJ
cana-5719	111	18	bottlenecks	bottleneck	NOUN
cana-5719	111	19	,	,	PUNCT
cana-5719	111	20	inverted	inverted	ADJ
cana-5719	111	21	residuals	residual	NOUN
cana-5719	111	22	,	,	PUNCT
cana-5719	111	23	and	and	CCONJ
cana-5719	111	24	depthwise	depthwise	VERB
cana-5719	111	25	separable	separable	ADJ
cana-5719	111	26	convolutions	convolution	NOUN
cana-5719	111	27	.	.	PUNCT
cana-5719	112	1	to	to	PART
cana-5719	112	2	maintain	maintain	VERB
cana-5719	112	3	high	high	ADJ
cana-5719	112	4	accuracy	accuracy	NOUN
cana-5719	112	5	while	while	SCONJ
cana-5719	112	6	lowering	lower	VERB
cana-5719	112	7	the	the	DET
cana-5719	112	8	computing	computing	NOUN
cana-5719	112	9	cost	cost	NOUN
cana-5719	112	10	of	of	ADP
cana-5719	112	11	the	the	DET
cana-5719	112	12	model	model	NOUN
cana-5719	112	13	,	,	PUNCT
cana-5719	112	14	each	each	PRON
cana-5719	112	15	of	of	ADP
cana-5719	112	16	these	these	DET
cana-5719	112	17	qualities	quality	NOUN
cana-5719	112	18	is	be	AUX
cana-5719	112	19	crucial	crucial	ADJ
cana-5719	112	20	.	.	PUNCT
cana-5719	113	1	now	now	ADV
cana-5719	113	2	that	that	SCONJ
cana-5719	113	3	we	we	PRON
cana-5719	113	4	have	have	VERB
cana-5719	113	5	a	a	DET
cana-5719	113	6	firm	firm	ADJ
cana-5719	113	7	grasp	grasp	NOUN
cana-5719	113	8	on	on	ADP
cana-5719	113	9	the	the	DET
cana-5719	113	10	characteristics	characteristic	NOUN
cana-5719	113	11	and	and	CCONJ
cana-5719	113	12	architecture	architecture	NOUN
cana-5719	113	13	of	of	ADP
cana-5719	113	14	mobilenetv2	mobilenetv2	PROPN
cana-5719	113	15	,	,	PUNCT
cana-5719	113	16	as	as	SCONJ
cana-5719	113	17	shown	show	VERB
cana-5719	113	18	in	in	ADP
cana-5719	113	19	figure	figure	NOUN
cana-5719	113	20	4	4	NUM
cana-5719	113	21	,	,	PUNCT
cana-5719	113	22	we	we	PRON
cana-5719	113	23	can	can	AUX
cana-5719	113	24	move	move	VERB
cana-5719	113	25	on	on	ADP
cana-5719	113	26	to	to	ADP
cana-5719	113	27	the	the	DET
cana-5719	113	28	process	process	NOUN
cana-5719	113	29	of	of	ADP
cana-5719	113	30	training	train	VERB
cana-5719	113	31	the	the	DET
cana-5719	113	32	model	model	NOUN
cana-5719	113	33	.	.	PUNCT
cana-5719	114	1	communications	communication	NOUN
cana-5719	114	2	on	on	ADP
cana-5719	114	3	applied	apply	VERB
cana-5719	114	4	nonlinear	nonlinear	ADJ
cana-5719	114	5	analysis	analysis	NOUN
cana-5719	114	6	issn	issn	NOUN
cana-5719	114	7	:	:	PUNCT
cana-5719	114	8	1074	1074	NUM
cana-5719	114	9	-	-	PUNCT
cana-5719	114	10	133x	133x	NUM
cana-5719	114	11	vol	vol	VERB
cana-5719	114	12	32	32	NUM
cana-5719	114	13	no	no	NOUN
cana-5719	114	14	.	.	PUNCT
cana-5719	115	1	10s	10	NOUN
cana-5719	115	2	(	(	PUNCT
cana-5719	115	3	2025	2025	NUM
cana-5719	115	4	)	)	PUNCT
cana-5719	115	5	2785	2785	NUM
cana-5719	115	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5719	116	1	fig.3	fig.3	PROPN
cana-5719	116	2	.	.	PROPN
cana-5719	116	3	proposed	propose	VERB
cana-5719	116	4	cnn	cnn	PROPN
cana-5719	116	5	architecture	architecture	NOUN
cana-5719	116	6	of	of	ADP
cana-5719	116	7	mobile	mobile	ADJ
cana-5719	116	8	net	net	ADJ
cana-5719	116	9	v2	v2	PROPN
cana-5719	116	10	with	with	ADP
cana-5719	116	11	transfer	transfer	NOUN
cana-5719	116	12	learning	learn	VERB
cana-5719	116	13	fig.4	fig.4	PROPN
cana-5719	116	14	.	.	PUNCT
cana-5719	116	15	architecture	architecture	NOUN
cana-5719	116	16	of	of	ADP
cana-5719	116	17	mobilenetv2	mobilenetv2	PROPN
cana-5719	116	18	communications	communication	NOUN
cana-5719	116	19	on	on	ADP
cana-5719	116	20	applied	apply	VERB
cana-5719	116	21	nonlinear	nonlinear	ADJ
cana-5719	116	22	analysis	analysis	NOUN
cana-5719	116	23	issn	issn	NOUN
cana-5719	116	24	:	:	PUNCT
cana-5719	116	25	1074	1074	NUM
cana-5719	116	26	-	-	PUNCT
cana-5719	116	27	133x	133x	NUM
cana-5719	116	28	vol	vol	VERB
cana-5719	116	29	32	32	NUM
cana-5719	116	30	no	no	NOUN
cana-5719	116	31	.	.	PUNCT
cana-5719	117	1	10s	10	NOUN
cana-5719	117	2	(	(	PUNCT
cana-5719	117	3	2025	2025	NUM
cana-5719	117	4	)	)	PUNCT
cana-5719	117	5	2786	2786	NUM
cana-5719	117	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5719	117	7	3.4	3.4	NUM
cana-5719	117	8	transfer	transfer	NOUN
cana-5719	117	9	learning	learning	NOUN
cana-5719	117	10	:	:	PUNCT
cana-5719	117	11	a	a	DET
cana-5719	117	12	deep	deep	ADJ
cana-5719	117	13	learning	learning	NOUN
cana-5719	117	14	method	method	NOUN
cana-5719	117	15	called	call	VERB
cana-5719	117	16	transfer	transfer	NOUN
cana-5719	117	17	learning	learning	NOUN
cana-5719	117	18	uses	use	VERB
cana-5719	117	19	a	a	DET
cana-5719	117	20	model	model	NOUN
cana-5719	117	21	that	that	PRON
cana-5719	117	22	has	have	AUX
cana-5719	117	23	already	already	ADV
cana-5719	117	24	been	be	AUX
cana-5719	117	25	trained	train	VERB
cana-5719	117	26	on	on	ADP
cana-5719	117	27	one	one	NUM
cana-5719	117	28	task	task	NOUN
cana-5719	117	29	as	as	ADP
cana-5719	117	30	a	a	DET
cana-5719	117	31	basis	basis	NOUN
cana-5719	117	32	for	for	ADP
cana-5719	117	33	creating	create	VERB
cana-5719	117	34	a	a	DET
cana-5719	117	35	model	model	NOUN
cana-5719	117	36	on	on	ADP
cana-5719	117	37	another	another	PRON
cana-5719	117	38	,	,	PUNCT
cana-5719	117	39	frequently	frequently	ADV
cana-5719	117	40	related	relate	VERB
cana-5719	117	41	activity	activity	NOUN
cana-5719	117	42	.	.	PUNCT
cana-5719	118	1	this	this	DET
cana-5719	118	2	method	method	NOUN
cana-5719	118	3	works	work	VERB
cana-5719	118	4	particularly	particularly	ADV
cana-5719	118	5	well	well	ADV
cana-5719	118	6	when	when	SCONJ
cana-5719	118	7	there	there	PRON
cana-5719	118	8	is	be	VERB
cana-5719	118	9	little	little	ADJ
cana-5719	118	10	data	datum	NOUN
cana-5719	118	11	for	for	ADP
cana-5719	118	12	the	the	DET
cana-5719	118	13	second	second	ADJ
cana-5719	118	14	task	task	NOUN
cana-5719	118	15	.	.	PUNCT
cana-5719	119	1	the	the	DET
cana-5719	119	2	model	model	NOUN
cana-5719	119	3	can	can	AUX
cana-5719	119	4	learn	learn	VERB
cana-5719	119	5	more	more	ADV
cana-5719	119	6	quickly	quickly	ADV
cana-5719	119	7	and	and	CCONJ
cana-5719	119	8	effectively	effectively	ADV
cana-5719	119	9	on	on	ADP
cana-5719	119	10	the	the	DET
cana-5719	119	11	second	second	ADJ
cana-5719	119	12	challenge	challenge	NOUN
cana-5719	119	13	by	by	ADP
cana-5719	119	14	utilising	utilise	VERB
cana-5719	119	15	the	the	DET
cana-5719	119	16	features	feature	NOUN
cana-5719	119	17	it	it	PRON
cana-5719	119	18	learnt	learn	VERB
cana-5719	119	19	from	from	ADP
cana-5719	119	20	the	the	DET
cana-5719	119	21	previous	previous	ADJ
cana-5719	119	22	task	task	NOUN
cana-5719	119	23	.	.	PUNCT
cana-5719	120	1	additionally	additionally	ADV
cana-5719	120	2	,	,	PUNCT
cana-5719	120	3	because	because	SCONJ
cana-5719	120	4	the	the	DET
cana-5719	120	5	model	model	NOUN
cana-5719	120	6	has	have	AUX
cana-5719	120	7	already	already	ADV
cana-5719	120	8	gained	gain	VERB
cana-5719	120	9	generalisable	generalisable	ADJ
cana-5719	120	10	properties	property	NOUN
cana-5719	120	11	that	that	PRON
cana-5719	120	12	will	will	AUX
cana-5719	120	13	probably	probably	ADV
cana-5719	120	14	be	be	AUX
cana-5719	120	15	helpful	helpful	ADJ
cana-5719	120	16	for	for	ADP
cana-5719	120	17	the	the	DET
cana-5719	120	18	new	new	ADJ
cana-5719	120	19	job	job	NOUN
cana-5719	120	20	,	,	PUNCT
cana-5719	120	21	transfer	transfer	NOUN
cana-5719	120	22	learning	learning	NOUN
cana-5719	120	23	helps	help	VERB
cana-5719	120	24	lower	low	ADJ
cana-5719	120	25	the	the	DET
cana-5719	120	26	danger	danger	NOUN
cana-5719	120	27	of	of	ADP
cana-5719	120	28	overfitting	overfitte	VERB
cana-5719	120	29	.	.	PUNCT
cana-5719	121	1	this	this	PRON
cana-5719	121	2	gives	give	VERB
cana-5719	121	3	a	a	DET
cana-5719	121	4	broad	broad	ADJ
cana-5719	121	5	idea	idea	NOUN
cana-5719	121	6	of	of	ADP
cana-5719	121	7	how	how	SCONJ
cana-5719	121	8	transfer	transfer	NOUN
cana-5719	121	9	learning	learning	NOUN
cana-5719	121	10	operates	operate	VERB
cana-5719	121	11	.	.	PUNCT
cana-5719	122	1	fig.5	fig.5	NOUN
cana-5719	122	2	.	.	PUNCT
cana-5719	122	3	process	process	NOUN
cana-5719	122	4	of	of	ADP
cana-5719	122	5	transfer	transfer	NOUN
cana-5719	122	6	learning	learn	VERB
cana-5719	122	7	the	the	DET
cana-5719	122	8	transfer	transfer	NOUN
cana-5719	122	9	learning	learning	NOUN
cana-5719	122	10	process	process	NOUN
cana-5719	122	11	,	,	PUNCT
cana-5719	122	12	which	which	PRON
cana-5719	122	13	involves	involve	VERB
cana-5719	122	14	applying	apply	VERB
cana-5719	122	15	knowledge	knowledge	NOUN
cana-5719	122	16	from	from	ADP
cana-5719	122	17	a	a	DET
cana-5719	122	18	pre	pre	ADJ
cana-5719	122	19	-	-	ADJ
cana-5719	122	20	trained	train	VERB
cana-5719	122	21	model	model	NOUN
cana-5719	122	22	to	to	ADP
cana-5719	122	23	a	a	DET
cana-5719	122	24	new	new	ADJ
cana-5719	122	25	model	model	NOUN
cana-5719	122	26	for	for	ADP
cana-5719	122	27	a	a	DET
cana-5719	122	28	different	different	ADJ
cana-5719	122	29	task	task	NOUN
cana-5719	122	30	,	,	PUNCT
cana-5719	122	31	seems	seem	VERB
cana-5719	122	32	to	to	PART
cana-5719	122	33	be	be	AUX
cana-5719	122	34	illustrated	illustrate	VERB
cana-5719	122	35	in	in	ADP
cana-5719	122	36	figure	figure	NOUN
cana-5719	122	37	5	5	NUM
cana-5719	122	38	.	.	NOUN
cana-5719	122	39	4	4	NUM
cana-5719	122	40	.	.	X
cana-5719	122	41	experimental	experimental	ADJ
cana-5719	122	42	setting	set	VERB
cana-5719	122	43	this	this	DET
cana-5719	122	44	section	section	NOUN
cana-5719	122	45	presents	present	VERB
cana-5719	122	46	a	a	DET
cana-5719	122	47	comprehensive	comprehensive	ADJ
cana-5719	122	48	evaluation	evaluation	NOUN
cana-5719	122	49	process	process	NOUN
cana-5719	122	50	for	for	ADP
cana-5719	122	51	our	our	PRON
cana-5719	122	52	proposed	propose	VERB
cana-5719	122	53	automated	automate	VERB
cana-5719	122	54	method	method	NOUN
cana-5719	122	55	of	of	ADP
cana-5719	122	56	identifying	identify	VERB
cana-5719	122	57	and	and	CCONJ
cana-5719	122	58	classifying	classify	VERB
cana-5719	122	59	brain	brain	NOUN
cana-5719	122	60	tumors	tumor	NOUN
cana-5719	122	61	using	use	VERB
cana-5719	122	62	2,400	2,400	NUM
cana-5719	122	63	images	image	NOUN
cana-5719	122	64	.	.	PUNCT
cana-5719	123	1	4.1	4.1	NUM
cana-5719	123	2	workframe	workframe	NOUN
cana-5719	123	3	of	of	ADP
cana-5719	123	4	the	the	DET
cana-5719	123	5	proposed	propose	VERB
cana-5719	123	6	model	model	NOUN
cana-5719	123	7	mobilenetv2	mobilenetv2	PROPN
cana-5719	123	8	is	be	AUX
cana-5719	123	9	one	one	NUM
cana-5719	123	10	of	of	ADP
cana-5719	123	11	the	the	DET
cana-5719	123	12	two	two	NUM
cana-5719	123	13	pretrained	pretraine	VERB
cana-5719	123	14	cnn	cnn	PROPN
cana-5719	123	15	architectures	architecture	NOUN
cana-5719	123	16	used	use	VERB
cana-5719	123	17	in	in	ADP
cana-5719	123	18	the	the	DET
cana-5719	123	19	suggested	suggest	VERB
cana-5719	123	20	framework	framework	NOUN
cana-5719	123	21	.	.	PUNCT
cana-5719	124	1	three	three	NUM
cana-5719	124	2	primary	primary	ADJ
cana-5719	124	3	steps	step	NOUN
cana-5719	124	4	comprise	comprise	VERB
cana-5719	124	5	the	the	DET
cana-5719	124	6	model	model	NOUN
cana-5719	124	7	's	's	PART
cana-5719	124	8	workflow	workflow	NOUN
cana-5719	124	9	:	:	PUNCT
cana-5719	124	10	preprocessing	preprocessing	NOUN
cana-5719	124	11	,	,	PUNCT
cana-5719	124	12	feature	feature	NOUN
cana-5719	124	13	extraction	extraction	NOUN
cana-5719	124	14	,	,	PUNCT
cana-5719	124	15	and	and	CCONJ
cana-5719	124	16	classification	classification	NOUN
cana-5719	124	17	/	/	SYM
cana-5719	124	18	detection	detection	NOUN
cana-5719	124	19	.	.	PUNCT
cana-5719	125	1	using	use	VERB
cana-5719	125	2	a	a	DET
cana-5719	125	3	contrast	contrast	NOUN
cana-5719	125	4	stretching	stretch	VERB
cana-5719	125	5	approach	approach	NOUN
cana-5719	125	6	,	,	PUNCT
cana-5719	125	7	mri	mri	NOUN
cana-5719	125	8	pictures	picture	NOUN
cana-5719	125	9	are	be	AUX
cana-5719	125	10	improved	improve	VERB
cana-5719	125	11	in	in	ADP
cana-5719	125	12	the	the	DET
cana-5719	125	13	first	first	ADJ
cana-5719	125	14	phase	phase	NOUN
cana-5719	125	15	.	.	PUNCT
cana-5719	126	1	in	in	ADP
cana-5719	126	2	the	the	DET
cana-5719	126	3	second	second	ADJ
cana-5719	126	4	stage	stage	NOUN
cana-5719	126	5	,	,	PUNCT
cana-5719	126	6	the	the	DET
cana-5719	126	7	target	target	NOUN
cana-5719	126	8	brain	brain	NOUN
cana-5719	126	9	tumour	tumour	NOUN
cana-5719	126	10	dataset	dataset	NOUN
cana-5719	126	11	from	from	ADP
cana-5719	126	12	kaggle	kaggle	PROPN
cana-5719	126	13	is	be	AUX
cana-5719	126	14	used	use	VERB
cana-5719	126	15	to	to	PART
cana-5719	126	16	train	train	VERB
cana-5719	126	17	pretrained	pretraine	VERB
cana-5719	126	18	cnn	cnn	PROPN
cana-5719	126	19	architectures	architecture	NOUN
cana-5719	126	20	based	base	VERB
cana-5719	126	21	on	on	ADP
cana-5719	126	22	mobilenetv2	mobilenetv2	PROPN
cana-5719	126	23	in	in	ADP
cana-5719	126	24	order	order	NOUN
cana-5719	126	25	to	to	PART
cana-5719	126	26	extract	extract	VERB
cana-5719	126	27	distinctive	distinctive	ADJ
cana-5719	126	28	visual	visual	ADJ
cana-5719	126	29	characteristics	characteristic	NOUN
cana-5719	126	30	from	from	ADP
cana-5719	126	31	the	the	DET
cana-5719	126	32	mri	mri	NOUN
cana-5719	126	33	pictures	picture	NOUN
cana-5719	126	34	.	.	PUNCT
cana-5719	127	1	at	at	ADP
cana-5719	127	2	the	the	DET
cana-5719	127	3	last	last	ADJ
cana-5719	127	4	stage	stage	NOUN
cana-5719	127	5	,	,	PUNCT
cana-5719	127	6	mobilenetv2	mobilenetv2	PROPN
cana-5719	127	7	is	be	AUX
cana-5719	127	8	used	use	VERB
cana-5719	127	9	to	to	PART
cana-5719	127	10	apply	apply	VERB
cana-5719	127	11	the	the	DET
cana-5719	127	12	transfer	transfer	NOUN
cana-5719	127	13	learning	learn	VERB
cana-5719	127	14	techniques	technique	NOUN
cana-5719	127	15	of	of	ADP
cana-5719	127	16	freezing	freeze	VERB
cana-5719	127	17	layers	layer	NOUN
cana-5719	127	18	and	and	CCONJ
cana-5719	127	19	fine	fine	ADV
cana-5719	127	20	-	-	PUNCT
cana-5719	127	21	tuning	tune	VERB
cana-5719	127	22	them	they	PRON
cana-5719	127	23	.	.	PUNCT
cana-5719	128	1	while	while	SCONJ
cana-5719	128	2	utilising	utilise	VERB
cana-5719	128	3	the	the	DET
cana-5719	128	4	freeze	freeze	NOUN
cana-5719	128	5	layers	layer	NOUN
cana-5719	128	6	technique	technique	NOUN
cana-5719	128	7	,	,	PUNCT
cana-5719	128	8	automated	automate	VERB
cana-5719	128	9	features	feature	NOUN
cana-5719	128	10	are	be	AUX
cana-5719	128	11	classified	classify	VERB
cana-5719	128	12	using	use	VERB
cana-5719	128	13	a	a	DET
cana-5719	128	14	linear	linear	ADJ
cana-5719	128	15	classifier	classifier	NOUN
cana-5719	128	16	;	;	PUNCT
cana-5719	128	17	while	while	SCONJ
cana-5719	128	18	utilising	utilise	VERB
cana-5719	128	19	the	the	DET
cana-5719	128	20	fine	fine	ADV
cana-5719	128	21	-	-	PUNCT
cana-5719	128	22	tuned	tune	VERB
cana-5719	128	23	layers	layer	NOUN
cana-5719	128	24	strategy	strategy	NOUN
cana-5719	128	25	,	,	PUNCT
cana-5719	128	26	the	the	DET
cana-5719	128	27	log	log	NOUN
cana-5719	128	28	-	-	PUNCT
cana-5719	128	29	softmax	softmax	NOUN
cana-5719	128	30	layer	layer	NOUN
cana-5719	128	31	is	be	AUX
cana-5719	128	32	used	use	VERB
cana-5719	128	33	.	.	PUNCT
cana-5719	129	1	figure	figure	NOUN
cana-5719	129	2	6	6	NUM
cana-5719	129	3	is	be	AUX
cana-5719	129	4	an	an	DET
cana-5719	129	5	illustration	illustration	NOUN
cana-5719	129	6	of	of	ADP
cana-5719	129	7	the	the	DET
cana-5719	129	8	suggested	suggest	VERB
cana-5719	129	9	model	model	NOUN
cana-5719	129	10	's	's	PART
cana-5719	129	11	framework	framework	NOUN
cana-5719	129	12	.	.	PUNCT
cana-5719	130	1	communications	communication	NOUN
cana-5719	130	2	on	on	ADP
cana-5719	130	3	applied	apply	VERB
cana-5719	130	4	nonlinear	nonlinear	ADJ
cana-5719	130	5	analysis	analysis	NOUN
cana-5719	130	6	issn	issn	NOUN
cana-5719	130	7	:	:	PUNCT
cana-5719	130	8	1074	1074	NUM
cana-5719	130	9	-	-	PUNCT
cana-5719	130	10	133x	133x	NUM
cana-5719	130	11	vol	vol	VERB
cana-5719	130	12	32	32	NUM
cana-5719	130	13	no	no	NOUN
cana-5719	130	14	.	.	PUNCT
cana-5719	131	1	10s	10	NOUN
cana-5719	131	2	(	(	PUNCT
cana-5719	131	3	2025	2025	NUM
cana-5719	131	4	)	)	PUNCT
cana-5719	131	5	2787	2787	NUM
cana-5719	131	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5719	131	7	fig.6	fig.6	PROPN
cana-5719	131	8	.	.	PUNCT
cana-5719	132	1	work	work	NOUN
cana-5719	132	2	flow	flow	NOUN
cana-5719	132	3	of	of	ADP
cana-5719	132	4	proposed	propose	VERB
cana-5719	132	5	model	model	NOUN
cana-5719	132	6	4.2	4.2	NUM
cana-5719	132	7	algorithms	algorithm	NOUN
cana-5719	132	8	for	for	ADP
cana-5719	132	9	proposed	propose	VERB
cana-5719	132	10	model	model	NOUN
cana-5719	132	11	:	:	PUNCT
cana-5719	132	12	two	two	NUM
cana-5719	132	13	algorithms	algorithm	NOUN
cana-5719	132	14	are	be	AUX
cana-5719	132	15	design	design	NOUN
cana-5719	132	16	for	for	ADP
cana-5719	132	17	the	the	DET
cana-5719	132	18	proposed	propose	VERB
cana-5719	132	19	model	model	NOUN
cana-5719	132	20	,	,	PUNCT
cana-5719	132	21	which	which	PRON
cana-5719	132	22	are	be	AUX
cana-5719	132	23	mention	mention	NOUN
cana-5719	132	24	below	below	ADV
cana-5719	132	25	.	.	PUNCT
cana-5719	133	1	communications	communication	NOUN
cana-5719	133	2	on	on	ADP
cana-5719	133	3	applied	apply	VERB
cana-5719	133	4	nonlinear	nonlinear	ADJ
cana-5719	133	5	analysis	analysis	NOUN
cana-5719	133	6	issn	issn	NOUN
cana-5719	133	7	:	:	PUNCT
cana-5719	133	8	1074	1074	NUM
cana-5719	133	9	-	-	PUNCT
cana-5719	133	10	133x	133x	NUM
cana-5719	133	11	vol	vol	VERB
cana-5719	133	12	32	32	NUM
cana-5719	133	13	no	no	NOUN
cana-5719	133	14	.	.	PUNCT
cana-5719	134	1	10s	10	NOUN
cana-5719	134	2	(	(	PUNCT
cana-5719	134	3	2025	2025	NUM
cana-5719	134	4	)	)	PUNCT
cana-5719	134	5	2788	2788	NUM
cana-5719	134	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5719	134	7	algorithm	algorithm	NOUN
cana-5719	134	8	1	1	NUM
cana-5719	134	9	:	:	PUNCT
cana-5719	134	10	tumor	tumor	NOUN
cana-5719	134	11	and	and	CCONJ
cana-5719	134	12	non	non	ADJ
cana-5719	134	13	-	-	ADJ
cana-5719	134	14	tumor	tumor	ADJ
cana-5719	134	15	classification	classification	NOUN
cana-5719	134	16	using	use	VERB
cana-5719	134	17	mobilenetv2	mobilenetv2	PROPN
cana-5719	134	18	begin	begin	VERB
cana-5719	134	19	:	:	PUNCT
cana-5719	134	20	step-1	step-1	NUM
cana-5719	134	21	:	:	PUNCT
cana-5719	134	22	//	//	NUM
cana-5719	134	23	load	load	NOUN
cana-5719	134	24	mobilenetv2	mobilenetv2	PROPN
cana-5719	135	1	pre	pre	VERB
cana-5719	135	2	-	-	ADJ
cana-5719	135	3	trained	train	VERB
cana-5719	135	4	weights	weight	NOUN
cana-5719	135	5	mobilenet_model	mobilenet_model	PROPN
cana-5719	135	6	←	←	PROPN
cana-5719	135	7	mobilenetv2.load_pretrained_model	mobilenetv2.load_pretrained_model	PROPN
cana-5719	135	8	(	(	PUNCT
cana-5719	135	9	)	)	PUNCT
cana-5719	135	10	step-2	step-2	PROPN
cana-5719	135	11	:	:	PUNCT
cana-5719	135	12	//modify	//modify	PUNCT
cana-5719	136	1	the	the	DET
cana-5719	136	2	top	top	ADJ
cana-5719	136	3	layers	layer	NOUN
cana-5719	136	4	for	for	ADP
cana-5719	136	5	binary	binary	ADJ
cana-5719	136	6	classification	classification	NOUN
cana-5719	136	7	(	(	PUNCT
cana-5719	136	8	tumor	tumor	NOUN
cana-5719	136	9	type	type	NOUN
cana-5719	136	10	and	and	CCONJ
cana-5719	136	11	non	non	ADJ
cana-5719	136	12	-	-	ADJ
cana-5719	136	13	tumor	tumor	ADJ
cana-5719	136	14	)	)	PUNCT
cana-5719	136	15	model	model	NOUN
cana-5719	136	16	←	←	PROPN
cana-5719	136	17	modify_top_layers	modify_top_layer	NOUN
cana-5719	136	18	(	(	PUNCT
cana-5719	136	19	mobilenet_model	mobilenet_model	NOUN
cana-5719	136	20	)	)	PUNCT
cana-5719	136	21	step-3	step-3	NUM
cana-5719	136	22	:	:	PUNCT
cana-5719	136	23	//	//	NUM
cana-5719	136	24	load	load	NOUN
cana-5719	136	25	and	and	CCONJ
cana-5719	136	26	preprocess	preprocess	NOUN
cana-5719	136	27	the	the	DET
cana-5719	136	28	dataset	dataset	NOUN
cana-5719	136	29	train_data	train_data	PROPN
cana-5719	136	30	,	,	PUNCT
cana-5719	136	31	test_data	test_data	ADJ
cana-5719	136	32	←	←	PROPN
cana-5719	136	33	load_and_preprocess_data	load_and_preprocess_data	PROPN
cana-5719	136	34	(	(	PUNCT
cana-5719	136	35	)	)	PUNCT
cana-5719	136	36	step-4	step-4	NUM
cana-5719	136	37	:	:	PUNCT
cana-5719	136	38	//define	//define	PUNCT
cana-5719	136	39	training	training	NOUN
cana-5719	136	40	parameters	parameter	NOUN
cana-5719	136	41	epochs	epoch	NOUN
cana-5719	136	42	←	←	PROPN
cana-5719	136	43	150	150	NUM
cana-5719	136	44	batch_size	batch_size	NOUN
cana-5719	136	45	←20	←20	ADP
cana-5719	136	46	step-5	step-5	NUM
cana-5719	136	47	:	:	PUNCT
cana-5719	136	48	//	//	NUM
cana-5719	136	49	compile	compile	NOUN
cana-5719	136	50	the	the	DET
cana-5719	136	51	model	model	NOUN
cana-5719	136	52	model.compile	model.compile	PROPN
cana-5719	136	53	(	(	PUNCT
cana-5719	136	54	optimizer='adam	optimizer='adam	NOUN
cana-5719	136	55	'	'	PART
cana-5719	136	56	,	,	PUNCT
cana-5719	136	57	loss='binary_crossentropy	loss='binary_crossentropy	NOUN
cana-5719	136	58	'	'	PUNCT
cana-5719	136	59	,	,	PUNCT
cana-5719	136	60	metrics=['accuracy	metrics=['accuracy	NOUN
cana-5719	136	61	'	'	PUNCT
cana-5719	136	62	]	]	PUNCT
cana-5719	136	63	)	)	PUNCT
cana-5719	137	1	step-6	step-6	NUM
cana-5719	137	2	:	:	PUNCT
cana-5719	137	3	//	//	NUM
cana-5719	137	4	train	train	VERB
cana-5719	137	5	the	the	DET
cana-5719	137	6	model	model	NOUN
cana-5719	137	7	using	use	VERB
cana-5719	137	8	transfer	transfer	NOUN
cana-5719	137	9	learning	learn	VERB
cana-5719	137	10	model.fit	model.fit	PRON
cana-5719	137	11	(	(	PUNCT
cana-5719	137	12	train_data	train_data	PROPN
cana-5719	137	13	,	,	PUNCT
cana-5719	137	14	epochs	epoch	NOUN
cana-5719	137	15	=	=	NOUN
cana-5719	137	16	epochs	epoch	NOUN
cana-5719	137	17	,	,	PUNCT
cana-5719	137	18	batch_size	batch_size	NOUN
cana-5719	137	19	=	=	SYM
cana-5719	137	20	batch_size	batch_size	NOUN
cana-5719	137	21	,	,	PUNCT
cana-5719	137	22	validation_data	validation_data	NOUN
cana-5719	137	23	=	=	SYM
cana-5719	137	24	test_data	test_data	NOUN
cana-5719	137	25	)	)	PUNCT
cana-5719	137	26	step-7	step-7	NUM
cana-5719	137	27	:	:	PUNCT
cana-5719	137	28	//evaluate	//evaluate	PUNCT
cana-5719	137	29	the	the	DET
cana-5719	137	30	model	model	NOUN
cana-5719	137	31	on	on	ADP
cana-5719	137	32	the	the	DET
cana-5719	137	33	test	test	NOUN
cana-5719	137	34	set	set	VERB
cana-5719	137	35	accuracy	accuracy	NOUN
cana-5719	137	36	=	=	SYM
cana-5719	137	37	model.evaluate(test_data	model.evaluate(test_data	PROPN
cana-5719	137	38	)	)	PUNCT
cana-5719	137	39	step-8	step-8	NUM
cana-5719	137	40	:	:	PUNCT
cana-5719	137	41	//	//	PUNCT
cana-5719	137	42	save	save	VERB
cana-5719	137	43	the	the	DET
cana-5719	137	44	trained	train	VERB
cana-5719	137	45	model	model	NOUN
cana-5719	137	46	model.save	model.save	NOUN
cana-5719	137	47	(	(	PUNCT
cana-5719	137	48	'	'	PUNCT
cana-5719	137	49	tumor_classification_model.h5	tumor_classification_model.h5	NUM
cana-5719	137	50	'	'	PUNCT
cana-5719	137	51	)	)	PUNCT
cana-5719	138	1	step-9	step-9	NUM
cana-5719	138	2	:	:	PUNCT
cana-5719	138	3	make	make	VERB
cana-5719	138	4	predictions	prediction	NOUN
cana-5719	138	5	on	on	ADP
cana-5719	138	6	new	new	ADJ
cana-5719	138	7	data	datum	NOUN
cana-5719	138	8	new_data	new_data	NOUN
cana-5719	138	9	=	=	SYM
cana-5719	138	10	preprocess_new_data(new_data	preprocess_new_data(new_data	ADJ
cana-5719	138	11	)	)	PUNCT
cana-5719	138	12	predictions	prediction	NOUN
cana-5719	138	13	=	=	SYM
cana-5719	138	14	model.predict	model.predict	PROPN
cana-5719	138	15	(	(	PUNCT
cana-5719	138	16	new_data	new_data	NOUN
cana-5719	138	17	)	)	PUNCT
cana-5719	138	18	//	//	NOUN
cana-5719	138	19	set	set	VERB
cana-5719	138	20	threshold	threshold	NOUN
cana-5719	138	21	(	(	PUNCT
cana-5719	138	22	tr)←	tr)←	NOUN
cana-5719	138	23	0.5	0.5	NUM
cana-5719	138	24	if	if	SCONJ
cana-5719	138	25	predictions	prediction	NOUN
cana-5719	138	26	>	>	PUNCT
cana-5719	138	27	tr	tr	VERB
cana-5719	138	28	then	then	ADV
cana-5719	138	29	classify	classify	VERB
cana-5719	138	30	as	as	SCONJ
cana-5719	138	31	tumor	tumor	NOUN
cana-5719	138	32	(	(	PUNCT
cana-5719	138	33	gliomas	glioma	NOUN
cana-5719	138	34	,	,	PUNCT
cana-5719	138	35	meningiomas	meningioma	NOUN
cana-5719	138	36	,	,	PUNCT
cana-5719	138	37	&	&	CCONJ
cana-5719	138	38	pituitary	pituitary	ADJ
cana-5719	138	39	)	)	PUNCT
cana-5719	138	40	else	else	ADV
cana-5719	138	41	classify	classify	VERB
cana-5719	138	42	as	as	ADP
cana-5719	138	43	non	non	ADJ
cana-5719	138	44	-	-	ADJ
cana-5719	138	45	tumor	tumor	ADJ
cana-5719	138	46	end	end	NOUN
cana-5719	138	47	if	if	SCONJ
cana-5719	138	48	end	end	VERB
cana-5719	138	49	communications	communication	NOUN
cana-5719	138	50	on	on	ADP
cana-5719	138	51	applied	apply	VERB
cana-5719	138	52	nonlinear	nonlinear	ADJ
cana-5719	138	53	analysis	analysis	NOUN
cana-5719	138	54	issn	issn	NOUN
cana-5719	138	55	:	:	PUNCT
cana-5719	138	56	1074	1074	NUM
cana-5719	138	57	-	-	PUNCT
cana-5719	138	58	133x	133x	NUM
cana-5719	138	59	vol	vol	VERB
cana-5719	138	60	32	32	NUM
cana-5719	138	61	no	no	NOUN
cana-5719	138	62	.	.	PUNCT
cana-5719	139	1	10s	10	NOUN
cana-5719	139	2	(	(	PUNCT
cana-5719	139	3	2025	2025	NUM
cana-5719	139	4	)	)	PUNCT
cana-5719	139	5	2789	2789	NUM
cana-5719	139	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5719	139	7	algorithm	algorithm	NOUN
cana-5719	139	8	2	2	NUM
cana-5719	139	9	:	:	PUNCT
cana-5719	139	10	modify_top_layers	modify_top_layer	NOUN
cana-5719	139	11	(	(	PUNCT
cana-5719	139	12	mobilenet	mobilenet	NOUN
cana-5719	139	13	v2_model	v2_model	PROPN
cana-5719	139	14	)	)	PUNCT
cana-5719	139	15	{	{	PUNCT
cana-5719	139	16	//extract	//extract	VERB
cana-5719	139	17	all	all	DET
cana-5719	139	18	the	the	DET
cana-5719	139	19	layers	layer	NOUN
cana-5719	139	20	from	from	ADP
cana-5719	139	21	mobilenet_model	mobilenet_model	NOUN
cana-5719	139	22	layers←	layers←	PROPN
cana-5719	139	23	mobilenet_model.layers	mobilenet_model.layer	NOUN
cana-5719	139	24	//	//	SYM
cana-5719	139	25	freeze	freeze	VERB
cana-5719	139	26	25	25	NUM
cana-5719	139	27	layers	layer	NOUN
cana-5719	139	28	top	top	ADJ
cana-5719	139	29	←	←	PROPN
cana-5719	139	30	0	0	PUNCT
cana-5719	139	31	do	do	VERB
cana-5719	139	32	{	{	PUNCT
cana-5719	139	33	layers[top].trainable	layers[top].trainable	ADJ
cana-5719	139	34	=	=	ADJ
cana-5719	139	35	false	false	ADJ
cana-5719	139	36	top	top	ADJ
cana-5719	139	37	=	=	SYM
cana-5719	139	38	top+1	top+1	NOUN
cana-5719	139	39	}	}	PUNCT
cana-5719	139	40	while	while	SCONJ
cana-5719	139	41	(	(	PUNCT
cana-5719	139	42	top	top	ADJ
cana-5719	139	43	≤	≤	NUM
cana-5719	139	44	25	25	NUM
cana-5719	139	45	)	)	PUNCT
cana-5719	139	46	//	//	NOUN
cana-5719	140	1	x	x	X
cana-5719	140	2	is	be	AUX
cana-5719	140	3	the	the	DET
cana-5719	140	4	output	output	NOUN
cana-5719	140	5	feature	feature	NOUN
cana-5719	140	6	map	map	NOUN
cana-5719	140	7	from	from	ADP
cana-5719	140	8	mobilenet_model	mobilenet_model	PROPN
cana-5719	140	9	x	x	SYM
cana-5719	140	10	=	=	PUNCT
cana-5719	140	11	mobilenet_model.output	mobilenet_model.output	NOUN
cana-5719	140	12	x	x	X
cana-5719	140	13	=	=	SYM
cana-5719	140	14	layers.globalaveragepooling2d()(x	layers.globalaveragepooling2d()(x	PROPN
cana-5719	140	15	)	)	PUNCT
cana-5719	140	16	x	x	X
cana-5719	141	1	=	=	SYM
cana-5719	141	2	dense(units=512	dense(units=512	X
cana-5719	141	3	,	,	PUNCT
cana-5719	141	4	activation='relu')(x	activation='relu')(x	NOUN
cana-5719	141	5	)	)	PUNCT
cana-5719	141	6	x	x	PUNCT
cana-5719	142	1	=	=	PUNCT
cana-5719	142	2	dense	dense	ADJ
cana-5719	142	3	(	(	PUNCT
cana-5719	142	4	units=128	units=128	NOUN
cana-5719	142	5	,	,	PUNCT
cana-5719	142	6	activation='relu')(x	activation='relu')(x	NOUN
cana-5719	142	7	)	)	PUNCT
cana-5719	142	8	x	x	PUNCT
cana-5719	143	1	=	=	PUNCT
cana-5719	143	2	dense	dense	ADJ
cana-5719	143	3	(	(	PUNCT
cana-5719	143	4	units=64	units=64	ADJ
cana-5719	143	5	,	,	PUNCT
cana-5719	143	6	activation='relu')(x	activation='relu')(x	NOUN
cana-5719	143	7	)	)	PUNCT
cana-5719	143	8	x	x	PUNCT
cana-5719	144	1	=	=	PUNCT
cana-5719	144	2	dense	dense	ADJ
cana-5719	144	3	(	(	PUNCT
cana-5719	144	4	units=32	units=32	NOUN
cana-5719	144	5	,	,	PUNCT
cana-5719	144	6	activation='relu')(x	activation='relu')(x	NOUN
cana-5719	144	7	)	)	PUNCT
cana-5719	144	8	predictions	prediction	NOUN
cana-5719	144	9	=	=	SYM
cana-5719	144	10	layers.dense	layers.dense	X
cana-5719	144	11	(	(	PUNCT
cana-5719	144	12	2	2	NUM
cana-5719	144	13	,	,	PUNCT
cana-5719	144	14	activation='softmax')(x	activation='softmax')(x	NOUN
cana-5719	144	15	)	)	PUNCT
cana-5719	144	16	model	model	NOUN
cana-5719	144	17	=	=	PUNCT
cana-5719	144	18	model(mobilenet_model.input	model(mobilenet_model.input	ADJ
cana-5719	144	19	,	,	PUNCT
cana-5719	144	20	predictions	prediction	NOUN
cana-5719	144	21	)	)	PUNCT
cana-5719	144	22	return	return	NOUN
cana-5719	144	23	(	(	PUNCT
cana-5719	144	24	model	model	NOUN
cana-5719	144	25	)	)	PUNCT
cana-5719	144	26	}	}	PUNCT
cana-5719	144	27	4.3	4.3	NUM
cana-5719	144	28	preprocessing	preprocessing	NOUN
cana-5719	144	29	and	and	CCONJ
cana-5719	144	30	data	datum	NOUN
cana-5719	144	31	augmentation	augmentation	NOUN
cana-5719	144	32	data	datum	NOUN
cana-5719	144	33	cleaning	cleaning	NOUN
cana-5719	144	34	is	be	AUX
cana-5719	144	35	a	a	DET
cana-5719	144	36	step	step	NOUN
cana-5719	144	37	in	in	ADP
cana-5719	144	38	preprocessing	preprocessing	NOUN
cana-5719	144	39	that	that	PRON
cana-5719	144	40	aims	aim	VERB
cana-5719	144	41	to	to	PART
cana-5719	144	42	improve	improve	VERB
cana-5719	144	43	and	and	CCONJ
cana-5719	144	44	refine	refine	VERB
cana-5719	144	45	the	the	DET
cana-5719	144	46	input	input	NOUN
cana-5719	144	47	data	datum	NOUN
cana-5719	144	48	for	for	ADP
cana-5719	144	49	task	task	NOUN
cana-5719	144	50	that	that	PRON
cana-5719	144	51	come	come	VERB
cana-5719	144	52	after	after	ADV
cana-5719	144	53	.	.	PUNCT
cana-5719	145	1	cleaning	clean	VERB
cana-5719	145	2	mri	mri	NOUN
cana-5719	145	3	images	image	NOUN
cana-5719	145	4	is	be	AUX
cana-5719	145	5	the	the	DET
cana-5719	145	6	main	main	ADJ
cana-5719	145	7	goal	goal	NOUN
cana-5719	145	8	of	of	ADP
cana-5719	145	9	medical	medical	ADJ
cana-5719	145	10	image	image	NOUN
cana-5719	145	11	analysis	analysis	NOUN
cana-5719	145	12	.	.	PUNCT
cana-5719	146	1	these	these	DET
cana-5719	146	2	images	image	NOUN
cana-5719	146	3	can	can	AUX
cana-5719	146	4	include	include	VERB
cana-5719	146	5	artefacts	artefact	NOUN
cana-5719	146	6	and	and	CCONJ
cana-5719	146	7	inaccurate	inaccurate	ADJ
cana-5719	146	8	intensity	intensity	NOUN
cana-5719	146	9	levels	level	NOUN
cana-5719	146	10	because	because	SCONJ
cana-5719	146	11	they	they	PRON
cana-5719	146	12	were	be	AUX
cana-5719	146	13	obtained	obtain	VERB
cana-5719	146	14	using	use	VERB
cana-5719	146	15	different	different	ADJ
cana-5719	146	16	modalities	modality	NOUN
cana-5719	146	17	.	.	PUNCT
cana-5719	147	1	different	different	ADJ
cana-5719	147	2	dl	dl	PROPN
cana-5719	147	3	algorithms	algorithm	NOUN
cana-5719	147	4	are	be	AUX
cana-5719	147	5	used	use	VERB
cana-5719	147	6	to	to	PART
cana-5719	147	7	improve	improve	VERB
cana-5719	147	8	the	the	DET
cana-5719	147	9	contrast	contrast	NOUN
cana-5719	147	10	of	of	ADP
cana-5719	147	11	mri	mri	NOUN
cana-5719	147	12	images	image	NOUN
cana-5719	147	13	in	in	ADP
cana-5719	147	14	order	order	NOUN
cana-5719	147	15	to	to	PART
cana-5719	147	16	address	address	VERB
cana-5719	147	17	this	this	PRON
cana-5719	147	18	.	.	PUNCT
cana-5719	148	1	our	our	PRON
cana-5719	148	2	method	method	NOUN
cana-5719	148	3	produced	produce	VERB
cana-5719	148	4	high	high	ADJ
cana-5719	148	5	-	-	PUNCT
cana-5719	148	6	resolution	resolution	NOUN
cana-5719	148	7	,	,	PUNCT
cana-5719	148	8	contrast	contrast	NOUN
cana-5719	148	9	-	-	PUNCT
cana-5719	148	10	enhanced	enhance	VERB
cana-5719	148	11	images	image	NOUN
cana-5719	148	12	by	by	ADP
cana-5719	148	13	preprocessing	preprocesse	VERB
cana-5719	148	14	using	use	VERB
cana-5719	148	15	the	the	DET
cana-5719	148	16	contrast	contrast	NOUN
cana-5719	148	17	stretching	stretch	VERB
cana-5719	148	18	algorithm	algorithm	NOUN
cana-5719	148	19	.	.	PUNCT
cana-5719	149	1	various	various	ADJ
cana-5719	149	2	image	image	NOUN
cana-5719	149	3	variations	variation	NOUN
cana-5719	149	4	were	be	AUX
cana-5719	149	5	created	create	VERB
cana-5719	149	6	utilising	utilise	VERB
cana-5719	149	7	conventional	conventional	ADJ
cana-5719	149	8	data	datum	NOUN
cana-5719	149	9	augmentation	augmentation	NOUN
cana-5719	149	10	techniques	technique	NOUN
cana-5719	149	11	,	,	PUNCT
cana-5719	149	12	which	which	PRON
cana-5719	149	13	help	help	VERB
cana-5719	149	14	reduce	reduce	VERB
cana-5719	149	15	overfitting	overfitting	NOUN
cana-5719	149	16	during	during	ADP
cana-5719	149	17	cnn	cnn	PROPN
cana-5719	149	18	training	training	NOUN
cana-5719	149	19	.	.	PUNCT
cana-5719	150	1	rotations	rotation	NOUN
cana-5719	150	2	and	and	CCONJ
cana-5719	150	3	flipping	flipping	NOUN
cana-5719	150	4	were	be	AUX
cana-5719	150	5	two	two	NUM
cana-5719	150	6	of	of	ADP
cana-5719	150	7	the	the	DET
cana-5719	150	8	augmentation	augmentation	NOUN
cana-5719	150	9	techniques	technique	NOUN
cana-5719	150	10	we	we	PRON
cana-5719	150	11	used	use	VERB
cana-5719	150	12	to	to	PART
cana-5719	150	13	expand	expand	VERB
cana-5719	150	14	the	the	DET
cana-5719	150	15	training	training	NOUN
cana-5719	150	16	dataset	dataset	NOUN
cana-5719	150	17	and	and	CCONJ
cana-5719	150	18	provide	provide	VERB
cana-5719	150	19	the	the	DET
cana-5719	150	20	cnns	cnn	NOUN
cana-5719	150	21	a	a	DET
cana-5719	150	22	bigger	big	ADJ
cana-5719	150	23	input	input	NOUN
cana-5719	150	24	area	area	NOUN
cana-5719	150	25	.	.	PUNCT
cana-5719	151	1	rotation	rotation	NOUN
cana-5719	151	2	is	be	AUX
cana-5719	151	3	a	a	DET
cana-5719	151	4	basic	basic	ADJ
cana-5719	151	5	augmentation	augmentation	NOUN
cana-5719	151	6	technique	technique	NOUN
cana-5719	151	7	that	that	PRON
cana-5719	151	8	involves	involve	VERB
cana-5719	151	9	rotating	rotate	VERB
cana-5719	151	10	input	input	NOUN
cana-5719	151	11	communications	communication	NOUN
cana-5719	151	12	on	on	ADP
cana-5719	151	13	applied	apply	VERB
cana-5719	151	14	nonlinear	nonlinear	ADJ
cana-5719	151	15	analysis	analysis	NOUN
cana-5719	151	16	issn	issn	NOUN
cana-5719	151	17	:	:	PUNCT
cana-5719	151	18	1074	1074	NUM
cana-5719	151	19	-	-	PUNCT
cana-5719	151	20	133x	133x	NUM
cana-5719	151	21	vol	vol	VERB
cana-5719	151	22	32	32	NUM
cana-5719	151	23	no	no	NOUN
cana-5719	151	24	.	.	PUNCT
cana-5719	152	1	10s	10	NOUN
cana-5719	152	2	(	(	PUNCT
cana-5719	152	3	2025	2025	NUM
cana-5719	152	4	)	)	PUNCT
cana-5719	152	5	2790	2790	NUM
cana-5719	152	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5719	152	7	images	image	NOUN
cana-5719	152	8	at	at	ADP
cana-5719	152	9	various	various	ADJ
cana-5719	152	10	angles	angle	NOUN
cana-5719	152	11	,	,	PUNCT
cana-5719	152	12	including	include	VERB
cana-5719	152	13	90	90	NUM
cana-5719	152	14	°	°	NOUN
cana-5719	152	15	,	,	PUNCT
cana-5719	152	16	180	180	NUM
cana-5719	152	17	°	°	NOUN
cana-5719	152	18	,	,	PUNCT
cana-5719	152	19	and	and	CCONJ
cana-5719	152	20	270	270	NUM
cana-5719	152	21	°	°	NOUN
cana-5719	152	22	.	.	PUNCT
cana-5719	153	1	furthermore	furthermore	ADV
cana-5719	153	2	,	,	PUNCT
cana-5719	153	3	images	image	NOUN
cana-5719	153	4	were	be	AUX
cana-5719	153	5	flipped	flip	VERB
cana-5719	153	6	,	,	PUNCT
cana-5719	153	7	or	or	CCONJ
cana-5719	153	8	mirrored	mirror	VERB
cana-5719	153	9	along	along	ADP
cana-5719	153	10	both	both	CCONJ
cana-5719	153	11	the	the	DET
cana-5719	153	12	vertical	vertical	ADJ
cana-5719	153	13	and	and	CCONJ
cana-5719	153	14	horizontal	horizontal	ADJ
cana-5719	153	15	axes	axis	NOUN
cana-5719	153	16	.	.	PUNCT
cana-5719	154	1	4.4	4.4	NUM
cana-5719	154	2	cnn	cnn	PROPN
cana-5719	154	3	-	-	PUNCT
cana-5719	154	4	based	base	VERB
cana-5719	154	5	feature	feature	NOUN
cana-5719	154	6	extraction	extraction	NOUN
cana-5719	154	7	the	the	DET
cana-5719	154	8	process	process	NOUN
cana-5719	154	9	of	of	ADP
cana-5719	154	10	extracting	extract	VERB
cana-5719	154	11	discriminative	discriminative	NOUN
cana-5719	154	12	and	and	CCONJ
cana-5719	154	13	visual	visual	ADJ
cana-5719	154	14	features	feature	NOUN
cana-5719	154	15	to	to	PART
cana-5719	154	16	describe	describe	VERB
cana-5719	154	17	the	the	DET
cana-5719	154	18	qualities	quality	NOUN
cana-5719	154	19	of	of	ADP
cana-5719	154	20	the	the	DET
cana-5719	154	21	data	data	NOUN
cana-5719	154	22	is	be	AUX
cana-5719	154	23	the	the	DET
cana-5719	154	24	next	next	ADJ
cana-5719	154	25	stage	stage	NOUN
cana-5719	154	26	after	after	ADP
cana-5719	154	27	data	data	NOUN
cana-5719	154	28	augmentation	augmentation	NOUN
cana-5719	154	29	,	,	PUNCT
cana-5719	154	30	which	which	PRON
cana-5719	154	31	generates	generate	VERB
cana-5719	154	32	a	a	DET
cana-5719	154	33	huge	huge	ADJ
cana-5719	154	34	set	set	NOUN
cana-5719	154	35	of	of	ADP
cana-5719	154	36	image	image	NOUN
cana-5719	154	37	samples	sample	NOUN
cana-5719	154	38	for	for	ADP
cana-5719	154	39	the	the	DET
cana-5719	154	40	training	training	NOUN
cana-5719	154	41	dataset	dataset	NOUN
cana-5719	154	42	.	.	PUNCT
cana-5719	155	1	one	one	NUM
cana-5719	155	2	significant	significant	ADJ
cana-5719	155	3	development	development	NOUN
cana-5719	155	4	in	in	ADP
cana-5719	155	5	cnns	cnns	PROPN
cana-5719	155	6	'	'	PART
cana-5719	155	7	feature	feature	NOUN
cana-5719	155	8	extraction	extraction	NOUN
cana-5719	155	9	techniques	technique	NOUN
cana-5719	155	10	is	be	AUX
cana-5719	155	11	transfer	transfer	NOUN
cana-5719	155	12	learning	learning	NOUN
cana-5719	155	13	,	,	PUNCT
cana-5719	155	14	which	which	PRON
cana-5719	155	15	is	be	AUX
cana-5719	155	16	especially	especially	ADV
cana-5719	155	17	helpful	helpful	ADJ
cana-5719	155	18	in	in	ADP
cana-5719	155	19	situations	situation	NOUN
cana-5719	155	20	like	like	ADP
cana-5719	155	21	this	this	DET
cana-5719	155	22	one	one	NOUN
cana-5719	155	23	where	where	SCONJ
cana-5719	155	24	there	there	PRON
cana-5719	155	25	are	be	VERB
cana-5719	155	26	n't	not	PART
cana-5719	155	27	as	as	ADV
cana-5719	155	28	many	many	ADJ
cana-5719	155	29	dataset	dataset	ADJ
cana-5719	155	30	samples	sample	NOUN
cana-5719	155	31	available	available	ADJ
cana-5719	155	32	.	.	PUNCT
cana-5719	156	1	for	for	ADP
cana-5719	156	2	feature	feature	NOUN
cana-5719	156	3	extraction	extraction	NOUN
cana-5719	156	4	in	in	ADP
cana-5719	156	5	this	this	DET
cana-5719	156	6	study	study	NOUN
cana-5719	156	7	,	,	PUNCT
cana-5719	156	8	we	we	PRON
cana-5719	156	9	used	use	VERB
cana-5719	156	10	the	the	DET
cana-5719	156	11	mobilenet	mobilenet	NOUN
cana-5719	156	12	v2	v2	PROPN
cana-5719	156	13	pretrained	pretraine	VERB
cana-5719	156	14	cnn	cnn	PROPN
cana-5719	156	15	architecture	architecture	NOUN
cana-5719	156	16	.	.	PUNCT
cana-5719	157	1	two	two	NUM
cana-5719	157	2	scenarios	scenario	NOUN
cana-5719	157	3	of	of	ADP
cana-5719	157	4	transfer	transfer	NOUN
cana-5719	157	5	learning	learning	NOUN
cana-5719	157	6	were	be	AUX
cana-5719	157	7	used	use	VERB
cana-5719	157	8	to	to	PART
cana-5719	157	9	extract	extract	VERB
cana-5719	157	10	the	the	DET
cana-5719	157	11	discriminative	discriminative	NOUN
cana-5719	157	12	visual	visual	ADJ
cana-5719	157	13	features	feature	NOUN
cana-5719	157	14	:	:	PUNCT
cana-5719	157	15	freezing	freeze	VERB
cana-5719	157	16	and	and	CCONJ
cana-5719	157	17	fine	fine	ADV
cana-5719	157	18	-	-	PUNCT
cana-5719	157	19	tuning	tuning	NOUN
cana-5719	157	20	.	.	PUNCT
cana-5719	158	1	4.5	4.5	NUM
cana-5719	158	2	classification	classification	NOUN
cana-5719	158	3	and	and	CCONJ
cana-5719	158	4	detection	detection	NOUN
cana-5719	158	5	of	of	ADP
cana-5719	158	6	brain	brain	NOUN
cana-5719	158	7	tumors	tumor	NOUN
cana-5719	158	8	classification	classification	NOUN
cana-5719	158	9	and	and	CCONJ
cana-5719	158	10	detection	detection	NOUN
cana-5719	158	11	on	on	ADP
cana-5719	158	12	the	the	DET
cana-5719	158	13	target	target	NOUN
cana-5719	158	14	dataset	dataset	NOUN
cana-5719	158	15	are	be	AUX
cana-5719	158	16	the	the	DET
cana-5719	158	17	next	next	ADJ
cana-5719	158	18	steps	step	NOUN
cana-5719	158	19	to	to	PART
cana-5719	158	20	take	take	VERB
cana-5719	158	21	after	after	ADP
cana-5719	158	22	employing	employ	VERB
cana-5719	158	23	transfer	transfer	NOUN
cana-5719	158	24	learning	learn	VERB
cana-5719	158	25	algorithms	algorithm	NOUN
cana-5719	158	26	to	to	PART
cana-5719	158	27	successfully	successfully	ADV
cana-5719	158	28	extract	extract	VERB
cana-5719	158	29	significant	significant	ADJ
cana-5719	158	30	visual	visual	ADJ
cana-5719	158	31	features	feature	NOUN
cana-5719	158	32	and	and	CCONJ
cana-5719	158	33	patterns	pattern	NOUN
cana-5719	158	34	.	.	PUNCT
cana-5719	159	1	in	in	ADP
cana-5719	159	2	the	the	DET
cana-5719	159	3	first	first	ADJ
cana-5719	159	4	case	case	NOUN
cana-5719	159	5	,	,	PUNCT
cana-5719	159	6	a	a	DET
cana-5719	159	7	softmax	softmax	NOUN
cana-5719	159	8	layer	layer	NOUN
cana-5719	159	9	is	be	AUX
cana-5719	159	10	used	use	VERB
cana-5719	159	11	to	to	PART
cana-5719	159	12	classify	classify	VERB
cana-5719	159	13	brain	brain	NOUN
cana-5719	159	14	tumours	tumour	NOUN
cana-5719	159	15	,	,	PUNCT
cana-5719	159	16	and	and	CCONJ
cana-5719	159	17	the	the	DET
cana-5719	159	18	number	number	NOUN
cana-5719	159	19	of	of	ADP
cana-5719	159	20	neurones	neurone	NOUN
cana-5719	159	21	is	be	AUX
cana-5719	159	22	set	set	VERB
cana-5719	159	23	up	up	ADP
cana-5719	159	24	for	for	ADP
cana-5719	159	25	two	two	NUM
cana-5719	159	26	classes	class	NOUN
cana-5719	159	27	.	.	PUNCT
cana-5719	160	1	each	each	DET
cana-5719	160	2	cnn	cnn	PROPN
cana-5719	160	3	architecture	architecture	NOUN
cana-5719	160	4	's	's	PART
cana-5719	160	5	acquired	acquire	VERB
cana-5719	160	6	visual	visual	ADJ
cana-5719	160	7	features	feature	NOUN
cana-5719	160	8	are	be	AUX
cana-5719	160	9	adjusted	adjust	VERB
cana-5719	160	10	to	to	PART
cana-5719	160	11	match	match	VERB
cana-5719	160	12	the	the	DET
cana-5719	160	13	target	target	NOUN
cana-5719	160	14	dataset	dataset	VERB
cana-5719	160	15	.	.	PUNCT
cana-5719	161	1	in	in	ADP
cana-5719	161	2	order	order	NOUN
cana-5719	161	3	to	to	PART
cana-5719	161	4	improve	improve	VERB
cana-5719	161	5	performance	performance	NOUN
cana-5719	161	6	,	,	PUNCT
cana-5719	161	7	the	the	DET
cana-5719	161	8	network	network	NOUN
cana-5719	161	9	does	do	AUX
cana-5719	161	10	not	not	PART
cana-5719	161	11	automatically	automatically	ADV
cana-5719	161	12	set	set	VERB
cana-5719	161	13	the	the	DET
cana-5719	161	14	fine	fine	ADV
cana-5719	161	15	-	-	PUNCT
cana-5719	161	16	tuning	tune	VERB
cana-5719	161	17	parameters	parameter	NOUN
cana-5719	161	18	;	;	PUNCT
cana-5719	161	19	instead	instead	ADV
cana-5719	161	20	,	,	PUNCT
cana-5719	161	21	the	the	DET
cana-5719	161	22	parameters	parameter	NOUN
cana-5719	161	23	must	must	AUX
cana-5719	161	24	be	be	AUX
cana-5719	161	25	explicitly	explicitly	ADV
cana-5719	161	26	configured	configure	VERB
cana-5719	161	27	and	and	CCONJ
cana-5719	161	28	optimised	optimise	VERB
cana-5719	161	29	based	base	VERB
cana-5719	161	30	on	on	ADP
cana-5719	161	31	the	the	DET
cana-5719	161	32	training	training	NOUN
cana-5719	161	33	outcomes	outcome	NOUN
cana-5719	161	34	.	.	PUNCT
cana-5719	162	1	based	base	VERB
cana-5719	162	2	on	on	ADP
cana-5719	162	3	validation	validation	NOUN
cana-5719	162	4	criteria	criterion	NOUN
cana-5719	162	5	,	,	PUNCT
cana-5719	162	6	the	the	DET
cana-5719	162	7	ideal	ideal	ADJ
cana-5719	162	8	number	number	NOUN
cana-5719	162	9	of	of	ADP
cana-5719	162	10	epochs	epoch	NOUN
cana-5719	162	11	was	be	AUX
cana-5719	162	12	modified	modify	VERB
cana-5719	162	13	,	,	PUNCT
cana-5719	162	14	with	with	ADP
cana-5719	162	15	a	a	DET
cana-5719	162	16	50	50	NUM
cana-5719	162	17	-	-	PUNCT
cana-5719	162	18	iteration	iteration	NOUN
cana-5719	162	19	validation	validation	NOUN
cana-5719	162	20	frequency	frequency	NOUN
cana-5719	162	21	.	.	PUNCT
cana-5719	163	1	with	with	ADP
cana-5719	163	2	mobilenet	mobilenet	PROPN
cana-5719	163	3	v2	v2	PROPN
cana-5719	163	4	,	,	PUNCT
cana-5719	163	5	the	the	DET
cana-5719	163	6	top	top	ADV
cana-5719	163	7	-	-	PUNCT
cana-5719	163	8	performing	perform	VERB
cana-5719	163	9	network	network	NOUN
cana-5719	163	10	attained	attain	VERB
cana-5719	163	11	a	a	DET
cana-5719	163	12	maximum	maximum	ADJ
cana-5719	163	13	accuracy	accuracy	NOUN
cana-5719	163	14	of	of	ADP
cana-5719	163	15	97.00	97.00	NUM
cana-5719	163	16	%	%	NOUN
cana-5719	163	17	.	.	PUNCT
cana-5719	164	1	the	the	DET
cana-5719	164	2	pretrained	pretraine	VERB
cana-5719	164	3	networks	network	NOUN
cana-5719	164	4	'	'	PART
cana-5719	164	5	frozen	frozen	ADJ
cana-5719	164	6	layers	layer	NOUN
cana-5719	164	7	were	be	AUX
cana-5719	164	8	fed	feed	VERB
cana-5719	164	9	into	into	ADP
cana-5719	164	10	a	a	DET
cana-5719	164	11	softmax	softmax	NOUN
cana-5719	164	12	classifier	classifier	NOUN
cana-5719	164	13	in	in	ADP
cana-5719	164	14	the	the	DET
cana-5719	164	15	second	second	ADJ
cana-5719	164	16	classification	classification	NOUN
cana-5719	164	17	case	case	NOUN
cana-5719	164	18	.	.	PUNCT
cana-5719	165	1	this	this	DET
cana-5719	165	2	method	method	NOUN
cana-5719	165	3	established	establish	VERB
cana-5719	165	4	a	a	DET
cana-5719	165	5	validation	validation	NOUN
cana-5719	165	6	frequency	frequency	NOUN
cana-5719	165	7	of	of	ADP
cana-5719	165	8	every	every	DET
cana-5719	165	9	50	50	NUM
cana-5719	165	10	iterations	iteration	NOUN
cana-5719	165	11	,	,	PUNCT
cana-5719	165	12	frozen	freeze	VERB
cana-5719	165	13	25	25	NUM
cana-5719	165	14	layers	layer	NOUN
cana-5719	165	15	,	,	PUNCT
cana-5719	165	16	and	and	CCONJ
cana-5719	165	17	varied	vary	VERB
cana-5719	165	18	the	the	DET
cana-5719	165	19	number	number	NOUN
cana-5719	165	20	of	of	ADP
cana-5719	165	21	epochs	epoch	NOUN
cana-5719	165	22	according	accord	VERB
cana-5719	165	23	to	to	ADP
cana-5719	165	24	validation	validation	NOUN
cana-5719	165	25	requirements	requirement	NOUN
cana-5719	165	26	.	.	PUNCT
cana-5719	166	1	this	this	DET
cana-5719	166	2	method	method	NOUN
cana-5719	166	3	produced	produce	VERB
cana-5719	166	4	98.33	98.33	NUM
cana-5719	166	5	%	%	NOUN
cana-5719	166	6	accuracy	accuracy	NOUN
cana-5719	166	7	,	,	PUNCT
cana-5719	166	8	which	which	PRON
cana-5719	166	9	is	be	AUX
cana-5719	166	10	a	a	DET
cana-5719	166	11	much	much	ADV
cana-5719	166	12	higher	high	ADJ
cana-5719	166	13	result	result	NOUN
cana-5719	166	14	.	.	PUNCT
cana-5719	167	1	5	5	X
cana-5719	167	2	.	.	X
cana-5719	167	3	comparative	comparative	ADJ
cana-5719	167	4	result	result	NOUN
cana-5719	167	5	analysis	analysis	NOUN
cana-5719	167	6	,	,	PUNCT
cana-5719	167	7	and	and	CCONJ
cana-5719	167	8	discussion	discussion	VERB
cana-5719	167	9	a	a	DET
cana-5719	167	10	publicly	publicly	ADV
cana-5719	167	11	accessible	accessible	ADJ
cana-5719	167	12	dataset	dataset	NOUN
cana-5719	167	13	of	of	ADP
cana-5719	167	14	2,400	2,400	NUM
cana-5719	167	15	images	image	NOUN
cana-5719	167	16	related	relate	VERB
cana-5719	167	17	to	to	ADP
cana-5719	167	18	brain	brain	NOUN
cana-5719	167	19	tumours	tumour	NOUN
cana-5719	167	20	was	be	AUX
cana-5719	167	21	used	use	VERB
cana-5719	167	22	for	for	ADP
cana-5719	167	23	the	the	DET
cana-5719	167	24	experimental	experimental	ADJ
cana-5719	167	25	investigation	investigation	NOUN
cana-5719	167	26	on	on	ADP
cana-5719	167	27	kaggle	kaggle	PROPN
cana-5719	167	28	.	.	PUNCT
cana-5719	168	1	the	the	DET
cana-5719	168	2	cnn	cnn	PROPN
cana-5719	168	3	design	design	NOUN
cana-5719	168	4	makes	make	VERB
cana-5719	168	5	use	use	NOUN
cana-5719	168	6	of	of	ADP
cana-5719	168	7	different	different	ADJ
cana-5719	168	8	numbers	number	NOUN
cana-5719	168	9	of	of	ADP
cana-5719	168	10	features	feature	NOUN
cana-5719	168	11	and	and	CCONJ
cana-5719	168	12	parameters	parameter	NOUN
cana-5719	168	13	were	be	AUX
cana-5719	168	14	changed	change	VERB
cana-5719	168	15	to	to	ADP
cana-5719	168	16	fine	fine	ADJ
cana-5719	168	17	-	-	PUNCT
cana-5719	168	18	tune	tune	NOUN
cana-5719	168	19	the	the	DET
cana-5719	168	20	pretrained	pretraine	VERB
cana-5719	168	21	mobilenetv2	mobilenetv2	PROPN
cana-5719	168	22	model	model	NOUN
cana-5719	168	23	.	.	PUNCT
cana-5719	169	1	during	during	ADP
cana-5719	169	2	these	these	DET
cana-5719	169	3	research	research	NOUN
cana-5719	169	4	,	,	PUNCT
cana-5719	169	5	the	the	DET
cana-5719	169	6	maximum	maximum	ADJ
cana-5719	169	7	accuracy	accuracy	NOUN
cana-5719	169	8	attained	attain	VERB
cana-5719	169	9	was	be	AUX
cana-5719	169	10	98.33	98.33	NUM
cana-5719	169	11	%	%	NOUN
cana-5719	169	12	.	.	PUNCT
cana-5719	170	1	we	we	PRON
cana-5719	170	2	employed	employ	VERB
cana-5719	170	3	a	a	DET
cana-5719	170	4	cross	cross	ADJ
cana-5719	170	5	-	-	ADJ
cana-5719	170	6	validation	validation	ADJ
cana-5719	170	7	strategy	strategy	NOUN
cana-5719	170	8	to	to	PART
cana-5719	170	9	record	record	VERB
cana-5719	170	10	training	training	NOUN
cana-5719	170	11	and	and	CCONJ
cana-5719	170	12	validation	validation	NOUN
cana-5719	170	13	accuracy	accuracy	NOUN
cana-5719	170	14	with	with	ADP
cana-5719	170	15	split	split	ADJ
cana-5719	170	16	ratios	ratio	NOUN
cana-5719	170	17	of	of	ADP
cana-5719	170	18	70:30	70:30	NUM
cana-5719	170	19	,	,	PUNCT
cana-5719	170	20	80:20	80:20	NUM
cana-5719	170	21	,	,	PUNCT
cana-5719	170	22	and	and	CCONJ
cana-5719	170	23	90:10	90:10	NUM
cana-5719	170	24	.	.	PUNCT
cana-5719	171	1	5.1	5.1	NUM
cana-5719	171	2	cross	cross	ADJ
cana-5719	171	3	-	-	ADJ
cana-5719	171	4	validation	validation	ADJ
cana-5719	171	5	strategy	strategy	NOUN
cana-5719	171	6	:	:	PUNCT
cana-5719	171	7	a	a	DET
cana-5719	171	8	statistical	statistical	ADJ
cana-5719	171	9	method	method	NOUN
cana-5719	171	10	called	call	VERB
cana-5719	171	11	cross	cross	ADJ
cana-5719	171	12	-	-	ADJ
cana-5719	171	13	validation	validation	ADJ
cana-5719	171	14	divides	divide	VERB
cana-5719	171	15	the	the	DET
cana-5719	171	16	dataset	dataset	NOUN
cana-5719	171	17	into	into	ADP
cana-5719	171	18	several	several	ADJ
cana-5719	171	19	subgroups	subgroup	NOUN
cana-5719	171	20	in	in	ADP
cana-5719	171	21	order	order	NOUN
cana-5719	171	22	to	to	PART
cana-5719	171	23	assess	assess	VERB
cana-5719	171	24	how	how	SCONJ
cana-5719	171	25	well	well	ADV
cana-5719	171	26	deep	deep	ADJ
cana-5719	171	27	learning	learning	NOUN
cana-5719	171	28	models	model	NOUN
cana-5719	171	29	perform	perform	VERB
cana-5719	171	30	.	.	PUNCT
cana-5719	172	1	by	by	ADP
cana-5719	172	2	ensuring	ensure	VERB
cana-5719	172	3	that	that	SCONJ
cana-5719	172	4	the	the	DET
cana-5719	172	5	model	model	NOUN
cana-5719	172	6	is	be	AUX
cana-5719	172	7	evaluated	evaluate	VERB
cana-5719	172	8	on	on	ADP
cana-5719	172	9	unknown	unknown	ADJ
cana-5719	172	10	data	datum	NOUN
cana-5719	172	11	,	,	PUNCT
cana-5719	172	12	it	it	PRON
cana-5719	172	13	helps	help	VERB
cana-5719	172	14	to	to	PART
cana-5719	172	15	mitigate	mitigate	VERB
cana-5719	172	16	overfitting	overfitte	VERB
cana-5719	172	17	and	and	CCONJ
cana-5719	172	18	underfitting	underfitting	NOUN
cana-5719	172	19	problems	problem	NOUN
cana-5719	172	20	and	and	CCONJ
cana-5719	172	21	evaluate	evaluate	VERB
cana-5719	172	22	the	the	DET
cana-5719	172	23	model	model	NOUN
cana-5719	172	24	's	's	PART
cana-5719	172	25	capacity	capacity	NOUN
cana-5719	172	26	for	for	ADP
cana-5719	172	27	generalisation	generalisation	NOUN
cana-5719	172	28	.	.	PUNCT
cana-5719	173	1	(	(	PUNCT
cana-5719	173	2	a	a	X
cana-5719	173	3	)	)	PUNCT
cana-5719	173	4	split	split	ADJ
cana-5719	173	5	ratio	ratio	NOUN
cana-5719	173	6	70:30"model	70:30"model	NOUN
cana-5719	173	7	accuracy	accuracy	NOUN
cana-5719	173	8	,	,	PUNCT
cana-5719	173	9	"	"	PUNCT
cana-5719	173	10	a	a	DET
cana-5719	173	11	line	line	NOUN
cana-5719	173	12	graph	graph	NOUN
cana-5719	173	13	that	that	PRON
cana-5719	173	14	shows	show	VERB
cana-5719	173	15	the	the	DET
cana-5719	173	16	accuracy	accuracy	NOUN
cana-5719	173	17	of	of	ADP
cana-5719	173	18	training	training	NOUN
cana-5719	173	19	and	and	CCONJ
cana-5719	173	20	validation	validation	NOUN
cana-5719	173	21	data	datum	NOUN
cana-5719	173	22	over	over	ADP
cana-5719	173	23	50	50	NUM
cana-5719	173	24	epochs	epoch	NOUN
cana-5719	173	25	,	,	PUNCT
cana-5719	173	26	is	be	AUX
cana-5719	173	27	the	the	DET
cana-5719	173	28	image	image	NOUN
cana-5719	173	29	that	that	PRON
cana-5719	173	30	was	be	AUX
cana-5719	173	31	uploaded	upload	VERB
cana-5719	173	32	as	as	SCONJ
cana-5719	173	33	shown	show	VERB
cana-5719	173	34	in	in	ADP
cana-5719	173	35	figure	figure	NOUN
cana-5719	173	36	7	7	NUM
cana-5719	173	37	.	.	PUNCT
cana-5719	174	1	the	the	DET
cana-5719	174	2	y	y	NOUN
cana-5719	174	3	-	-	PUNCT
cana-5719	174	4	axis	axis	NOUN
cana-5719	174	5	indicates	indicate	VERB
cana-5719	174	6	accuracy	accuracy	NOUN
cana-5719	174	7	,	,	PUNCT
cana-5719	174	8	and	and	CCONJ
cana-5719	174	9	the	the	DET
cana-5719	174	10	x	x	ADJ
cana-5719	174	11	-	-	ADJ
cana-5719	174	12	axis	axis	NOUN
cana-5719	174	13	shows	show	VERB
cana-5719	174	14	the	the	DET
cana-5719	174	15	number	number	NOUN
cana-5719	174	16	of	of	ADP
cana-5719	174	17	epochs	epoch	NOUN
cana-5719	174	18	.	.	PUNCT
cana-5719	175	1	communications	communication	NOUN
cana-5719	175	2	on	on	ADP
cana-5719	175	3	applied	apply	VERB
cana-5719	175	4	nonlinear	nonlinear	ADJ
cana-5719	175	5	analysis	analysis	NOUN
cana-5719	175	6	issn	issn	NOUN
cana-5719	175	7	:	:	PUNCT
cana-5719	175	8	1074	1074	NUM
cana-5719	175	9	-	-	PUNCT
cana-5719	175	10	133x	133x	NUM
cana-5719	175	11	vol	vol	VERB
cana-5719	175	12	32	32	NUM
cana-5719	175	13	no	no	NOUN
cana-5719	175	14	.	.	PUNCT
cana-5719	176	1	10s	10	NOUN
cana-5719	176	2	(	(	PUNCT
cana-5719	176	3	2025	2025	NUM
cana-5719	176	4	)	)	PUNCT
cana-5719	176	5	2791	2791	NUM
cana-5719	176	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5719	177	1	fig.7	fig.7	ADV
cana-5719	177	2	.	.	PUNCT
cana-5719	178	1	shows	show	VERB
cana-5719	178	2	the	the	DET
cana-5719	178	3	model	model	NOUN
cana-5719	178	4	accuracy	accuracy	NOUN
cana-5719	178	5	and	and	CCONJ
cana-5719	178	6	loss	loss	NOUN
cana-5719	178	7	over	over	ADP
cana-5719	178	8	50	50	NUM
cana-5719	178	9	epochs	epoch	NOUN
cana-5719	178	10	with	with	ADP
cana-5719	178	11	split	split	ADJ
cana-5719	178	12	ratio	ratio	NOUN
cana-5719	178	13	70:30	70:30	NUM
cana-5719	178	14	(	(	PUNCT
cana-5719	178	15	b	b	NOUN
cana-5719	178	16	)	)	PUNCT
cana-5719	178	17	split	split	ADJ
cana-5719	178	18	ratio	ratio	NOUN
cana-5719	178	19	80:20	80:20	NUM
cana-5719	178	20	:	:	PUNCT
cana-5719	178	21	the	the	DET
cana-5719	178	22	graph	graph	NOUN
cana-5719	178	23	depicts	depict	VERB
cana-5719	178	24	the	the	DET
cana-5719	178	25	model	model	NOUN
cana-5719	178	26	's	's	PART
cana-5719	178	27	accuracy	accuracy	NOUN
cana-5719	178	28	during	during	ADP
cana-5719	178	29	training	training	NOUN
cana-5719	178	30	and	and	CCONJ
cana-5719	178	31	validation	validation	NOUN
cana-5719	178	32	over	over	ADP
cana-5719	178	33	50	50	NUM
cana-5719	178	34	epochs	epoch	NOUN
cana-5719	178	35	as	as	SCONJ
cana-5719	178	36	shown	show	VERB
cana-5719	178	37	in	in	ADP
cana-5719	178	38	figure	figure	NOUN
cana-5719	178	39	8	8	NUM
cana-5719	178	40	.	.	PUNCT
cana-5719	179	1	fig.8	fig.8	PROPN
cana-5719	179	2	.	.	PUNCT
cana-5719	180	1	shows	show	VERB
cana-5719	180	2	the	the	DET
cana-5719	180	3	model	model	NOUN
cana-5719	180	4	accuracy	accuracy	NOUN
cana-5719	180	5	and	and	CCONJ
cana-5719	180	6	loss	loss	NOUN
cana-5719	180	7	over	over	ADP
cana-5719	180	8	50	50	NUM
cana-5719	180	9	epochs	epoch	NOUN
cana-5719	180	10	with	with	ADP
cana-5719	180	11	split	split	ADJ
cana-5719	180	12	ratio	ratio	NOUN
cana-5719	180	13	80:20	80:20	NUM
cana-5719	180	14	(	(	PUNCT
cana-5719	180	15	c	c	NOUN
cana-5719	180	16	)	)	PUNCT
cana-5719	180	17	split	split	VERB
cana-5719	180	18	ratio	ratio	NOUN
cana-5719	180	19	90:10	90:10	NUM
cana-5719	180	20	:	:	PUNCT
cana-5719	180	21	this	this	DET
cana-5719	180	22	graph	graph	NOUN
cana-5719	180	23	depicts	depict	VERB
cana-5719	180	24	the	the	DET
cana-5719	180	25	accuracy	accuracy	NOUN
cana-5719	180	26	of	of	ADP
cana-5719	180	27	a	a	DET
cana-5719	180	28	deep	deep	ADJ
cana-5719	180	29	learning	learning	NOUN
cana-5719	180	30	model	model	NOUN
cana-5719	180	31	over	over	ADP
cana-5719	180	32	50	50	NUM
cana-5719	180	33	epochs	epoch	NOUN
cana-5719	180	34	,	,	PUNCT
cana-5719	180	35	showing	show	VERB
cana-5719	180	36	both	both	CCONJ
cana-5719	180	37	the	the	DET
cana-5719	180	38	training	training	NOUN
cana-5719	180	39	and	and	CCONJ
cana-5719	180	40	validation	validation	NOUN
cana-5719	180	41	accuracies	accuracy	NOUN
cana-5719	180	42	as	as	SCONJ
cana-5719	180	43	shown	show	VERB
cana-5719	180	44	in	in	ADP
cana-5719	180	45	figure	figure	NOUN
cana-5719	180	46	9	9	NUM
cana-5719	180	47	.	.	PUNCT
cana-5719	180	48	5.2	5.2	NUM
cana-5719	180	49	comparative	comparative	ADJ
cana-5719	180	50	results	result	NOUN
cana-5719	180	51	:	:	PUNCT
cana-5719	180	52	this	this	DET
cana-5719	180	53	table	table	NOUN
cana-5719	180	54	2	2	NUM
cana-5719	180	55	presents	present	VERB
cana-5719	180	56	the	the	DET
cana-5719	180	57	training	training	NOUN
cana-5719	180	58	and	and	CCONJ
cana-5719	180	59	testing	testing	NOUN
cana-5719	180	60	accuracy	accuracy	NOUN
cana-5719	180	61	,	,	PUNCT
cana-5719	180	62	along	along	ADP
cana-5719	180	63	with	with	ADP
cana-5719	180	64	loss	loss	NOUN
cana-5719	180	65	values	value	NOUN
cana-5719	180	66	,	,	PUNCT
cana-5719	180	67	for	for	ADP
cana-5719	180	68	various	various	ADJ
cana-5719	180	69	cnn	cnn	PROPN
cana-5719	180	70	models	model	NOUN
cana-5719	180	71	using	use	VERB
cana-5719	180	72	a	a	DET
cana-5719	180	73	70:30	70:30	NUM
cana-5719	180	74	train	train	NOUN
cana-5719	180	75	-	-	PUNCT
cana-5719	180	76	test	test	NOUN
cana-5719	180	77	split	split	NOUN
cana-5719	180	78	.	.	PUNCT
cana-5719	181	1	mobilenetv2	mobilenetv2	PROPN
cana-5719	182	1	with	with	ADP
cana-5719	182	2	freeze	freeze	NOUN
cana-5719	182	3	layer	layer	NOUN
cana-5719	182	4	achieves	achieve	VERB
cana-5719	182	5	the	the	DET
cana-5719	182	6	highest	high	ADJ
cana-5719	182	7	training	training	NOUN
cana-5719	182	8	(	(	PUNCT
cana-5719	182	9	98.14	98.14	NUM
cana-5719	182	10	%	%	NOUN
cana-5719	182	11	)	)	PUNCT
cana-5719	182	12	and	and	CCONJ
cana-5719	182	13	test	test	NOUN
cana-5719	182	14	accuracy	accuracy	NOUN
cana-5719	182	15	(	(	PUNCT
cana-5719	182	16	98.09	98.09	NUM
cana-5719	182	17	%	%	NOUN
cana-5719	182	18	)	)	PUNCT
cana-5719	182	19	,	,	PUNCT
cana-5719	182	20	while	while	SCONJ
cana-5719	182	21	maintaining	maintain	VERB
cana-5719	182	22	low	low	ADJ
cana-5719	182	23	training	training	NOUN
cana-5719	182	24	(	(	PUNCT
cana-5719	182	25	0.0160	0.0160	NUM
cana-5719	182	26	)	)	PUNCT
cana-5719	182	27	and	and	CCONJ
cana-5719	182	28	testing	test	VERB
cana-5719	182	29	loss	loss	NOUN
cana-5719	182	30	(	(	PUNCT
cana-5719	182	31	0.0370	0.0370	NUM
cana-5719	182	32	)	)	PUNCT
cana-5719	182	33	.	.	PUNCT
cana-5719	183	1	mobilenetv2	mobilenetv2	PROPN
cana-5719	183	2	without	without	ADP
cana-5719	183	3	freeze	freeze	NOUN
cana-5719	183	4	layer	layer	NOUN
cana-5719	183	5	follows	follow	VERB
cana-5719	183	6	closely	closely	ADV
cana-5719	183	7	,	,	PUNCT
cana-5719	183	8	with	with	ADP
cana-5719	183	9	98.09	98.09	NUM
cana-5719	183	10	%	%	NOUN
cana-5719	183	11	training	training	NOUN
cana-5719	183	12	accuracy	accuracy	NOUN
cana-5719	183	13	and	and	CCONJ
cana-5719	183	14	98.06	98.06	NUM
cana-5719	183	15	%	%	NOUN
cana-5719	183	16	test	test	NOUN
cana-5719	183	17	accuracy	accuracy	NOUN
cana-5719	183	18	,	,	PUNCT
cana-5719	183	19	having	have	VERB
cana-5719	183	20	the	the	DET
cana-5719	183	21	lowest	low	ADJ
cana-5719	183	22	training	training	NOUN
cana-5719	183	23	loss	loss	NOUN
cana-5719	183	24	(	(	PUNCT
cana-5719	183	25	0.0157	0.0157	NUM
cana-5719	183	26	)	)	PUNCT
cana-5719	183	27	.	.	PUNCT
cana-5719	184	1	resnet-50	resnet-50	PROPN
cana-5719	184	2	and	and	CCONJ
cana-5719	184	3	inception	inception	NOUN
cana-5719	184	4	also	also	ADV
cana-5719	184	5	perform	perform	VERB
cana-5719	184	6	well	well	ADV
cana-5719	184	7	,	,	PUNCT
cana-5719	184	8	with	with	ADP
cana-5719	184	9	training	training	NOUN
cana-5719	184	10	accuracy	accuracy	NOUN
cana-5719	184	11	of	of	ADP
cana-5719	184	12	97.51	97.51	NUM
cana-5719	184	13	%	%	NOUN
cana-5719	184	14	and	and	CCONJ
cana-5719	184	15	97.88	97.88	NUM
cana-5719	184	16	%	%	NOUN
cana-5719	184	17	,	,	PUNCT
cana-5719	184	18	and	and	CCONJ
cana-5719	184	19	test	test	NOUN
cana-5719	184	20	accuracy	accuracy	NOUN
cana-5719	184	21	of	of	ADP
cana-5719	184	22	97.10	97.10	NUM
cana-5719	184	23	%	%	NOUN
cana-5719	184	24	and	and	CCONJ
cana-5719	184	25	96.90	96.90	NUM
cana-5719	184	26	%	%	NOUN
cana-5719	184	27	,	,	PUNCT
cana-5719	184	28	respectively	respectively	ADV
cana-5719	184	29	.	.	PUNCT
cana-5719	185	1	meanwhile	meanwhile	ADV
cana-5719	185	2	,	,	PUNCT
cana-5719	185	3	vgg-19	vgg-19	ADV
cana-5719	185	4	and	and	CCONJ
cana-5719	185	5	vgg-16	vgg-16	NOUN
cana-5719	185	6	show	show	VERB
cana-5719	185	7	slightly	slightly	ADV
cana-5719	185	8	lower	low	ADJ
cana-5719	185	9	test	test	NOUN
cana-5719	185	10	communications	communication	NOUN
cana-5719	185	11	on	on	ADP
cana-5719	185	12	applied	apply	VERB
cana-5719	185	13	nonlinear	nonlinear	ADJ
cana-5719	185	14	analysis	analysis	NOUN
cana-5719	185	15	issn	issn	NOUN
cana-5719	185	16	:	:	PUNCT
cana-5719	185	17	1074	1074	NUM
cana-5719	185	18	-	-	PUNCT
cana-5719	185	19	133x	133x	NUM
cana-5719	185	20	vol	vol	VERB
cana-5719	185	21	32	32	NUM
cana-5719	185	22	no	no	NOUN
cana-5719	185	23	.	.	PUNCT
cana-5719	185	24	10s	10	NOUN
cana-5719	185	25	(	(	PUNCT
cana-5719	185	26	2025	2025	NUM
cana-5719	185	27	)	)	PUNCT
cana-5719	185	28	2792	2792	NUM
cana-5719	185	29	https://internationalpubls.com	https://internationalpubls.com	X
cana-5719	185	30	accuracy	accuracy	NOUN
cana-5719	185	31	(	(	PUNCT
cana-5719	185	32	95.65	95.65	NUM
cana-5719	185	33	%	%	NOUN
cana-5719	185	34	and	and	CCONJ
cana-5719	185	35	95.35	95.35	NUM
cana-5719	185	36	%	%	NOUN
cana-5719	185	37	)	)	PUNCT
cana-5719	185	38	,	,	PUNCT
cana-5719	185	39	with	with	ADP
cana-5719	185	40	vgg-16	vgg-16	NOUN
cana-5719	185	41	exhibiting	exhibit	VERB
cana-5719	185	42	the	the	DET
cana-5719	185	43	highest	high	ADJ
cana-5719	185	44	test	test	NOUN
cana-5719	185	45	loss	loss	NOUN
cana-5719	185	46	(	(	PUNCT
cana-5719	185	47	0.0701	0.0701	NUM
cana-5719	185	48	)	)	PUNCT
cana-5719	185	49	,	,	PUNCT
cana-5719	185	50	indicating	indicate	VERB
cana-5719	185	51	potential	potential	ADJ
cana-5719	185	52	overfitting	overfitting	NOUN
cana-5719	185	53	.	.	PUNCT
cana-5719	186	1	fig.9	fig.9	PROPN
cana-5719	186	2	.	.	PUNCT
cana-5719	187	1	shows	show	VERB
cana-5719	187	2	the	the	DET
cana-5719	187	3	model	model	NOUN
cana-5719	187	4	accuracy	accuracy	NOUN
cana-5719	187	5	and	and	CCONJ
cana-5719	187	6	loss	loss	NOUN
cana-5719	187	7	over	over	ADP
cana-5719	187	8	50	50	NUM
cana-5719	187	9	epochs	epoch	NOUN
cana-5719	187	10	with	with	ADP
cana-5719	187	11	split	split	ADJ
cana-5719	187	12	ratio	ratio	NOUN
cana-5719	187	13	90:10	90:10	NUM
cana-5719	187	14	table2	table2	NOUN
cana-5719	187	15	.	.	PUNCT
cana-5719	188	1	results	result	NOUN
cana-5719	188	2	of	of	ADP
cana-5719	188	3	different	different	ADJ
cana-5719	188	4	models	model	NOUN
cana-5719	188	5	with	with	ADP
cana-5719	188	6	split	split	ADJ
cana-5719	188	7	ratio	ratio	NOUN
cana-5719	188	8	70:30	70:30	NUM
cana-5719	188	9	model	model	NOUN
cana-5719	188	10	split	split	VERB
cana-5719	188	11	ratios	ratio	NOUN
cana-5719	188	12	acc(train	acc(train	NUM
cana-5719	188	13	)	)	PUNCT
cana-5719	188	14	loss(train	loss(train	PROPN
cana-5719	188	15	)	)	PUNCT
cana-5719	188	16	acc(test	acc(test	NUM
cana-5719	188	17	)	)	PUNCT
cana-5719	188	18	loss(test	loss(test	NUM
cana-5719	188	19	)	)	PUNCT
cana-5719	189	1	vgg-19	vgg-19	ADP
cana-5719	189	2	70:30	70:30	NUM
cana-5719	189	3	96.82	96.82	NUM
cana-5719	189	4	0.0311	0.0311	NUM
cana-5719	189	5	95.65	95.65	NUM
cana-5719	189	6	0.0430	0.0430	NUM
cana-5719	189	7	vgg-16	vgg-16	X
cana-5719	189	8	96.96	96.96	NUM
cana-5719	189	9	0.0364	0.0364	NUM
cana-5719	189	10	95.35	95.35	NUM
cana-5719	189	11	0.0701	0.0701	NUM
cana-5719	189	12	resnet	resnet	NOUN
cana-5719	189	13	50	50	NUM
cana-5719	189	14	97.51	97.51	NUM
cana-5719	189	15	0.0349	0.0349	NUM
cana-5719	189	16	97.10	97.10	NUM
cana-5719	189	17	0.1200	0.1200	NUM
cana-5719	189	18	inception	inception	NOUN
cana-5719	189	19	97.88	97.88	NUM
cana-5719	189	20	0.0415	0.0415	NUM
cana-5719	189	21	96.90	96.90	NUM
cana-5719	189	22	0.1117	0.1117	NUM
cana-5719	189	23	mobilenet	mobilenet	NOUN
cana-5719	189	24	v2	v2	VERB
cana-5719	190	1	98.09	98.09	NUM
cana-5719	190	2	0.0157	0.0157	NUM
cana-5719	190	3	98.06	98.06	NUM
cana-5719	190	4	0.0368	0.0368	NUM
cana-5719	190	5	mobilenetv2	mobilenetv2	PROPN
cana-5719	190	6	with	with	ADP
cana-5719	190	7	freeze	freeze	NOUN
cana-5719	190	8	layer	layer	NOUN
cana-5719	190	9	98.14	98.14	NUM
cana-5719	190	10	0.0160	0.0160	NUM
cana-5719	190	11	98.09	98.09	NUM
cana-5719	190	12	0.0370	0.0370	NUM
cana-5719	190	13	the	the	DET
cana-5719	190	14	table	table	NOUN
cana-5719	190	15	3	3	NUM
cana-5719	190	16	presents	present	VERB
cana-5719	190	17	the	the	DET
cana-5719	190	18	classification	classification	NOUN
cana-5719	190	19	performance	performance	NOUN
cana-5719	190	20	metrics	metric	NOUN
cana-5719	190	21	(	(	PUNCT
cana-5719	190	22	precision	precision	NOUN
cana-5719	190	23	,	,	PUNCT
cana-5719	190	24	recall	recall	NOUN
cana-5719	190	25	,	,	PUNCT
cana-5719	190	26	and	and	CCONJ
cana-5719	190	27	specificity	specificity	NOUN
cana-5719	190	28	)	)	PUNCT
cana-5719	190	29	for	for	ADP
cana-5719	190	30	different	different	ADJ
cana-5719	190	31	tumor	tumor	NOUN
cana-5719	190	32	types	type	NOUN
cana-5719	190	33	(	(	PUNCT
cana-5719	190	34	gliomas	glioma	NOUN
cana-5719	190	35	,	,	PUNCT
cana-5719	190	36	meningiomas	meningioma	NOUN
cana-5719	190	37	,	,	PUNCT
cana-5719	190	38	and	and	CCONJ
cana-5719	190	39	pituitary	pituitary	NOUN
cana-5719	190	40	)	)	PUNCT
cana-5719	190	41	using	use	VERB
cana-5719	190	42	a	a	DET
cana-5719	190	43	70:30	70:30	NUM
cana-5719	190	44	train	train	NOUN
cana-5719	190	45	-	-	PUNCT
cana-5719	190	46	test	test	NOUN
cana-5719	190	47	split	split	NOUN
cana-5719	190	48	ratio	ratio	NOUN
cana-5719	190	49	.	.	PUNCT
cana-5719	191	1	key	key	ADJ
cana-5719	191	2	insights	insight	NOUN
cana-5719	191	3	:	:	PUNCT
cana-5719	191	4	•	•	NUM
cana-5719	191	5	gliomas	glioma	NOUN
cana-5719	191	6	achieve	achieve	VERB
cana-5719	191	7	a	a	DET
cana-5719	191	8	precision	precision	NOUN
cana-5719	191	9	of	of	ADP
cana-5719	191	10	96.2	96.2	NUM
cana-5719	191	11	%	%	NOUN
cana-5719	191	12	,	,	PUNCT
cana-5719	191	13	recall	recall	NOUN
cana-5719	191	14	of	of	ADP
cana-5719	191	15	97.8	97.8	NUM
cana-5719	191	16	%	%	NOUN
cana-5719	191	17	,	,	PUNCT
cana-5719	191	18	and	and	CCONJ
cana-5719	191	19	specificity	specificity	NOUN
cana-5719	191	20	of	of	ADP
cana-5719	191	21	97.5	97.5	NUM
cana-5719	191	22	%	%	NOUN
cana-5719	191	23	,	,	PUNCT
cana-5719	191	24	indicating	indicate	VERB
cana-5719	191	25	strong	strong	ADJ
cana-5719	191	26	detection	detection	NOUN
cana-5719	191	27	capability	capability	NOUN
cana-5719	191	28	with	with	ADP
cana-5719	191	29	minimal	minimal	ADJ
cana-5719	191	30	false	false	ADJ
cana-5719	191	31	negatives	negative	NOUN
cana-5719	191	32	.	.	PUNCT
cana-5719	192	1	•	•	NUM
cana-5719	192	2	meningiomas	meningioma	NOUN
cana-5719	192	3	demonstrate	demonstrate	VERB
cana-5719	192	4	high	high	ADJ
cana-5719	192	5	accuracy	accuracy	NOUN
cana-5719	192	6	,	,	PUNCT
cana-5719	192	7	with	with	ADP
cana-5719	192	8	a	a	DET
cana-5719	192	9	precision	precision	NOUN
cana-5719	192	10	of	of	ADP
cana-5719	192	11	97.8	97.8	NUM
cana-5719	192	12	%	%	NOUN
cana-5719	192	13	,	,	PUNCT
cana-5719	192	14	recall	recall	NOUN
cana-5719	192	15	of	of	ADP
cana-5719	192	16	98.1	98.1	NUM
cana-5719	192	17	%	%	NOUN
cana-5719	192	18	,	,	PUNCT
cana-5719	192	19	and	and	CCONJ
cana-5719	192	20	specificity	specificity	NOUN
cana-5719	192	21	of	of	ADP
cana-5719	192	22	98.2	98.2	NUM
cana-5719	192	23	%	%	NOUN
cana-5719	192	24	,	,	PUNCT
cana-5719	192	25	ensuring	ensure	VERB
cana-5719	192	26	reliable	reliable	ADJ
cana-5719	192	27	classification	classification	NOUN
cana-5719	192	28	.	.	PUNCT
cana-5719	193	1	communications	communication	NOUN
cana-5719	193	2	on	on	ADP
cana-5719	193	3	applied	apply	VERB
cana-5719	193	4	nonlinear	nonlinear	ADJ
cana-5719	193	5	analysis	analysis	NOUN
cana-5719	193	6	issn	issn	NOUN
cana-5719	193	7	:	:	PUNCT
cana-5719	193	8	1074	1074	NUM
cana-5719	193	9	-	-	PUNCT
cana-5719	193	10	133x	133x	NUM
cana-5719	193	11	vol	vol	VERB
cana-5719	193	12	32	32	NUM
cana-5719	193	13	no	no	NOUN
cana-5719	193	14	.	.	PUNCT
cana-5719	194	1	10s	10	NOUN
cana-5719	194	2	(	(	PUNCT
cana-5719	194	3	2025	2025	NUM
cana-5719	194	4	)	)	PUNCT
cana-5719	194	5	2793	2793	NUM
cana-5719	194	6	https://internationalpubls.com	https://internationalpubls.com	SYM
cana-5719	194	7	•	•	NOUN
cana-5719	194	8	pituitary	pituitary	ADJ
cana-5719	194	9	tumors	tumor	NOUN
cana-5719	194	10	exhibit	exhibit	VERB
cana-5719	194	11	balanced	balanced	ADJ
cana-5719	194	12	and	and	CCONJ
cana-5719	194	13	excellent	excellent	ADJ
cana-5719	194	14	performance	performance	NOUN
cana-5719	194	15	,	,	PUNCT
cana-5719	194	16	with	with	ADP
cana-5719	194	17	precision	precision	NOUN
cana-5719	194	18	,	,	PUNCT
cana-5719	194	19	recall	recall	NOUN
cana-5719	194	20	,	,	PUNCT
cana-5719	194	21	and	and	CCONJ
cana-5719	194	22	specificity	specificity	NOUN
cana-5719	194	23	all	all	ADV
cana-5719	194	24	at	at	ADP
cana-5719	194	25	98.1	98.1	NUM
cana-5719	194	26	.	.	PUNCT
cana-5719	195	1	overall	overall	ADV
cana-5719	195	2	,	,	PUNCT
cana-5719	195	3	the	the	DET
cana-5719	195	4	high	high	ADJ
cana-5719	195	5	classification	classification	NOUN
cana-5719	195	6	accuracy	accuracy	NOUN
cana-5719	195	7	across	across	ADP
cana-5719	195	8	all	all	DET
cana-5719	195	9	tumor	tumor	NOUN
cana-5719	195	10	types	type	NOUN
cana-5719	195	11	,	,	PUNCT
cana-5719	195	12	making	make	VERB
cana-5719	195	13	it	it	PRON
cana-5719	195	14	highly	highly	ADV
cana-5719	195	15	effective	effective	ADJ
cana-5719	195	16	for	for	ADP
cana-5719	195	17	tumor	tumor	NOUN
cana-5719	195	18	detection	detection	NOUN
cana-5719	195	19	and	and	CCONJ
cana-5719	195	20	diagnosis	diagnosis	NOUN
cana-5719	195	21	table	table	NOUN
cana-5719	195	22	3	3	NUM
cana-5719	195	23	.	.	PUNCT
cana-5719	195	24	class	class	NOUN
cana-5719	195	25	-	-	PUNCT
cana-5719	195	26	specific	specific	ADJ
cana-5719	195	27	evaluation	evaluation	NOUN
cana-5719	195	28	of	of	ADP
cana-5719	195	29	brain	brain	NOUN
cana-5719	195	30	tumor	tumor	NOUN
cana-5719	195	31	classifier	classifier	NOUN
cana-5719	195	32	tumor	tumor	NOUN
cana-5719	195	33	type	type	NOUN
cana-5719	195	34	split	split	VERB
cana-5719	195	35	ratios	ratio	NOUN
cana-5719	195	36	precision	precision	NOUN
cana-5719	195	37	recall	recall	NOUN
cana-5719	195	38	specificity	specificity	NOUN
cana-5719	195	39	gliomas	glioma	VERB
cana-5719	195	40	70:30	70:30	NUM
cana-5719	195	41	96.2	96.2	NUM
cana-5719	195	42	97.8	97.8	NUM
cana-5719	195	43	97.5	97.5	NUM
cana-5719	195	44	meningiomas	meningioma	NOUN
cana-5719	195	45	97.8	97.8	NUM
cana-5719	195	46	98.1	98.1	NUM
cana-5719	195	47	98.2	98.2	NUM
cana-5719	195	48	pituitary	pituitary	NOUN
cana-5719	195	49	98.1	98.1	NUM
cana-5719	195	50	98.1	98.1	NUM
cana-5719	195	51	98.1	98.1	NUM
cana-5719	195	52	this	this	DET
cana-5719	195	53	table	table	NOUN
cana-5719	195	54	4	4	NUM
cana-5719	195	55	presents	present	VERB
cana-5719	195	56	the	the	DET
cana-5719	195	57	training	training	NOUN
cana-5719	195	58	and	and	CCONJ
cana-5719	195	59	testing	testing	NOUN
cana-5719	195	60	accuracy	accuracy	NOUN
cana-5719	195	61	,	,	PUNCT
cana-5719	195	62	along	along	ADP
cana-5719	195	63	with	with	ADP
cana-5719	195	64	loss	loss	NOUN
cana-5719	195	65	values	value	NOUN
cana-5719	195	66	,	,	PUNCT
cana-5719	195	67	for	for	ADP
cana-5719	195	68	various	various	ADJ
cana-5719	195	69	cnn	cnn	PROPN
cana-5719	195	70	models	model	NOUN
cana-5719	195	71	using	use	VERB
cana-5719	195	72	an	an	DET
cana-5719	195	73	80:20	80:20	NUM
cana-5719	195	74	train	train	NOUN
cana-5719	195	75	-	-	PUNCT
cana-5719	195	76	test	test	NOUN
cana-5719	195	77	split	split	NOUN
cana-5719	195	78	.	.	PUNCT
cana-5719	196	1	mobilenetv2	mobilenetv2	PROPN
cana-5719	196	2	with	with	ADP
cana-5719	196	3	a	a	DET
cana-5719	196	4	freeze	freeze	NOUN
cana-5719	196	5	layer	layer	NOUN
cana-5719	196	6	achieves	achieve	VERB
cana-5719	196	7	the	the	DET
cana-5719	196	8	highest	high	ADJ
cana-5719	196	9	test	test	NOUN
cana-5719	196	10	accuracy	accuracy	NOUN
cana-5719	196	11	(	(	PUNCT
cana-5719	196	12	98.33	98.33	NUM
cana-5719	196	13	%	%	NOUN
cana-5719	196	14	)	)	PUNCT
cana-5719	196	15	,	,	PUNCT
cana-5719	196	16	slightly	slightly	ADV
cana-5719	196	17	surpassing	surpass	VERB
cana-5719	196	18	mobilenetv2	mobilenetv2	NOUN
cana-5719	196	19	without	without	ADP
cana-5719	196	20	a	a	DET
cana-5719	196	21	freeze	freeze	NOUN
cana-5719	196	22	layer	layer	NOUN
cana-5719	196	23	(	(	PUNCT
cana-5719	196	24	98.07	98.07	NUM
cana-5719	196	25	%	%	NOUN
cana-5719	196	26	)	)	PUNCT
cana-5719	196	27	.	.	PUNCT
cana-5719	197	1	vgg-19	vgg-19	PROPN
cana-5719	197	2	also	also	ADV
cana-5719	197	3	performs	perform	VERB
cana-5719	197	4	well	well	ADV
cana-5719	197	5	,	,	PUNCT
cana-5719	197	6	with	with	ADP
cana-5719	197	7	a	a	DET
cana-5719	197	8	test	test	NOUN
cana-5719	197	9	accuracy	accuracy	NOUN
cana-5719	197	10	of	of	ADP
cana-5719	197	11	97.25	97.25	NUM
cana-5719	197	12	%	%	NOUN
cana-5719	197	13	and	and	CCONJ
cana-5719	197	14	a	a	DET
cana-5719	197	15	low	low	ADJ
cana-5719	197	16	test	test	NOUN
cana-5719	197	17	loss	loss	NOUN
cana-5719	197	18	(	(	PUNCT
cana-5719	197	19	0.0142	0.0142	NUM
cana-5719	197	20	)	)	PUNCT
cana-5719	197	21	;	;	PUNCT
cana-5719	197	22	however	however	ADV
cana-5719	197	23	,	,	PUNCT
cana-5719	197	24	its	its	PRON
cana-5719	197	25	high	high	ADJ
cana-5719	197	26	training	training	NOUN
cana-5719	197	27	accuracy	accuracy	NOUN
cana-5719	197	28	(	(	PUNCT
cana-5719	197	29	98.80	98.80	NUM
cana-5719	197	30	%	%	NOUN
cana-5719	197	31	)	)	PUNCT
cana-5719	197	32	suggests	suggest	VERB
cana-5719	197	33	some	some	DET
cana-5719	197	34	overfitting	overfitting	NOUN
cana-5719	197	35	.	.	PUNCT
cana-5719	198	1	vgg-16	vgg-16	NOUN
cana-5719	198	2	and	and	CCONJ
cana-5719	198	3	resnet-50	resnet-50	PROPN
cana-5719	198	4	follow	follow	VERB
cana-5719	198	5	with	with	ADP
cana-5719	198	6	test	test	NOUN
cana-5719	198	7	accuracies	accuracy	NOUN
cana-5719	198	8	of	of	ADP
cana-5719	198	9	96.55	96.55	NUM
cana-5719	198	10	%	%	NOUN
cana-5719	198	11	and	and	CCONJ
cana-5719	198	12	97.30	97.30	NUM
cana-5719	198	13	%	%	NOUN
cana-5719	198	14	,	,	PUNCT
cana-5719	198	15	respectively	respectively	ADV
cana-5719	198	16	,	,	PUNCT
cana-5719	198	17	though	though	SCONJ
cana-5719	198	18	resnet-50	resnet-50	NOUN
cana-5719	198	19	exhibits	exhibit	VERB
cana-5719	198	20	a	a	DET
cana-5719	198	21	significantly	significantly	ADV
cana-5719	198	22	high	high	ADJ
cana-5719	198	23	test	test	NOUN
cana-5719	198	24	loss	loss	NOUN
cana-5719	198	25	(	(	PUNCT
cana-5719	198	26	0.1210	0.1210	NUM
cana-5719	198	27	)	)	PUNCT
cana-5719	198	28	,	,	PUNCT
cana-5719	198	29	indicating	indicate	VERB
cana-5719	198	30	overfitting	overfitte	VERB
cana-5719	198	31	.	.	PUNCT
cana-5719	199	1	inception	inception	NOUN
cana-5719	199	2	shows	show	VERB
cana-5719	199	3	the	the	DET
cana-5719	199	4	largest	large	ADJ
cana-5719	199	5	accuracy	accuracy	NOUN
cana-5719	199	6	drop	drop	NOUN
cana-5719	199	7	,	,	PUNCT
cana-5719	199	8	from	from	ADP
cana-5719	199	9	98.10	98.10	NUM
cana-5719	199	10	%	%	NOUN
cana-5719	199	11	(	(	PUNCT
cana-5719	199	12	train	train	NOUN
cana-5719	199	13	)	)	PUNCT
cana-5719	199	14	to	to	ADP
cana-5719	199	15	94.97	94.97	NUM
cana-5719	199	16	%	%	NOUN
cana-5719	199	17	(	(	PUNCT
cana-5719	199	18	test	test	NOUN
cana-5719	199	19	)	)	PUNCT
cana-5719	199	20	,	,	PUNCT
cana-5719	199	21	highlighting	highlight	VERB
cana-5719	199	22	poor	poor	ADJ
cana-5719	199	23	generalization	generalization	NOUN
cana-5719	199	24	.	.	PUNCT
cana-5719	200	1	table	table	NOUN
cana-5719	200	2	4	4	NUM
cana-5719	200	3	.	.	PUNCT
cana-5719	201	1	results	result	NOUN
cana-5719	201	2	of	of	ADP
cana-5719	201	3	different	different	ADJ
cana-5719	201	4	models	model	NOUN
cana-5719	201	5	with	with	ADP
cana-5719	201	6	split	split	ADJ
cana-5719	201	7	ratio	ratio	NOUN
cana-5719	201	8	80:20	80:20	NUM
cana-5719	201	9	model	model	NOUN
cana-5719	201	10	split	split	VERB
cana-5719	201	11	ratios	ratio	NOUN
cana-5719	201	12	acc(train	acc(train	NUM
cana-5719	201	13	)	)	PUNCT
cana-5719	201	14	loss(train	loss(train	PROPN
cana-5719	201	15	)	)	PUNCT
cana-5719	201	16	acc(test	acc(test	NUM
cana-5719	201	17	)	)	PUNCT
cana-5719	201	18	loss(test	loss(test	NUM
cana-5719	201	19	)	)	PUNCT
cana-5719	202	1	vgg-19	vgg-19	CCONJ
cana-5719	202	2	80:20	80:20	NUM
cana-5719	202	3	98.80	98.80	NUM
cana-5719	202	4	0.0364	0.0364	NUM
cana-5719	202	5	97.25	97.25	NUM
cana-5719	202	6	0.0142	0.0142	NUM
cana-5719	202	7	vgg-16	vgg-16	X
cana-5719	202	8	97.98	97.98	NUM
cana-5719	202	9	0.0212	0.0212	NUM
cana-5719	202	10	96.55	96.55	NUM
cana-5719	202	11	0.0331	0.0331	NUM
cana-5719	202	12	resnet	resnet	VERB
cana-5719	202	13	50	50	NUM
cana-5719	202	14	98.00	98.00	NUM
cana-5719	202	15	0.0140	0.0140	NUM
cana-5719	202	16	97.30	97.30	NUM
cana-5719	202	17	0.1210	0.1210	NUM
cana-5719	202	18	inception	inception	VERB
cana-5719	202	19	98.10	98.10	NUM
cana-5719	202	20	0.0312	0.0312	NUM
cana-5719	202	21	94.97	94.97	NUM
cana-5719	202	22	0.0126	0.0126	NUM
cana-5719	202	23	mobilenet	mobilenet	NOUN
cana-5719	202	24	v2	v2	VERB
cana-5719	202	25	98.20	98.20	NUM
cana-5719	202	26	0.0360	0.0360	NUM
cana-5719	202	27	98.07	98.07	NUM
cana-5719	202	28	0.0205	0.0205	NUM
cana-5719	202	29	mobilenetv2	mobilenetv2	NOUN
cana-5719	202	30	with	with	ADP
cana-5719	202	31	freeze	freeze	NOUN
cana-5719	202	32	layer	layer	NOUN
cana-5719	202	33	98.33	98.33	NUM
cana-5719	202	34	0.0365	0.0365	NUM
cana-5719	202	35	98.16	98.16	NUM
cana-5719	202	36	0.0208	0.0208	NUM
cana-5719	202	37	the	the	DET
cana-5719	202	38	table	table	NOUN
cana-5719	202	39	5	5	NUM
cana-5719	202	40	presents	present	VERB
cana-5719	202	41	classification	classification	NOUN
cana-5719	202	42	performance	performance	NOUN
cana-5719	202	43	metrics	metric	NOUN
cana-5719	202	44	(	(	PUNCT
cana-5719	202	45	precision	precision	NOUN
cana-5719	202	46	,	,	PUNCT
cana-5719	202	47	recall	recall	NOUN
cana-5719	202	48	,	,	PUNCT
cana-5719	202	49	and	and	CCONJ
cana-5719	202	50	specificity	specificity	NOUN
cana-5719	202	51	)	)	PUNCT
cana-5719	202	52	for	for	ADP
cana-5719	202	53	different	different	ADJ
cana-5719	202	54	tumor	tumor	NOUN
cana-5719	202	55	types	type	NOUN
cana-5719	202	56	(	(	PUNCT
cana-5719	202	57	gliomas	glioma	NOUN
cana-5719	202	58	,	,	PUNCT
cana-5719	202	59	meningiomas	meningioma	NOUN
cana-5719	202	60	,	,	PUNCT
cana-5719	202	61	and	and	CCONJ
cana-5719	202	62	pituitary	pituitary	NOUN
cana-5719	202	63	)	)	PUNCT
cana-5719	202	64	using	use	VERB
cana-5719	202	65	an	an	DET
cana-5719	202	66	80:20	80:20	NUM
cana-5719	202	67	train	train	NOUN
cana-5719	202	68	-	-	PUNCT
cana-5719	202	69	test	test	NOUN
cana-5719	202	70	split	split	NOUN
cana-5719	202	71	ratio	ratio	NOUN
cana-5719	202	72	.	.	PUNCT
cana-5719	203	1	key	key	ADJ
cana-5719	203	2	insights	insight	NOUN
cana-5719	203	3	:	:	PUNCT
cana-5719	203	4	communications	communication	NOUN
cana-5719	203	5	on	on	ADP
cana-5719	203	6	applied	apply	VERB
cana-5719	203	7	nonlinear	nonlinear	ADJ
cana-5719	203	8	analysis	analysis	NOUN
cana-5719	203	9	issn	issn	NOUN
cana-5719	203	10	:	:	PUNCT
cana-5719	203	11	1074	1074	NUM
cana-5719	203	12	-	-	PUNCT
cana-5719	203	13	133x	133x	NUM
cana-5719	203	14	vol	vol	VERB
cana-5719	203	15	32	32	NUM
cana-5719	203	16	no	no	NOUN
cana-5719	203	17	.	.	PUNCT
cana-5719	204	1	10s	10	NOUN
cana-5719	204	2	(	(	PUNCT
cana-5719	204	3	2025	2025	NUM
cana-5719	204	4	)	)	PUNCT
cana-5719	204	5	2794	2794	NUM
cana-5719	204	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5719	204	7	•	•	NUM
cana-5719	204	8	gliomas	glioma	NOUN
cana-5719	204	9	achieve	achieve	VERB
cana-5719	204	10	a	a	DET
cana-5719	204	11	precision	precision	NOUN
cana-5719	204	12	of	of	ADP
cana-5719	204	13	98.1	98.1	NUM
cana-5719	204	14	%	%	NOUN
cana-5719	204	15	,	,	PUNCT
cana-5719	204	16	recall	recall	NOUN
cana-5719	204	17	of	of	ADP
cana-5719	204	18	97.2	97.2	NUM
cana-5719	204	19	%	%	NOUN
cana-5719	204	20	,	,	PUNCT
cana-5719	204	21	and	and	CCONJ
cana-5719	204	22	specificity	specificity	NOUN
cana-5719	204	23	of	of	ADP
cana-5719	204	24	96.5	96.5	NUM
cana-5719	204	25	%	%	NOUN
cana-5719	204	26	,	,	PUNCT
cana-5719	204	27	ensuring	ensure	VERB
cana-5719	204	28	strong	strong	ADJ
cana-5719	204	29	detection	detection	NOUN
cana-5719	204	30	with	with	ADP
cana-5719	204	31	minimal	minimal	ADJ
cana-5719	204	32	false	false	ADJ
cana-5719	204	33	positives	positive	NOUN
cana-5719	204	34	.	.	PUNCT
cana-5719	205	1	•	•	NUM
cana-5719	205	2	meningiomas	meningioma	NOUN
cana-5719	205	3	demonstrate	demonstrate	VERB
cana-5719	205	4	reliable	reliable	ADJ
cana-5719	205	5	classification	classification	NOUN
cana-5719	205	6	,	,	PUNCT
cana-5719	205	7	with	with	ADP
cana-5719	205	8	a	a	DET
cana-5719	205	9	precision	precision	NOUN
cana-5719	205	10	of	of	ADP
cana-5719	205	11	96.9	96.9	NUM
cana-5719	205	12	%	%	NOUN
cana-5719	205	13	,	,	PUNCT
cana-5719	205	14	recall	recall	NOUN
cana-5719	205	15	of	of	ADP
cana-5719	205	16	97.5	97.5	NUM
cana-5719	205	17	%	%	NOUN
cana-5719	205	18	,	,	PUNCT
cana-5719	205	19	and	and	CCONJ
cana-5719	205	20	specificity	specificity	NOUN
cana-5719	205	21	of	of	ADP
cana-5719	205	22	97.5	97.5	NUM
cana-5719	205	23	%	%	NOUN
cana-5719	205	24	.	.	PUNCT
cana-5719	206	1	•	•	NUM
cana-5719	206	2	pituitary	pituitary	ADJ
cana-5719	206	3	tumors	tumor	NOUN
cana-5719	206	4	exhibit	exhibit	VERB
cana-5719	206	5	the	the	DET
cana-5719	206	6	highest	high	ADJ
cana-5719	206	7	performance	performance	NOUN
cana-5719	206	8	,	,	PUNCT
cana-5719	206	9	with	with	ADP
cana-5719	206	10	precision	precision	NOUN
cana-5719	206	11	at	at	ADP
cana-5719	206	12	98.1	98.1	NUM
cana-5719	206	13	%	%	NOUN
cana-5719	206	14	,	,	PUNCT
cana-5719	206	15	recall	recall	VERB
cana-5719	206	16	at	at	ADP
cana-5719	206	17	98.3	98.3	NUM
cana-5719	206	18	%	%	NOUN
cana-5719	206	19	,	,	PUNCT
cana-5719	206	20	and	and	CCONJ
cana-5719	206	21	specificity	specificity	NOUN
cana-5719	206	22	at	at	ADP
cana-5719	206	23	98.1	98.1	NUM
cana-5719	206	24	%	%	NOUN
cana-5719	206	25	,	,	PUNCT
cana-5719	206	26	reflecting	reflect	VERB
cana-5719	206	27	a	a	DET
cana-5719	206	28	well	well	ADV
cana-5719	206	29	-	-	PUNCT
cana-5719	206	30	balanced	balanced	ADJ
cana-5719	206	31	and	and	CCONJ
cana-5719	206	32	highly	highly	ADV
cana-5719	206	33	accurate	accurate	ADJ
cana-5719	206	34	classification	classification	NOUN
cana-5719	206	35	.	.	PUNCT
cana-5719	207	1	overall	overall	ADV
cana-5719	207	2	,	,	PUNCT
cana-5719	207	3	the	the	DET
cana-5719	207	4	model	model	NOUN
cana-5719	207	5	maintains	maintain	VERB
cana-5719	207	6	high	high	ADJ
cana-5719	207	7	classification	classification	NOUN
cana-5719	207	8	accuracy	accuracy	NOUN
cana-5719	207	9	across	across	ADP
cana-5719	207	10	all	all	DET
cana-5719	207	11	tumor	tumor	NOUN
cana-5719	207	12	types	type	NOUN
cana-5719	207	13	,	,	PUNCT
cana-5719	207	14	making	make	VERB
cana-5719	207	15	it	it	PRON
cana-5719	207	16	effective	effective	ADJ
cana-5719	207	17	for	for	ADP
cana-5719	207	18	precise	precise	ADJ
cana-5719	207	19	tumor	tumor	NOUN
cana-5719	207	20	detection	detection	NOUN
cana-5719	207	21	and	and	CCONJ
cana-5719	207	22	diagnosis	diagnosis	NOUN
cana-5719	207	23	.	.	PUNCT
cana-5719	208	1	table	table	NOUN
cana-5719	208	2	5.class	5.class	NUM
cana-5719	208	3	-	-	PUNCT
cana-5719	208	4	specific	specific	ADJ
cana-5719	208	5	evaluation	evaluation	NOUN
cana-5719	208	6	of	of	ADP
cana-5719	208	7	brain	brain	NOUN
cana-5719	208	8	tumor	tumor	NOUN
cana-5719	208	9	classifier	classifier	NOUN
cana-5719	208	10	tumor	tumor	NOUN
cana-5719	208	11	type	type	NOUN
cana-5719	208	12	split	split	VERB
cana-5719	208	13	ratios	ratio	NOUN
cana-5719	208	14	precision	precision	NOUN
cana-5719	208	15	recall	recall	NOUN
cana-5719	208	16	specificity	specificity	NOUN
cana-5719	208	17	gliomas	glioma	VERB
cana-5719	208	18	80:20	80:20	NUM
cana-5719	208	19	98.1	98.1	NUM
cana-5719	208	20	97.2	97.2	NUM
cana-5719	208	21	96.5	96.5	NUM
cana-5719	208	22	meningiomas	meningioma	NOUN
cana-5719	208	23	96.9	96.9	NUM
cana-5719	208	24	97.5	97.5	NUM
cana-5719	208	25	97.5	97.5	NUM
cana-5719	208	26	pituitary	pituitary	NOUN
cana-5719	208	27	98.1	98.1	NUM
cana-5719	208	28	98.3	98.3	NUM
cana-5719	208	29	98.1	98.1	NUM
cana-5719	208	30	this	this	DET
cana-5719	208	31	table	table	NOUN
cana-5719	208	32	6	6	NUM
cana-5719	208	33	presents	present	VERB
cana-5719	208	34	the	the	DET
cana-5719	208	35	training	training	NOUN
cana-5719	208	36	and	and	CCONJ
cana-5719	208	37	testing	testing	NOUN
cana-5719	208	38	accuracy	accuracy	NOUN
cana-5719	208	39	,	,	PUNCT
cana-5719	208	40	along	along	ADP
cana-5719	208	41	with	with	ADP
cana-5719	208	42	loss	loss	NOUN
cana-5719	208	43	values	value	NOUN
cana-5719	208	44	,	,	PUNCT
cana-5719	208	45	for	for	ADP
cana-5719	208	46	various	various	ADJ
cana-5719	208	47	cnn	cnn	PROPN
cana-5719	208	48	models	model	NOUN
cana-5719	208	49	using	use	VERB
cana-5719	208	50	a	a	DET
cana-5719	208	51	90:10	90:10	NUM
cana-5719	208	52	train	train	NOUN
cana-5719	208	53	-	-	PUNCT
cana-5719	208	54	test	test	NOUN
cana-5719	208	55	split	split	NOUN
cana-5719	208	56	.	.	PUNCT
cana-5719	209	1	mobilenetv2	mobilenetv2	PROPN
cana-5719	209	2	with	with	ADP
cana-5719	209	3	a	a	DET
cana-5719	209	4	freeze	freeze	NOUN
cana-5719	209	5	layer	layer	NOUN
cana-5719	209	6	achieves	achieve	VERB
cana-5719	209	7	the	the	DET
cana-5719	209	8	highest	high	ADJ
cana-5719	209	9	test	test	NOUN
cana-5719	209	10	accuracy	accuracy	NOUN
cana-5719	209	11	(	(	PUNCT
cana-5719	209	12	98.08	98.08	NUM
cana-5719	209	13	%	%	NOUN
cana-5719	209	14	)	)	PUNCT
cana-5719	209	15	,	,	PUNCT
cana-5719	209	16	making	make	VERB
cana-5719	209	17	it	it	PRON
cana-5719	209	18	the	the	DET
cana-5719	209	19	top	top	ADV
cana-5719	209	20	-	-	PUNCT
cana-5719	209	21	performing	perform	VERB
cana-5719	209	22	model	model	NOUN
cana-5719	209	23	for	for	ADP
cana-5719	209	24	this	this	DET
cana-5719	209	25	split	split	NOUN
cana-5719	209	26	.	.	PUNCT
cana-5719	210	1	mobilenetv2	mobilenetv2	PROPN
cana-5719	210	2	without	without	ADP
cana-5719	210	3	a	a	DET
cana-5719	210	4	freeze	freeze	NOUN
cana-5719	210	5	layer	layer	NOUN
cana-5719	210	6	follows	follow	VERB
cana-5719	210	7	closely	closely	ADV
cana-5719	210	8	with	with	ADP
cana-5719	210	9	97.07	97.07	NUM
cana-5719	210	10	%	%	NOUN
cana-5719	210	11	test	test	NOUN
cana-5719	210	12	accuracy	accuracy	NOUN
cana-5719	210	13	and	and	CCONJ
cana-5719	210	14	a	a	DET
cana-5719	210	15	lower	low	ADJ
cana-5719	210	16	test	test	NOUN
cana-5719	210	17	loss	loss	NOUN
cana-5719	210	18	(	(	PUNCT
cana-5719	210	19	0.0203	0.0203	NUM
cana-5719	210	20	)	)	PUNCT
cana-5719	210	21	.	.	PUNCT
cana-5719	211	1	resnet-50	resnet-50	NOUN
cana-5719	211	2	attains	attain	VERB
cana-5719	211	3	97.20	97.20	NUM
cana-5719	211	4	%	%	NOUN
cana-5719	211	5	test	test	NOUN
cana-5719	211	6	accuracy	accuracy	NOUN
cana-5719	211	7	but	but	CCONJ
cana-5719	211	8	exhibits	exhibit	VERB
cana-5719	211	9	a	a	DET
cana-5719	211	10	high	high	ADJ
cana-5719	211	11	test	test	NOUN
cana-5719	211	12	loss	loss	NOUN
cana-5719	211	13	(	(	PUNCT
cana-5719	211	14	0.1210	0.1210	NUM
cana-5719	211	15	)	)	PUNCT
cana-5719	211	16	,	,	PUNCT
cana-5719	211	17	indicating	indicate	VERB
cana-5719	211	18	potential	potential	ADJ
cana-5719	211	19	overfitting	overfitting	NOUN
cana-5719	211	20	.	.	PUNCT
cana-5719	212	1	vgg-16	vgg-16	X
cana-5719	212	2	(	(	PUNCT
cana-5719	212	3	96.54	96.54	NUM
cana-5719	212	4	%	%	NOUN
cana-5719	212	5	)	)	PUNCT
cana-5719	212	6	and	and	CCONJ
cana-5719	212	7	vgg-19	vgg-19	NUM
cana-5719	212	8	(	(	PUNCT
cana-5719	212	9	96.20	96.20	NUM
cana-5719	212	10	%	%	NOUN
cana-5719	212	11	)	)	PUNCT
cana-5719	212	12	perform	perform	VERB
cana-5719	212	13	reasonably	reasonably	ADV
cana-5719	212	14	well	well	ADV
cana-5719	212	15	but	but	CCONJ
cana-5719	212	16	have	have	VERB
cana-5719	212	17	higher	high	ADJ
cana-5719	212	18	test	test	NOUN
cana-5719	212	19	losses	loss	NOUN
cana-5719	212	20	than	than	ADP
cana-5719	212	21	the	the	DET
cana-5719	212	22	mobilenetv2	mobilenetv2	PROPN
cana-5719	212	23	models	model	NOUN
cana-5719	212	24	.	.	PUNCT
cana-5719	213	1	inception	inception	NOUN
cana-5719	213	2	shows	show	VERB
cana-5719	213	3	the	the	DET
cana-5719	213	4	lowest	low	ADJ
cana-5719	213	5	test	test	NOUN
cana-5719	213	6	accuracy	accuracy	NOUN
cana-5719	213	7	(	(	PUNCT
cana-5719	213	8	94.90	94.90	NUM
cana-5719	213	9	%	%	NOUN
cana-5719	213	10	)	)	PUNCT
cana-5719	213	11	but	but	CCONJ
cana-5719	213	12	also	also	ADV
cana-5719	213	13	the	the	DET
cana-5719	213	14	lowest	low	ADJ
cana-5719	213	15	test	test	NOUN
cana-5719	213	16	loss	loss	NOUN
cana-5719	213	17	(	(	PUNCT
cana-5719	213	18	0.0116	0.0116	NUM
cana-5719	213	19	)	)	PUNCT
cana-5719	213	20	,	,	PUNCT
cana-5719	213	21	suggesting	suggest	VERB
cana-5719	213	22	possible	possible	ADJ
cana-5719	213	23	underfitting	underfitting	NOUN
cana-5719	213	24	or	or	CCONJ
cana-5719	213	25	suboptimal	suboptimal	ADJ
cana-5719	213	26	learning	learning	NOUN
cana-5719	213	27	.	.	PUNCT
cana-5719	214	1	table	table	NOUN
cana-5719	214	2	6	6	NUM
cana-5719	214	3	.	.	PUNCT
cana-5719	215	1	results	result	NOUN
cana-5719	215	2	of	of	ADP
cana-5719	215	3	different	different	ADJ
cana-5719	215	4	models	model	NOUN
cana-5719	215	5	with	with	ADP
cana-5719	215	6	split	split	ADJ
cana-5719	215	7	ratio	ratio	NOUN
cana-5719	215	8	90:10	90:10	NUM
cana-5719	215	9	model	model	NOUN
cana-5719	215	10	split	split	VERB
cana-5719	215	11	ratios	ratio	NOUN
cana-5719	215	12	acc(train	acc(train	NUM
cana-5719	215	13	)	)	PUNCT
cana-5719	215	14	loss(train	loss(train	PROPN
cana-5719	215	15	)	)	PUNCT
cana-5719	215	16	acc(test	acc(test	NUM
cana-5719	215	17	)	)	PUNCT
cana-5719	215	18	loss(test	loss(test	NUM
cana-5719	215	19	)	)	PUNCT
cana-5719	216	1	vgg-19	vgg-19	CCONJ
cana-5719	216	2	90:10	90:10	NUM
cana-5719	216	3	97.81	97.81	NUM
cana-5719	216	4	0.0362	0.0362	NUM
cana-5719	216	5	96.20	96.20	NUM
cana-5719	216	6	0.0241	0.0241	NUM
cana-5719	216	7	vgg-16	vgg-16	X
cana-5719	216	8	97.93	97.93	NUM
cana-5719	216	9	0.0215	0.0215	NUM
cana-5719	216	10	96.54	96.54	NUM
cana-5719	216	11	0.0333	0.0333	NUM
cana-5719	216	12	resnet	resnet	VERB
cana-5719	216	13	50	50	NUM
cana-5719	216	14	97.98	97.98	NUM
cana-5719	216	15	0.0249	0.0249	NUM
cana-5719	216	16	97.20	97.20	NUM
cana-5719	216	17	0.1210	0.1210	NUM
cana-5719	216	18	inception	inception	NOUN
cana-5719	216	19	97.54	97.54	NUM
cana-5719	216	20	0.0313	0.0313	NUM
cana-5719	216	21	94.90	94.90	NUM
cana-5719	216	22	0.0116	0.0116	NUM
cana-5719	216	23	mobilenet	mobilenet	NOUN
cana-5719	216	24	v2	v2	VERB
cana-5719	216	25	98.01	98.01	NUM
cana-5719	216	26	0.0360	0.0360	NUM
cana-5719	216	27	97.07	97.07	NUM
cana-5719	216	28	0.0203	0.0203	NUM
cana-5719	216	29	mobilenetv2	mobilenetv2	NOUN
cana-5719	216	30	with	with	ADP
cana-5719	216	31	freeze	freeze	NOUN
cana-5719	216	32	layer	layer	NOUN
cana-5719	216	33	98.17	98.17	NUM
cana-5719	216	34	0.0363	0.0363	NUM
cana-5719	216	35	98.08	98.08	NUM
cana-5719	216	36	0.0304	0.0304	NUM
cana-5719	216	37	communications	communication	NOUN
cana-5719	216	38	on	on	ADP
cana-5719	216	39	applied	apply	VERB
cana-5719	216	40	nonlinear	nonlinear	ADJ
cana-5719	216	41	analysis	analysis	NOUN
cana-5719	216	42	issn	issn	NOUN
cana-5719	216	43	:	:	PUNCT
cana-5719	216	44	1074	1074	NUM
cana-5719	216	45	-	-	PUNCT
cana-5719	216	46	133x	133x	NUM
cana-5719	216	47	vol	vol	VERB
cana-5719	216	48	32	32	NUM
cana-5719	216	49	no	no	NOUN
cana-5719	216	50	.	.	PUNCT
cana-5719	217	1	10s	10	NOUN
cana-5719	217	2	(	(	PUNCT
cana-5719	217	3	2025	2025	NUM
cana-5719	217	4	)	)	PUNCT
cana-5719	217	5	2795	2795	NUM
cana-5719	217	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5719	218	1	the	the	DET
cana-5719	218	2	table	table	NOUN
cana-5719	218	3	7	7	NUM
cana-5719	218	4	presents	present	VERB
cana-5719	218	5	classification	classification	NOUN
cana-5719	218	6	performance	performance	NOUN
cana-5719	218	7	metrics	metric	NOUN
cana-5719	218	8	(	(	PUNCT
cana-5719	218	9	precision	precision	NOUN
cana-5719	218	10	,	,	PUNCT
cana-5719	218	11	recall	recall	NOUN
cana-5719	218	12	,	,	PUNCT
cana-5719	218	13	and	and	CCONJ
cana-5719	218	14	specificity	specificity	NOUN
cana-5719	218	15	)	)	PUNCT
cana-5719	218	16	for	for	ADP
cana-5719	218	17	different	different	ADJ
cana-5719	218	18	tumor	tumor	NOUN
cana-5719	218	19	types	type	NOUN
cana-5719	218	20	(	(	PUNCT
cana-5719	218	21	gliomas	glioma	NOUN
cana-5719	218	22	,	,	PUNCT
cana-5719	218	23	meningiomas	meningioma	NOUN
cana-5719	218	24	,	,	PUNCT
cana-5719	218	25	and	and	CCONJ
cana-5719	218	26	pituitary	pituitary	NOUN
cana-5719	218	27	)	)	PUNCT
cana-5719	218	28	using	use	VERB
cana-5719	218	29	a	a	DET
cana-5719	218	30	90:10	90:10	NUM
cana-5719	218	31	train	train	NOUN
cana-5719	218	32	-	-	PUNCT
cana-5719	218	33	test	test	NOUN
cana-5719	218	34	split	split	NOUN
cana-5719	218	35	ratio	ratio	NOUN
cana-5719	218	36	.	.	PUNCT
cana-5719	219	1	key	key	ADJ
cana-5719	219	2	observations	observation	NOUN
cana-5719	219	3	:	:	PUNCT
cana-5719	219	4	•	•	NUM
cana-5719	219	5	gliomas	glioma	NOUN
cana-5719	219	6	achieve	achieve	VERB
cana-5719	219	7	a	a	DET
cana-5719	219	8	precision	precision	NOUN
cana-5719	219	9	of	of	ADP
cana-5719	219	10	98.1	98.1	NUM
cana-5719	219	11	%	%	NOUN
cana-5719	219	12	,	,	PUNCT
cana-5719	219	13	recall	recall	NOUN
cana-5719	219	14	of	of	ADP
cana-5719	219	15	97.3	97.3	NUM
cana-5719	219	16	%	%	NOUN
cana-5719	219	17	,	,	PUNCT
cana-5719	219	18	and	and	CCONJ
cana-5719	219	19	specificity	specificity	NOUN
cana-5719	219	20	of	of	ADP
cana-5719	219	21	97.5	97.5	NUM
cana-5719	219	22	%	%	NOUN
cana-5719	219	23	,	,	PUNCT
cana-5719	219	24	ensuring	ensure	VERB
cana-5719	219	25	strong	strong	ADJ
cana-5719	219	26	classification	classification	NOUN
cana-5719	219	27	accuracy	accuracy	NOUN
cana-5719	219	28	with	with	ADP
cana-5719	219	29	minimal	minimal	ADJ
cana-5719	219	30	false	false	ADJ
cana-5719	219	31	negatives	negative	NOUN
cana-5719	219	32	.	.	PUNCT
cana-5719	220	1	•	•	NUM
cana-5719	220	2	meningiomas	meningioma	NOUN
cana-5719	220	3	demonstrate	demonstrate	VERB
cana-5719	220	4	reliable	reliable	ADJ
cana-5719	220	5	detection	detection	NOUN
cana-5719	220	6	with	with	ADP
cana-5719	220	7	a	a	DET
cana-5719	220	8	precision	precision	NOUN
cana-5719	220	9	of	of	ADP
cana-5719	220	10	97.9	97.9	NUM
cana-5719	220	11	%	%	NOUN
cana-5719	220	12	,	,	PUNCT
cana-5719	220	13	recall	recall	NOUN
cana-5719	220	14	of	of	ADP
cana-5719	220	15	97.1	97.1	NUM
cana-5719	220	16	%	%	NOUN
cana-5719	220	17	,	,	PUNCT
cana-5719	220	18	and	and	CCONJ
cana-5719	220	19	specificity	specificity	NOUN
cana-5719	220	20	of	of	ADP
cana-5719	220	21	96.5	96.5	NUM
cana-5719	220	22	%	%	NOUN
cana-5719	220	23	,	,	PUNCT
cana-5719	220	24	indicating	indicate	VERB
cana-5719	220	25	balanced	balanced	ADJ
cana-5719	220	26	performance	performance	NOUN
cana-5719	220	27	.	.	PUNCT
cana-5719	221	1	•	•	NUM
cana-5719	221	2	pituitary	pituitary	ADJ
cana-5719	221	3	tumors	tumor	NOUN
cana-5719	221	4	exhibit	exhibit	VERB
cana-5719	221	5	the	the	DET
cana-5719	221	6	highest	high	ADJ
cana-5719	221	7	classification	classification	NOUN
cana-5719	221	8	accuracy	accuracy	NOUN
cana-5719	221	9	,	,	PUNCT
cana-5719	221	10	with	with	ADP
cana-5719	221	11	precision	precision	NOUN
cana-5719	221	12	at	at	ADP
cana-5719	221	13	98.1	98.1	NUM
cana-5719	221	14	%	%	NOUN
cana-5719	221	15	,	,	PUNCT
cana-5719	221	16	recall	recall	VERB
cana-5719	221	17	at	at	ADP
cana-5719	221	18	98.3	98.3	NUM
cana-5719	221	19	%	%	NOUN
cana-5719	221	20	,	,	PUNCT
cana-5719	221	21	and	and	CCONJ
cana-5719	221	22	specificity	specificity	NOUN
cana-5719	221	23	at	at	ADP
cana-5719	221	24	98.1	98.1	NUM
cana-5719	221	25	%	%	NOUN
cana-5719	221	26	,	,	PUNCT
cana-5719	221	27	ensuring	ensure	VERB
cana-5719	221	28	minimal	minimal	ADJ
cana-5719	221	29	misclassification	misclassification	NOUN
cana-5719	221	30	.	.	PUNCT
cana-5719	222	1	overall	overall	ADV
cana-5719	222	2	,	,	PUNCT
cana-5719	222	3	the	the	DET
cana-5719	222	4	model	model	NOUN
cana-5719	222	5	maintains	maintain	VERB
cana-5719	222	6	high	high	ADJ
cana-5719	222	7	performance	performance	NOUN
cana-5719	222	8	across	across	ADP
cana-5719	222	9	all	all	DET
cana-5719	222	10	tumor	tumor	NOUN
cana-5719	222	11	types	type	NOUN
cana-5719	222	12	,	,	PUNCT
cana-5719	222	13	making	make	VERB
cana-5719	222	14	it	it	PRON
cana-5719	222	15	well	well	ADV
cana-5719	222	16	-	-	PUNCT
cana-5719	222	17	suited	suit	VERB
cana-5719	222	18	for	for	ADP
cana-5719	222	19	precise	precise	ADJ
cana-5719	222	20	tumor	tumor	NOUN
cana-5719	222	21	detection	detection	NOUN
cana-5719	222	22	and	and	CCONJ
cana-5719	222	23	diagnosis	diagnosis	NOUN
cana-5719	222	24	.	.	PUNCT
cana-5719	223	1	table	table	NOUN
cana-5719	223	2	7.class	7.class	NUM
cana-5719	223	3	-	-	PUNCT
cana-5719	223	4	specific	specific	ADJ
cana-5719	223	5	evaluation	evaluation	NOUN
cana-5719	223	6	of	of	ADP
cana-5719	223	7	brain	brain	NOUN
cana-5719	223	8	tumor	tumor	NOUN
cana-5719	223	9	classifier	classifier	NOUN
cana-5719	223	10	tumor	tumor	NOUN
cana-5719	223	11	type	type	NOUN
cana-5719	223	12	split	split	VERB
cana-5719	223	13	ratios	ratio	NOUN
cana-5719	223	14	precision	precision	NOUN
cana-5719	223	15	recall	recall	NOUN
cana-5719	223	16	specificity	specificity	NOUN
cana-5719	223	17	gliomas	glioma	VERB
cana-5719	223	18	90:10	90:10	NUM
cana-5719	223	19	98.1	98.1	NUM
cana-5719	223	20	97.3	97.3	NUM
cana-5719	223	21	97.5	97.5	NUM
cana-5719	223	22	meningiomas	meningioma	NOUN
cana-5719	223	23	97.9	97.9	NUM
cana-5719	223	24	97.1	97.1	NUM
cana-5719	223	25	96.5	96.5	NUM
cana-5719	223	26	pituitary	pituitary	NOUN
cana-5719	223	27	98.1	98.1	NUM
cana-5719	223	28	98.3	98.3	NUM
cana-5719	223	29	98.1	98.1	NUM
cana-5719	223	30	based	base	VERB
cana-5719	223	31	on	on	ADP
cana-5719	223	32	the	the	DET
cana-5719	223	33	comparative	comparative	ADJ
cana-5719	223	34	analysis	analysis	NOUN
cana-5719	223	35	of	of	ADP
cana-5719	223	36	the	the	DET
cana-5719	223	37	split	split	NOUN
cana-5719	223	38	ratio	ratio	NOUN
cana-5719	223	39	,	,	PUNCT
cana-5719	223	40	the	the	DET
cana-5719	223	41	80:20	80:20	NUM
cana-5719	223	42	strategies	strategy	NOUN
cana-5719	223	43	outperformed	outperform	VERB
cana-5719	223	44	the	the	DET
cana-5719	223	45	other	other	ADJ
cana-5719	223	46	approaches	approach	NOUN
cana-5719	223	47	.	.	PUNCT
cana-5719	224	1	consequently	consequently	ADV
cana-5719	224	2	,	,	PUNCT
cana-5719	224	3	we	we	PRON
cana-5719	224	4	proceeded	proceed	VERB
cana-5719	224	5	with	with	ADP
cana-5719	224	6	the	the	DET
cana-5719	224	7	80:20	80:20	NUM
cana-5719	224	8	split	split	NOUN
cana-5719	224	9	for	for	ADP
cana-5719	224	10	further	further	ADJ
cana-5719	224	11	analysis	analysis	NOUN
cana-5719	224	12	.	.	PUNCT
cana-5719	225	1	5.3	5.3	NUM
cana-5719	225	2	.	.	PUNCT
cana-5719	225	3	comparative	comparative	ADJ
cana-5719	225	4	classification	classification	NOUN
cana-5719	225	5	report	report	NOUN
cana-5719	225	6	:	:	PUNCT
cana-5719	225	7	the	the	DET
cana-5719	225	8	table	table	NOUN
cana-5719	225	9	8	8	NUM
cana-5719	225	10	presents	present	VERB
cana-5719	225	11	the	the	DET
cana-5719	225	12	performance	performance	NOUN
cana-5719	225	13	metrics	metric	NOUN
cana-5719	225	14	(	(	PUNCT
cana-5719	225	15	precision	precision	NOUN
cana-5719	225	16	,	,	PUNCT
cana-5719	225	17	recall	recall	NOUN
cana-5719	225	18	,	,	PUNCT
cana-5719	225	19	and	and	CCONJ
cana-5719	225	20	specificity	specificity	NOUN
cana-5719	225	21	)	)	PUNCT
cana-5719	225	22	of	of	ADP
cana-5719	225	23	different	different	ADJ
cana-5719	225	24	cnn	cnn	PROPN
cana-5719	225	25	models	model	NOUN
cana-5719	225	26	across	across	ADP
cana-5719	225	27	three	three	NUM
cana-5719	225	28	train	train	NOUN
cana-5719	225	29	-	-	PUNCT
cana-5719	225	30	tests	test	NOUN
cana-5719	225	31	split	split	ADJ
cana-5719	225	32	ratios	ratio	NOUN
cana-5719	225	33	(	(	PUNCT
cana-5719	225	34	70:30	70:30	NUM
cana-5719	225	35	,	,	PUNCT
cana-5719	225	36	80:20	80:20	NUM
cana-5719	225	37	,	,	PUNCT
cana-5719	225	38	and	and	CCONJ
cana-5719	225	39	90:10	90:10	NUM
cana-5719	225	40	)	)	PUNCT
cana-5719	225	41	.	.	PUNCT
cana-5719	226	1	key	key	ADJ
cana-5719	226	2	observations	observation	NOUN
cana-5719	226	3	:	:	PUNCT
cana-5719	226	4	•	•	NUM
cana-5719	226	5	vgg-19	vgg-19	NUM
cana-5719	226	6	shows	show	VERB
cana-5719	226	7	fluctuating	fluctuate	VERB
cana-5719	226	8	performance	performance	NOUN
cana-5719	226	9	,	,	PUNCT
cana-5719	226	10	achieving	achieve	VERB
cana-5719	226	11	its	its	PRON
cana-5719	226	12	highest	high	ADJ
cana-5719	226	13	precision	precision	NOUN
cana-5719	226	14	(	(	PUNCT
cana-5719	226	15	0.93	0.93	NUM
cana-5719	226	16	)	)	PUNCT
cana-5719	226	17	in	in	ADP
cana-5719	226	18	the	the	DET
cana-5719	226	19	80:20	80:20	NUM
cana-5719	226	20	split	split	NOUN
cana-5719	226	21	,	,	PUNCT
cana-5719	226	22	while	while	SCONJ
cana-5719	226	23	recall	recall	NOUN
cana-5719	226	24	peaks	peak	NOUN
cana-5719	226	25	at	at	ADP
cana-5719	226	26	0.93	0.93	NUM
cana-5719	226	27	in	in	ADP
cana-5719	226	28	the	the	DET
cana-5719	226	29	90:10	90:10	NUM
cana-5719	226	30	split	split	NOUN
cana-5719	226	31	.	.	PUNCT
cana-5719	227	1	•	•	NUM
cana-5719	227	2	vgg-16	vgg-16	NOUN
cana-5719	227	3	maintains	maintain	VERB
cana-5719	227	4	high	high	ADJ
cana-5719	227	5	recall	recall	NOUN
cana-5719	227	6	across	across	ADP
cana-5719	227	7	splits	split	NOUN
cana-5719	227	8	,	,	PUNCT
cana-5719	227	9	with	with	ADP
cana-5719	227	10	precision	precision	NOUN
cana-5719	227	11	ranging	range	VERB
cana-5719	227	12	between	between	ADP
cana-5719	227	13	0.88	0.88	NUM
cana-5719	227	14	and	and	CCONJ
cana-5719	227	15	0.92	0.92	NUM
cana-5719	227	16	,	,	PUNCT
cana-5719	227	17	and	and	CCONJ
cana-5719	227	18	specificity	specificity	NOUN
cana-5719	227	19	remaining	remain	VERB
cana-5719	227	20	above	above	ADP
cana-5719	227	21	0.94	0.94	NUM
cana-5719	227	22	.	.	PUNCT
cana-5719	228	1	•	•	NOUN
cana-5719	228	2	resnet-50	resnet-50	PROPN
cana-5719	228	3	delivers	deliver	VERB
cana-5719	228	4	stable	stable	ADJ
cana-5719	228	5	performance	performance	NOUN
cana-5719	228	6	,	,	PUNCT
cana-5719	228	7	with	with	ADP
cana-5719	228	8	precision	precision	NOUN
cana-5719	228	9	and	and	CCONJ
cana-5719	228	10	recall	recall	VERB
cana-5719	228	11	consistently	consistently	ADV
cana-5719	228	12	above	above	ADP
cana-5719	228	13	0.92	0.92	NUM
cana-5719	228	14	and	and	CCONJ
cana-5719	228	15	specificity	specificity	NOUN
cana-5719	228	16	peaking	peak	VERB
cana-5719	228	17	at	at	ADP
cana-5719	228	18	0.96	0.96	NUM
cana-5719	228	19	in	in	ADP
cana-5719	228	20	the	the	DET
cana-5719	228	21	90:10	90:10	NUM
cana-5719	228	22	split	split	NOUN
cana-5719	228	23	.	.	PUNCT
cana-5719	229	1	•	•	NUM
cana-5719	229	2	inception	inception	NOUN
cana-5719	229	3	achieves	achieve	VERB
cana-5719	229	4	strong	strong	ADJ
cana-5719	229	5	results	result	NOUN
cana-5719	229	6	,	,	PUNCT
cana-5719	229	7	with	with	ADP
cana-5719	229	8	the	the	DET
cana-5719	229	9	highest	high	ADJ
cana-5719	229	10	recall	recall	NOUN
cana-5719	229	11	(	(	PUNCT
cana-5719	229	12	0.97	0.97	NUM
cana-5719	229	13	)	)	PUNCT
cana-5719	229	14	in	in	ADP
cana-5719	229	15	the	the	DET
cana-5719	229	16	70:30	70:30	NUM
cana-5719	229	17	split	split	NOUN
cana-5719	229	18	and	and	CCONJ
cana-5719	229	19	specificity	specificity	NOUN
cana-5719	229	20	reaching	reach	VERB
cana-5719	229	21	0.98	0.98	NUM
cana-5719	229	22	in	in	ADP
cana-5719	229	23	the	the	DET
cana-5719	229	24	90:10	90:10	NUM
cana-5719	229	25	split	split	NOUN
cana-5719	229	26	.	.	PUNCT
cana-5719	230	1	•	•	NUM
cana-5719	230	2	mobilenetv2	mobilenetv2	NOUN
cana-5719	230	3	demonstrates	demonstrate	VERB
cana-5719	230	4	robust	robust	ADJ
cana-5719	230	5	classification	classification	NOUN
cana-5719	230	6	,	,	PUNCT
cana-5719	230	7	with	with	ADP
cana-5719	230	8	precision	precision	NOUN
cana-5719	230	9	reaching	reach	VERB
cana-5719	230	10	0.97	0.97	NUM
cana-5719	230	11	in	in	ADP
cana-5719	230	12	the	the	DET
cana-5719	230	13	70:30	70:30	NUM
cana-5719	230	14	split	split	NOUN
cana-5719	230	15	and	and	CCONJ
cana-5719	230	16	recall	recall	NOUN
cana-5719	230	17	peaking	peak	VERB
cana-5719	230	18	at	at	ADP
cana-5719	230	19	0.97	0.97	NUM
cana-5719	230	20	in	in	ADP
cana-5719	230	21	both	both	CCONJ
cana-5719	230	22	the	the	DET
cana-5719	230	23	80:20	80:20	NUM
cana-5719	230	24	and	and	CCONJ
cana-5719	230	25	90:10	90:10	NUM
cana-5719	230	26	splits	split	NOUN
cana-5719	230	27	.	.	PUNCT
cana-5719	231	1	communications	communication	NOUN
cana-5719	231	2	on	on	ADP
cana-5719	231	3	applied	apply	VERB
cana-5719	231	4	nonlinear	nonlinear	ADJ
cana-5719	231	5	analysis	analysis	NOUN
cana-5719	231	6	issn	issn	NOUN
cana-5719	231	7	:	:	PUNCT
cana-5719	231	8	1074	1074	NUM
cana-5719	231	9	-	-	PUNCT
cana-5719	231	10	133x	133x	NUM
cana-5719	231	11	vol	vol	VERB
cana-5719	231	12	32	32	NUM
cana-5719	231	13	no	no	NOUN
cana-5719	231	14	.	.	PUNCT
cana-5719	232	1	10s	10	NOUN
cana-5719	232	2	(	(	PUNCT
cana-5719	232	3	2025	2025	NUM
cana-5719	232	4	)	)	PUNCT
cana-5719	232	5	2796	2796	NUM
cana-5719	232	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5719	232	7	•	•	NUM
cana-5719	232	8	mobilenetv2	mobilenetv2	NOUN
cana-5719	232	9	with	with	ADP
cana-5719	232	10	a	a	DET
cana-5719	232	11	frozen	frozen	ADJ
cana-5719	232	12	layer	layer	NOUN
cana-5719	232	13	outperforms	outperform	VERB
cana-5719	232	14	other	other	ADJ
cana-5719	232	15	models	model	NOUN
cana-5719	232	16	,	,	PUNCT
cana-5719	232	17	maintaining	maintain	VERB
cana-5719	232	18	the	the	DET
cana-5719	232	19	highest	high	ADJ
cana-5719	232	20	precision	precision	NOUN
cana-5719	232	21	(	(	PUNCT
cana-5719	232	22	0.98	0.98	NUM
cana-5719	232	23	)	)	PUNCT
cana-5719	232	24	in	in	ADP
cana-5719	232	25	the	the	DET
cana-5719	232	26	70:30	70:30	NUM
cana-5719	232	27	and	and	CCONJ
cana-5719	232	28	80:20	80:20	NUM
cana-5719	232	29	splits	split	NOUN
cana-5719	232	30	,	,	PUNCT
cana-5719	232	31	with	with	ADP
cana-5719	232	32	specificity	specificity	NOUN
cana-5719	232	33	reaching	reach	VERB
cana-5719	232	34	0.99	0.99	NUM
cana-5719	232	35	in	in	ADP
cana-5719	232	36	the	the	DET
cana-5719	232	37	80:20	80:20	NUM
cana-5719	232	38	split	split	NOUN
cana-5719	232	39	.	.	PUNCT
cana-5719	233	1	table	table	NOUN
cana-5719	233	2	8	8	NUM
cana-5719	233	3	.	.	PUNCT
cana-5719	234	1	comparative	comparative	ADJ
cana-5719	234	2	classification	classification	NOUN
cana-5719	234	3	report	report	NOUN
cana-5719	234	4	for	for	ADP
cana-5719	234	5	cross	cross	ADJ
cana-5719	234	6	-	-	ADJ
cana-5719	234	7	validation	validation	ADJ
cana-5719	234	8	strategy	strategy	NOUN
cana-5719	234	9	ratio	ratio	NOUN
cana-5719	234	10	70:30	70:30	NUM
cana-5719	234	11	,	,	PUNCT
cana-5719	234	12	80:20	80:20	NUM
cana-5719	234	13	,	,	PUNCT
cana-5719	234	14	and	and	CCONJ
cana-5719	234	15	90:10	90:10	NUM
cana-5719	234	16	cnn	cnn	PROPN
cana-5719	234	17	model	model	NOUN
cana-5719	234	18	split	split	VERB
cana-5719	234	19	ratios	ratio	NOUN
cana-5719	234	20	70:30	70:30	NUM
cana-5719	234	21	80:20	80:20	NUM
cana-5719	234	22	90:10	90:10	NUM
cana-5719	234	23	precision	precision	NOUN
cana-5719	234	24	recall	recall	NOUN
cana-5719	234	25	specificity	specificity	PROPN
cana-5719	234	26	precision	precision	NOUN
cana-5719	234	27	recall	recall	NOUN
cana-5719	234	28	specificity	specificity	NOUN
cana-5719	234	29	precision	precision	NOUN
cana-5719	234	30	recall	recall	VERB
cana-5719	234	31	specificity	specificity	NOUN
cana-5719	234	32	vgg-19	vgg-19	CCONJ
cana-5719	234	33	0.80	0.80	NUM
cana-5719	234	34	0.78	0.78	NUM
cana-5719	234	35	0.91	0.91	NUM
cana-5719	234	36	0.93	0.93	NUM
cana-5719	234	37	0.91	0.91	NUM
cana-5719	234	38	0.92	0.92	NUM
cana-5719	234	39	0.82	0.82	NUM
cana-5719	234	40	0.93	0.93	NUM
cana-5719	234	41	0.93	0.93	NUM
cana-5719	234	42	vgg-16	vgg-16	NOUN
cana-5719	234	43	0.91	0.91	NUM
cana-5719	234	44	0.95	0.95	NUM
cana-5719	234	45	0.96	0.96	NUM
cana-5719	234	46	0.88	0.88	NUM
cana-5719	234	47	0.93	0.93	NUM
cana-5719	234	48	0.95	0.95	NUM
cana-5719	234	49	0.92	0.92	NUM
cana-5719	234	50	0.94	0.94	NUM
cana-5719	234	51	0.94	0.94	NUM
cana-5719	234	52	resnet	resnet	VERB
cana-5719	234	53	50	50	NUM
cana-5719	234	54	0.92	0.92	NUM
cana-5719	234	55	0.94	0.94	NUM
cana-5719	234	56	0.95	0.95	NUM
cana-5719	234	57	0.92	0.92	NUM
cana-5719	234	58	0.95	0.95	NUM
cana-5719	234	59	0.95	0.95	NUM
cana-5719	234	60	0.93	0.93	NUM
cana-5719	234	61	0.94	0.94	NUM
cana-5719	234	62	0.96	0.96	NUM
cana-5719	234	63	inception	inception	NOUN
cana-5719	234	64	0.96	0.96	NUM
cana-5719	234	65	0.97	0.97	NUM
cana-5719	234	66	0.97	0.97	NUM
cana-5719	234	67	0.93	0.93	NUM
cana-5719	234	68	0.96	0.96	NUM
cana-5719	234	69	0.96	0.96	NUM
cana-5719	234	70	0.95	0.95	NUM
cana-5719	234	71	0.96	0.96	NUM
cana-5719	234	72	0.98	0.98	NUM
cana-5719	234	73	mobilenet	mobilenet	NOUN
cana-5719	234	74	v2	v2	VERB
cana-5719	234	75	0.97	0.97	NUM
cana-5719	234	76	0.90	0.90	NUM
cana-5719	234	77	0.96	0.96	NUM
cana-5719	234	78	0.95	0.95	NUM
cana-5719	234	79	0.97	0.97	NUM
cana-5719	234	80	0.97	0.97	NUM
cana-5719	234	81	0.96	0.96	NUM
cana-5719	234	82	0.97	0.97	NUM
cana-5719	234	83	0.97	0.97	NUM
cana-5719	234	84	mobile	mobile	ADJ
cana-5719	234	85	netv2	netv2	NOUN
cana-5719	234	86	with	with	ADP
cana-5719	234	87	freeze	freeze	NOUN
cana-5719	234	88	layer	layer	NOUN
cana-5719	234	89	0.98	0.98	NUM
cana-5719	234	90	0.96	0.96	NUM
cana-5719	234	91	0.97	0.97	NUM
cana-5719	234	92	0.98	0.98	NUM
cana-5719	234	93	0.97	0.97	NUM
cana-5719	234	94	0.99	0.99	NUM
cana-5719	234	95	0.97	0.97	NUM
cana-5719	234	96	0.97	0.97	NUM
cana-5719	234	97	0.96	0.96	NUM
cana-5719	234	98	overall	overall	NOUN
cana-5719	234	99	,	,	PUNCT
cana-5719	234	100	mobilenetv2	mobilenetv2	PROPN
cana-5719	234	101	with	with	ADP
cana-5719	234	102	a	a	DET
cana-5719	234	103	frozen	frozen	ADJ
cana-5719	234	104	layer	layer	NOUN
cana-5719	234	105	exhibits	exhibit	VERB
cana-5719	234	106	the	the	DET
cana-5719	234	107	most	most	ADV
cana-5719	234	108	consistent	consistent	ADJ
cana-5719	234	109	and	and	CCONJ
cana-5719	234	110	superior	superior	ADJ
cana-5719	234	111	performance	performance	NOUN
cana-5719	234	112	across	across	ADP
cana-5719	234	113	all	all	DET
cana-5719	234	114	splits	split	NOUN
cana-5719	234	115	,	,	PUNCT
cana-5719	234	116	making	make	VERB
cana-5719	234	117	it	it	PRON
cana-5719	234	118	the	the	DET
cana-5719	234	119	most	most	ADV
cana-5719	234	120	effective	effective	ADJ
cana-5719	234	121	model	model	NOUN
cana-5719	234	122	for	for	ADP
cana-5719	234	123	classification	classification	NOUN
cana-5719	234	124	tasks	task	NOUN
cana-5719	234	125	.	.	PUNCT
cana-5719	235	1	5.4	5.4	NUM
cana-5719	235	2	.	.	PUNCT
cana-5719	235	3	confusion	confusion	NOUN
cana-5719	235	4	matrix	matrix	NOUN
cana-5719	235	5	:	:	PUNCT
cana-5719	235	6	the	the	DET
cana-5719	235	7	figure	figure	NOUN
cana-5719	235	8	10	10	NUM
cana-5719	235	9	presents	present	VERB
cana-5719	235	10	a	a	DET
cana-5719	235	11	confusion	confusion	NOUN
cana-5719	235	12	matrix	matrix	NOUN
cana-5719	235	13	that	that	PRON
cana-5719	235	14	compares	compare	VERB
cana-5719	235	15	the	the	DET
cana-5719	235	16	actual	actual	ADJ
cana-5719	235	17	and	and	CCONJ
cana-5719	235	18	predicted	predict	VERB
cana-5719	235	19	labels	label	NOUN
cana-5719	235	20	,	,	PUNCT
cana-5719	235	21	providing	provide	VERB
cana-5719	235	22	an	an	DET
cana-5719	235	23	evaluation	evaluation	NOUN
cana-5719	235	24	of	of	ADP
cana-5719	235	25	the	the	DET
cana-5719	235	26	classification	classification	NOUN
cana-5719	235	27	model	model	NOUN
cana-5719	235	28	's	's	PART
cana-5719	235	29	performance	performance	NOUN
cana-5719	235	30	in	in	ADP
cana-5719	235	31	detecting	detect	VERB
cana-5719	235	32	four	four	NUM
cana-5719	235	33	types	type	NOUN
cana-5719	235	34	of	of	ADP
cana-5719	235	35	brain	brain	NOUN
cana-5719	235	36	tumors	tumor	NOUN
cana-5719	235	37	:	:	PUNCT
cana-5719	235	38	glioma	glioma	NOUN
cana-5719	235	39	,	,	PUNCT
cana-5719	235	40	meningioma	meningioma	NOUN
cana-5719	235	41	,	,	PUNCT
cana-5719	235	42	notumor	notumor	ADJ
cana-5719	235	43	,	,	PUNCT
cana-5719	235	44	and	and	CCONJ
cana-5719	235	45	pituitary	pituitary	NOUN
cana-5719	235	46	.	.	PUNCT
cana-5719	236	1	in	in	ADP
cana-5719	236	2	this	this	DET
cana-5719	236	3	matrix	matrix	NOUN
cana-5719	236	4	,	,	PUNCT
cana-5719	236	5	rows	row	NOUN
cana-5719	236	6	correspond	correspond	VERB
cana-5719	236	7	to	to	ADP
cana-5719	236	8	the	the	DET
cana-5719	236	9	actual	actual	ADJ
cana-5719	236	10	classes	class	NOUN
cana-5719	236	11	,	,	PUNCT
cana-5719	236	12	while	while	SCONJ
cana-5719	236	13	columns	column	NOUN
cana-5719	236	14	represent	represent	VERB
cana-5719	236	15	the	the	DET
cana-5719	236	16	predicted	predict	VERB
cana-5719	236	17	classes	class	NOUN
cana-5719	236	18	.	.	PUNCT
cana-5719	237	1	the	the	DET
cana-5719	237	2	model	model	NOUN
cana-5719	237	3	demonstrates	demonstrate	VERB
cana-5719	237	4	strong	strong	ADJ
cana-5719	237	5	performance	performance	NOUN
cana-5719	237	6	in	in	ADP
cana-5719	237	7	accurately	accurately	ADV
cana-5719	237	8	identifying	identify	VERB
cana-5719	237	9	notumor	notumor	ADJ
cana-5719	237	10	and	and	CCONJ
cana-5719	237	11	pituitary	pituitary	ADJ
cana-5719	237	12	cases	case	NOUN
cana-5719	237	13	.	.	PUNCT
cana-5719	238	1	however	however	ADV
cana-5719	238	2	,	,	PUNCT
cana-5719	238	3	the	the	DET
cana-5719	238	4	highest	high	ADJ
cana-5719	238	5	level	level	NOUN
cana-5719	238	6	of	of	ADP
cana-5719	238	7	misclassification	misclassification	NOUN
cana-5719	238	8	occurs	occur	VERB
cana-5719	238	9	between	between	ADP
cana-5719	238	10	meningioma	meningioma	NOUN
cana-5719	238	11	and	and	CCONJ
cana-5719	238	12	notumor	notumor	NOUN
cana-5719	238	13	,	,	PUNCT
cana-5719	238	14	with	with	SCONJ
cana-5719	238	15	35	35	NUM
cana-5719	238	16	meningioma	meningioma	NOUN
cana-5719	238	17	instances	instance	NOUN
cana-5719	238	18	incorrectly	incorrectly	ADV
cana-5719	238	19	predicted	predict	VERB
cana-5719	238	20	as	as	ADP
cana-5719	238	21	notumor	notumor	ADJ
cana-5719	238	22	.	.	PUNCT
cana-5719	239	1	despite	despite	SCONJ
cana-5719	239	2	this	this	PRON
cana-5719	239	3	,	,	PUNCT
cana-5719	239	4	the	the	DET
cana-5719	239	5	prominent	prominent	ADJ
cana-5719	239	6	values	value	NOUN
cana-5719	239	7	along	along	ADP
cana-5719	239	8	the	the	DET
cana-5719	239	9	diagonal	diagonal	ADJ
cana-5719	239	10	indicate	indicate	VERB
cana-5719	239	11	that	that	SCONJ
cana-5719	239	12	the	the	DET
cana-5719	239	13	model	model	NOUN
cana-5719	239	14	generally	generally	ADV
cana-5719	239	15	achieves	achieve	VERB
cana-5719	239	16	good	good	ADJ
cana-5719	239	17	classification	classification	NOUN
cana-5719	239	18	accuracy	accuracy	NOUN
cana-5719	239	19	.	.	PUNCT
cana-5719	240	1	the	the	DET
cana-5719	240	2	accompanying	accompany	VERB
cana-5719	240	3	image	image	NOUN
cana-5719	240	4	shows	show	VERB
cana-5719	240	5	the	the	DET
cana-5719	240	6	confusion	confusion	NOUN
cana-5719	240	7	matrix	matrix	NOUN
cana-5719	240	8	that	that	PRON
cana-5719	240	9	generated	generate	VERB
cana-5719	240	10	by	by	ADP
cana-5719	240	11	the	the	DET
cana-5719	240	12	analysis	analysis	NOUN
cana-5719	240	13	of	of	ADP
cana-5719	240	14	our	our	PRON
cana-5719	240	15	approach	approach	NOUN
cana-5719	240	16	utilising	utilise	VERB
cana-5719	240	17	brain	brain	NOUN
cana-5719	240	18	tumour	tumour	NOUN
cana-5719	240	19	test	test	NOUN
cana-5719	240	20	datasets	dataset	NOUN
cana-5719	240	21	.	.	PUNCT
cana-5719	241	1	the	the	DET
cana-5719	241	2	accurate	accurate	ADJ
cana-5719	241	3	and	and	CCONJ
cana-5719	241	4	incorrect	incorrect	ADJ
cana-5719	241	5	classifications	classification	NOUN
cana-5719	241	6	for	for	ADP
cana-5719	241	7	each	each	DET
cana-5719	241	8	form	form	NOUN
cana-5719	241	9	of	of	ADP
cana-5719	241	10	tumour	tumour	NOUN
cana-5719	241	11	are	be	AUX
cana-5719	241	12	broken	break	VERB
cana-5719	241	13	down	down	ADP
cana-5719	241	14	in	in	ADP
cana-5719	241	15	detail	detail	NOUN
cana-5719	241	16	in	in	ADP
cana-5719	241	17	the	the	DET
cana-5719	241	18	following	follow	VERB
cana-5719	241	19	table	table	NOUN
cana-5719	241	20	9	9	NUM
cana-5719	241	21	.	.	PUNCT
cana-5719	242	1	communications	communication	NOUN
cana-5719	242	2	on	on	ADP
cana-5719	242	3	applied	apply	VERB
cana-5719	242	4	nonlinear	nonlinear	ADJ
cana-5719	242	5	analysis	analysis	NOUN
cana-5719	242	6	issn	issn	NOUN
cana-5719	242	7	:	:	PUNCT
cana-5719	242	8	1074	1074	NUM
cana-5719	242	9	-	-	PUNCT
cana-5719	242	10	133x	133x	NUM
cana-5719	242	11	vol	vol	VERB
cana-5719	242	12	32	32	NUM
cana-5719	242	13	no	no	NOUN
cana-5719	242	14	.	.	PUNCT
cana-5719	243	1	10s	10	NOUN
cana-5719	243	2	(	(	PUNCT
cana-5719	243	3	2025	2025	NUM
cana-5719	243	4	)	)	PUNCT
cana-5719	243	5	2797	2797	NUM
cana-5719	243	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5719	243	7	fig.10.confusion	fig.10.confusion	NOUN
cana-5719	243	8	matrix	matrix	NOUN
cana-5719	243	9	table	table	NOUN
cana-5719	243	10	10.classification	10.classification	NUM
cana-5719	243	11	and	and	CCONJ
cana-5719	243	12	misclassification	misclassification	NOUN
cana-5719	243	13	sl	sl	VERB
cana-5719	243	14	no	no	DET
cana-5719	243	15	tumour	tumour	NOUN
cana-5719	243	16	types	type	NOUN
cana-5719	243	17	classification	classification	NOUN
cana-5719	243	18	type	type	NOUN
cana-5719	243	19	misclassification	misclassification	NOUN
cana-5719	243	20	type	type	NOUN
cana-5719	243	21	01	01	NUM
cana-5719	243	22	gliomas	glioma	NOUN
cana-5719	243	23	289	289	NUM
cana-5719	243	24	23	23	NUM
cana-5719	243	25	02	02	NUM
cana-5719	243	26	meningiomas	meningioma	NOUN
cana-5719	244	1	254	254	NUM
cana-5719	244	2	12	12	NUM
cana-5719	244	3	03	03	NUM
cana-5719	244	4	notumor	notumor	ADJ
cana-5719	244	5	400	400	NUM
cana-5719	244	6	35	35	NUM
cana-5719	244	7	04	04	NUM
cana-5719	244	8	pituitary	pituitary	NOUN
cana-5719	244	9	292	292	NUM
cana-5719	244	10	06	06	NUM
cana-5719	244	11	5.5	5.5	NUM
cana-5719	244	12	comparison	comparison	NOUN
cana-5719	244	13	with	with	ADP
cana-5719	244	14	existing	exist	VERB
cana-5719	244	15	models	model	NOUN
cana-5719	244	16	:	:	PUNCT
cana-5719	244	17	the	the	DET
cana-5719	244	18	proposed	propose	VERB
cana-5719	244	19	approach	approach	NOUN
cana-5719	244	20	is	be	AUX
cana-5719	244	21	contrasted	contrast	VERB
cana-5719	244	22	with	with	ADP
cana-5719	244	23	other	other	ADJ
cana-5719	244	24	models	model	NOUN
cana-5719	244	25	,	,	PUNCT
cana-5719	244	26	and	and	CCONJ
cana-5719	244	27	table	table	NOUN
cana-5719	244	28	11	11	NUM
cana-5719	244	29	shows	show	VERB
cana-5719	244	30	a	a	DET
cana-5719	244	31	comparison	comparison	NOUN
cana-5719	244	32	of	of	ADP
cana-5719	244	33	the	the	DET
cana-5719	244	34	suggested	suggest	VERB
cana-5719	244	35	strategy	strategy	NOUN
cana-5719	244	36	with	with	ADP
cana-5719	244	37	the	the	DET
cana-5719	244	38	current	current	ADJ
cana-5719	244	39	approaches	approach	NOUN
cana-5719	244	40	.	.	PUNCT
cana-5719	245	1	table	table	NOUN
cana-5719	245	2	11	11	NUM
cana-5719	245	3	.	.	PUNCT
cana-5719	246	1	comparative	comparative	ADJ
cana-5719	246	2	study	study	NOUN
cana-5719	246	3	of	of	ADP
cana-5719	246	4	existing	exist	VERB
cana-5719	246	5	methods	method	NOUN
cana-5719	246	6	with	with	ADP
cana-5719	246	7	proposed	propose	VERB
cana-5719	246	8	method	method	NOUN
cana-5719	246	9	sl.no	sl.no	NOUN
cana-5719	246	10	author	author	NOUN
cana-5719	246	11	methodology	methodology	NOUN
cana-5719	246	12	adopted	adopt	VERB
cana-5719	246	13	overall	overall	ADJ
cana-5719	246	14	accuracy	accuracy	NOUN
cana-5719	246	15	(	(	PUNCT
cana-5719	246	16	in	in	ADP
cana-5719	246	17	%	%	NOUN
cana-5719	246	18	)	)	PUNCT
cana-5719	246	19	01	01	NUM
cana-5719	246	20	tonmoy	tonmoy	PROPN
cana-5719	246	21	hossain	hossain	PROPN
cana-5719	246	22	et.al	et.al	PROPN
cana-5719	247	1	[	[	X
cana-5719	247	2	20	20	NUM
cana-5719	247	3	]	]	PUNCT
cana-5719	247	4	cnn	cnn	PROPN
cana-5719	247	5	97.87	97.87	NUM
cana-5719	247	6	02	02	NUM
cana-5719	247	7	francisco	francisco	PROPN
cana-5719	247	8	javier	javier	PROPN
cana-5719	247	9	mccn	mccn	PROPN
cana-5719	247	10	97.30	97.30	NUM
cana-5719	247	11	díaz	díaz	NOUN
cana-5719	247	12	-	-	PUNCT
cana-5719	247	13	pernas	pernas	NOUN
cana-5719	247	14	et.al	et.al	PROPN
cana-5719	248	1	[	[	X
cana-5719	248	2	21	21	NUM
cana-5719	248	3	]	]	SYM
cana-5719	248	4	03	03	NUM
cana-5719	248	5	maad	maad	PROPN
cana-5719	248	6	m.	m.	NOUN
cana-5719	248	7	mijwil	mijwil	PROPN
cana-5719	248	8	et.al	et.al	PROPN
cana-5719	248	9	[	[	X
cana-5719	248	10	22	22	NUM
cana-5719	248	11	]	]	X
cana-5719	248	12	mobilenetv1	mobilenetv1	NOUN
cana-5719	248	13	97.00	97.00	NUM
cana-5719	248	14	communications	communication	NOUN
cana-5719	248	15	on	on	ADP
cana-5719	248	16	applied	apply	VERB
cana-5719	248	17	nonlinear	nonlinear	ADJ
cana-5719	248	18	analysis	analysis	NOUN
cana-5719	248	19	issn	issn	NOUN
cana-5719	248	20	:	:	PUNCT
cana-5719	248	21	1074	1074	NUM
cana-5719	248	22	-	-	PUNCT
cana-5719	248	23	133x	133x	NUM
cana-5719	248	24	vol	vol	VERB
cana-5719	248	25	32	32	NUM
cana-5719	248	26	no	no	NOUN
cana-5719	248	27	.	.	PUNCT
cana-5719	249	1	10s	10	NOUN
cana-5719	249	2	(	(	PUNCT
cana-5719	249	3	2025	2025	NUM
cana-5719	249	4	)	)	PUNCT
cana-5719	249	5	2798	2798	NUM
cana-5719	249	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5719	249	7	04	04	NUM
cana-5719	249	8	marco	marco	PROPN
cana-5719	249	9	antonio	antonio	PROPN
cana-5719	249	10	gómez	gómez	NOUN
cana-5719	249	11	-	-	PUNCT
cana-5719	249	12	guzmán	guzmán	ADJ
cana-5719	249	13	et.al	et.al	NOUN
cana-5719	250	1	[	[	X
cana-5719	250	2	23	23	NUM
cana-5719	250	3	]	]	PUNCT
cana-5719	250	4	inception	inception	PROPN
cana-5719	250	5	v3	v3	PROPN
cana-5719	250	6	97.12	97.12	NUM
cana-5719	250	7	05	05	NUM
cana-5719	251	1	lu	lu	NOUN
cana-5719	251	2	xu	xu	PROPN
cana-5719	252	1	et.al	et.al	PROPN
cana-5719	253	1	[	[	X
cana-5719	253	2	24	24	NUM
cana-5719	253	3	]	]	X
cana-5719	253	4	mn	mn	PROPN
cana-5719	253	5	-	-	PUNCT
cana-5719	253	6	v2	v2	NOUN
cana-5719	253	7	/	/	SYM
cana-5719	253	8	cfo	cfo	NOUN
cana-5719	253	9	97.32	97.32	NUM
cana-5719	253	10	06	06	NUM
cana-5719	253	11	narayanan	narayanan	PROPN
cana-5719	253	12	krishanasamy	krishanasamy	PROPN
cana-5719	253	13	fcns	fcns	PROPN
cana-5719	253	14	and	and	CCONJ
cana-5719	253	15	resnets	resnet	NOUN
cana-5719	253	16	93.90	93.90	NUM
cana-5719	253	17	et.al	et.al	NOUN
cana-5719	253	18	[	[	X
cana-5719	253	19	25	25	NUM
cana-5719	253	20	]	]	SYM
cana-5719	253	21	07	07	NUM
cana-5719	253	22	proposed	propose	VERB
cana-5719	253	23	method	method	NOUN
cana-5719	253	24	mobilenetv2	mobilenetv2	PROPN
cana-5719	254	1	98.33	98.33	NUM
cana-5719	254	2	6	6	NUM
cana-5719	254	3	.	.	PUNCT
cana-5719	255	1	conclusion	conclusion	NOUN
cana-5719	255	2	this	this	DET
cana-5719	255	3	research	research	NOUN
cana-5719	255	4	paper	paper	NOUN
cana-5719	255	5	introduces	introduce	VERB
cana-5719	255	6	a	a	DET
cana-5719	255	7	novel	novel	ADJ
cana-5719	255	8	method	method	NOUN
cana-5719	255	9	for	for	ADP
cana-5719	255	10	classifying	classify	VERB
cana-5719	255	11	brain	brain	NOUN
cana-5719	255	12	tumours	tumour	NOUN
cana-5719	255	13	using	use	VERB
cana-5719	255	14	transfer	transfer	NOUN
cana-5719	255	15	learning	learning	NOUN
cana-5719	255	16	and	and	CCONJ
cana-5719	255	17	the	the	DET
cana-5719	255	18	mobilenetv2	mobilenetv2	PROPN
cana-5719	255	19	architecture	architecture	NOUN
cana-5719	255	20	.	.	PUNCT
cana-5719	256	1	using	use	VERB
cana-5719	256	2	a	a	DET
cana-5719	256	3	dataset	dataset	NOUN
cana-5719	256	4	of	of	ADP
cana-5719	256	5	brain	brain	NOUN
cana-5719	256	6	mri	mri	NOUN
cana-5719	256	7	images	image	NOUN
cana-5719	256	8	,	,	PUNCT
cana-5719	256	9	we	we	PRON
cana-5719	256	10	successfully	successfully	ADV
cana-5719	256	11	differentiated	differentiate	VERB
cana-5719	256	12	between	between	ADP
cana-5719	256	13	tumour	tumour	NOUN
cana-5719	256	14	(	(	PUNCT
cana-5719	256	15	gliomas	glioma	NOUN
cana-5719	256	16	,	,	PUNCT
cana-5719	256	17	meningiomas	meningioma	NOUN
cana-5719	256	18	,	,	PUNCT
cana-5719	256	19	&	&	CCONJ
cana-5719	256	20	pituitary	pituitary	NOUN
cana-5719	256	21	)	)	PUNCT
cana-5719	256	22	and	and	CCONJ
cana-5719	256	23	non	non	ADJ
cana-5719	256	24	-	-	ADJ
cana-5719	256	25	tumor	tumor	ADJ
cana-5719	256	26	classes	class	NOUN
cana-5719	256	27	by	by	ADP
cana-5719	256	28	utilising	utilise	VERB
cana-5719	256	29	powerful	powerful	ADJ
cana-5719	256	30	features	feature	NOUN
cana-5719	256	31	.	.	PUNCT
cana-5719	257	1	discriminative	discriminative	NOUN
cana-5719	257	2	visual	visual	ADJ
cana-5719	257	3	features	feature	NOUN
cana-5719	257	4	and	and	CCONJ
cana-5719	257	5	patterns	pattern	NOUN
cana-5719	257	6	were	be	AUX
cana-5719	257	7	extracted	extract	VERB
cana-5719	257	8	from	from	ADP
cana-5719	257	9	mri	mri	NOUN
cana-5719	257	10	slices	slice	NOUN
cana-5719	257	11	using	use	VERB
cana-5719	257	12	transfer	transfer	NOUN
cana-5719	257	13	learning	learning	NOUN
cana-5719	257	14	,	,	PUNCT
cana-5719	257	15	which	which	PRON
cana-5719	257	16	allowed	allow	VERB
cana-5719	257	17	the	the	DET
cana-5719	257	18	suggested	suggested	ADJ
cana-5719	257	19	model	model	NOUN
cana-5719	257	20	to	to	PART
cana-5719	257	21	reach	reach	VERB
cana-5719	257	22	a	a	DET
cana-5719	257	23	maximum	maximum	ADJ
cana-5719	257	24	accuracy	accuracy	NOUN
cana-5719	257	25	of	of	ADP
cana-5719	257	26	98.33	98.33	NUM
cana-5719	257	27	%	%	NOUN
cana-5719	257	28	.	.	PUNCT
cana-5719	258	1	the	the	DET
cana-5719	258	2	future	future	ADJ
cana-5719	258	3	direction	direction	NOUN
cana-5719	258	4	will	will	AUX
cana-5719	258	5	investigate	investigate	VERB
cana-5719	258	6	sophisticated	sophisticated	ADJ
cana-5719	258	7	deep	deep	ADJ
cana-5719	258	8	neural	neural	ADJ
cana-5719	258	9	network	network	NOUN
cana-5719	258	10	designs	design	NOUN
cana-5719	258	11	for	for	ADP
cana-5719	258	12	brain	brain	NOUN
cana-5719	258	13	tumour	tumour	NOUN
cana-5719	258	14	classification	classification	NOUN
cana-5719	258	15	.	.	PUNCT
cana-5719	259	1	refrences	refrence	VERB
cana-5719	260	1	[	[	X
cana-5719	260	2	1	1	NUM
cana-5719	260	3	]	]	X
cana-5719	260	4	razzak	razzak	PROPN
cana-5719	260	5	m.	m.	NOUN
cana-5719	260	6	i	i	PRON
cana-5719	260	7	,	,	PUNCT
cana-5719	260	8	naz	naz	PROPN
cana-5719	260	9	s	s	PROPN
cana-5719	260	10	,	,	PUNCT
cana-5719	260	11	zaib	zaib	PROPN
cana-5719	260	12	a	a	PRON
cana-5719	260	13	,	,	PUNCT
cana-5719	260	14	“	"	PUNCT
cana-5719	260	15	deep	deep	ADJ
cana-5719	260	16	learning	learning	NOUN
cana-5719	260	17	for	for	ADP
cana-5719	260	18	medical	medical	ADJ
cana-5719	260	19	image	image	NOUN
cana-5719	260	20	processing	processing	NOUN
cana-5719	260	21	:	:	PUNCT
cana-5719	260	22	overview	overview	NOUN
cana-5719	260	23	,	,	PUNCT
cana-5719	260	24	challenges	challenge	NOUN
cana-5719	260	25	and	and	CCONJ
cana-5719	260	26	the	the	DET
cana-5719	260	27	future	future	NOUN
cana-5719	260	28	”	"	PUNCT
cana-5719	260	29	,	,	PUNCT
cana-5719	260	30	computer	computer	NOUN
cana-5719	260	31	vision	vision	NOUN
cana-5719	260	32	and	and	CCONJ
cana-5719	260	33	pattern	pattern	NOUN
cana-5719	260	34	recognition	recognition	NOUN
cana-5719	260	35	,	,	PUNCT
cana-5719	260	36	(	(	PUNCT
cana-5719	260	37	2018	2018	NUM
cana-5719	260	38	)	)	PUNCT
cana-5719	260	39	.	.	PUNCT
cana-5719	261	1	https://doi.org/10.48550/arxiv.1704.06825	https://doi.org/10.48550/arxiv.1704.06825	PROPN
cana-5719	262	1	[	[	X
cana-5719	262	2	2	2	NUM
cana-5719	262	3	]	]	PUNCT
cana-5719	262	4	rehman	rehman	NOUN
cana-5719	262	5	a	a	PROPN
cana-5719	262	6	,	,	PUNCT
cana-5719	262	7	naz	naz	PROPN
cana-5719	262	8	s	s	PROPN
cana-5719	262	9	,	,	PUNCT
cana-5719	262	10	razzak	razzak	PROPN
cana-5719	262	11	,	,	PUNCT
cana-5719	262	12	hameed	hameed	PROPN
cana-5719	262	13	a.i	a.i	PROPN
cana-5719	262	14	,	,	PUNCT
cana-5719	262	15	“	"	PUNCT
cana-5719	262	16	automatic	automatic	ADJ
cana-5719	262	17	visual	visual	ADJ
cana-5719	262	18	features	feature	NOUN
cana-5719	262	19	for	for	ADP
cana-5719	262	20	writer	writer	NOUN
cana-5719	262	21	identification	identification	NOUN
cana-5719	262	22	:	:	PUNCT
cana-5719	262	23	a	a	DET
cana-5719	262	24	deep	deep	ADJ
cana-5719	262	25	learning	learning	NOUN
cana-5719	262	26	approach	approach	NOUN
cana-5719	262	27	”	"	PUNCT
cana-5719	262	28	,	,	PUNCT
cana-5719	262	29	ieee	ieee	NOUN
cana-5719	262	30	access	access	NOUN
cana-5719	262	31	,	,	PUNCT
cana-5719	262	32	(	(	PUNCT
cana-5719	262	33	2019	2019	NUM
cana-5719	262	34	)	)	PUNCT
cana-5719	262	35	.	.	PUNCT
cana-5719	263	1	http://doi.org/10.1109/access.2018.2890810	http://doi.org/10.1109/access.2018.2890810	PROPN
cana-5719	264	1	[	[	X
cana-5719	264	2	3	3	X
cana-5719	264	3	]	]	X
cana-5719	264	4	bauer	bauer	PROPN
cana-5719	264	5	s	s	PROPN
cana-5719	264	6	,	,	PUNCT
cana-5719	264	7	may	may	AUX
cana-5719	264	8	c	c	X
cana-5719	264	9	,	,	PUNCT
cana-5719	264	10	dionysiou	dionysiou	NOUN
cana-5719	264	11	d	d	PROPN
cana-5719	264	12	,	,	PUNCT
cana-5719	264	13	stamatakos	stamatakos	VERB
cana-5719	264	14	g	g	NOUN
cana-5719	264	15	,	,	PUNCT
cana-5719	264	16	buchler	buchler	NOUN
cana-5719	264	17	p	p	NOUN
cana-5719	264	18	,	,	PUNCT
cana-5719	264	19	reyes	reyes	PROPN
cana-5719	264	20	m	m	PROPN
cana-5719	264	21	,	,	PUNCT
cana-5719	264	22	“	"	PUNCT
cana-5719	264	23	multi	multi	ADJ
cana-5719	264	24	-	-	NOUN
cana-5719	264	25	scale	scale	ADJ
cana-5719	264	26	modeling	modeling	NOUN
cana-5719	264	27	for	for	ADP
cana-5719	264	28	image	image	NOUN
cana-5719	264	29	analysis	analysis	NOUN
cana-5719	264	30	of	of	ADP
cana-5719	264	31	brain	brain	NOUN
cana-5719	264	32	tumor	tumor	NOUN
cana-5719	264	33	studies	study	NOUN
cana-5719	264	34	”	"	PUNCT
cana-5719	264	35	,	,	PUNCT
cana-5719	264	36	transactions	transaction	NOUN
cana-5719	264	37	on	on	ADP
cana-5719	264	38	biomedical	biomedical	ADJ
cana-5719	264	39	engineering	engineering	NOUN
cana-5719	264	40	,	,	PUNCT
cana-5719	264	41	(	(	PUNCT
cana-5719	264	42	2011	2011	NUM
cana-5719	264	43	)	)	PUNCT
cana-5719	264	44	.	.	PUNCT
cana-5719	265	1	http://doi.org/10.1109/tbme.2011.2163406	http://doi.org/10.1109/tbme.2011.2163406	PROPN
cana-5719	265	2	[	[	X
cana-5719	265	3	4	4	X
cana-5719	265	4	]	]	X
cana-5719	265	5	liu	liu	PROPN
cana-5719	265	6	j	j	PROPN
cana-5719	265	7	,	,	PUNCT
cana-5719	265	8	li	li	PROPN
cana-5719	265	9	min	min	PROPN
cana-5719	265	10	,	,	PUNCT
cana-5719	265	11	wang	wang	PROPN
cana-5719	265	12	j	j	PROPN
cana-5719	265	13	,	,	PUNCT
cana-5719	265	14	wu	wu	PROPN
cana-5719	265	15	f	f	PROPN
cana-5719	265	16	,	,	PUNCT
cana-5719	265	17	liu	liu	PROPN
cana-5719	265	18	t	t	PROPN
cana-5719	265	19	,	,	PUNCT
cana-5719	265	20	pan	pan	PROPN
cana-5719	265	21	y	y	PROPN
cana-5719	265	22	,	,	PUNCT
cana-5719	265	23	“	"	PUNCT
cana-5719	265	24	a	a	DET
cana-5719	265	25	survey	survey	NOUN
cana-5719	265	26	of	of	ADP
cana-5719	265	27	mri	mri	NOUN
cana-5719	265	28	-	-	PUNCT
cana-5719	265	29	based	base	VERB
cana-5719	265	30	brain	brain	NOUN
cana-5719	265	31	tumor	tumor	NOUN
cana-5719	265	32	segmentation	segmentation	NOUN
cana-5719	265	33	methods	method	NOUN
cana-5719	265	34	,	,	PUNCT
cana-5719	265	35	tsinghua	tsinghua	PROPN
cana-5719	265	36	science	science	PROPN
cana-5719	265	37	and	and	CCONJ
cana-5719	265	38	technology	technology	NOUN
cana-5719	265	39	”	"	PUNCT
cana-5719	265	40	,	,	PUNCT
cana-5719	265	41	(	(	PUNCT
cana-5719	265	42	2014	2014	NUM
cana-5719	265	43	)	)	PUNCT
cana-5719	265	44	.	.	PUNCT
cana-5719	266	1	http://doi.org/10.1109/tst.2014.6961028	http://doi.org/10.1109/tst.2014.6961028	NOUN
cana-5719	267	1	[	[	X
cana-5719	267	2	5	5	NUM
cana-5719	267	3	]	]	PUNCT
cana-5719	267	4	menze	menze	PROPN
cana-5719	267	5	b	b	PROPN
cana-5719	267	6	h	h	PROPN
cana-5719	267	7	,	,	PUNCT
cana-5719	267	8	reyes	reyes	PROPN
cana-5719	267	9	m	m	PROPN
cana-5719	267	10	,	,	PUNCT
cana-5719	267	11	leemput	leemput	PROPN
cana-5719	267	12	van	van	PROPN
cana-5719	267	13	,	,	PUNCT
cana-5719	267	14	“	"	PUNCT
cana-5719	267	15	the	the	DET
cana-5719	267	16	multimodal	multimodal	ADJ
cana-5719	267	17	brain	brain	NOUN
cana-5719	267	18	tumor	tumor	NOUN
cana-5719	267	19	image	image	NOUN
cana-5719	267	20	segmentation	segmentation	NOUN
cana-5719	267	21	benchmark	benchmark	NOUN
cana-5719	267	22	(	(	PUNCT
cana-5719	267	23	brats	brat	NOUN
cana-5719	267	24	)	)	PUNCT
cana-5719	267	25	”	"	PUNCT
cana-5719	267	26	,	,	PUNCT
cana-5719	267	27	ieee	ieee	NOUN
cana-5719	267	28	transactions	transaction	NOUN
cana-5719	267	29	on	on	ADP
cana-5719	267	30	medical	medical	ADJ
cana-5719	267	31	imaging	imaging	NOUN
cana-5719	267	32	,	,	PUNCT
cana-5719	267	33	(	(	PUNCT
cana-5719	267	34	2015	2015	NUM
cana-5719	267	35	)	)	PUNCT
cana-5719	267	36	.	.	PUNCT
cana-5719	268	1	http://doi.org/10.1109/tmi.2014.2377694	http://doi.org/10.1109/tmi.2014.2377694	NOUN
cana-5719	269	1	[	[	X
cana-5719	269	2	6	6	NUM
cana-5719	269	3	]	]	X
cana-5719	269	4	mohsen	mohsen	PROPN
cana-5719	269	5	h	h	PROPN
cana-5719	269	6	,	,	PUNCT
cana-5719	269	7	el	el	PROPN
cana-5719	269	8	-	-	PROPN
cana-5719	269	9	dahshan	dahshan	PROPN
cana-5719	269	10	e.a	e.a	PROPN
cana-5719	269	11	,	,	PUNCT
cana-5719	269	12	el	el	PROPN
cana-5719	269	13	-	-	PROPN
cana-5719	269	14	horbaty	horbaty	PROPN
cana-5719	269	15	e.m	e.m	PROPN
cana-5719	269	16	,	,	PUNCT
cana-5719	269	17	salem	salem	PROPN
cana-5719	269	18	a.m	a.m	PROPN
cana-5719	269	19	,	,	PUNCT
cana-5719	269	20	“	"	PUNCT
cana-5719	269	21	classification	classification	NOUN
cana-5719	269	22	using	use	VERB
cana-5719	269	23	deep	deep	ADJ
cana-5719	269	24	learning	learn	VERB
cana-5719	269	25	neural	neural	ADJ
cana-5719	269	26	networks	network	NOUN
cana-5719	269	27	for	for	ADP
cana-5719	269	28	brain	brain	NOUN
cana-5719	269	29	tumors	tumor	NOUN
cana-5719	269	30	”	"	PUNCT
cana-5719	269	31	,	,	PUNCT
cana-5719	269	32	future	future	ADJ
cana-5719	269	33	computing	computing	NOUN
cana-5719	269	34	and	and	CCONJ
cana-5719	269	35	informatics	informatics	PROPN
cana-5719	269	36	journal	journal	PROPN
cana-5719	269	37	(	(	PUNCT
cana-5719	269	38	2017	2017	NUM
cana-5719	269	39	)	)	PUNCT
cana-5719	269	40	,	,	PUNCT
cana-5719	269	41	https://doi.org/10.1016/j.fcij.2017.12.001	https://doi.org/10.1016/j.fcij.2017.12.001	PROPN
cana-5719	269	42	[	[	X
cana-5719	269	43	7	7	X
cana-5719	269	44	]	]	X
cana-5719	269	45	mohammed	mohammed	PROPN
cana-5719	269	46	b	b	PROPN
cana-5719	269	47	a	a	PROPN
cana-5719	269	48	,	,	PUNCT
cana-5719	269	49	al	al	PROPN
cana-5719	269	50	-	-	PUNCT
cana-5719	269	51	ani	ani	NOUN
cana-5719	269	52	m	m	PROPN
cana-5719	269	53	s	s	NOUN
cana-5719	269	54	,	,	PUNCT
cana-5719	269	55	“	"	PUNCT
cana-5719	269	56	an	an	DET
cana-5719	269	57	efficient	efficient	ADJ
cana-5719	269	58	approach	approach	NOUN
cana-5719	269	59	to	to	AUX
cana-5719	269	60	diagnose	diagnose	NOUN
cana-5719	269	61	brain	brain	NOUN
cana-5719	269	62	tumors	tumor	NOUN
cana-5719	269	63	through	through	ADP
cana-5719	269	64	deep	deep	ADJ
cana-5719	269	65	cnn	cnn	PROPN
cana-5719	269	66	”	"	PUNCT
cana-5719	269	67	,	,	PUNCT
cana-5719	269	68	mathematical	mathematical	ADJ
cana-5719	269	69	biosciences	bioscience	NOUN
cana-5719	269	70	and	and	CCONJ
cana-5719	269	71	engineering	engineering	NOUN
cana-5719	269	72	,	,	PUNCT
cana-5719	269	73	(	(	PUNCT
cana-5719	269	74	2021	2021	NUM
cana-5719	269	75	)	)	PUNCT
cana-5719	269	76	.	.	PUNCT
cana-5719	270	1	https://doi.org/	https://doi.org/	VERB
cana-5719	270	2	10.3934	10.3934	NUM
cana-5719	270	3	/	/	SYM
cana-5719	270	4	mbe.2021045	mbe.2021045	NUM
cana-5719	270	5	[	[	X
cana-5719	270	6	8	8	NUM
cana-5719	270	7	]	]	X
cana-5719	270	8	irmak	irmak	PROPN
cana-5719	270	9	emrah	emrah	PROPN
cana-5719	270	10	,	,	PUNCT
cana-5719	270	11	“	"	PUNCT
cana-5719	270	12	multi	multi	ADJ
cana-5719	270	13	-	-	NOUN
cana-5719	270	14	classification	classification	NOUN
cana-5719	270	15	of	of	ADP
cana-5719	270	16	brain	brain	NOUN
cana-5719	270	17	tumor	tumor	NOUN
cana-5719	270	18	mri	mri	NOUN
cana-5719	270	19	images	image	NOUN
cana-5719	270	20	using	use	VERB
cana-5719	270	21	deep	deep	ADJ
cana-5719	270	22	convolutional	convolutional	ADJ
cana-5719	270	23	neural	neural	ADJ
cana-5719	270	24	network	network	NOUN
cana-5719	270	25	with	with	ADP
cana-5719	270	26	fully	fully	ADV
cana-5719	270	27	optimized	optimize	VERB
cana-5719	270	28	framework	framework	NOUN
cana-5719	270	29	”	"	PUNCT
cana-5719	270	30	,	,	PUNCT
cana-5719	270	31	iranian	iranian	ADJ
cana-5719	270	32	journal	journal	PROPN
cana-5719	270	33	of	of	ADP
cana-5719	270	34	science	science	PROPN
cana-5719	270	35	and	and	CCONJ
cana-5719	270	36	https://doi.org/10.48550/arxiv.1704.06825	https://doi.org/10.48550/arxiv.1704.06825	PROPN
cana-5719	270	37	http://doi.org/10.1109/access.2018.2890810	http://doi.org/10.1109/access.2018.2890810	PROPN
cana-5719	271	1	https://doi.org/10.1109/tbme.2011.2163406	https://doi.org/10.1109/tbme.2011.2163406	PROPN
cana-5719	271	2	https://doi.org/10.1109/tst.2014.6961028	https://doi.org/10.1109/tst.2014.6961028	ADJ
cana-5719	271	3	http://doi.org/10.1109/tmi.2014.2377694	http://doi.org/10.1109/tmi.2014.2377694	NOUN
cana-5719	271	4	https://doi.org/10.1016/j.fcij.2017.12.001	https://doi.org/10.1016/j.fcij.2017.12.001	PROPN
cana-5719	271	5	communications	communication	NOUN
cana-5719	271	6	on	on	ADP
cana-5719	271	7	applied	apply	VERB
cana-5719	271	8	nonlinear	nonlinear	ADJ
cana-5719	271	9	analysis	analysis	NOUN
cana-5719	271	10	issn	issn	NOUN
cana-5719	271	11	:	:	PUNCT
cana-5719	271	12	1074	1074	NUM
cana-5719	271	13	-	-	PUNCT
cana-5719	271	14	133x	133x	NUM
cana-5719	271	15	vol	vol	VERB
cana-5719	271	16	32	32	NUM
cana-5719	271	17	no	no	NOUN
cana-5719	271	18	.	.	PUNCT
cana-5719	272	1	10s	10	NOUN
cana-5719	272	2	(	(	PUNCT
cana-5719	272	3	2025	2025	NUM
cana-5719	272	4	)	)	PUNCT
cana-5719	272	5	2799	2799	NUM
cana-5719	272	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5719	272	7	technology	technology	NOUN
cana-5719	272	8	,	,	PUNCT
cana-5719	272	9	transactions	transaction	NOUN
cana-5719	272	10	of	of	ADP
cana-5719	272	11	electrical	electrical	ADJ
cana-5719	272	12	engineering	engineering	NOUN
cana-5719	272	13	,	,	PUNCT
cana-5719	272	14	(	(	PUNCT
cana-5719	272	15	2021	2021	NUM
cana-5719	272	16	)	)	PUNCT
cana-5719	272	17	.	.	PUNCT
cana-5719	273	1	https://doi.org/10.1007/s40998021-00426-9	https://doi.org/10.1007/s40998021-00426-9	PROPN
cana-5719	274	1	[	[	X
cana-5719	274	2	9	9	NUM
cana-5719	274	3	]	]	X
cana-5719	274	4	khan	khan	PROPN
cana-5719	274	5	m	m	PROPN
cana-5719	274	6	s	s	PROPN
cana-5719	274	7	i	i	PROPN
cana-5719	274	8	,	,	PUNCT
cana-5719	274	9	rahman	rahman	PROPN
cana-5719	274	10	a	a	PROPN
cana-5719	274	11	,	,	PUNCT
cana-5719	274	12	debnath	debnath	PROPN
cana-5719	274	13	t	t	PROPN
cana-5719	274	14	,	,	PUNCT
cana-5719	274	15	karim	karim	PROPN
cana-5719	274	16	m	m	PROPN
cana-5719	274	17	r	r	PROPN
cana-5719	274	18	,	,	PUNCT
cana-5719	274	19	nasir	nasir	PROPN
cana-5719	274	20	m	m	PROPN
cana-5719	274	21	k	k	PROPN
cana-5719	274	22	,	,	PUNCT
cana-5719	274	23	band	band	NOUN
cana-5719	274	24	s	s	PART
cana-5719	274	25	,	,	PUNCT
cana-5719	274	26	mosavi	mosavi	VERB
cana-5719	274	27	a	a	PRON
cana-5719	274	28	,	,	PUNCT
cana-5719	274	29	dehzangi	dehzangi	VERB
cana-5719	274	30	i	i	PRON
cana-5719	274	31	,	,	PUNCT
cana-5719	274	32	“	"	PUNCT
cana-5719	274	33	accurate	accurate	ADJ
cana-5719	274	34	brain	brain	NOUN
cana-5719	274	35	tumor	tumor	NOUN
cana-5719	274	36	detection	detection	NOUN
cana-5719	274	37	using	use	VERB
cana-5719	274	38	deep	deep	ADJ
cana-5719	274	39	convolutional	convolutional	ADJ
cana-5719	274	40	neural	neural	ADJ
cana-5719	274	41	network	network	NOUN
cana-5719	274	42	”	"	PUNCT
cana-5719	274	43	,	,	PUNCT
cana-5719	274	44	computational	computational	ADJ
cana-5719	274	45	and	and	CCONJ
cana-5719	274	46	structural	structural	ADJ
cana-5719	274	47	biotechnology	biotechnology	NOUN
cana-5719	274	48	journal	journal	NOUN
cana-5719	274	49	,	,	PUNCT
cana-5719	274	50	(	(	PUNCT
cana-5719	274	51	2022	2022	NUM
cana-5719	274	52	)	)	PUNCT
cana-5719	274	53	.	.	PUNCT
cana-5719	275	1	https://doi.org/10.1016/j.csbj.2022.08.039	https://doi.org/10.1016/j.csbj.2022.08.039	NOUN
cana-5719	276	1	[	[	X
cana-5719	276	2	10	10	NUM
cana-5719	276	3	]	]	X
cana-5719	276	4	shanthi	shanthi	PROPN
cana-5719	276	5	s	s	PROPN
cana-5719	276	6	,	,	PUNCT
cana-5719	276	7	saradha	saradha	PROPN
cana-5719	276	8	s	s	PROPN
cana-5719	276	9	,	,	PUNCT
cana-5719	276	10	smitha	smitha	PROPN
cana-5719	276	11	j	j	PROPN
cana-5719	276	12	a	a	PROPN
cana-5719	276	13	,	,	PUNCT
cana-5719	276	14	prasath	prasath	NOUN
cana-5719	276	15	n	n	CCONJ
cana-5719	276	16	,	,	PUNCT
cana-5719	276	17	anandakumar	anandakumar	PROPN
cana-5719	276	18	h	h	PROPN
cana-5719	276	19	,	,	PUNCT
cana-5719	276	20	“	"	PUNCT
cana-5719	276	21	an	an	DET
cana-5719	276	22	efficient	efficient	ADJ
cana-5719	276	23	automatic	automatic	ADJ
cana-5719	276	24	brain	brain	NOUN
cana-5719	276	25	tumor	tumor	NOUN
cana-5719	276	26	classification	classification	NOUN
cana-5719	276	27	using	use	VERB
cana-5719	276	28	optimized	optimize	VERB
cana-5719	276	29	hybrid	hybrid	ADJ
cana-5719	276	30	deep	deep	ADJ
cana-5719	276	31	neural	neural	ADJ
cana-5719	276	32	network	network	NOUN
cana-5719	276	33	,	,	PUNCT
cana-5719	276	34	international	international	ADJ
cana-5719	276	35	journal	journal	NOUN
cana-5719	276	36	of	of	ADP
cana-5719	276	37	intelligent	intelligent	ADJ
cana-5719	276	38	networks	network	NOUN
cana-5719	276	39	”	"	PUNCT
cana-5719	276	40	,	,	PUNCT
cana-5719	276	41	(	(	PUNCT
cana-5719	276	42	2022	2022	NUM
cana-5719	276	43	)	)	PUNCT
cana-5719	276	44	.	.	PUNCT
cana-5719	277	1	https://doi.org/10.1016/j.ijin.2022.11.003	https://doi.org/10.1016/j.ijin.2022.11.003	X
cana-5719	278	1	[	[	X
cana-5719	278	2	11	11	NUM
cana-5719	278	3	]	]	PUNCT
cana-5719	278	4	alsubai	alsubai	NOUN
cana-5719	278	5	s	s	PROPN
cana-5719	278	6	,	,	PUNCT
cana-5719	278	7	khan	khan	PROPN
cana-5719	278	8	h	h	PROPN
cana-5719	278	9	u	u	PROPN
cana-5719	278	10	,	,	PUNCT
cana-5719	278	11	alqahtani	alqahtani	X
cana-5719	278	12	a	a	PRON
cana-5719	278	13	,	,	PUNCT
cana-5719	278	14	sha	sha	PROPN
cana-5719	278	15	m	m	PROPN
cana-5719	278	16	,	,	PUNCT
cana-5719	278	17	abbas	abbas	PROPN
cana-5719	278	18	s	s	PART
cana-5719	278	19	,	,	PUNCT
cana-5719	278	20	mohammad	mohammad	PROPN
cana-5719	278	21	u	u	NOUN
cana-5719	278	22	g	g	PROPN
cana-5719	278	23	,	,	PUNCT
cana-5719	278	24	“	"	PUNCT
cana-5719	278	25	ensemble	ensemble	ADJ
cana-5719	278	26	deep	deep	ADJ
cana-5719	278	27	learning	learning	NOUN
cana-5719	278	28	for	for	ADP
cana-5719	278	29	brain	brain	NOUN
cana-5719	278	30	tumor	tumor	NOUN
cana-5719	278	31	detection	detection	NOUN
cana-5719	278	32	”	"	PUNCT
cana-5719	278	33	,	,	PUNCT
cana-5719	278	34	frontiers	frontier	NOUN
cana-5719	278	35	in	in	ADP
cana-5719	278	36	computational	computational	ADJ
cana-5719	278	37	neuroscience	neuroscience	NOUN
cana-5719	278	38	,	,	PUNCT
cana-5719	278	39	(	(	PUNCT
cana-5719	278	40	2022	2022	NUM
cana-5719	278	41	)	)	PUNCT
cana-5719	278	42	.	.	PUNCT
cana-5719	279	1	doi	doi	NOUN
cana-5719	279	2	:	:	PUNCT
cana-5719	279	3	10.3389	10.3389	NUM
cana-5719	279	4	/	/	SYM
cana-5719	279	5	fncom.2022.1005617	fncom.2022.1005617	PROPN
cana-5719	279	6	[	[	X
cana-5719	279	7	12	12	NUM
cana-5719	279	8	]	]	X
cana-5719	279	9	sarkar	sarkar	PROPN
cana-5719	279	10	a	a	PROPN
cana-5719	279	11	,	,	PUNCT
cana-5719	279	12	maniruzzaman	maniruzzaman	NOUN
cana-5719	279	13	m	m	PROPN
cana-5719	279	14	,	,	PUNCT
cana-5719	279	15	alahe	alahe	PROPN
cana-5719	279	16	m	m	PROPN
cana-5719	279	17	a	a	PROPN
cana-5719	279	18	,	,	PUNCT
cana-5719	279	19	ahmad	ahmad	PROPN
cana-5719	279	20	m	m	PROPN
cana-5719	279	21	,	,	PUNCT
cana-5719	279	22	“	"	PUNCT
cana-5719	279	23	an	an	DET
cana-5719	279	24	effective	effective	ADJ
cana-5719	279	25	and	and	CCONJ
cana-5719	279	26	novel	novel	ADJ
cana-5719	279	27	approach	approach	NOUN
cana-5719	279	28	for	for	ADP
cana-5719	279	29	brain	brain	NOUN
cana-5719	279	30	tumor	tumor	NOUN
cana-5719	279	31	classification	classification	NOUN
cana-5719	279	32	using	use	VERB
cana-5719	279	33	alexnet	alexnet	ADJ
cana-5719	279	34	cnn	cnn	PROPN
cana-5719	279	35	feature	feature	NOUN
cana-5719	279	36	extractor	extractor	NOUN
cana-5719	279	37	and	and	CCONJ
cana-5719	279	38	multiple	multiple	ADJ
cana-5719	279	39	eminent	eminent	ADJ
cana-5719	279	40	machine	machine	NOUN
cana-5719	279	41	learning	learn	VERB
cana-5719	279	42	classifiers	classifier	NOUN
cana-5719	279	43	in	in	ADP
cana-5719	279	44	mris	mris	PROPN
cana-5719	279	45	”	"	PUNCT
cana-5719	279	46	,	,	PUNCT
cana-5719	279	47	hindawi	hindawi	ADJ
cana-5719	279	48	,	,	PUNCT
cana-5719	279	49	journal	journal	NOUN
cana-5719	279	50	of	of	ADP
cana-5719	279	51	sensors	sensor	NOUN
cana-5719	279	52	,	,	PUNCT
cana-5719	279	53	(	(	PUNCT
cana-5719	279	54	2023	2023	NUM
cana-5719	279	55	)	)	PUNCT
cana-5719	279	56	.	.	PUNCT
cana-5719	280	1	https://doi.org/10.1155/2023/1224619	https://doi.org/10.1155/2023/1224619	PROPN
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cana-5719	281	2	13	13	NUM
cana-5719	281	3	]	]	SYM
cana-5719	281	4	talukder	talukder	NOUN
cana-5719	281	5	m	m	PROPN
cana-5719	281	6	a	a	PROPN
cana-5719	281	7	,	,	PUNCT
cana-5719	281	8	islam	islam	PROPN
cana-5719	281	9	m	m	PROPN
cana-5719	281	10	,	,	PUNCT
cana-5719	281	11	uddin	uddin	PROPN
cana-5719	281	12	m	m	VERB
cana-5719	281	13	a	a	NOUN
cana-5719	281	14	,	,	PUNCT
cana-5719	281	15	akhter	akhter	NOUN
cana-5719	281	16	a	a	NOUN
cana-5719	281	17	,	,	PUNCT
cana-5719	281	18	pramanik	pramanik	VERB
cana-5719	281	19	m	m	VERB
cana-5719	281	20	a	a	DET
cana-5719	281	21	j	j	PROPN
cana-5719	281	22	,	,	PUNCT
cana-5719	281	23	aryal	aryal	PROPN
cana-5719	281	24	s	s	PROPN
cana-5719	281	25	,	,	PUNCT
cana-5719	281	26	almoyad	almoyad	DET
cana-5719	281	27	m	m	VERB
cana-5719	281	28	a	a	PRON
cana-5719	281	29	,	,	PUNCT
cana-5719	282	1	hasan	hasan	PROPN
cana-5719	282	2	k	k	PROPN
cana-5719	282	3	f	f	PROPN
cana-5719	282	4	,	,	PUNCT
cana-5719	282	5	moni	moni	PROPN
cana-5719	282	6	m	m	PROPN
cana-5719	282	7	a	a	NOUN
cana-5719	282	8	,	,	PUNCT
cana-5719	282	9	“	"	PUNCT
cana-5719	282	10	an	an	DET
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cana-5719	282	12	deep	deep	ADJ
cana-5719	282	13	learning	learning	NOUN
cana-5719	282	14	model	model	NOUN
cana-5719	282	15	to	to	PART
cana-5719	282	16	categorize	categorize	VERB
cana-5719	282	17	brain	brain	NOUN
cana-5719	282	18	tumor	tumor	NOUN
cana-5719	282	19	using	use	VERB
cana-5719	282	20	reconstruction	reconstruction	NOUN
cana-5719	282	21	and	and	CCONJ
cana-5719	282	22	fine	fine	ADJ
cana-5719	282	23	tuning	tuning	NOUN
cana-5719	282	24	”	"	PUNCT
cana-5719	282	25	,	,	PUNCT
cana-5719	282	26	elsevier	elsevier	NOUN
cana-5719	282	27	,	,	PUNCT
cana-5719	282	28	expert	expert	NOUN
cana-5719	282	29	systems	system	NOUN
cana-5719	282	30	with	with	ADP
cana-5719	282	31	applications	application	NOUN
cana-5719	282	32	,	,	PUNCT
cana-5719	282	33	(	(	PUNCT
cana-5719	282	34	2023	2023	NUM
cana-5719	282	35	)	)	PUNCT
cana-5719	282	36	.	.	PUNCT
cana-5719	283	1	https://doi.org/10.1016/j.eswa.2023.120534	https://doi.org/10.1016/j.eswa.2023.120534	PROPN
cana-5719	283	2	[	[	X
cana-5719	283	3	14	14	NUM
cana-5719	283	4	]	]	X
cana-5719	283	5	mohsen	mohsen	PROPN
cana-5719	283	6	s	s	PROPN
cana-5719	283	7	,	,	PUNCT
cana-5719	283	8	ali	ali	X
cana-5719	283	9	a	a	DET
cana-5719	283	10	m	m	PROPN
cana-5719	283	11	,	,	PUNCT
cana-5719	283	12	elrabaie	elrabaie	PROPN
cana-5719	283	13	e	e	PROPN
cana-5719	283	14	m	m	PROPN
cana-5719	283	15	,	,	PUNCT
cana-5719	283	16	elkaseer	elkaseer	VERB
cana-5719	283	17	a	a	PRON
cana-5719	283	18	,	,	PUNCT
cana-5719	283	19	scholz	scholz	PROPN
cana-5719	283	20	s	s	VERB
cana-5719	283	21	g	g	NOUN
cana-5719	283	22	,	,	PUNCT
cana-5719	283	23	hassan	hassan	PROPN
cana-5719	283	24	a	a	DET
cana-5719	283	25	m	m	VERB
cana-5719	283	26	a	a	PRON
cana-5719	283	27	,	,	PUNCT
cana-5719	283	28	“	"	PUNCT
cana-5719	283	29	brain	brain	NOUN
cana-5719	283	30	tumor	tumor	NOUN
cana-5719	283	31	classification	classification	NOUN
cana-5719	283	32	using	use	VERB
cana-5719	283	33	hybrid	hybrid	ADJ
cana-5719	283	34	single	single	ADJ
cana-5719	283	35	image	image	NOUN
cana-5719	283	36	super	super	ADJ
cana-5719	283	37	-	-	ADJ
cana-5719	283	38	resolution	resolution	ADJ
cana-5719	283	39	technique	technique	NOUN
cana-5719	283	40	with	with	ADP
cana-5719	283	41	resnext101_32	resnext101_32	NOUN
cana-5719	283	42	×	×	NOUN
cana-5719	283	43	8d	8d	NUM
cana-5719	283	44	and	and	CCONJ
cana-5719	283	45	vgg19	vgg19	PROPN
cana-5719	283	46	pre	pre	ADJ
cana-5719	283	47	-	-	ADJ
cana-5719	283	48	trained	train	VERB
cana-5719	283	49	models	model	NOUN
cana-5719	283	50	”	"	PUNCT
cana-5719	283	51	,	,	PUNCT
cana-5719	283	52	ieee	ieee	NOUN
cana-5719	283	53	,	,	PUNCT
cana-5719	283	54	(	(	PUNCT
cana-5719	283	55	2023	2023	NUM
cana-5719	283	56	)	)	PUNCT
cana-5719	283	57	.	.	PUNCT
cana-5719	284	1	https://doi.org	https://doi.org	VERB
cana-5719	284	2	/10.1109	/10.1109	PROPN
cana-5719	284	3	/	/	SYM
cana-5719	284	4	access.2023.3281529	access.2023.3281529	NOUN
cana-5719	284	5	[	[	X
cana-5719	284	6	15	15	NUM
cana-5719	284	7	]	]	AUX
cana-5719	284	8	tazin	tazin	PROPN
cana-5719	284	9	t	t	PROPN
cana-5719	284	10	,	,	PUNCT
cana-5719	284	11	sarker	sarker	NOUN
cana-5719	284	12	s	s	PROPN
cana-5719	284	13	,	,	PUNCT
cana-5719	284	14	gupta	gupta	PROPN
cana-5719	284	15	p	p	PROPN
cana-5719	284	16	,	,	PUNCT
cana-5719	284	17	ayaz	ayaz	PROPN
cana-5719	284	18	f	f	PROPN
cana-5719	285	1	i	i	PROPN
cana-5719	285	2	,	,	PUNCT
cana-5719	285	3	islam	islam	PROPN
cana-5719	285	4	s	s	PROPN
cana-5719	285	5	,	,	PUNCT
cana-5719	285	6	khan	khan	PROPN
cana-5719	285	7	m	m	PROPN
cana-5719	285	8	,	,	PUNCT
cana-5719	285	9	bourouis	bourouis	PROPN
cana-5719	285	10	s	s	PROPN
cana-5719	285	11	,	,	PUNCT
cana-5719	285	12	idris	idris	PROPN
cana-5719	285	13	s	s	PROPN
cana-5719	285	14	a	a	DET
cana-5719	285	15	,	,	PUNCT
cana-5719	285	16	alshazly	alshazly	NOUN
cana-5719	285	17	h	h	NOUN
cana-5719	285	18	,	,	PUNCT
cana-5719	285	19	“	"	PUNCT
cana-5719	285	20	a	a	DET
cana-5719	285	21	robust	robust	ADJ
cana-5719	285	22	and	and	CCONJ
cana-5719	285	23	novel	novel	ADJ
cana-5719	285	24	approach	approach	NOUN
cana-5719	285	25	for	for	ADP
cana-5719	285	26	brain	brain	NOUN
cana-5719	285	27	tumor	tumor	NOUN
cana-5719	285	28	classification	classification	NOUN
cana-5719	285	29	using	use	VERB
cana-5719	285	30	convolutional	convolutional	ADJ
cana-5719	285	31	neural	neural	ADJ
cana-5719	285	32	network	network	NOUN
cana-5719	285	33	”	"	PUNCT
cana-5719	285	34	,	,	PUNCT
cana-5719	285	35	hindawi	hindawi	ADJ
cana-5719	285	36	computational	computational	ADJ
cana-5719	285	37	intelligence	intelligence	NOUN
cana-5719	285	38	and	and	CCONJ
cana-5719	285	39	neuroscience	neuroscience	NOUN
cana-5719	285	40	,	,	PUNCT
cana-5719	285	41	(	(	PUNCT
cana-5719	285	42	2023	2023	NUM
cana-5719	285	43	)	)	PUNCT
cana-5719	285	44	.	.	PUNCT
cana-5719	286	1	https://doi.org/10.1155/2023/9760861	https://doi.org/10.1155/2023/9760861	NOUN
cana-5719	287	1	[	[	X
cana-5719	287	2	16	16	NUM
cana-5719	287	3	]	]	X
cana-5719	287	4	parvin	parvin	PROPN
cana-5719	287	5	f	f	PROPN
cana-5719	287	6	,	,	PUNCT
cana-5719	287	7	mamun	mamun	PROPN
cana-5719	287	8	m	m	PROPN
cana-5719	287	9	a	a	NOUN
cana-5719	287	10	,	,	PUNCT
cana-5719	287	11	“	"	PUNCT
cana-5719	287	12	an	an	DET
cana-5719	287	13	effective	effective	ADJ
cana-5719	287	14	brain	brain	NOUN
cana-5719	287	15	tumor	tumor	NOUN
cana-5719	287	16	classification	classification	NOUN
cana-5719	287	17	approach	approach	NOUN
cana-5719	287	18	using	use	VERB
cana-5719	287	19	parallel	parallel	ADJ
cana-5719	287	20	deep	deep	ADJ
cana-5719	287	21	convolutional	convolutional	ADJ
cana-5719	287	22	neural	neural	ADJ
cana-5719	287	23	networks	network	NOUN
cana-5719	287	24	”	"	PUNCT
cana-5719	287	25	,	,	PUNCT
cana-5719	287	26	ieee	ieee	NOUN
cana-5719	287	27	,	,	PUNCT
cana-5719	287	28	(	(	PUNCT
cana-5719	287	29	2023	2023	NUM
cana-5719	287	30	)	)	PUNCT
cana-5719	287	31	.	.	PUNCT
cana-5719	288	1	doi	doi	NOUN
cana-5719	288	2	:	:	PUNCT
cana-5719	288	3	10.1109	10.1109	NUM
cana-5719	288	4	/	/	SYM
cana-5719	288	5	ncim59001.2023.10212650	ncim59001.2023.10212650	NOUN
cana-5719	289	1	[	[	X
cana-5719	289	2	17	17	NUM
cana-5719	289	3	]	]	PUNCT
cana-5719	289	4	suryawanshi	suryawanshi	NOUN
cana-5719	289	5	s	s	PROPN
cana-5719	289	6	,	,	PUNCT
cana-5719	289	7	patil	patil	PROPN
cana-5719	289	8	s	s	PROPN
cana-5719	289	9	b	b	PROPN
cana-5719	289	10	,	,	PUNCT
cana-5719	289	11	“	"	PUNCT
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cana-5719	289	13	brain	brain	NOUN
cana-5719	289	14	tumor	tumor	NOUN
cana-5719	289	15	classification	classification	NOUN
cana-5719	289	16	with	with	ADP
cana-5719	289	17	a	a	DET
cana-5719	289	18	hybrid	hybrid	ADJ
cana-5719	289	19	cnn	cnn	PROPN
cana-5719	289	20	-	-	PUNCT
cana-5719	289	21	svm	svm	ADJ
cana-5719	289	22	approach	approach	NOUN
cana-5719	289	23	in	in	ADP
cana-5719	289	24	mri	mri	NOUN
cana-5719	289	25	”	"	PUNCT
cana-5719	289	26	,	,	PUNCT
cana-5719	289	27	journal	journal	NOUN
cana-5719	289	28	of	of	ADP
cana-5719	289	29	advances	advance	NOUN
cana-5719	289	30	in	in	ADP
cana-5719	289	31	information	information	NOUN
cana-5719	289	32	technology	technology	NOUN
cana-5719	289	33	,	,	PUNCT
cana-5719	289	34	(	(	PUNCT
cana-5719	289	35	2024	2024	NUM
cana-5719	289	36	)	)	PUNCT
cana-5719	289	37	.	.	PUNCT
cana-5719	290	1	doi	doi	NOUN
cana-5719	290	2	:	:	PUNCT
cana-5719	290	3	10.12720	10.12720	NUM
cana-5719	290	4	/	/	SYM
cana-5719	290	5	jait.15.3.340	jait.15.3.340	NOUN
cana-5719	290	6	-	-	SYM
cana-5719	290	7	354	354	NUM
cana-5719	291	1	[	[	X
cana-5719	291	2	18	18	NUM
cana-5719	291	3	]	]	PUNCT
cana-5719	291	4	reyes	reyes	PROPN
cana-5719	291	5	d	d	PROPN
cana-5719	291	6	,	,	PUNCT
cana-5719	291	7	sánchez	sánchez	PROPN
cana-5719	291	8	j	j	PROPN
cana-5719	291	9	,	,	PUNCT
cana-5719	291	10	“	"	PUNCT
cana-5719	291	11	performance	performance	NOUN
cana-5719	291	12	of	of	ADP
cana-5719	291	13	convolutional	convolutional	ADJ
cana-5719	291	14	neural	neural	ADJ
cana-5719	291	15	networks	network	NOUN
cana-5719	291	16	for	for	ADP
cana-5719	291	17	the	the	DET
cana-5719	291	18	classification	classification	NOUN
cana-5719	291	19	of	of	ADP
cana-5719	291	20	brain	brain	NOUN
cana-5719	291	21	tumors	tumor	NOUN
cana-5719	291	22	using	use	VERB
cana-5719	291	23	magnetic	magnetic	ADJ
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cana-5719	291	25	imaging	imaging	NOUN
cana-5719	291	26	”	"	PUNCT
cana-5719	291	27	,	,	PUNCT
cana-5719	291	28	heliyon	heliyon	NOUN
cana-5719	291	29	,	,	PUNCT
cana-5719	291	30	(	(	PUNCT
cana-5719	291	31	2024	2024	NUM
cana-5719	291	32	)	)	PUNCT
cana-5719	291	33	.	.	PUNCT
cana-5719	292	1	https://doi.org/10.1016/j.heliyon.2024.e25468	https://doi.org/10.1016/j.heliyon.2024.e25468	VERB
cana-5719	293	1	[	[	X
cana-5719	293	2	19	19	NUM
cana-5719	293	3	]	]	PUNCT
cana-5719	293	4	j.cheng	j.cheng	X
cana-5719	293	5	,	,	PUNCT
cana-5719	293	6	“	"	PUNCT
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cana-5719	293	8	tumor	tumor	NOUN
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cana-5719	293	10	,	,	PUNCT
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cana-5719	293	12	,	,	PUNCT
cana-5719	293	13	dataset	dataset	NOUN
cana-5719	293	14	”	"	PUNCT
cana-5719	293	15	,	,	PUNCT
cana-5719	293	16	(	(	PUNCT
cana-5719	293	17	2018	2018	NUM
cana-5719	293	18	)	)	PUNCT
cana-5719	293	19	.	.	PUNCT
cana-5719	294	1	https://doi.org/10.6084/m9.figshare.1512427.v5	https://doi.org/10.6084/m9.figshare.1512427.v5	PROPN
cana-5719	294	2	.	.	PUNCT
cana-5719	295	1	2018	2018	NUM
cana-5719	295	2	.	.	PUNCT
cana-5719	296	1	[	[	X
cana-5719	296	2	20	20	NUM
cana-5719	296	3	]	]	PUNCT
cana-5719	296	4	tonmoy	tonmoy	PROPN
cana-5719	296	5	hossain	hossain	PROPN
cana-5719	296	6	,	,	PUNCT
cana-5719	296	7	fairuz	fairuz	PROPN
cana-5719	296	8	shadmani	shadmani	PROPN
cana-5719	296	9	shishir	shishir	PROPN
cana-5719	296	10	,	,	PUNCT
cana-5719	296	11	mohsena	mohsena	PROPN
cana-5719	296	12	ashraf	ashraf	PROPN
cana-5719	296	13	,	,	PUNCT
cana-5719	296	14	md	md	PROPN
cana-5719	296	15	abdullah	abdullah	PROPN
cana-5719	296	16	al	al	PROPN
cana-5719	296	17	nasim	nasim	PROPN
cana-5719	296	18	,	,	PUNCT
cana-5719	296	19	faisal	faisal	PROPN
cana-5719	296	20	muhammad	muhammad	PROPN
cana-5719	296	21	shah	shah	PROPN
cana-5719	296	22	,	,	PUNCT
cana-5719	296	23	“	"	PUNCT
cana-5719	296	24	brain	brain	NOUN
cana-5719	296	25	tumor	tumor	NOUN
cana-5719	296	26	detection	detection	NOUN
cana-5719	296	27	using	use	VERB
cana-5719	296	28	convolutional	convolutional	ADJ
cana-5719	296	29	neural	neural	ADJ
cana-5719	296	30	network	network	NOUN
cana-5719	296	31	”	"	PUNCT
cana-5719	296	32	,	,	PUNCT
cana-5719	296	33	ieee,(2019	ieee,(2019	NOUN
cana-5719	296	34	)	)	PUNCT
cana-5719	296	35	.	.	PUNCT
cana-5719	297	1	doi:10.1109	doi:10.1109	VERB
cana-5719	297	2	/	/	SYM
cana-5719	297	3	icasert.2019.8934561	icasert.2019.8934561	PROPN
cana-5719	297	4	https://doi.org/10.1007/s40998-021-00426-9	https://doi.org/10.1007/s40998-021-00426-9	NUM
cana-5719	297	5	https://doi.org/10.1007/s40998-021-00426-9	https://doi.org/10.1007/s40998-021-00426-9	NUM
cana-5719	297	6	https://doi.org/10.1016/j.csbj.2022.08.039	https://doi.org/10.1016/j.csbj.2022.08.039	NOUN
cana-5719	297	7	https://doi.org/10.1016/j.ijin.2022.11.003	https://doi.org/10.1016/j.ijin.2022.11.003	NUM
cana-5719	297	8	https://doi.org/10.1155/2023/1224619	https://doi.org/10.1155/2023/1224619	PROPN
cana-5719	297	9	https://doi.org/10.1016/j.eswa.2023.120534	https://doi.org/10.1016/j.eswa.2023.120534	PROPN
cana-5719	297	10	https://doi.org/10.1155/2023/9760861	https://doi.org/10.1155/2023/9760861	PROPN
cana-5719	297	11	https://ieeexplore.ieee.org/author/37088545666	https://ieeexplore.ieee.org/author/37088545666	PROPN
cana-5719	297	12	https://ieeexplore.ieee.org/author/37657633200	https://ieeexplore.ieee.org/author/37657633200	PROPN
cana-5719	297	13	https://doi.org/10.1109/ncim59001.2023.10212650	https://doi.org/10.1109/ncim59001.2023.10212650	PROPN
cana-5719	297	14	https://doi.org/10.6084/m9.figshare.1512427.v5.%202018	https://doi.org/10.6084/m9.figshare.1512427.v5.%202018	PROPN
cana-5719	297	15	communications	communication	NOUN
cana-5719	297	16	on	on	ADP
cana-5719	297	17	applied	apply	VERB
cana-5719	297	18	nonlinear	nonlinear	ADJ
cana-5719	297	19	analysis	analysis	NOUN
cana-5719	297	20	issn	issn	NOUN
cana-5719	297	21	:	:	PUNCT
cana-5719	297	22	1074	1074	NUM
cana-5719	297	23	-	-	PUNCT
cana-5719	297	24	133x	133x	NUM
cana-5719	297	25	vol	vol	VERB
cana-5719	297	26	32	32	NUM
cana-5719	297	27	no	no	NOUN
cana-5719	297	28	.	.	PUNCT
cana-5719	298	1	10s	10	NOUN
cana-5719	298	2	(	(	PUNCT
cana-5719	298	3	2025	2025	NUM
cana-5719	298	4	)	)	PUNCT
cana-5719	298	5	2800	2800	NUM
cana-5719	298	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5719	299	1	[	[	X
cana-5719	299	2	21	21	NUM
cana-5719	299	3	]	]	SYM
cana-5719	299	4	francisco	francisco	PROPN
cana-5719	299	5	javier	javier	PROPN
cana-5719	299	6	díaz	díaz	PROPN
cana-5719	299	7	-	-	PUNCT
cana-5719	299	8	pernas	pernas	PROPN
cana-5719	299	9	,	,	PUNCT
cana-5719	299	10	mario	mario	PROPN
cana-5719	299	11	martínez	martínez	PROPN
cana-5719	299	12	-	-	PUNCT
cana-5719	299	13	zarzuela	zarzuela	PROPN
cana-5719	299	14	,	,	PUNCT
cana-5719	299	15	míriam	míriam	PROPN
cana-5719	299	16	antón	antón	PROPN
cana-5719	299	17	-	-	PUNCT
cana-5719	299	18	rodríguez	rodríguez	PROPN
cana-5719	299	19	,	,	PUNCT
cana-5719	299	20	david	david	PROPN
cana-5719	299	21	gonzález	gonzález	PROPN
cana-5719	299	22	-	-	PUNCT
cana-5719	299	23	ortega	ortega	PROPN
cana-5719	299	24	,	,	PUNCT
cana-5719	299	25	“	"	PUNCT
cana-5719	299	26	a	a	DET
cana-5719	299	27	deep	deep	ADJ
cana-5719	299	28	learning	learning	NOUN
cana-5719	299	29	approach	approach	NOUN
cana-5719	299	30	for	for	ADP
cana-5719	299	31	brain	brain	NOUN
cana-5719	299	32	tumor	tumor	NOUN
cana-5719	299	33	classificationand	classificationand	NOUN
cana-5719	299	34	segmentation	segmentation	NOUN
cana-5719	299	35	using	use	VERB
cana-5719	299	36	a	a	DET
cana-5719	299	37	multiscale	multiscale	ADJ
cana-5719	299	38	convolutional	convolutional	ADJ
cana-5719	299	39	neural	neural	ADJ
cana-5719	299	40	network	network	NOUN
cana-5719	299	41	”	"	PUNCT
cana-5719	299	42	,	,	PUNCT
cana-5719	299	43	healthcare	healthcare	NOUN
cana-5719	299	44	2021	2021	NUM
cana-5719	299	45	.	.	PUNCT
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cana-5719	299	47	[	[	X
cana-5719	299	48	22	22	NUM
cana-5719	299	49	]	]	PUNCT
cana-5719	299	50	maad	maad	PROPN
cana-5719	299	51	m.	m.	PROPN
cana-5719	299	52	mijwil	mijwil	PROPN
cana-5719	299	53	,	,	PUNCT
cana-5719	299	54	ruchi	ruchi	PROPN
cana-5719	299	55	doshi	doshi	PROPN
cana-5719	299	56	,	,	PUNCT
cana-5719	299	57	kamal	kamal	PROPN
cana-5719	299	58	kant	kant	PROPN
cana-5719	299	59	hiran	hiran	PROPN
cana-5719	299	60	,	,	PUNCT
cana-5719	299	61	omega	omega	NOUN
cana-5719	299	62	john	john	PROPN
cana-5719	299	63	unogwu	unogwu	PROPN
cana-5719	299	64	,	,	PUNCT
cana-5719	299	65	indu	indu	NOUN
cana-5719	299	66	bala	bala	PROPN
cana-5719	299	67	,	,	PUNCT
cana-5719	299	68	“	"	PUNCT
cana-5719	299	69	mobilenetv1	mobilenetv1	NOUN
cana-5719	299	70	-	-	PUNCT
cana-5719	299	71	based	base	VERB
cana-5719	299	72	deep	deep	ADJ
cana-5719	299	73	learning	learning	NOUN
cana-5719	299	74	model	model	NOUN
cana-5719	299	75	for	for	ADP
cana-5719	299	76	accurate	accurate	ADJ
cana-5719	299	77	brain	brain	NOUN
cana-5719	299	78	tumor	tumor	NOUN
cana-5719	299	79	classification	classification	NOUN
cana-5719	299	80	”	"	PUNCT
cana-5719	299	81	,	,	PUNCT
cana-5719	299	82	mesopotamian	mesopotamian	PROPN
cana-5719	299	83	journal	journal	PROPN
cana-5719	299	84	of	of	ADP
cana-5719	299	85	computer	computer	NOUN
cana-5719	299	86	science	science	NOUN
cana-5719	299	87	,	,	PUNCT
cana-5719	299	88	2023	2023	NUM
cana-5719	299	89	.	.	PUNCT
cana-5719	300	1	doi	doi	NOUN
cana-5719	300	2	:	:	PUNCT
cana-5719	300	3	https://doi.org/10.58496/mjcsc/2023/005	https://doi.org/10.58496/mjcsc/2023/005	PROPN
cana-5719	301	1	[	[	X
cana-5719	301	2	23	23	NUM
cana-5719	301	3	]	]	X
cana-5719	301	4	marco	marco	PROPN
cana-5719	301	5	antonio	antonio	PROPN
cana-5719	301	6	gómez	gómez	PROPN
cana-5719	301	7	-	-	PUNCT
cana-5719	301	8	guzmán	guzmán	NOUN
cana-5719	301	9	,	,	PUNCT
cana-5719	301	10	laura	laura	PROPN
cana-5719	301	11	jiménez	jiménez	PROPN
cana-5719	301	12	-	-	PUNCT
cana-5719	301	13	beristaín	beristaín	PROPN
cana-5719	301	14	,	,	PUNCT
cana-5719	301	15	“	"	PUNCT
cana-5719	301	16	classifying	classify	VERB
cana-5719	301	17	brain	brain	NOUN
cana-5719	301	18	tumors	tumor	NOUN
cana-5719	301	19	on	on	ADP
cana-5719	301	20	magnetic	magnetic	ADJ
cana-5719	301	21	resonance	resonance	NOUN
cana-5719	301	22	imaging	imaging	NOUN
cana-5719	301	23	by	by	ADP
cana-5719	301	24	using	use	VERB
cana-5719	301	25	convolutional	convolutional	ADJ
cana-5719	301	26	neural	neural	ADJ
cana-5719	301	27	networks	network	NOUN
cana-5719	301	28	”	"	PUNCT
cana-5719	301	29	,	,	PUNCT
cana-5719	301	30	electronics	electronic	NOUN
cana-5719	301	31	2023	2023	NUM
cana-5719	301	32	.	.	PUNCT
cana-5719	302	1	https://doi.org/10.3390/electronics12040955	https://doi.org/10.3390/electronics12040955	NOUN
cana-5719	303	1	[	[	X
cana-5719	303	2	24	24	NUM
cana-5719	303	3	]	]	X
cana-5719	303	4	lu	lu	PROPN
cana-5719	303	5	xu	xu	INTJ
cana-5719	303	6	,	,	PUNCT
cana-5719	303	7	morteza	morteza	NOUN
cana-5719	303	8	mohammadi	mohammadi	NOUN
cana-5719	303	9	,	,	PUNCT
cana-5719	303	10	“	"	PUNCT
cana-5719	303	11	brain	brain	NOUN
cana-5719	303	12	tumor	tumor	NOUN
cana-5719	303	13	diagnosis	diagnosis	NOUN
cana-5719	303	14	from	from	ADP
cana-5719	303	15	mri	mri	NOUN
cana-5719	303	16	based	base	VERB
cana-5719	303	17	on	on	ADP
cana-5719	303	18	mobilenetv2	mobilenetv2	PROPN
cana-5719	303	19	optimized	optimize	VERB
cana-5719	303	20	by	by	ADP
cana-5719	303	21	contracted	contract	VERB
cana-5719	303	22	fox	fox	PROPN
cana-5719	303	23	optimization	optimization	NOUN
cana-5719	303	24	algorithm	algorithm	NOUN
cana-5719	303	25	”	"	PUNCT
cana-5719	303	26	,	,	PUNCT
cana-5719	303	27	heliyon	heliyon	NOUN
cana-5719	303	28	2024	2024	NUM
cana-5719	303	29	.	.	PUNCT
cana-5719	304	1	https://doi.org/10.1016/j.heliyon.2023.e23866	https://doi.org/10.1016/j.heliyon.2023.e23866	X
cana-5719	305	1	[	[	X
cana-5719	305	2	25	25	NUM
cana-5719	305	3	]	]	X
cana-5719	305	4	narayanan	narayanan	PROPN
cana-5719	305	5	krishnasamy	krishnasamy	PROPN
cana-5719	305	6	,	,	PUNCT
cana-5719	305	7	thangaraj	thangaraj	ADJ
cana-5719	305	8	ponnusamy	ponnusamy	NOUN
cana-5719	305	9	,	,	PUNCT
cana-5719	305	10	“	"	PUNCT
cana-5719	305	11	deep	deep	ADJ
cana-5719	305	12	learning	learning	NOUN
cana-5719	305	13	-	-	PUNCT
cana-5719	305	14	based	base	VERB
cana-5719	305	15	robust	robust	ADJ
cana-5719	305	16	hybrid	hybrid	ADJ
cana-5719	305	17	approaches	approach	NOUN
cana-5719	305	18	for	for	ADP
cana-5719	305	19	brain	brain	NOUN
cana-5719	305	20	tumor	tumor	NOUN
cana-5719	305	21	classification	classification	NOUN
cana-5719	305	22	in	in	ADP
cana-5719	305	23	magnetic	magnetic	ADJ
cana-5719	305	24	resonance	resonance	NOUN
cana-5719	305	25	images	image	NOUN
cana-5719	305	26	”	"	PUNCT
cana-5719	305	27	,	,	PUNCT
cana-5719	305	28	i	i	PRON
cana-5719	305	29	m	m	VERB
cana-5719	305	30	a	a	PROPN
cana-5719	305	31	,	,	PUNCT
cana-5719	305	32	10	10	NUM
cana-5719	305	33	october	october	PROPN
cana-5719	305	34	2023	2023	NUM
cana-5719	305	35	.	.	PUNCT
cana-5719	306	1	https://doi.org/10.1002/ima.22974	https://doi.org/10.1002/ima.22974	PROPN
cana-5719	306	2	https://doi.org/10.58496/mjcsc/2023/005	https://doi.org/10.58496/mjcsc/2023/005	PROPN
cana-5719	306	3	https://doi.org/10.3390/electronics12040955	https://doi.org/10.3390/electronics12040955	PROPN
cana-5719	306	4	https://doi.org/10.1016/j.heliyon.2023.e23866	https://doi.org/10.1016/j.heliyon.2023.e23866	PROPN
cana-5719	306	5	https://doi.org/10.1002/ima.22974	https://doi.org/10.1002/ima.22974	PROPN