id	sid	tid	token	lemma	pos
cana-652	1	1	communications	communication	NOUN
cana-652	1	2	on	on	ADP
cana-652	1	3	applied	apply	VERB
cana-652	1	4	nonlinear	nonlinear	ADJ
cana-652	1	5	analysis	analysis	NOUN
cana-652	1	6	issn	issn	NOUN
cana-652	1	7	:	:	PUNCT
cana-652	1	8	1074	1074	NUM
cana-652	1	9	-	-	PUNCT
cana-652	1	10	133x	133x	NUM
cana-652	1	11	vol	vol	NOUN
cana-652	1	12	31	31	NUM
cana-652	1	13	no	no	NOUN
cana-652	1	14	.	.	PUNCT
cana-652	2	1	2s	2s	NUM
cana-652	2	2	(	(	PUNCT
cana-652	2	3	2024	2024	NUM
cana-652	2	4	)	)	PUNCT
cana-652	2	5	320	320	NUM
cana-652	2	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-652	2	7	nonlinear	nonlinear	ADJ
cana-652	2	8	analysis	analysis	NOUN
cana-652	2	9	in	in	ADP
cana-652	2	10	skin	skin	NOUN
cana-652	2	11	cancer	cancer	NOUN
cana-652	2	12	detection	detection	NOUN
cana-652	2	13	:	:	PUNCT
cana-652	2	14	customized	customize	VERB
cana-652	2	15	convolutional	convolutional	ADJ
cana-652	2	16	neural	neural	ADJ
cana-652	2	17	networks	network	NOUN
cana-652	2	18	approach	approach	PROPN
cana-652	2	19	deepak	deepak	PROPN
cana-652	2	20	mane	mane	PROPN
cana-652	2	21	,	,	PUNCT
cana-652	2	22	sangita	sangita	PROPN
cana-652	2	23	jaybhaye	jaybhaye	PROPN
cana-652	2	24	,	,	PUNCT
cana-652	2	25	atharva	atharva	PROPN
cana-652	2	26	sawleshwarkar	sawleshwarkar	NOUN
cana-652	2	27	,	,	PUNCT
cana-652	2	28	shraddha	shraddha	PROPN
cana-652	2	29	shaha	shaha	PROPN
cana-652	2	30	,	,	PUNCT
cana-652	2	31	farhan	farhan	PROPN
cana-652	2	32	shaikh	shaikh	PROPN
cana-652	2	33	,	,	PUNCT
cana-652	2	34	pratik	pratik	NOUN
cana-652	2	35	yeole	yeole	PROPN
cana-652	2	36	1,2,3,4,5,6vishwakarma	1,2,3,4,5,6vishwakarma	NUM
cana-652	2	37	institute	institute	PROPN
cana-652	2	38	of	of	ADP
cana-652	2	39	technology	technology	PROPN
cana-652	2	40	,	,	PUNCT
cana-652	2	41	pune	pune	NOUN
cana-652	2	42	,	,	PUNCT
cana-652	2	43	pune-411037	pune-411037	NOUN
cana-652	2	44	,	,	PUNCT
cana-652	2	45	maharashtra	maharashtra	PROPN
cana-652	2	46	,	,	PUNCT
cana-652	2	47	india	india	PROPN
cana-652	2	48	deepak.mane@vit.edu	deepak.mane@vit.edu	PROPN
cana-652	2	49	sangita.jaybhaye@vit.edu	sangita.jaybhaye@vit.edu	PROPN
cana-652	2	50	atharva.sawleshwarkar20@vit.edu	atharva.sawleshwarkar20@vit.edu	PROPN
cana-652	2	51	shraddha.shaha21@vit.edu	shraddha.shaha21@vit.edu	ADJ
cana-652	2	52	farhan.shaikh20@vit.edu	farhan.shaikh20@vit.edu	NOUN
cana-652	2	53	pratik.yeole20@vit.edu	pratik.yeole20@vit.edu	PROPN
cana-652	2	54	article	article	NOUN
cana-652	2	55	history	history	NOUN
cana-652	2	56	:	:	PUNCT
cana-652	2	57	received	receive	VERB
cana-652	2	58	:	:	PUNCT
cana-652	2	59	26	26	NUM
cana-652	2	60	-	-	SYM
cana-652	2	61	03	03	NUM
cana-652	2	62	-	-	PUNCT
cana-652	2	63	2024	2024	NUM
cana-652	2	64	revised	revise	VERB
cana-652	2	65	:	:	PUNCT
cana-652	2	66	09	09	NUM
cana-652	2	67	-	-	PUNCT
cana-652	2	68	05	05	NUM
cana-652	2	69	-	-	PUNCT
cana-652	2	70	2024	2024	NUM
cana-652	2	71	accepted	accept	VERB
cana-652	2	72	:	:	PUNCT
cana-652	2	73	22	22	NUM
cana-652	2	74	-	-	SYM
cana-652	2	75	05	05	NUM
cana-652	2	76	-	-	PUNCT
cana-652	2	77	2024	2024	NUM
cana-652	2	78	abstract	abstract	NOUN
cana-652	2	79	:	:	PUNCT
cana-652	2	80	skin	skin	NOUN
cana-652	2	81	cancer	cancer	NOUN
cana-652	2	82	is	be	AUX
cana-652	2	83	a	a	DET
cana-652	2	84	common	common	ADJ
cana-652	2	85	and	and	CCONJ
cana-652	2	86	possibly	possibly	ADV
cana-652	2	87	fatal	fatal	ADJ
cana-652	2	88	illness	illness	NOUN
cana-652	2	89	,	,	PUNCT
cana-652	2	90	emphasizing	emphasize	VERB
cana-652	2	91	the	the	DET
cana-652	2	92	critical	critical	ADJ
cana-652	2	93	importance	importance	NOUN
cana-652	2	94	of	of	ADP
cana-652	2	95	early	early	ADJ
cana-652	2	96	detection	detection	NOUN
cana-652	2	97	for	for	ADP
cana-652	2	98	effective	effective	ADJ
cana-652	2	99	treatment	treatment	NOUN
cana-652	2	100	.	.	PUNCT
cana-652	3	1	convolutional	convolutional	ADJ
cana-652	3	2	neural	neural	ADJ
cana-652	3	3	networks	network	NOUN
cana-652	3	4	(	(	PUNCT
cana-652	3	5	cnns	cnns	PROPN
cana-652	3	6	)	)	PUNCT
cana-652	3	7	have	have	AUX
cana-652	3	8	become	become	VERB
cana-652	3	9	effective	effective	ADJ
cana-652	3	10	methods	method	NOUN
cana-652	3	11	for	for	ADP
cana-652	3	12	automating	automate	VERB
cana-652	3	13	the	the	DET
cana-652	3	14	detection	detection	NOUN
cana-652	3	15	of	of	ADP
cana-652	3	16	skin	skin	NOUN
cana-652	3	17	cancer	cancer	NOUN
cana-652	3	18	in	in	ADP
cana-652	3	19	recent	recent	ADJ
cana-652	3	20	years	year	NOUN
cana-652	3	21	.	.	PUNCT
cana-652	4	1	this	this	DET
cana-652	4	2	paper	paper	NOUN
cana-652	4	3	proposed	propose	VERB
cana-652	4	4	a	a	DET
cana-652	4	5	novel	novel	ADJ
cana-652	4	6	approach	approach	NOUN
cana-652	4	7	to	to	ADP
cana-652	4	8	skin	skin	NOUN
cana-652	4	9	cancer	cancer	NOUN
cana-652	4	10	detection	detection	NOUN
cana-652	4	11	,	,	PUNCT
cana-652	4	12	aiming	aim	VERB
cana-652	4	13	to	to	PART
cana-652	4	14	develop	develop	VERB
cana-652	4	15	a	a	DET
cana-652	4	16	robust	robust	ADJ
cana-652	4	17	classification	classification	NOUN
cana-652	4	18	system	system	NOUN
cana-652	4	19	which	which	PRON
cana-652	4	20	will	will	AUX
cana-652	4	21	be	be	AUX
cana-652	4	22	able	able	ADJ
cana-652	4	23	to	to	PART
cana-652	4	24	differentiate	differentiate	VERB
cana-652	4	25	between	between	ADP
cana-652	4	26	skin	skin	NOUN
cana-652	4	27	lesions	lesion	NOUN
cana-652	4	28	with	with	ADP
cana-652	4	29	different	different	ADJ
cana-652	4	30	types	type	NOUN
cana-652	4	31	.	.	PUNCT
cana-652	5	1	the	the	DET
cana-652	5	2	ham10000	ham10000	PROPN
cana-652	5	3	dataset	dataset	PROPN
cana-652	5	4	contains	contain	VERB
cana-652	5	5	a	a	DET
cana-652	5	6	total	total	NOUN
cana-652	5	7	of	of	ADP
cana-652	5	8	10,015	10,015	NUM
cana-652	5	9	images	image	NOUN
cana-652	5	10	of	of	ADP
cana-652	5	11	different	different	ADJ
cana-652	5	12	skin	skin	NOUN
cana-652	5	13	lesions	lesion	NOUN
cana-652	5	14	.	.	PUNCT
cana-652	6	1	there	there	PRON
cana-652	6	2	are	be	VERB
cana-652	6	3	7	7	NUM
cana-652	6	4	different	different	ADJ
cana-652	6	5	kinds	kind	NOUN
cana-652	6	6	of	of	ADP
cana-652	6	7	skin	skin	NOUN
cana-652	6	8	cancer	cancer	NOUN
cana-652	6	9	photos	photo	NOUN
cana-652	6	10	in	in	ADP
cana-652	6	11	this	this	DET
cana-652	6	12	collection	collection	NOUN
cana-652	6	13	,	,	PUNCT
cana-652	6	14	each	each	PRON
cana-652	6	15	sized	size	VERB
cana-652	6	16	450x600	450x600	NUM
cana-652	6	17	pixels	pixel	NOUN
cana-652	6	18	with	with	ADP
cana-652	6	19	three	three	NUM
cana-652	6	20	color	color	NOUN
cana-652	6	21	channels	channel	NOUN
cana-652	6	22	.	.	PUNCT
cana-652	7	1	to	to	PART
cana-652	7	2	address	address	VERB
cana-652	7	3	class	class	NOUN
cana-652	7	4	imbalance	imbalance	NOUN
cana-652	7	5	,	,	PUNCT
cana-652	7	6	oversampling	oversample	VERB
cana-652	7	7	was	be	AUX
cana-652	7	8	applied	apply	VERB
cana-652	7	9	,	,	PUNCT
cana-652	7	10	and	and	CCONJ
cana-652	7	11	data	datum	NOUN
cana-652	7	12	augmentation	augmentation	NOUN
cana-652	7	13	was	be	AUX
cana-652	7	14	used	use	VERB
cana-652	7	15	to	to	PART
cana-652	7	16	reduce	reduce	VERB
cana-652	7	17	the	the	DET
cana-652	7	18	risk	risk	NOUN
cana-652	7	19	of	of	ADP
cana-652	7	20	model	model	NOUN
cana-652	7	21	overfitting	overfitting	NOUN
cana-652	7	22	.	.	PUNCT
cana-652	8	1	our	our	PRON
cana-652	8	2	proposed	propose	VERB
cana-652	8	3	model	model	NOUN
cana-652	8	4	comprised	comprise	VERB
cana-652	8	5	a	a	DET
cana-652	8	6	customized	customized	ADJ
cana-652	8	7	cnn	cnn	PROPN
cana-652	8	8	model	model	NOUN
cana-652	8	9	,	,	PUNCT
cana-652	8	10	including	include	VERB
cana-652	8	11	convolutional	convolutional	ADJ
cana-652	8	12	layer	layer	NOUN
cana-652	8	13	,	,	PUNCT
cana-652	8	14	input	input	NOUN
cana-652	8	15	layer	layer	NOUN
cana-652	8	16	,	,	PUNCT
cana-652	8	17	batch	batch	VERB
cana-652	8	18	normalization	normalization	NOUN
cana-652	8	19	layer	layer	NOUN
cana-652	8	20	,	,	PUNCT
cana-652	8	21	max	max	PROPN
cana-652	8	22	-	-	PUNCT
cana-652	8	23	pooling	pool	VERB
cana-652	8	24	layers	layer	NOUN
cana-652	8	25	and	and	CCONJ
cana-652	8	26	many	many	ADJ
cana-652	8	27	more	more	ADJ
cana-652	8	28	.	.	PUNCT
cana-652	9	1	additionally	additionally	ADV
cana-652	9	2	,	,	PUNCT
cana-652	9	3	we	we	PRON
cana-652	9	4	utilized	utilize	VERB
cana-652	9	5	a	a	DET
cana-652	9	6	customized	customized	ADJ
cana-652	9	7	mobilenet	mobilenet	NOUN
cana-652	9	8	model	model	NOUN
cana-652	9	9	incorporating	incorporate	VERB
cana-652	9	10	various	various	ADJ
cana-652	9	11	layers	layer	NOUN
cana-652	9	12	,	,	PUNCT
cana-652	9	13	such	such	ADJ
cana-652	9	14	as	as	ADP
cana-652	9	15	dense	dense	ADJ
cana-652	9	16	layer	layer	NOUN
cana-652	9	17	,	,	PUNCT
cana-652	9	18	flattened	flatten	VERB
cana-652	9	19	layer	layer	NOUN
cana-652	9	20	,	,	PUNCT
cana-652	9	21	dropout	dropout	NOUN
cana-652	9	22	layer	layer	NOUN
cana-652	9	23	,	,	PUNCT
cana-652	9	24	etc	etc	X
cana-652	9	25	,	,	PUNCT
cana-652	9	26	to	to	PART
cana-652	9	27	predict	predict	VERB
cana-652	9	28	the	the	DET
cana-652	9	29	disease	disease	NOUN
cana-652	9	30	precisely	precisely	ADV
cana-652	9	31	.	.	PUNCT
cana-652	10	1	training	train	VERB
cana-652	10	2	optimization	optimization	NOUN
cana-652	10	3	involved	involve	VERB
cana-652	10	4	a	a	DET
cana-652	10	5	learning	learning	NOUN
cana-652	10	6	rate	rate	NOUN
cana-652	10	7	reduction	reduction	NOUN
cana-652	10	8	strategy	strategy	NOUN
cana-652	10	9	using	use	VERB
cana-652	10	10	callbacks	callback	NOUN
cana-652	10	11	.	.	PUNCT
cana-652	11	1	comprehensive	comprehensive	ADJ
cana-652	11	2	model	model	NOUN
cana-652	11	3	evaluation	evaluation	NOUN
cana-652	11	4	,	,	PUNCT
cana-652	11	5	utilizing	utilize	VERB
cana-652	11	6	various	various	ADJ
cana-652	11	7	techniques	technique	NOUN
cana-652	11	8	,	,	PUNCT
cana-652	11	9	yielded	yield	VERB
cana-652	11	10	an	an	DET
cana-652	11	11	accuracy	accuracy	NOUN
cana-652	11	12	of	of	ADP
cana-652	11	13	98.5	98.5	NUM
cana-652	11	14	%	%	NOUN
cana-652	11	15	for	for	ADP
cana-652	11	16	the	the	DET
cana-652	11	17	cnn	cnn	PROPN
cana-652	11	18	model	model	NOUN
cana-652	11	19	and	and	CCONJ
cana-652	11	20	92	92	NUM
cana-652	11	21	%	%	NOUN
cana-652	11	22	for	for	ADP
cana-652	11	23	the	the	DET
cana-652	11	24	mobilenet	mobilenet	NOUN
cana-652	11	25	model	model	NOUN
cana-652	11	26	keywords	keyword	NOUN
cana-652	11	27	:	:	PUNCT
cana-652	11	28	convolutional	convolutional	ADJ
cana-652	11	29	neural	neural	ADJ
cana-652	11	30	network	network	NOUN
cana-652	11	31	(	(	PUNCT
cana-652	11	32	cnn	cnn	PROPN
cana-652	11	33	)	)	PUNCT
cana-652	11	34	,	,	PUNCT
cana-652	11	35	mobilenet	mobilenet	NOUN
cana-652	11	36	,	,	PUNCT
cana-652	11	37	deep	deep	ADJ
cana-652	11	38	learning	learning	NOUN
cana-652	11	39	,	,	PUNCT
cana-652	11	40	skin	skin	NOUN
cana-652	11	41	cancer	cancer	NOUN
cana-652	11	42	,	,	PUNCT
cana-652	11	43	pattern	pattern	NOUN
cana-652	11	44	classification	classification	NOUN
cana-652	11	45	.	.	PUNCT
cana-652	12	1	1	1	X
cana-652	12	2	.	.	X
cana-652	12	3	introduction	introduction	NOUN
cana-652	12	4	skin	skin	NOUN
cana-652	12	5	cancer	cancer	NOUN
cana-652	12	6	is	be	AUX
cana-652	12	7	a	a	DET
cana-652	12	8	common	common	ADJ
cana-652	12	9	and	and	CCONJ
cana-652	12	10	potentially	potentially	ADV
cana-652	12	11	fatal	fatal	ADJ
cana-652	12	12	malignancy	malignancy	NOUN
cana-652	12	13	that	that	PRON
cana-652	12	14	affects	affect	VERB
cana-652	12	15	millions	million	NOUN
cana-652	12	16	of	of	ADP
cana-652	12	17	individuals	individual	NOUN
cana-652	12	18	worldwide	worldwide	ADV
cana-652	12	19	.	.	PUNCT
cana-652	13	1	it	it	PRON
cana-652	13	2	encompasses	encompass	VERB
cana-652	13	3	various	various	ADJ
cana-652	13	4	subtypes	subtype	NOUN
cana-652	13	5	,	,	PUNCT
cana-652	13	6	including	include	VERB
cana-652	13	7	basal	basal	ADJ
cana-652	13	8	cell	cell	NOUN
cana-652	13	9	carcinoma	carcinoma	NOUN
cana-652	13	10	,	,	PUNCT
cana-652	13	11	melanoma	melanoma	NOUN
cana-652	13	12	,	,	PUNCT
cana-652	13	13	and	and	CCONJ
cana-652	13	14	squamous	squamous	ADJ
cana-652	13	15	cell	cell	NOUN
cana-652	13	16	carcinoma	carcinoma	NOUN
cana-652	13	17	,	,	PUNCT
cana-652	13	18	each	each	PRON
cana-652	13	19	presenting	present	VERB
cana-652	13	20	distinct	distinct	ADJ
cana-652	13	21	challenges	challenge	NOUN
cana-652	13	22	in	in	ADP
cana-652	13	23	terms	term	NOUN
cana-652	13	24	of	of	ADP
cana-652	13	25	diagnosis	diagnosis	NOUN
cana-652	13	26	and	and	CCONJ
cana-652	13	27	treatment	treatment	NOUN
cana-652	13	28	.	.	PUNCT
cana-652	14	1	among	among	ADP
cana-652	14	2	these	these	PRON
cana-652	14	3	,	,	PUNCT
cana-652	14	4	melanoma	melanoma	NOUN
cana-652	14	5	stands	stand	VERB
cana-652	14	6	out	out	ADP
cana-652	14	7	as	as	ADP
cana-652	14	8	the	the	DET
cana-652	14	9	most	most	ADV
cana-652	14	10	aggressive	aggressive	ADJ
cana-652	14	11	form	form	NOUN
cana-652	14	12	,	,	PUNCT
cana-652	14	13	known	know	VERB
cana-652	14	14	for	for	ADP
cana-652	14	15	its	its	PRON
cana-652	14	16	rapid	rapid	ADJ
cana-652	14	17	progression	progression	NOUN
cana-652	14	18	and	and	CCONJ
cana-652	14	19	high	high	ADJ
cana-652	14	20	metastatic	metastatic	ADJ
cana-652	14	21	potential	potential	NOUN
cana-652	14	22	.	.	PUNCT
cana-652	15	1	timely	timely	ADJ
cana-652	15	2	detection	detection	NOUN
cana-652	15	3	of	of	ADP
cana-652	15	4	skin	skin	NOUN
cana-652	15	5	cancer	cancer	NOUN
cana-652	15	6	plays	play	VERB
cana-652	15	7	a	a	DET
cana-652	15	8	pivotal	pivotal	ADJ
cana-652	15	9	role	role	NOUN
cana-652	15	10	in	in	ADP
cana-652	15	11	reducing	reduce	VERB
cana-652	15	12	morbidity	morbidity	NOUN
cana-652	15	13	and	and	CCONJ
cana-652	15	14	mortality	mortality	NOUN
cana-652	15	15	rates	rate	NOUN
cana-652	15	16	associated	associate	VERB
cana-652	15	17	with	with	ADP
cana-652	15	18	the	the	DET
cana-652	15	19	disease	disease	NOUN
cana-652	15	20	.	.	PUNCT
cana-652	16	1	conventional	conventional	ADJ
cana-652	16	2	diagnosis	diagnosis	NOUN
cana-652	16	3	heavily	heavily	ADV
cana-652	16	4	relies	rely	VERB
cana-652	16	5	on	on	ADP
cana-652	16	6	the	the	DET
cana-652	16	7	expertise	expertise	NOUN
cana-652	16	8	of	of	ADP
cana-652	16	9	dermatologists	dermatologist	NOUN
cana-652	16	10	who	who	PRON
cana-652	16	11	conduct	conduct	VERB
cana-652	16	12	visual	visual	ADJ
cana-652	16	13	examinations	examination	NOUN
cana-652	16	14	and	and	CCONJ
cana-652	16	15	employ	employ	NOUN
cana-652	16	16	dermoscopy	dermoscopy	NOUN
cana-652	16	17	,	,	PUNCT
cana-652	16	18	a	a	DET
cana-652	16	19	non	non	ADJ
cana-652	16	20	-	-	ADJ
cana-652	16	21	invasive	invasive	ADJ
cana-652	16	22	imaging	imaging	NOUN
cana-652	16	23	method	method	NOUN
cana-652	16	24	for	for	ADP
cana-652	16	25	evaluating	evaluate	VERB
cana-652	16	26	lesions	lesion	NOUN
cana-652	16	27	.	.	PUNCT
cana-652	17	1	while	while	SCONJ
cana-652	17	2	these	these	DET
cana-652	17	3	methods	method	NOUN
cana-652	17	4	have	have	AUX
cana-652	17	5	been	be	AUX
cana-652	17	6	fundamental	fundamental	ADJ
cana-652	17	7	in	in	ADP
cana-652	17	8	identifying	identify	VERB
cana-652	17	9	suspicious	suspicious	ADJ
cana-652	17	10	lesions	lesion	NOUN
cana-652	17	11	,	,	PUNCT
cana-652	17	12	the	the	DET
cana-652	17	13	accuracy	accuracy	NOUN
cana-652	17	14	of	of	ADP
cana-652	17	15	diagnosis	diagnosis	NOUN
cana-652	17	16	remains	remain	VERB
cana-652	17	17	subjective	subjective	ADJ
cana-652	17	18	,	,	PUNCT
cana-652	17	19	contingent	contingent	ADJ
cana-652	17	20	upon	upon	SCONJ
cana-652	17	21	the	the	DET
cana-652	17	22	clinician	clinician	NOUN
cana-652	17	23	's	's	PART
cana-652	17	24	experience	experience	NOUN
cana-652	17	25	,	,	PUNCT
cana-652	17	26	and	and	CCONJ
cana-652	17	27	sometimes	sometimes	ADV
cana-652	17	28	constrained	constrain	VERB
cana-652	17	29	by	by	ADP
cana-652	17	30	limited	limited	ADJ
cana-652	17	31	access	access	NOUN
cana-652	17	32	to	to	ADP
cana-652	17	33	specialized	specialized	ADJ
cana-652	17	34	care	care	NOUN
cana-652	18	1	[	[	X
cana-652	18	2	1	1	NUM
cana-652	18	3	]	]	PUNCT
cana-652	18	4	.	.	PUNCT
cana-652	19	1	mailto:deepak.mane@vit.edu	mailto:deepak.mane@vit.edu	PROPN
cana-652	19	2	mailto:atharva.sawleshwarkar20@vit.edu	mailto:atharva.sawleshwarkar20@vit.edu	PROPN
cana-652	19	3	mailto:shraddha.shaha21@vit.edu	mailto:shraddha.shaha21@vit.edu	NOUN
cana-652	19	4	mailto:farhan.shaik20@vit.edu	mailto:farhan.shaik20@vit.edu	PROPN
cana-652	19	5	mailto:pratik.yeole20@vit.edu	mailto:pratik.yeole20@vit.edu	VERB
cana-652	19	6	communications	communication	NOUN
cana-652	19	7	on	on	ADP
cana-652	19	8	applied	apply	VERB
cana-652	19	9	nonlinear	nonlinear	ADJ
cana-652	19	10	analysis	analysis	NOUN
cana-652	19	11	issn	issn	NOUN
cana-652	19	12	:	:	PUNCT
cana-652	19	13	1074	1074	NUM
cana-652	19	14	-	-	PUNCT
cana-652	19	15	133x	133x	NUM
cana-652	19	16	vol	vol	NOUN
cana-652	19	17	31	31	NUM
cana-652	19	18	no	no	NOUN
cana-652	19	19	.	.	PUNCT
cana-652	20	1	2s	2s	NUM
cana-652	20	2	(	(	PUNCT
cana-652	20	3	2024	2024	NUM
cana-652	20	4	)	)	PUNCT
cana-652	20	5	321	321	NUM
cana-652	20	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-652	20	7	in	in	ADP
cana-652	20	8	recent	recent	ADJ
cana-652	20	9	years	year	NOUN
cana-652	20	10	,	,	PUNCT
cana-652	20	11	the	the	DET
cana-652	20	12	convergence	convergence	NOUN
cana-652	20	13	of	of	ADP
cana-652	20	14	substantial	substantial	ADJ
cana-652	20	15	advancements	advancement	NOUN
cana-652	20	16	in	in	ADP
cana-652	20	17	the	the	DET
cana-652	20	18	analysis	analysis	NOUN
cana-652	20	19	of	of	ADP
cana-652	20	20	medical	medical	ADJ
cana-652	20	21	imaging	imaging	NOUN
cana-652	20	22	and	and	CCONJ
cana-652	20	23	the	the	DET
cana-652	20	24	availability	availability	NOUN
cana-652	20	25	of	of	ADP
cana-652	20	26	expansive	expansive	ADJ
cana-652	20	27	dermatoscopic	dermatoscopic	NOUN
cana-652	20	28	image	image	NOUN
cana-652	20	29	datasets	dataset	NOUN
cana-652	20	30	has	have	AUX
cana-652	20	31	sparked	spark	VERB
cana-652	20	32	significant	significant	ADJ
cana-652	20	33	interest	interest	NOUN
cana-652	20	34	in	in	ADP
cana-652	20	35	the	the	DET
cana-652	20	36	incorporation	incorporation	NOUN
cana-652	20	37	of	of	ADP
cana-652	20	38	ai	ai	ADJ
cana-652	20	39	methods	method	NOUN
cana-652	20	40	for	for	ADP
cana-652	20	41	spotting	spot	VERB
cana-652	20	42	skin	skin	NOUN
cana-652	20	43	cancer	cancer	NOUN
cana-652	20	44	.	.	PUNCT
cana-652	21	1	cnn	cnn	PROPN
cana-652	21	2	,	,	PUNCT
cana-652	21	3	a	a	DET
cana-652	21	4	class	class	NOUN
cana-652	21	5	of	of	ADP
cana-652	21	6	deep	deep	ADJ
cana-652	21	7	learning	learning	NOUN
cana-652	21	8	models	model	NOUN
cana-652	21	9	,	,	PUNCT
cana-652	21	10	in	in	ADP
cana-652	21	11	particular	particular	ADJ
cana-652	21	12	,	,	PUNCT
cana-652	21	13	have	have	AUX
cana-652	21	14	shown	show	VERB
cana-652	21	15	a	a	DET
cana-652	21	16	remarkable	remarkable	ADJ
cana-652	21	17	potential	potential	NOUN
cana-652	21	18	in	in	ADP
cana-652	21	19	addressing	address	VERB
cana-652	21	20	the	the	DET
cana-652	21	21	complexities	complexity	NOUN
cana-652	21	22	of	of	ADP
cana-652	21	23	dermatoscopic	dermatoscopic	ADJ
cana-652	21	24	image	image	NOUN
cana-652	21	25	analysis	analysis	NOUN
cana-652	21	26	.	.	PUNCT
cana-652	22	1	cnns	cnns	PROPN
cana-652	22	2	excel	excel	VERB
cana-652	22	3	in	in	ADP
cana-652	22	4	their	their	PRON
cana-652	22	5	ability	ability	NOUN
cana-652	22	6	to	to	PART
cana-652	22	7	automatically	automatically	ADV
cana-652	22	8	extract	extract	VERB
cana-652	22	9	intricate	intricate	ADJ
cana-652	22	10	and	and	CCONJ
cana-652	22	11	hierarchical	hierarchical	ADJ
cana-652	22	12	features	feature	NOUN
cana-652	22	13	from	from	ADP
cana-652	22	14	raw	raw	ADJ
cana-652	22	15	image	image	NOUN
cana-652	22	16	data	datum	NOUN
cana-652	22	17	,	,	PUNCT
cana-652	22	18	making	make	VERB
cana-652	22	19	them	they	PRON
cana-652	22	20	ideally	ideally	ADV
cana-652	22	21	suited	suit	VERB
cana-652	22	22	for	for	ADP
cana-652	22	23	identifying	identify	VERB
cana-652	22	24	subtle	subtle	ADJ
cana-652	22	25	patterns	pattern	NOUN
cana-652	22	26	indicative	indicative	ADJ
cana-652	22	27	of	of	ADP
cana-652	22	28	skin	skin	NOUN
cana-652	22	29	cancer	cancer	NOUN
cana-652	22	30	[	[	X
cana-652	22	31	2	2	NUM
cana-652	22	32	]	]	PUNCT
cana-652	22	33	.	.	PUNCT
cana-652	23	1	the	the	DET
cana-652	23	2	objectives	objective	NOUN
cana-652	23	3	of	of	ADP
cana-652	23	4	our	our	PRON
cana-652	23	5	system	system	NOUN
cana-652	23	6	is	be	AUX
cana-652	23	7	:	:	PUNCT
cana-652	23	8	●	●	PUNCT
cana-652	23	9	evaluate	evaluate	VERB
cana-652	23	10	diagnostic	diagnostic	ADJ
cana-652	23	11	accuracy	accuracy	NOUN
cana-652	23	12	and	and	CCONJ
cana-652	23	13	compare	compare	VERB
cana-652	23	14	performance	performance	NOUN
cana-652	23	15	to	to	ADP
cana-652	23	16	existing	exist	VERB
cana-652	23	17	methods	method	NOUN
cana-652	23	18	.	.	PUNCT
cana-652	24	1	●	●	PUNCT
cana-652	24	2	investigate	investigate	VERB
cana-652	24	3	the	the	DET
cana-652	24	4	reduction	reduction	NOUN
cana-652	24	5	of	of	ADP
cana-652	24	6	false	false	ADJ
cana-652	24	7	positives	positive	NOUN
cana-652	24	8	in	in	ADP
cana-652	24	9	skin	skin	NOUN
cana-652	24	10	cancer	cancer	NOUN
cana-652	24	11	detection	detection	NOUN
cana-652	24	12	.	.	PUNCT
cana-652	25	1	●	●	PUNCT
cana-652	25	2	assess	assess	NOUN
cana-652	25	3	scalability	scalability	NOUN
cana-652	25	4	and	and	CCONJ
cana-652	25	5	real	real	ADJ
cana-652	25	6	-	-	PUNCT
cana-652	25	7	world	world	NOUN
cana-652	25	8	applicability	applicability	NOUN
cana-652	25	9	on	on	ADP
cana-652	25	10	diverse	diverse	ADJ
cana-652	25	11	datasets	dataset	NOUN
cana-652	25	12	.	.	PUNCT
cana-652	26	1	●	●	PUNCT
cana-652	26	2	evaluate	evaluate	VERB
cana-652	26	3	the	the	DET
cana-652	26	4	user	user	NOUN
cana-652	26	5	-	-	PUNCT
cana-652	26	6	friendliness	friendliness	NOUN
cana-652	26	7	and	and	CCONJ
cana-652	26	8	speed	speed	NOUN
cana-652	26	9	of	of	ADP
cana-652	26	10	the	the	DET
cana-652	26	11	system	system	NOUN
cana-652	26	12	's	's	PART
cana-652	26	13	interface	interface	NOUN
cana-652	26	14	.	.	PUNCT
cana-652	27	1	the	the	DET
cana-652	27	2	research	research	NOUN
cana-652	27	3	paper	paper	NOUN
cana-652	27	4	is	be	AUX
cana-652	27	5	organized	organize	VERB
cana-652	27	6	into	into	ADP
cana-652	27	7	seven	seven	NUM
cana-652	27	8	sections	section	NOUN
cana-652	27	9	:	:	PUNCT
cana-652	27	10	introduction	introduction	NOUN
cana-652	27	11	(	(	PUNCT
cana-652	27	12	section	section	NOUN
cana-652	27	13	i	i	PRON
cana-652	27	14	)	)	PUNCT
cana-652	27	15	provides	provide	VERB
cana-652	27	16	context	context	NOUN
cana-652	27	17	and	and	CCONJ
cana-652	27	18	motivation	motivation	NOUN
cana-652	27	19	.	.	PUNCT
cana-652	28	1	background	background	NOUN
cana-652	28	2	and	and	CCONJ
cana-652	28	3	motivation	motivation	NOUN
cana-652	28	4	(	(	PUNCT
cana-652	28	5	section	section	NOUN
cana-652	28	6	ii	ii	PROPN
cana-652	28	7	)	)	PUNCT
cana-652	28	8	offers	offer	VERB
cana-652	28	9	a	a	DET
cana-652	28	10	deeper	deep	ADJ
cana-652	28	11	exploration	exploration	NOUN
cana-652	28	12	of	of	ADP
cana-652	28	13	the	the	DET
cana-652	28	14	research	research	NOUN
cana-652	28	15	context	context	NOUN
cana-652	28	16	.	.	PUNCT
cana-652	29	1	related	related	ADJ
cana-652	29	2	work	work	NOUN
cana-652	29	3	(	(	PUNCT
cana-652	29	4	section	section	NOUN
cana-652	29	5	iii	iii	NOUN
cana-652	29	6	)	)	PUNCT
cana-652	29	7	reviews	review	NOUN
cana-652	29	8	prior	prior	ADJ
cana-652	29	9	research	research	NOUN
cana-652	29	10	.	.	PUNCT
cana-652	30	1	research	research	NOUN
cana-652	30	2	gap	gap	NOUN
cana-652	30	3	and	and	CCONJ
cana-652	30	4	objectives	objective	NOUN
cana-652	30	5	(	(	PUNCT
cana-652	30	6	section	section	NOUN
cana-652	30	7	iv	iv	NUM
cana-652	30	8	)	)	PUNCT
cana-652	30	9	defines	define	NOUN
cana-652	30	10	study	study	NOUN
cana-652	30	11	goals	goal	NOUN
cana-652	30	12	.	.	PUNCT
cana-652	31	1	materials	material	NOUN
cana-652	31	2	and	and	CCONJ
cana-652	31	3	methods	method	NOUN
cana-652	31	4	(	(	PUNCT
cana-652	31	5	section	section	NOUN
cana-652	31	6	v	v	NOUN
cana-652	31	7	)	)	PUNCT
cana-652	31	8	explains	explain	VERB
cana-652	31	9	data	datum	NOUN
cana-652	31	10	and	and	CCONJ
cana-652	31	11	techniques	technique	NOUN
cana-652	31	12	.	.	PUNCT
cana-652	32	1	results	result	NOUN
cana-652	32	2	and	and	CCONJ
cana-652	32	3	discussions	discussion	NOUN
cana-652	32	4	(	(	PUNCT
cana-652	32	5	section	section	NOUN
cana-652	32	6	vi	vi	NOUN
cana-652	32	7	)	)	PUNCT
cana-652	32	8	present	present	ADJ
cana-652	32	9	findings	finding	NOUN
cana-652	32	10	and	and	CCONJ
cana-652	32	11	analysis	analysis	NOUN
cana-652	32	12	.	.	PUNCT
cana-652	33	1	conclusion	conclusion	NOUN
cana-652	33	2	and	and	CCONJ
cana-652	33	3	future	future	ADJ
cana-652	33	4	scope	scope	NOUN
cana-652	33	5	(	(	PUNCT
cana-652	33	6	section	section	NOUN
cana-652	33	7	vii	vii	PROPN
cana-652	33	8	)	)	PUNCT
cana-652	33	9	summarizes	summarize	NOUN
cana-652	33	10	results	result	NOUN
cana-652	33	11	and	and	CCONJ
cana-652	33	12	outlines	outline	VERB
cana-652	33	13	future	future	ADJ
cana-652	33	14	research	research	NOUN
cana-652	33	15	directions	direction	NOUN
cana-652	33	16	.	.	PUNCT
cana-652	34	1	2	2	X
cana-652	34	2	.	.	X
cana-652	34	3	background	background	NOUN
cana-652	34	4	and	and	CCONJ
cana-652	34	5	motivation	motivation	NOUN
cana-652	34	6	despite	despite	SCONJ
cana-652	34	7	the	the	DET
cana-652	34	8	significant	significant	ADJ
cana-652	34	9	advancements	advancement	NOUN
cana-652	34	10	in	in	ADP
cana-652	34	11	medical	medical	ADJ
cana-652	34	12	image	image	NOUN
cana-652	34	13	analysis	analysis	NOUN
cana-652	34	14	and	and	CCONJ
cana-652	34	15	the	the	DET
cana-652	34	16	availability	availability	NOUN
cana-652	34	17	of	of	ADP
cana-652	34	18	dermatoscopic	dermatoscopic	ADJ
cana-652	34	19	imaging	imaging	NOUN
cana-652	34	20	techniques	technique	NOUN
cana-652	34	21	,	,	PUNCT
cana-652	34	22	the	the	DET
cana-652	34	23	accurate	accurate	ADJ
cana-652	34	24	and	and	CCONJ
cana-652	34	25	timely	timely	ADJ
cana-652	34	26	skin	skin	NOUN
cana-652	34	27	cancer	cancer	NOUN
cana-652	34	28	diagnosis	diagnosis	NOUN
cana-652	34	29	,	,	PUNCT
cana-652	34	30	including	include	VERB
cana-652	34	31	non	non	ADJ
cana-652	34	32	-	-	ADJ
cana-652	34	33	melanoma	melanoma	ADJ
cana-652	34	34	and	and	CCONJ
cana-652	34	35	melanoma	melanoma	NOUN
cana-652	34	36	skin	skin	NOUN
cana-652	34	37	cancers	cancer	NOUN
cana-652	34	38	,	,	PUNCT
cana-652	34	39	is	be	AUX
cana-652	34	40	still	still	ADV
cana-652	34	41	a	a	DET
cana-652	34	42	difficult	difficult	ADJ
cana-652	34	43	task	task	NOUN
cana-652	34	44	.	.	PUNCT
cana-652	35	1	the	the	DET
cana-652	35	2	current	current	ADJ
cana-652	35	3	reliance	reliance	NOUN
cana-652	35	4	on	on	ADP
cana-652	35	5	subjective	subjective	ADJ
cana-652	35	6	clinical	clinical	ADJ
cana-652	35	7	assessments	assessment	NOUN
cana-652	35	8	and	and	CCONJ
cana-652	35	9	dermatoscopy	dermatoscopy	NOUN
cana-652	35	10	,	,	PUNCT
cana-652	35	11	which	which	PRON
cana-652	35	12	are	be	AUX
cana-652	35	13	highly	highly	ADV
cana-652	35	14	dependent	dependent	ADJ
cana-652	35	15	on	on	ADP
cana-652	35	16	the	the	DET
cana-652	35	17	expertise	expertise	NOUN
cana-652	35	18	of	of	ADP
cana-652	35	19	dermatologists	dermatologist	NOUN
cana-652	35	20	,	,	PUNCT
cana-652	35	21	presents	present	VERB
cana-652	35	22	limitations	limitation	NOUN
cana-652	35	23	in	in	ADP
cana-652	35	24	terms	term	NOUN
cana-652	35	25	of	of	ADP
cana-652	35	26	diagnostic	diagnostic	ADJ
cana-652	35	27	accuracy	accuracy	NOUN
cana-652	35	28	and	and	CCONJ
cana-652	35	29	accessibility	accessibility	NOUN
cana-652	35	30	to	to	ADP
cana-652	35	31	specialized	specialized	ADJ
cana-652	35	32	care	care	NOUN
cana-652	35	33	.	.	PUNCT
cana-652	36	1	furthermore	furthermore	ADV
cana-652	36	2	,	,	PUNCT
cana-652	36	3	the	the	DET
cana-652	36	4	global	global	ADJ
cana-652	36	5	prevalence	prevalence	NOUN
cana-652	36	6	of	of	ADP
cana-652	36	7	skin	skin	NOUN
cana-652	36	8	cancer	cancer	NOUN
cana-652	36	9	demands	demand	VERB
cana-652	36	10	innovative	innovative	ADJ
cana-652	36	11	solutions	solution	NOUN
cana-652	36	12	to	to	PART
cana-652	36	13	expedite	expedite	VERB
cana-652	36	14	the	the	DET
cana-652	36	15	detection	detection	NOUN
cana-652	36	16	process	process	NOUN
cana-652	36	17	and	and	CCONJ
cana-652	36	18	reduce	reduce	VERB
cana-652	36	19	the	the	DET
cana-652	36	20	burden	burden	NOUN
cana-652	36	21	on	on	ADP
cana-652	36	22	healthcare	healthcare	NOUN
cana-652	36	23	systems	system	NOUN
cana-652	36	24	[	[	X
cana-652	36	25	3	3	NUM
cana-652	36	26	]	]	PUNCT
cana-652	36	27	.	.	PUNCT
cana-652	37	1	to	to	PART
cana-652	37	2	address	address	VERB
cana-652	37	3	these	these	DET
cana-652	37	4	challenges	challenge	NOUN
cana-652	37	5	,	,	PUNCT
cana-652	37	6	this	this	DET
cana-652	37	7	research	research	NOUN
cana-652	37	8	paper	paper	NOUN
cana-652	37	9	aims	aim	VERB
cana-652	37	10	to	to	PART
cana-652	37	11	leverage	leverage	VERB
cana-652	37	12	the	the	DET
cana-652	37	13	potential	potential	NOUN
cana-652	37	14	of	of	ADP
cana-652	37	15	convolutional	convolutional	ADJ
cana-652	37	16	neural	neural	ADJ
cana-652	37	17	networks	network	NOUN
cana-652	37	18	in	in	ADP
cana-652	37	19	the	the	DET
cana-652	37	20	realm	realm	NOUN
cana-652	37	21	of	of	ADP
cana-652	37	22	dermatoscopic	dermatoscopic	ADJ
cana-652	37	23	image	image	NOUN
cana-652	37	24	analysis	analysis	NOUN
cana-652	37	25	for	for	ADP
cana-652	37	26	skin	skin	NOUN
cana-652	37	27	cancer	cancer	NOUN
cana-652	37	28	detection	detection	NOUN
cana-652	37	29	.	.	PUNCT
cana-652	38	1	the	the	DET
cana-652	38	2	primary	primary	ADJ
cana-652	38	3	objective	objective	NOUN
cana-652	38	4	is	be	AUX
cana-652	38	5	to	to	PART
cana-652	38	6	design	design	VERB
cana-652	38	7	,	,	PUNCT
cana-652	38	8	develop	develop	VERB
cana-652	38	9	,	,	PUNCT
cana-652	38	10	and	and	CCONJ
cana-652	38	11	rigorously	rigorously	ADV
cana-652	38	12	evaluate	evaluate	VERB
cana-652	38	13	a	a	DET
cana-652	38	14	cnn	cnn	PROPN
cana-652	38	15	-	-	PUNCT
cana-652	38	16	based	base	VERB
cana-652	38	17	model	model	NOUN
cana-652	38	18	capable	capable	ADJ
cana-652	38	19	of	of	ADP
cana-652	38	20	accurately	accurately	ADV
cana-652	38	21	detecting	detect	VERB
cana-652	38	22	skin	skin	NOUN
cana-652	38	23	cancer	cancer	NOUN
cana-652	38	24	lesions	lesion	NOUN
cana-652	38	25	.	.	PUNCT
cana-652	39	1	this	this	DET
cana-652	39	2	model	model	NOUN
cana-652	39	3	seeks	seek	VERB
cana-652	39	4	to	to	PART
cana-652	39	5	reduce	reduce	VERB
cana-652	39	6	diagnostic	diagnostic	ADJ
cana-652	39	7	subjectivity	subjectivity	NOUN
cana-652	39	8	,	,	PUNCT
cana-652	39	9	enhance	enhance	VERB
cana-652	39	10	the	the	DET
cana-652	39	11	accessibility	accessibility	NOUN
cana-652	39	12	of	of	ADP
cana-652	39	13	skin	skin	NOUN
cana-652	39	14	cancer	cancer	NOUN
cana-652	39	15	diagnosis	diagnosis	NOUN
cana-652	39	16	,	,	PUNCT
cana-652	39	17	and	and	CCONJ
cana-652	39	18	contribute	contribute	VERB
cana-652	39	19	to	to	ADP
cana-652	39	20	the	the	DET
cana-652	39	21	global	global	ADJ
cana-652	39	22	efforts	effort	NOUN
cana-652	39	23	to	to	PART
cana-652	39	24	improve	improve	VERB
cana-652	39	25	early	early	ADJ
cana-652	39	26	detection	detection	NOUN
cana-652	39	27	,	,	PUNCT
cana-652	39	28	thereby	thereby	ADV
cana-652	39	29	minimizing	minimize	VERB
cana-652	39	30	the	the	DET
cana-652	39	31	morbidity	morbidity	NOUN
cana-652	39	32	and	and	CCONJ
cana-652	39	33	mortality	mortality	NOUN
cana-652	39	34	associated	associate	VERB
cana-652	39	35	with	with	ADP
cana-652	39	36	this	this	DET
cana-652	39	37	widespread	widespread	ADJ
cana-652	39	38	disease	disease	NOUN
cana-652	39	39	[	[	X
cana-652	39	40	4	4	NUM
cana-652	39	41	]	]	PUNCT
cana-652	39	42	.	.	PUNCT
cana-652	40	1	a.	a.	NOUN
cana-652	40	2	skin	skin	NOUN
cana-652	40	3	cancer	cancer	NOUN
cana-652	40	4	types	type	NOUN
cana-652	40	5	:	:	PUNCT
cana-652	40	6	(	(	PUNCT
cana-652	40	7	i	i	NOUN
cana-652	40	8	)	)	PUNCT
cana-652	40	9	melanoma	melanoma	NOUN
cana-652	40	10	:	:	PUNCT
cana-652	40	11	it	it	PRON
cana-652	40	12	is	be	AUX
cana-652	40	13	a	a	DET
cana-652	40	14	highly	highly	ADV
cana-652	40	15	malignant	malignant	ADJ
cana-652	40	16	skin	skin	NOUN
cana-652	40	17	cancer	cancer	NOUN
cana-652	40	18	type	type	NOUN
cana-652	40	19	that	that	PRON
cana-652	40	20	develops	develop	VERB
cana-652	40	21	in	in	ADP
cana-652	40	22	melanocytes	melanocyte	NOUN
cana-652	40	23	,	,	PUNCT
cana-652	40	24	the	the	DET
cana-652	40	25	cells	cell	NOUN
cana-652	40	26	that	that	PRON
cana-652	40	27	produce	produce	VERB
cana-652	40	28	pigment	pigment	NOUN
cana-652	40	29	.	.	PUNCT
cana-652	41	1	it	it	PRON
cana-652	41	2	is	be	AUX
cana-652	41	3	known	know	VERB
cana-652	41	4	for	for	ADP
cana-652	41	5	its	its	PRON
cana-652	41	6	potential	potential	NOUN
cana-652	41	7	to	to	PART
cana-652	41	8	metastasize	metastasize	VERB
cana-652	41	9	,	,	PUNCT
cana-652	41	10	making	make	VERB
cana-652	41	11	early	early	ADJ
cana-652	41	12	detection	detection	NOUN
cana-652	41	13	crucial	crucial	ADJ
cana-652	41	14	.	.	PUNCT
cana-652	42	1	(	(	PUNCT
cana-652	42	2	ii	ii	NOUN
cana-652	42	3	)	)	PUNCT
cana-652	42	4	melanocytic	melanocytic	ADJ
cana-652	42	5	nevus	nevus	NOUN
cana-652	42	6	:	:	PUNCT
cana-652	42	7	melanocytic	melanocytic	ADJ
cana-652	42	8	nevi	nevi	NOUN
cana-652	42	9	,	,	PUNCT
cana-652	42	10	often	often	ADV
cana-652	42	11	referred	refer	VERB
cana-652	42	12	to	to	ADP
cana-652	42	13	as	as	ADP
cana-652	42	14	moles	mole	NOUN
cana-652	42	15	,	,	PUNCT
cana-652	42	16	are	be	AUX
cana-652	42	17	common	common	ADJ
cana-652	42	18	benign	benign	ADJ
cana-652	42	19	skin	skin	NOUN
cana-652	42	20	growths	growth	NOUN
cana-652	42	21	characterized	characterize	VERB
cana-652	42	22	by	by	ADP
cana-652	42	23	a	a	DET
cana-652	42	24	cluster	cluster	NOUN
cana-652	42	25	of	of	ADP
cana-652	42	26	melanocytes	melanocyte	NOUN
cana-652	42	27	.	.	PUNCT
cana-652	43	1	they	they	PRON
cana-652	43	2	come	come	VERB
cana-652	43	3	in	in	ADP
cana-652	43	4	various	various	ADJ
cana-652	43	5	shapes	shape	NOUN
cana-652	43	6	and	and	CCONJ
cana-652	43	7	colors	color	NOUN
cana-652	43	8	.	.	PUNCT
cana-652	44	1	communications	communication	NOUN
cana-652	44	2	on	on	ADP
cana-652	44	3	applied	apply	VERB
cana-652	44	4	nonlinear	nonlinear	ADJ
cana-652	44	5	analysis	analysis	NOUN
cana-652	44	6	issn	issn	NOUN
cana-652	44	7	:	:	PUNCT
cana-652	44	8	1074	1074	NUM
cana-652	44	9	-	-	PUNCT
cana-652	44	10	133x	133x	NUM
cana-652	44	11	vol	vol	NOUN
cana-652	44	12	31	31	NUM
cana-652	44	13	no	no	NOUN
cana-652	44	14	.	.	PUNCT
cana-652	45	1	2s	2s	NUM
cana-652	45	2	(	(	PUNCT
cana-652	45	3	2024	2024	NUM
cana-652	45	4	)	)	PUNCT
cana-652	45	5	322	322	NUM
cana-652	45	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-652	45	7	(	(	PUNCT
cana-652	45	8	iii)basal	iii)basal	NOUN
cana-652	45	9	cell	cell	NOUN
cana-652	45	10	carcinoma	carcinoma	NOUN
cana-652	45	11	:	:	PUNCT
cana-652	45	12	the	the	DET
cana-652	45	13	most	most	ADV
cana-652	45	14	common	common	ADJ
cana-652	45	15	form	form	NOUN
cana-652	45	16	of	of	ADP
cana-652	45	17	skin	skin	NOUN
cana-652	45	18	cancer	cancer	NOUN
cana-652	45	19	is	be	AUX
cana-652	45	20	basal	basal	ADJ
cana-652	45	21	cell	cell	NOUN
cana-652	45	22	carcinoma	carcinoma	NOUN
cana-652	45	23	.	.	PUNCT
cana-652	46	1	it	it	PRON
cana-652	46	2	typically	typically	ADV
cana-652	46	3	arises	arise	VERB
cana-652	46	4	in	in	ADP
cana-652	46	5	the	the	DET
cana-652	46	6	basal	basal	ADJ
cana-652	46	7	cells	cell	NOUN
cana-652	46	8	of	of	ADP
cana-652	46	9	the	the	DET
cana-652	46	10	epidermis	epidermis	NOUN
cana-652	46	11	and	and	CCONJ
cana-652	46	12	is	be	AUX
cana-652	46	13	generally	generally	ADV
cana-652	46	14	slow	slow	ADV
cana-652	46	15	-	-	PUNCT
cana-652	46	16	growing	grow	VERB
cana-652	46	17	,	,	PUNCT
cana-652	46	18	with	with	ADP
cana-652	46	19	a	a	DET
cana-652	46	20	low	low	ADJ
cana-652	46	21	risk	risk	NOUN
cana-652	46	22	of	of	ADP
cana-652	46	23	metastasis	metastasis	NOUN
cana-652	46	24	.	.	PUNCT
cana-652	47	1	(	(	PUNCT
cana-652	47	2	iv	iv	X
cana-652	47	3	)	)	PUNCT
cana-652	47	4	actinic	actinic	ADJ
cana-652	47	5	keratosis	keratosis	NOUN
cana-652	47	6	:	:	PUNCT
cana-652	47	7	actinic	actinic	ADJ
cana-652	47	8	keratosis	keratosis	NOUN
cana-652	47	9	is	be	AUX
cana-652	47	10	a	a	DET
cana-652	47	11	precancerous	precancerous	ADJ
cana-652	47	12	skin	skin	NOUN
cana-652	47	13	condition	condition	NOUN
cana-652	47	14	resulting	result	VERB
cana-652	47	15	from	from	ADP
cana-652	47	16	sun	sun	NOUN
cana-652	47	17	damage	damage	NOUN
cana-652	47	18	.	.	PUNCT
cana-652	48	1	it	it	PRON
cana-652	48	2	appears	appear	VERB
cana-652	48	3	as	as	ADP
cana-652	48	4	scaly	scaly	NOUN
cana-652	48	5	or	or	CCONJ
cana-652	48	6	crusty	crusty	ADJ
cana-652	48	7	patches	patch	NOUN
cana-652	48	8	and	and	CCONJ
cana-652	48	9	can	can	AUX
cana-652	48	10	develop	develop	VERB
cana-652	48	11	into	into	ADP
cana-652	48	12	squamous	squamous	ADJ
cana-652	48	13	cell	cell	NOUN
cana-652	48	14	carcinoma	carcinoma	NOUN
cana-652	48	15	if	if	SCONJ
cana-652	48	16	left	leave	VERB
cana-652	48	17	untreated	untreated	ADJ
cana-652	48	18	.	.	PUNCT
cana-652	49	1	(	(	PUNCT
cana-652	49	2	v	v	NOUN
cana-652	49	3	)	)	PUNCT
cana-652	49	4	benign	benign	ADJ
cana-652	49	5	keratosis	keratosis	NOUN
cana-652	49	6	:	:	PUNCT
cana-652	49	7	benign	benign	ADJ
cana-652	49	8	keratosis	keratosis	NOUN
cana-652	49	9	are	be	AUX
cana-652	49	10	non	non	ADJ
cana-652	49	11	-	-	ADJ
cana-652	49	12	cancerous	cancerous	ADJ
cana-652	49	13	skin	skin	NOUN
cana-652	49	14	growths	growth	NOUN
cana-652	49	15	often	often	ADV
cana-652	49	16	caused	cause	VERB
cana-652	49	17	by	by	ADP
cana-652	49	18	excess	excess	ADJ
cana-652	49	19	keratin	keratin	NOUN
cana-652	49	20	.	.	PUNCT
cana-652	50	1	they	they	PRON
cana-652	50	2	can	can	AUX
cana-652	50	3	take	take	VERB
cana-652	50	4	various	various	ADJ
cana-652	50	5	forms	form	NOUN
cana-652	50	6	,	,	PUNCT
cana-652	50	7	such	such	ADJ
cana-652	50	8	as	as	ADP
cana-652	50	9	seborrheic	seborrheic	ADJ
cana-652	50	10	keratosis	keratosis	NOUN
cana-652	50	11	,	,	PUNCT
cana-652	50	12	and	and	CCONJ
cana-652	50	13	are	be	AUX
cana-652	50	14	generally	generally	ADV
cana-652	50	15	harmless	harmless	ADJ
cana-652	50	16	.	.	PUNCT
cana-652	51	1	(	(	PUNCT
cana-652	51	2	vi	vi	NOUN
cana-652	51	3	)	)	PUNCT
cana-652	51	4	dermatofibroma	dermatofibroma	NOUN
cana-652	51	5	:	:	PUNCT
cana-652	51	6	dermatofibromas	dermatofibroma	NOUN
cana-652	51	7	are	be	AUX
cana-652	51	8	usually	usually	ADV
cana-652	51	9	benign	benign	ADJ
cana-652	51	10	skin	skin	NOUN
cana-652	51	11	lesions	lesion	NOUN
cana-652	51	12	originating	originate	VERB
cana-652	51	13	in	in	ADP
cana-652	51	14	fibroblasts	fibroblast	NOUN
cana-652	51	15	.	.	PUNCT
cana-652	52	1	they	they	PRON
cana-652	52	2	manifest	manifest	VERB
cana-652	52	3	as	as	ADP
cana-652	52	4	firm	firm	ADJ
cana-652	52	5	,	,	PUNCT
cana-652	52	6	brownish	brownish	ADJ
cana-652	52	7	nodules	nodule	NOUN
cana-652	52	8	and	and	CCONJ
cana-652	52	9	are	be	AUX
cana-652	52	10	typically	typically	ADV
cana-652	52	11	non	non	ADJ
cana-652	52	12	-	-	ADJ
cana-652	52	13	threatening	threatening	ADJ
cana-652	52	14	.	.	PUNCT
cana-652	53	1	(	(	PUNCT
cana-652	53	2	vii	vii	NOUN
cana-652	53	3	)	)	PUNCT
cana-652	53	4	vascular	vascular	ADJ
cana-652	53	5	lesion	lesion	NOUN
cana-652	53	6	:	:	PUNCT
cana-652	53	7	vascular	vascular	ADJ
cana-652	53	8	lesions	lesion	NOUN
cana-652	53	9	involve	involve	VERB
cana-652	53	10	abnormalities	abnormality	NOUN
cana-652	53	11	in	in	ADP
cana-652	53	12	blood	blood	NOUN
cana-652	53	13	vessels	vessel	NOUN
cana-652	53	14	within	within	ADP
cana-652	53	15	the	the	DET
cana-652	53	16	skin	skin	NOUN
cana-652	53	17	.	.	PUNCT
cana-652	54	1	they	they	PRON
cana-652	54	2	can	can	AUX
cana-652	54	3	include	include	VERB
cana-652	54	4	conditions	condition	NOUN
cana-652	54	5	like	like	ADP
cana-652	54	6	hemangiomas	hemangioma	NOUN
cana-652	54	7	and	and	CCONJ
cana-652	54	8	port	port	NOUN
cana-652	54	9	-	-	PUNCT
cana-652	54	10	wine	wine	NOUN
cana-652	54	11	stains	stain	NOUN
cana-652	54	12	,	,	PUNCT
cana-652	54	13	which	which	PRON
cana-652	54	14	are	be	AUX
cana-652	54	15	usually	usually	ADV
cana-652	54	16	noncancerous	noncancerous	ADJ
cana-652	54	17	but	but	CCONJ
cana-652	54	18	may	may	AUX
cana-652	54	19	require	require	VERB
cana-652	54	20	medical	medical	ADJ
cana-652	54	21	attention	attention	NOUN
cana-652	54	22	.	.	PUNCT
cana-652	55	1	b.	b.	PROPN
cana-652	55	2	dermatoscopy	dermatoscopy	NOUN
cana-652	55	3	:	:	PUNCT
cana-652	55	4	dermatoscopy	dermatoscopy	VERB
cana-652	55	5	,	,	PUNCT
cana-652	55	6	also	also	ADV
cana-652	55	7	known	know	VERB
cana-652	55	8	as	as	ADP
cana-652	55	9	dermoscopy	dermoscopy	NOUN
cana-652	55	10	,	,	PUNCT
cana-652	55	11	is	be	AUX
cana-652	55	12	a	a	DET
cana-652	55	13	pivotal	pivotal	ADJ
cana-652	55	14	tool	tool	NOUN
cana-652	55	15	in	in	ADP
cana-652	55	16	dermatology	dermatology	NOUN
cana-652	55	17	,	,	PUNCT
cana-652	55	18	allowing	allow	VERB
cana-652	55	19	for	for	ADP
cana-652	55	20	the	the	DET
cana-652	55	21	magnified	magnify	VERB
cana-652	55	22	visualization	visualization	NOUN
cana-652	55	23	of	of	ADP
cana-652	55	24	skin	skin	NOUN
cana-652	55	25	lesions	lesion	NOUN
cana-652	55	26	.	.	PUNCT
cana-652	56	1	it	it	PRON
cana-652	56	2	enables	enable	VERB
cana-652	56	3	clinicians	clinician	NOUN
cana-652	56	4	to	to	PART
cana-652	56	5	assess	assess	VERB
cana-652	56	6	pigment	pigment	NOUN
cana-652	56	7	patterns	pattern	NOUN
cana-652	56	8	,	,	PUNCT
cana-652	56	9	vascularization	vascularization	NOUN
cana-652	56	10	,	,	PUNCT
cana-652	56	11	and	and	CCONJ
cana-652	56	12	architectural	architectural	ADJ
cana-652	56	13	features	feature	NOUN
cana-652	56	14	,	,	PUNCT
cana-652	56	15	providing	provide	VERB
cana-652	56	16	valuable	valuable	ADJ
cana-652	56	17	insights	insight	NOUN
cana-652	56	18	for	for	ADP
cana-652	56	19	accurate	accurate	ADJ
cana-652	56	20	diagnosis	diagnosis	NOUN
cana-652	56	21	.	.	PUNCT
cana-652	57	1	dermatoscopy	dermatoscopy	NOUN
cana-652	57	2	has	have	AUX
cana-652	57	3	significantly	significantly	ADV
cana-652	57	4	improved	improve	VERB
cana-652	57	5	diagnostic	diagnostic	ADJ
cana-652	57	6	accuracy	accuracy	NOUN
cana-652	57	7	,	,	PUNCT
cana-652	57	8	extending	extend	VERB
cana-652	57	9	beyond	beyond	ADP
cana-652	57	10	what	what	PRON
cana-652	57	11	is	be	AUX
cana-652	57	12	preciviable	preciviable	ADJ
cana-652	57	13	to	to	ADP
cana-652	57	14	the	the	DET
cana-652	57	15	normal	normal	ADJ
cana-652	57	16	eye	eye	NOUN
cana-652	57	17	.	.	PUNCT
cana-652	58	1	c.	c.	PROPN
cana-652	58	2	deep	deep	ADJ
cana-652	58	3	learning	learning	PROPN
cana-652	58	4	and	and	CCONJ
cana-652	58	5	cnns	cnns	PROPN
cana-652	58	6	:	:	PUNCT
cana-652	58	7	machine	machine	NOUN
cana-652	58	8	learning	learning	NOUN
cana-652	58	9	is	be	AUX
cana-652	58	10	a	a	DET
cana-652	58	11	superset	superset	NOUN
cana-652	58	12	of	of	ADP
cana-652	58	13	deep	deep	ADJ
cana-652	58	14	learning	learning	NOUN
cana-652	58	15	that	that	PRON
cana-652	58	16	has	have	AUX
cana-652	58	17	brought	bring	VERB
cana-652	58	18	about	about	ADP
cana-652	58	19	revolutionary	revolutionary	ADJ
cana-652	58	20	transformations	transformation	NOUN
cana-652	58	21	in	in	ADP
cana-652	58	22	various	various	ADJ
cana-652	58	23	fields	field	NOUN
cana-652	58	24	,	,	PUNCT
cana-652	58	25	including	include	VERB
cana-652	58	26	medical	medical	ADJ
cana-652	58	27	imaging	imaging	NOUN
cana-652	58	28	and	and	CCONJ
cana-652	58	29	health	health	NOUN
cana-652	58	30	care	care	NOUN
cana-652	58	31	.	.	PUNCT
cana-652	59	1	cnns	cnns	PROPN
cana-652	59	2	,	,	PUNCT
cana-652	59	3	as	as	ADP
cana-652	59	4	a	a	DET
cana-652	59	5	category	category	NOUN
cana-652	59	6	of	of	ADP
cana-652	59	7	deep	deep	ADJ
cana-652	59	8	neural	neural	ADJ
cana-652	59	9	network	network	NOUN
cana-652	59	10	architectures	architecture	NOUN
cana-652	59	11	,	,	PUNCT
cana-652	59	12	have	have	AUX
cana-652	59	13	gained	gain	VERB
cana-652	59	14	prominence	prominence	NOUN
cana-652	59	15	for	for	ADP
cana-652	59	16	their	their	PRON
cana-652	59	17	exceptional	exceptional	ADJ
cana-652	59	18	performance	performance	NOUN
cana-652	59	19	in	in	ADP
cana-652	59	20	image	image	NOUN
cana-652	59	21	analysis	analysis	NOUN
cana-652	59	22	tasks	task	NOUN
cana-652	59	23	.	.	PUNCT
cana-652	60	1	comprising	comprise	VERB
cana-652	60	2	convolutional	convolutional	ADJ
cana-652	60	3	and	and	CCONJ
cana-652	60	4	pooling	pool	VERB
cana-652	60	5	layers	layer	NOUN
cana-652	60	6	,	,	PUNCT
cana-652	60	7	cnns	cnns	PROPN
cana-652	60	8	excel	excel	VERB
cana-652	60	9	at	at	ADP
cana-652	60	10	automatically	automatically	ADV
cana-652	60	11	learning	learn	VERB
cana-652	60	12	complex	complex	ADJ
cana-652	60	13	features	feature	NOUN
cana-652	60	14	from	from	ADP
cana-652	60	15	raw	raw	ADJ
cana-652	60	16	image	image	NOUN
cana-652	60	17	data	datum	NOUN
cana-652	60	18	,	,	PUNCT
cana-652	60	19	rendering	render	VERB
cana-652	60	20	them	they	PRON
cana-652	60	21	well	well	ADV
cana-652	60	22	-	-	PUNCT
cana-652	60	23	suited	suited	ADJ
cana-652	60	24	for	for	ADP
cana-652	60	25	the	the	DET
cana-652	60	26	analysis	analysis	NOUN
cana-652	60	27	of	of	ADP
cana-652	60	28	dermatoscopic	dermatoscopic	ADJ
cana-652	60	29	images	image	NOUN
cana-652	60	30	.	.	PUNCT
cana-652	61	1	d.	d.	PROPN
cana-652	61	2	mobilenet	mobilenet	PROPN
cana-652	61	3	:	:	PUNCT
cana-652	61	4	mobilenet	mobilenet	PROPN
cana-652	61	5	is	be	AUX
cana-652	61	6	a	a	DET
cana-652	61	7	lightweight	lightweight	ADJ
cana-652	61	8	convolutional	convolutional	ADJ
cana-652	61	9	neural	neural	ADJ
cana-652	61	10	network	network	NOUN
cana-652	61	11	model	model	NOUN
cana-652	61	12	,	,	PUNCT
cana-652	61	13	primarily	primarily	ADV
cana-652	61	14	designed	design	VERB
cana-652	61	15	for	for	ADP
cana-652	61	16	resourceconstrained	resourceconstraine	VERB
cana-652	61	17	and	and	CCONJ
cana-652	61	18	efficient	efficient	ADJ
cana-652	61	19	environments	environment	NOUN
cana-652	61	20	,	,	PUNCT
cana-652	61	21	like	like	ADP
cana-652	61	22	embedded	embed	VERB
cana-652	61	23	systems	system	NOUN
cana-652	61	24	and	and	CCONJ
cana-652	61	25	mobile	mobile	ADJ
cana-652	61	26	devices	device	NOUN
cana-652	61	27	.	.	PUNCT
cana-652	62	1	it	it	PRON
cana-652	62	2	achieves	achieve	VERB
cana-652	62	3	remarkable	remarkable	ADJ
cana-652	62	4	performance	performance	NOUN
cana-652	62	5	by	by	ADP
cana-652	62	6	utilizing	utilize	VERB
cana-652	62	7	depth	depth	NOUN
cana-652	62	8	-	-	PUNCT
cana-652	62	9	wise	wise	ADJ
cana-652	62	10	separable	separable	ADJ
cana-652	62	11	convolutions	convolution	NOUN
cana-652	62	12	,	,	PUNCT
cana-652	62	13	which	which	PRON
cana-652	62	14	significantly	significantly	ADV
cana-652	62	15	reduce	reduce	VERB
cana-652	62	16	the	the	DET
cana-652	62	17	model	model	NOUN
cana-652	62	18	's	's	PART
cana-652	62	19	computational	computational	ADJ
cana-652	62	20	complexity	complexity	NOUN
cana-652	62	21	and	and	CCONJ
cana-652	62	22	memory	memory	NOUN
cana-652	62	23	footprint	footprint	NOUN
cana-652	62	24	while	while	SCONJ
cana-652	62	25	preserving	preserve	VERB
cana-652	62	26	accuracy	accuracy	NOUN
cana-652	62	27	.	.	PUNCT
cana-652	63	1	mobilenet	mobilenet	PROPN
cana-652	63	2	's	's	PART
cana-652	63	3	modular	modular	ADJ
cana-652	63	4	structure	structure	NOUN
cana-652	63	5	allows	allow	VERB
cana-652	63	6	for	for	ADP
cana-652	63	7	easy	easy	ADJ
cana-652	63	8	customization	customization	NOUN
cana-652	63	9	and	and	CCONJ
cana-652	63	10	adaptation	adaptation	NOUN
cana-652	63	11	to	to	ADP
cana-652	63	12	various	various	ADJ
cana-652	63	13	tasks	task	NOUN
cana-652	63	14	,	,	PUNCT
cana-652	63	15	making	make	VERB
cana-652	63	16	it	it	PRON
cana-652	63	17	an	an	DET
cana-652	63	18	attractive	attractive	ADJ
cana-652	63	19	choice	choice	NOUN
cana-652	63	20	for	for	ADP
cana-652	63	21	real	real	ADJ
cana-652	63	22	-	-	PUNCT
cana-652	63	23	time	time	NOUN
cana-652	63	24	image	image	NOUN
cana-652	63	25	classification	classification	NOUN
cana-652	63	26	and	and	CCONJ
cana-652	63	27	object	object	NOUN
cana-652	63	28	detection	detection	NOUN
cana-652	63	29	applications	application	NOUN
cana-652	63	30	in	in	ADP
cana-652	63	31	resourceconstrained	resourceconstraine	VERB
cana-652	63	32	scenarios	scenario	NOUN
cana-652	63	33	.	.	PUNCT
cana-652	64	1	3	3	X
cana-652	64	2	.	.	X
cana-652	64	3	related	relate	VERB
cana-652	64	4	work	work	NOUN
cana-652	64	5	the	the	DET
cana-652	64	6	ability	ability	NOUN
cana-652	64	7	to	to	PART
cana-652	64	8	classify	classify	VERB
cana-652	64	9	skin	skin	NOUN
cana-652	64	10	tumors	tumor	NOUN
cana-652	64	11	from	from	ADP
cana-652	64	12	photographs	photograph	NOUN
cana-652	64	13	has	have	AUX
cana-652	64	14	increased	increase	VERB
cana-652	64	15	deamatically	deamatically	ADV
cana-652	64	16	in	in	ADP
cana-652	64	17	past	past	ADJ
cana-652	64	18	years	year	NOUN
cana-652	64	19	.	.	PUNCT
cana-652	65	1	over	over	ADP
cana-652	65	2	the	the	DET
cana-652	65	3	years	year	NOUN
cana-652	65	4	,	,	PUNCT
cana-652	65	5	a	a	DET
cana-652	65	6	lot	lot	NOUN
cana-652	65	7	of	of	ADP
cana-652	65	8	deep	deep	ADJ
cana-652	65	9	learning	learning	NOUN
cana-652	65	10	approaches	approach	NOUN
cana-652	65	11	have	have	AUX
cana-652	65	12	been	be	AUX
cana-652	65	13	studied	study	VERB
cana-652	65	14	and	and	CCONJ
cana-652	65	15	attempted	attempt	VERB
cana-652	65	16	.	.	PUNCT
cana-652	66	1	the	the	DET
cana-652	66	2	2018	2018	NUM
cana-652	66	3	international	international	ADJ
cana-652	66	4	skin	skin	NOUN
cana-652	66	5	imaging	imaging	NOUN
cana-652	66	6	collaboration	collaboration	NOUN
cana-652	66	7	(	(	PUNCT
cana-652	66	8	isic	isic	NOUN
cana-652	66	9	)	)	PUNCT
cana-652	66	10	event	event	NOUN
cana-652	66	11	,	,	PUNCT
cana-652	66	12	which	which	PRON
cana-652	66	13	featured	feature	VERB
cana-652	66	14	a	a	DET
cana-652	66	15	challenge	challenge	NOUN
cana-652	66	16	contest	contest	NOUN
cana-652	66	17	,	,	PUNCT
cana-652	66	18	has	have	AUX
cana-652	66	19	evolved	evolve	VERB
cana-652	66	20	into	into	ADP
cana-652	66	21	a	a	DET
cana-652	66	22	de	de	X
cana-652	66	23	communications	communication	NOUN
cana-652	66	24	on	on	ADP
cana-652	66	25	applied	apply	VERB
cana-652	66	26	nonlinear	nonlinear	ADJ
cana-652	66	27	analysis	analysis	NOUN
cana-652	66	28	issn	issn	NOUN
cana-652	66	29	:	:	PUNCT
cana-652	66	30	1074	1074	NUM
cana-652	66	31	-	-	PUNCT
cana-652	66	32	133x	133x	NUM
cana-652	66	33	vol	vol	NOUN
cana-652	66	34	31	31	NUM
cana-652	66	35	no	no	NOUN
cana-652	66	36	.	.	PUNCT
cana-652	67	1	2s	2s	NUM
cana-652	67	2	(	(	PUNCT
cana-652	67	3	2024	2024	NUM
cana-652	67	4	)	)	PUNCT
cana-652	67	5	323	323	NUM
cana-652	67	6	https://internationalpubls.com	https://internationalpubls.com	NUM
cana-652	67	7	facto	facto	X
cana-652	67	8	standard	standard	NOUN
cana-652	67	9	for	for	ADP
cana-652	67	10	skin	skin	NOUN
cana-652	67	11	cancer	cancer	NOUN
cana-652	67	12	screening	screening	NOUN
cana-652	67	13	.	.	PUNCT
cana-652	68	1	[	[	X
cana-652	68	2	1	1	NUM
cana-652	68	3	]	]	PUNCT
cana-652	68	4	.	.	PUNCT
cana-652	69	1	even	even	ADV
cana-652	69	2	the	the	DET
cana-652	69	3	images	image	NOUN
cana-652	69	4	taken	take	VERB
cana-652	69	5	through	through	ADP
cana-652	69	6	mobile	mobile	ADJ
cana-652	69	7	phones	phone	NOUN
cana-652	69	8	are	be	AUX
cana-652	69	9	capable	capable	ADJ
cana-652	69	10	enough	enough	ADV
cana-652	69	11	to	to	PART
cana-652	69	12	get	get	AUX
cana-652	69	13	tested	test	VERB
cana-652	69	14	for	for	ADP
cana-652	69	15	convolutional	convolutional	ADJ
cana-652	69	16	neural	neural	ADJ
cana-652	69	17	networks	network	NOUN
cana-652	69	18	(	(	PUNCT
cana-652	69	19	cnn)[1	cnn)[1	PROPN
cana-652	69	20	]	]	PUNCT
cana-652	69	21	.	.	PUNCT
cana-652	70	1	cnn	cnn	PROPN
cana-652	70	2	basically	basically	ADV
cana-652	70	3	mimics	mimic	VERB
cana-652	70	4	human	human	ADJ
cana-652	70	5	visual	visual	ADJ
cana-652	70	6	cognition	cognition	NOUN
cana-652	70	7	systems	system	NOUN
cana-652	70	8	and	and	CCONJ
cana-652	70	9	is	be	AUX
cana-652	70	10	the	the	DET
cana-652	70	11	one	one	NUM
cana-652	70	12	of	of	ADP
cana-652	70	13	the	the	DET
cana-652	70	14	best	good	ADJ
cana-652	70	15	ways	way	NOUN
cana-652	70	16	to	to	PART
cana-652	70	17	recognise	recognise	VERB
cana-652	70	18	images	image	NOUN
cana-652	70	19	with	with	ADP
cana-652	70	20	the	the	DET
cana-652	70	21	help	help	NOUN
cana-652	70	22	of	of	ADP
cana-652	70	23	computer	computer	NOUN
cana-652	70	24	vision	vision	NOUN
cana-652	70	25	.	.	PUNCT
cana-652	71	1	m.	m.	NOUN
cana-652	71	2	acosta	acosta	PROPN
cana-652	71	3	et.al	et.al	PROPN
cana-652	71	4	.	.	PUNCT
cana-652	72	1	have	have	AUX
cana-652	72	2	suggested	suggest	VERB
cana-652	72	3	a	a	DET
cana-652	72	4	technique	technique	NOUN
cana-652	72	5	based	base	VERB
cana-652	72	6	on	on	ADP
cana-652	72	7	cnn	cnn	PROPN
cana-652	72	8	mask	mask	NOUN
cana-652	72	9	and	and	CCONJ
cana-652	72	10	resnet152	resnet152	PROPN
cana-652	72	11	.	.	PUNCT
cana-652	73	1	the	the	DET
cana-652	73	2	suggested	suggest	VERB
cana-652	73	3	procedure	procedure	NOUN
cana-652	73	4	consists	consist	VERB
cana-652	73	5	of	of	ADP
cana-652	73	6	two	two	NUM
cana-652	73	7	stages	stage	NOUN
cana-652	73	8	:	:	PUNCT
cana-652	73	9	a	a	DET
cana-652	73	10	first	first	ADJ
cana-652	73	11	stage	stage	NOUN
cana-652	73	12	that	that	PRON
cana-652	73	13	uses	use	VERB
cana-652	73	14	area	area	NOUN
cana-652	73	15	of	of	ADP
cana-652	73	16	interest	interest	NOUN
cana-652	73	17	in	in	ADP
cana-652	73	18	dermatoscopic	dermatoscopic	NOUN
cana-652	73	19	images	image	NOUN
cana-652	73	20	by	by	ADP
cana-652	73	21	cropping	crop	VERB
cana-652	73	22	it	it	PRON
cana-652	73	23	,	,	PUNCT
cana-652	73	24	this	this	DET
cana-652	73	25	technique	technique	NOUN
cana-652	73	26	uses	use	VERB
cana-652	73	27	mask	mask	NOUN
cana-652	73	28	and	and	CCONJ
cana-652	73	29	region	region	NOUN
cana-652	73	30	based	base	VERB
cana-652	73	31	cnn	cnn	PROPN
cana-652	73	32	,	,	PUNCT
cana-652	73	33	and	and	CCONJ
cana-652	73	34	a	a	DET
cana-652	73	35	second	second	ADJ
cana-652	73	36	stage	stage	NOUN
cana-652	73	37	that	that	PRON
cana-652	73	38	classify	classify	VERB
cana-652	73	39	lesions	lesion	NOUN
cana-652	73	40	into	into	ADP
cana-652	73	41	‘	'	PUNCT
cana-652	73	42	benign	benign	ADJ
cana-652	73	43	’	'	PUNCT
cana-652	73	44	and	and	CCONJ
cana-652	73	45	‘	'	PUNCT
cana-652	73	46	malignant	malignant	ADJ
cana-652	73	47	’	'	PUNCT
cana-652	73	48	and	and	CCONJ
cana-652	73	49	uses	use	VERB
cana-652	73	50	resnet52	resnet52	NOUN
cana-652	73	51	to	to	PART
cana-652	73	52	classify	classify	VERB
cana-652	73	53	it[2	it[2	PROPN
cana-652	73	54	]	]	PUNCT
cana-652	73	55	.	.	PUNCT
cana-652	74	1	proposed	propose	VERB
cana-652	74	2	model	model	NOUN
cana-652	74	3	by	by	ADP
cana-652	74	4	m.f.j	m.f.j	PROPN
cana-652	74	5	.	.	PROPN
cana-652	74	6	acosta	acosta	PROPN
cana-652	74	7	et	et	PROPN
cana-652	74	8	.	.	PUNCT
cana-652	75	1	al	al	PROPN
cana-652	75	2	.	.	PROPN
cana-652	75	3	reported	report	VERB
cana-652	75	4	highest	high	ADJ
cana-652	75	5	specificity	specificity	NOUN
cana-652	75	6	of	of	ADP
cana-652	75	7	96	96	NUM
cana-652	75	8	%	%	NOUN
cana-652	75	9	and	and	CCONJ
cana-652	75	10	maximum	maximum	ADJ
cana-652	75	11	accuracy	accuracy	NOUN
cana-652	75	12	of	of	ADP
cana-652	75	13	90.4	90.4	NUM
cana-652	75	14	%	%	NOUN
cana-652	75	15	for	for	ADP
cana-652	75	16	six	six	NUM
cana-652	75	17	different	different	ADJ
cana-652	75	18	testing	testing	NOUN
cana-652	75	19	models	model	NOUN
cana-652	75	20	.	.	PUNCT
cana-652	76	1	finally	finally	ADV
cana-652	76	2	,	,	PUNCT
cana-652	76	3	with	with	ADP
cana-652	76	4	a	a	DET
cana-652	76	5	better	well	ADJ
cana-652	76	6	ratio	ratio	NOUN
cana-652	76	7	between	between	ADP
cana-652	76	8	overall	overall	ADJ
cana-652	76	9	accuracy	accuracy	NOUN
cana-652	76	10	,	,	PUNCT
cana-652	76	11	sensitivity	sensitivity	NOUN
cana-652	76	12	and	and	CCONJ
cana-652	76	13	specificity	specificity	NOUN
cana-652	76	14	calculated	calculate	VERB
cana-652	76	15	0.82	0.82	NUM
cana-652	76	16	,	,	PUNCT
cana-652	76	17	0.904	0.904	NUM
cana-652	76	18	and	and	CCONJ
cana-652	76	19	0.925	0.925	NUM
cana-652	76	20	respectively	respectively	ADV
cana-652	76	21	,	,	PUNCT
cana-652	76	22	the	the	DET
cana-652	76	23	evida	evida	PROPN
cana-652	76	24	m6	m6	PROPN
cana-652	76	25	model	model	NOUN
cana-652	76	26	is	be	AUX
cana-652	76	27	a	a	DET
cana-652	76	28	dependable	dependable	ADJ
cana-652	76	29	predictor	predictor	NOUN
cana-652	76	30	.	.	PUNCT
cana-652	77	1	[	[	X
cana-652	77	2	2	2	NUM
cana-652	77	3	]	]	PUNCT
cana-652	77	4	.	.	PUNCT
cana-652	78	1	esteva	esteva	PROPN
cana-652	78	2	et	et	PROPN
cana-652	78	3	al	al	PROPN
cana-652	78	4	.	.	PROPN
cana-652	78	5	made	make	VERB
cana-652	78	6	significant	significant	ADJ
cana-652	78	7	progress	progress	NOUN
cana-652	78	8	in	in	ADP
cana-652	78	9	the	the	DET
cana-652	78	10	categorization	categorization	NOUN
cana-652	78	11	of	of	ADP
cana-652	78	12	skin	skin	NOUN
cana-652	78	13	cancer	cancer	NOUN
cana-652	78	14	using	use	VERB
cana-652	78	15	a	a	DET
cana-652	78	16	pre	pre	ADJ
cana-652	78	17	-	-	ADJ
cana-652	78	18	trained	train	VERB
cana-652	78	19	googlenet	googlenet	NOUN
cana-652	78	20	inception	inception	PROPN
cana-652	78	21	v3	v3	PROPN
cana-652	78	22	cnn	cnn	PROPN
cana-652	78	23	model	model	NOUN
cana-652	78	24	[	[	X
cana-652	78	25	3	3	NUM
cana-652	78	26	]	]	PUNCT
cana-652	78	27	.	.	PUNCT
cana-652	79	1	author	author	NOUN
cana-652	79	2	m.	m.	PROPN
cana-652	79	3	kadampur	kadampur	PROPN
cana-652	79	4	et	et	PROPN
cana-652	79	5	.	.	PUNCT
cana-652	80	1	al	al	PROPN
cana-652	80	2	had	have	AUX
cana-652	80	3	worked	work	VERB
cana-652	80	4	on	on	ADP
cana-652	80	5	five	five	NUM
cana-652	80	6	different	different	ADJ
cana-652	80	7	cnn	cnn	NOUN
cana-652	80	8	models	model	NOUN
cana-652	80	9	and	and	CCONJ
cana-652	80	10	tested	test	VERB
cana-652	80	11	on	on	ADP
cana-652	80	12	the	the	DET
cana-652	80	13	ham10000	ham10000	PROPN
cana-652	80	14	dataset	dataset	PROPN
cana-652	80	15	.	.	PUNCT
cana-652	81	1	they	they	PRON
cana-652	81	2	acquired	acquire	VERB
cana-652	81	3	accuracy	accuracy	NOUN
cana-652	81	4	of	of	ADP
cana-652	81	5	cnn	cnn	PROPN
cana-652	81	6	models	model	NOUN
cana-652	81	7	about	about	ADV
cana-652	81	8	94	94	NUM
cana-652	81	9	%	%	NOUN
cana-652	81	10	on	on	ADP
cana-652	81	11	ham10000	ham10000	PROPN
cana-652	81	12	.	.	PUNCT
cana-652	82	1	h.	h.	PROPN
cana-652	82	2	balaha	balaha	PROPN
cana-652	82	3	et	et	PROPN
cana-652	82	4	.	.	PUNCT
cana-652	83	1	al	al	PROPN
cana-652	83	2	.	.	PROPN
cana-652	83	3	have	have	AUX
cana-652	83	4	tried	try	VERB
cana-652	83	5	about	about	ADV
cana-652	83	6	eight	eight	NUM
cana-652	83	7	different	different	ADJ
cana-652	83	8	models	model	NOUN
cana-652	83	9	like	like	ADP
cana-652	83	10	vgg16	vgg16	PROPN
cana-652	83	11	,	,	PUNCT
cana-652	83	12	vgg19	vgg19	PROPN
cana-652	83	13	,	,	PUNCT
cana-652	83	14	mobilenet	mobilenet	NOUN
cana-652	83	15	mobilenetv3	mobilenetv3	PROPN
cana-652	83	16	,	,	PUNCT
cana-652	83	17	moblinenetv3large	moblinenetv3large	VERB
cana-652	83	18	,	,	PUNCT
cana-652	83	19	mobilenetv3small	mobilenetv3small	NOUN
cana-652	83	20	and	and	CCONJ
cana-652	83	21	reported	report	VERB
cana-652	83	22	a	a	DET
cana-652	83	23	maximum	maximum	NOUN
cana-652	83	24	of	of	ADP
cana-652	83	25	98	98	NUM
cana-652	83	26	%	%	NOUN
cana-652	83	27	accuracy	accuracy	NOUN
cana-652	84	1	[	[	X
cana-652	84	2	4	4	NUM
cana-652	84	3	]	]	PUNCT
cana-652	84	4	.	.	PUNCT
cana-652	85	1	the	the	DET
cana-652	85	2	accuracy	accuracy	NOUN
cana-652	85	3	of	of	ADP
cana-652	85	4	98	98	NUM
cana-652	85	5	%	%	NOUN
cana-652	85	6	is	be	AUX
cana-652	85	7	successfully	successfully	ADV
cana-652	85	8	reported	report	VERB
cana-652	85	9	by	by	ADP
cana-652	85	10	author	author	NOUN
cana-652	85	11	h.	h.	PROPN
cana-652	85	12	balaha	balaha	PROPN
cana-652	85	13	et.al	et.al	PROPN
cana-652	85	14	.	.	PUNCT
cana-652	86	1	using	use	VERB
cana-652	86	2	mobilenet	mobilenet	PROPN
cana-652	86	3	.	.	PUNCT
cana-652	87	1	m.	m.	NOUN
cana-652	87	2	tahir	tahir	PROPN
cana-652	87	3	et.al.proposeddscc_net	et.al.proposeddscc_net	PROPN
cana-652	87	4	based	base	VERB
cana-652	87	5	skin	skin	NOUN
cana-652	87	6	cancer	cancer	NOUN
cana-652	87	7	detection	detection	NOUN
cana-652	87	8	on	on	ADP
cana-652	87	9	standerted	standerte	VERB
cana-652	87	10	ham10000	ham10000	NOUN
cana-652	87	11	dataset	dataset	NOUN
cana-652	87	12	and	and	CCONJ
cana-652	87	13	acquired	acquire	VERB
cana-652	87	14	a	a	DET
cana-652	87	15	maximum	maximum	NOUN
cana-652	87	16	of	of	ADP
cana-652	87	17	92	92	NUM
cana-652	87	18	%	%	NOUN
cana-652	87	19	accuracy	accuracy	NOUN
cana-652	88	1	[	[	X
cana-652	88	2	5	5	NUM
cana-652	88	3	]	]	PUNCT
cana-652	88	4	.	.	PUNCT
cana-652	89	1	they	they	PRON
cana-652	89	2	used	use	VERB
cana-652	89	3	different	different	ADJ
cana-652	89	4	cnn	cnn	PROPN
cana-652	89	5	models	model	NOUN
cana-652	89	6	to	to	PART
cana-652	89	7	detect	detect	VERB
cana-652	89	8	skin	skin	NOUN
cana-652	89	9	cancer	cancer	NOUN
cana-652	89	10	classes	class	NOUN
cana-652	89	11	like	like	ADP
cana-652	89	12	vgg19	vgg19	PROPN
cana-652	89	13	,	,	PUNCT
cana-652	89	14	alex	alex	PROPN
cana-652	89	15	-	-	PUNCT
cana-652	89	16	net	net	NOUN
cana-652	89	17	,	,	PUNCT
cana-652	89	18	vgg16	vgg16	NOUN
cana-652	89	19	,	,	PUNCT
cana-652	89	20	resnet50	resnet50	NOUN
cana-652	89	21	,	,	PUNCT
cana-652	89	22	efficientnetb0	efficientnetb0	PROPN
cana-652	89	23	-	-	PUNCT
cana-652	89	24	b7	b7	PROPN
cana-652	89	25	,	,	PUNCT
cana-652	89	26	d	d	PROPN
cana-652	89	27	-	-	PUNCT
cana-652	89	28	cnn	cnn	PROPN
cana-652	89	29	and	and	CCONJ
cana-652	89	30	many	many	ADJ
cana-652	89	31	more	more	ADJ
cana-652	89	32	.	.	PUNCT
cana-652	90	1	a	a	DET
cana-652	90	2	study	study	NOUN
cana-652	90	3	comparing	compare	VERB
cana-652	90	4	deep	deep	ADJ
cana-652	90	5	neural	neural	ADJ
cana-652	90	6	networks	network	NOUN
cana-652	90	7	and	and	CCONJ
cana-652	90	8	convolutional	convolutional	ADJ
cana-652	90	9	neural	neural	ADJ
cana-652	90	10	networks	network	NOUN
cana-652	90	11	was	be	AUX
cana-652	90	12	proposed	propose	VERB
cana-652	90	13	by	by	ADP
cana-652	90	14	s.	s.	PROPN
cana-652	90	15	albawi1	albawi1	PROPN
cana-652	90	16	et	et	PROPN
cana-652	90	17	al	al	PROPN
cana-652	90	18	.	.	PUNCT
cana-652	91	1	"	"	PUNCT
cana-652	91	2	international	international	ADJ
cana-652	91	3	skin	skin	NOUN
cana-652	91	4	imaging	imaging	NOUN
cana-652	91	5	collaboration	collaboration	NOUN
cana-652	91	6	"	"	PUNCT
cana-652	91	7	released	release	VERB
cana-652	91	8	a	a	DET
cana-652	91	9	dataset	dataset	NOUN
cana-652	91	10	of	of	ADP
cana-652	91	11	skin	skin	NOUN
cana-652	91	12	cancer	cancer	NOUN
cana-652	91	13	dermatoscopic	dermatoscopic	NOUN
cana-652	91	14	images	image	NOUN
cana-652	91	15	to	to	ADP
cana-652	91	16	public	public	ADJ
cana-652	91	17	.	.	PUNCT
cana-652	92	1	authors	author	NOUN
cana-652	92	2	used	use	VERB
cana-652	92	3	this	this	DET
cana-652	92	4	dataset	dataset	NOUN
cana-652	92	5	for	for	ADP
cana-652	92	6	training	training	NOUN
cana-652	92	7	and	and	CCONJ
cana-652	92	8	testing	testing	NOUN
cana-652	92	9	purpose	purpose	NOUN
cana-652	92	10	of	of	ADP
cana-652	92	11	their	their	PRON
cana-652	92	12	model	model	NOUN
cana-652	92	13	.	.	PUNCT
cana-652	93	1	dataset	dataset	NOUN
cana-652	93	2	consists	consist	NOUN
cana-652	93	3	of	of	ADP
cana-652	93	4	about	about	ADV
cana-652	93	5	6400	6400	NUM
cana-652	93	6	images	image	NOUN
cana-652	93	7	,	,	PUNCT
cana-652	93	8	in	in	ADP
cana-652	93	9	which	which	PRON
cana-652	93	10	for	for	ADP
cana-652	93	11	training	training	NOUN
cana-652	93	12	and	and	CCONJ
cana-652	93	13	testing	testing	NOUN
cana-652	93	14	purpose	purpose	NOUN
cana-652	93	15	,	,	PUNCT
cana-652	93	16	data	datum	NOUN
cana-652	93	17	was	be	AUX
cana-652	93	18	split	split	VERB
cana-652	93	19	into	into	ADP
cana-652	93	20	the	the	DET
cana-652	93	21	ratio	ratio	NOUN
cana-652	93	22	of	of	ADP
cana-652	93	23	80:20	80:20	NUM
cana-652	94	1	[	[	X
cana-652	94	2	6	6	NUM
cana-652	94	3	]	]	PUNCT
cana-652	94	4	.	.	PUNCT
cana-652	95	1	with	with	ADP
cana-652	95	2	this	this	DET
cana-652	95	3	80:20	80:20	NUM
cana-652	95	4	training	training	NOUN
cana-652	95	5	-	-	PUNCT
cana-652	95	6	testing	testing	NOUN
cana-652	95	7	ratio	ratio	NOUN
cana-652	95	8	s.	s.	PROPN
cana-652	95	9	albawil	albawil	PROPN
cana-652	95	10	et.al	et.al	PROPN
cana-652	95	11	concluded	conclude	VERB
cana-652	95	12	about	about	ADP
cana-652	95	13	98.5	98.5	NUM
cana-652	95	14	%	%	NOUN
cana-652	95	15	accuracy	accuracy	NOUN
cana-652	95	16	on	on	ADP
cana-652	95	17	their	their	PRON
cana-652	95	18	dataset	dataset	NOUN
cana-652	95	19	.	.	PUNCT
cana-652	96	1	the	the	DET
cana-652	96	2	layers	layer	NOUN
cana-652	96	3	incorporated	incorporate	VERB
cana-652	96	4	in	in	ADP
cana-652	96	5	cnn	cnn	PROPN
cana-652	96	6	models	model	NOUN
cana-652	96	7	were	be	AUX
cana-652	96	8	effective	effective	ADJ
cana-652	96	9	in	in	ADP
cana-652	96	10	their	their	PRON
cana-652	96	11	case	case	NOUN
cana-652	96	12	.	.	PUNCT
cana-652	97	1	by	by	ADP
cana-652	97	2	changing	change	VERB
cana-652	97	3	the	the	DET
cana-652	97	4	training	training	NOUN
cana-652	97	5	-	-	PUNCT
cana-652	97	6	testing	testing	NOUN
cana-652	97	7	ratio	ratio	NOUN
cana-652	97	8	they	they	PRON
cana-652	97	9	got	get	VERB
cana-652	97	10	accuracy	accuracy	NOUN
cana-652	97	11	of	of	ADP
cana-652	97	12	cnn	cnn	PROPN
cana-652	97	13	models	model	NOUN
cana-652	97	14	varying	vary	VERB
cana-652	97	15	from	from	ADP
cana-652	97	16	60	60	NUM
cana-652	97	17	%	%	NOUN
cana-652	97	18	to	to	PART
cana-652	97	19	98	98	NUM
cana-652	97	20	%	%	NOUN
cana-652	97	21	[	[	X
cana-652	97	22	6	6	NUM
cana-652	97	23	]	]	PUNCT
cana-652	97	24	.	.	PUNCT
cana-652	98	1	mobilenet	mobilenet	NOUN
cana-652	98	2	convolutional	convolutional	ADJ
cana-652	98	3	neural	neural	ADJ
cana-652	98	4	network	network	NOUN
cana-652	98	5	implementation	implementation	NOUN
cana-652	98	6	by	by	ADP
cana-652	98	7	s.	s.	PROPN
cana-652	98	8	chatuvedi	chatuvedi	PROPN
cana-652	98	9	et	et	PROPN
cana-652	98	10	al	al	PROPN
cana-652	98	11	.	.	PROPN
cana-652	98	12	was	be	AUX
cana-652	98	13	pretrained	pretraine	VERB
cana-652	98	14	on	on	ADP
cana-652	98	15	12,80,000	12,80,000	NUM
cana-652	98	16	pictures	picture	NOUN
cana-652	98	17	with	with	ADP
cana-652	98	18	1000	1000	NUM
cana-652	98	19	item	item	NOUN
cana-652	98	20	types	type	NOUN
cana-652	98	21	[	[	X
cana-652	98	22	7	7	NUM
cana-652	98	23	]	]	PUNCT
cana-652	98	24	.	.	PUNCT
cana-652	99	1	they	they	PRON
cana-652	99	2	used	use	VERB
cana-652	99	3	transfer	transfer	NOUN
cana-652	99	4	learning	learning	NOUN
cana-652	99	5	methods	method	NOUN
cana-652	99	6	to	to	PART
cana-652	99	7	train	train	VERB
cana-652	99	8	models	model	NOUN
cana-652	99	9	with	with	ADP
cana-652	99	10	a	a	DET
cana-652	99	11	total	total	ADJ
cana-652	99	12	38,569	38,569	NUM
cana-652	99	13	images	image	NOUN
cana-652	99	14	.	.	PUNCT
cana-652	100	1	they	they	PRON
cana-652	100	2	used	use	VERB
cana-652	100	3	batch	batch	NOUN
cana-652	100	4	size	size	NOUN
cana-652	100	5	of	of	ADP
cana-652	100	6	10	10	NUM
cana-652	100	7	and	and	CCONJ
cana-652	100	8	epoch	epoch	PROPN
cana-652	100	9	50	50	NUM
cana-652	101	1	[	[	X
cana-652	101	2	7	7	NUM
cana-652	101	3	]	]	PUNCT
cana-652	101	4	.	.	PUNCT
cana-652	102	1	they	they	PRON
cana-652	102	2	have	have	AUX
cana-652	102	3	reported	report	VERB
cana-652	102	4	maximum	maximum	ADJ
cana-652	102	5	accuracy	accuracy	NOUN
cana-652	102	6	of	of	ADP
cana-652	102	7	95.34%.[7	95.34%.[7	NUM
cana-652	102	8	]	]	PUNCT
cana-652	102	9	.	.	PUNCT
cana-652	103	1	k.ali	k.ali	PROPN
cana-652	103	2	et.al	et.al	PROPN
cana-652	103	3	.	.	PUNCT
cana-652	104	1	trained	train	VERB
cana-652	104	2	efficientnet	efficientnet	PROPN
cana-652	104	3	b0	b0	NOUN
cana-652	104	4	-	-	PUNCT
cana-652	104	5	b7	b7	PROPN
cana-652	104	6	on	on	ADP
cana-652	104	7	ham10000	ham10000	PROPN
cana-652	104	8	dataset	dataset	PROPN
cana-652	105	1	[	[	X
cana-652	105	2	8	8	NUM
cana-652	105	3	]	]	PUNCT
cana-652	105	4	and	and	CCONJ
cana-652	105	5	achieved	achieve	VERB
cana-652	105	6	maximum	maximum	ADJ
cana-652	105	7	accuracy	accuracy	NOUN
cana-652	105	8	of	of	ADP
cana-652	105	9	87.91	87.91	NUM
cana-652	105	10	%	%	NOUN
cana-652	105	11	.	.	PUNCT
cana-652	106	1	according	accord	VERB
cana-652	106	2	to	to	ADP
cana-652	106	3	their	their	PRON
cana-652	106	4	findings	finding	NOUN
cana-652	106	5	,	,	PUNCT
cana-652	106	6	higher	high	ADJ
cana-652	106	7	accuracy	accuracy	NOUN
cana-652	106	8	is	be	AUX
cana-652	106	9	not	not	PART
cana-652	106	10	always	always	ADV
cana-652	106	11	implied	imply	VERB
cana-652	106	12	by	by	ADP
cana-652	106	13	increasing	increase	VERB
cana-652	106	14	model	model	NOUN
cana-652	106	15	accuracy	accuracy	NOUN
cana-652	106	16	[	[	X
cana-652	106	17	8	8	NUM
cana-652	106	18	]	]	PUNCT
cana-652	106	19	.	.	PUNCT
cana-652	107	1	the	the	DET
cana-652	107	2	most	most	ADV
cana-652	107	3	accurate	accurate	ADJ
cana-652	107	4	models	model	NOUN
cana-652	107	5	among	among	ADP
cana-652	107	6	the	the	DET
cana-652	107	7	b0	b0	NOUN
cana-652	107	8	-	-	PUNCT
cana-652	107	9	b7	b7	PROPN
cana-652	107	10	range	range	NOUN
cana-652	107	11	are	be	AUX
cana-652	107	12	the	the	DET
cana-652	107	13	b4	b4	NOUN
cana-652	107	14	and	and	CCONJ
cana-652	107	15	b5	b5	PROPN
cana-652	107	16	models	model	NOUN
cana-652	107	17	,	,	PUNCT
cana-652	107	18	which	which	PRON
cana-652	107	19	have	have	VERB
cana-652	107	20	intermediate	intermediate	ADJ
cana-652	107	21	complexity	complexity	NOUN
cana-652	107	22	.	.	PUNCT
cana-652	108	1	a.	a.	NOUN
cana-652	108	2	nugroho	nugroho	PROPN
cana-652	108	3	et.al	et.al	PROPN
cana-652	108	4	.	.	PUNCT
cana-652	108	5	implemented	implement	VERB
cana-652	108	6	9	9	NUM
cana-652	108	7	layered	layered	ADJ
cana-652	108	8	cnn	cnn	PROPN
cana-652	108	9	to	to	PART
cana-652	108	10	identify	identify	VERB
cana-652	108	11	cancer	cancer	NOUN
cana-652	108	12	classes	class	NOUN
cana-652	108	13	in	in	ADP
cana-652	108	14	ham10000	ham10000	PROPN
cana-652	108	15	dataset	dataset	PROPN
cana-652	108	16	.	.	PUNCT
cana-652	109	1	they	they	PRON
cana-652	109	2	split	split	VERB
cana-652	109	3	a	a	DET
cana-652	109	4	dataset	dataset	NOUN
cana-652	109	5	of	of	ADP
cana-652	109	6	10015	10015	NUM
cana-652	109	7	images	image	NOUN
cana-652	109	8	into	into	ADP
cana-652	109	9	train	train	NOUN
cana-652	109	10	dataset	dataset	NOUN
cana-652	109	11	,	,	PUNCT
cana-652	109	12	validation	validation	NOUN
cana-652	109	13	dataset	dataset	NOUN
cana-652	109	14	and	and	CCONJ
cana-652	109	15	test	test	NOUN
cana-652	109	16	dataset	dataset	VERB
cana-652	109	17	each	each	DET
cana-652	109	18	one	one	NUM
cana-652	109	19	consists	consist	VERB
cana-652	109	20	of	of	ADP
cana-652	109	21	7212	7212	NUM
cana-652	109	22	,	,	PUNCT
cana-652	109	23	2003	2003	NUM
cana-652	109	24	and	and	CCONJ
cana-652	109	25	800	800	NUM
cana-652	109	26	images	image	NOUN
cana-652	109	27	respectively	respectively	ADV
cana-652	109	28	[	[	X
cana-652	109	29	9	9	NUM
cana-652	109	30	]	]	PUNCT
cana-652	109	31	.	.	PUNCT
cana-652	110	1	they	they	PRON
cana-652	110	2	achieved	achieve	VERB
cana-652	110	3	accuracy	accuracy	NOUN
cana-652	110	4	of	of	ADP
cana-652	110	5	maximum	maximum	ADJ
cana-652	110	6	80	80	NUM
cana-652	110	7	%	%	NOUN
cana-652	110	8	with	with	ADP
cana-652	110	9	input	input	NOUN
cana-652	110	10	image	image	NOUN
cana-652	110	11	size	size	NOUN
cana-652	110	12	of	of	ADP
cana-652	110	13	90	90	NUM
cana-652	110	14	x	x	SYM
cana-652	110	15	120	120	NUM
cana-652	110	16	pixels	pixel	NOUN
cana-652	110	17	[	[	PUNCT
cana-652	110	18	9	9	NUM
cana-652	110	19	]	]	PUNCT
cana-652	110	20	.	.	PUNCT
cana-652	111	1	a.	a.	PROPN
cana-652	111	2	tajerian	tajerian	PROPN
cana-652	111	3	et.al	et.al	PROPN
cana-652	111	4	.	.	PROPN
cana-652	111	5	reported	report	VERB
cana-652	111	6	maximum	maximum	ADJ
cana-652	111	7	accuracy	accuracy	NOUN
cana-652	111	8	of	of	ADP
cana-652	111	9	84	84	NUM
cana-652	111	10	%	%	NOUN
cana-652	111	11	on	on	ADP
cana-652	111	12	detecting	detect	VERB
cana-652	111	13	skin	skin	NOUN
cana-652	111	14	cancer	cancer	NOUN
cana-652	111	15	in	in	ADP
cana-652	111	16	ham10000	ham10000	PROPN
cana-652	111	17	dataset	dataset	PROPN
cana-652	111	18	.	.	PUNCT
cana-652	112	1	they	they	PRON
cana-652	112	2	employed	employ	VERB
cana-652	112	3	efficientnet	efficientnet	NOUN
cana-652	112	4	-	-	PUNCT
cana-652	112	5	b0	b0	NOUN
cana-652	112	6	,	,	PUNCT
cana-652	112	7	a	a	DET
cana-652	112	8	variation	variation	NOUN
cana-652	112	9	of	of	ADP
cana-652	112	10	the	the	DET
cana-652	112	11	communications	communication	NOUN
cana-652	112	12	on	on	ADP
cana-652	112	13	applied	apply	VERB
cana-652	112	14	nonlinear	nonlinear	ADJ
cana-652	112	15	analysis	analysis	NOUN
cana-652	112	16	issn	issn	NOUN
cana-652	112	17	:	:	PUNCT
cana-652	112	18	1074	1074	NUM
cana-652	112	19	-	-	PUNCT
cana-652	112	20	133x	133x	NUM
cana-652	112	21	vol	vol	NOUN
cana-652	112	22	31	31	NUM
cana-652	112	23	no	no	NOUN
cana-652	112	24	.	.	PUNCT
cana-652	113	1	2s	2s	NUM
cana-652	113	2	(	(	PUNCT
cana-652	113	3	2024	2024	NUM
cana-652	113	4	)	)	PUNCT
cana-652	113	5	324	324	NUM
cana-652	113	6	https://internationalpubls.com	https://internationalpubls.com	NUM
cana-652	113	7	base	base	NOUN
cana-652	113	8	model	model	NOUN
cana-652	113	9	,	,	PUNCT
cana-652	113	10	and	and	CCONJ
cana-652	113	11	efficientnet	efficientnet	NOUN
cana-652	113	12	-	-	PUNCT
cana-652	113	13	b1	b1	NOUN
cana-652	113	14	.	.	PUNCT
cana-652	114	1	it	it	PRON
cana-652	114	2	was	be	AUX
cana-652	114	3	covered	cover	VERB
cana-652	114	4	by	by	ADP
cana-652	114	5	a	a	DET
cana-652	114	6	7	7	NUM
cana-652	114	7	-	-	PUNCT
cana-652	114	8	node	node	ADJ
cana-652	114	9	softmax	softmax	NOUN
cana-652	114	10	layer	layer	NOUN
cana-652	114	11	and	and	CCONJ
cana-652	114	12	a	a	DET
cana-652	114	13	2d	2d	NUM
cana-652	114	14	global	global	ADJ
cana-652	114	15	average	average	ADJ
cana-652	114	16	pooling	pool	VERB
cana-652	114	17	layer	layer	NOUN
cana-652	114	18	.	.	PUNCT
cana-652	115	1	[	[	X
cana-652	115	2	10	10	NUM
cana-652	115	3	]	]	PUNCT
cana-652	115	4	.	.	PUNCT
cana-652	116	1	alam	alam	PROPN
cana-652	116	2	tm	tm	PROPN
cana-652	116	3	et.al	et.al	PROPN
cana-652	116	4	.	.	PUNCT
cana-652	117	1	implemented	implement	VERB
cana-652	117	2	3	3	NUM
cana-652	117	3	different	different	ADJ
cana-652	117	4	models	model	NOUN
cana-652	117	5	alexnet	alexnet	ADJ
cana-652	117	6	,	,	PUNCT
cana-652	117	7	inceptionv3	inceptionv3	NOUN
cana-652	117	8	and	and	CCONJ
cana-652	117	9	regnety	regnety	NOUN
cana-652	117	10	-	-	PUNCT
cana-652	117	11	v3	v3	PROPN
cana-652	117	12	.	.	PUNCT
cana-652	118	1	ham10000	ham10000	PROPN
cana-652	118	2	dataset	dataset	PROPN
cana-652	118	3	was	be	AUX
cana-652	118	4	used	use	VERB
cana-652	118	5	to	to	PART
cana-652	118	6	train	train	VERB
cana-652	118	7	these	these	DET
cana-652	118	8	models	model	NOUN
cana-652	118	9	and	and	CCONJ
cana-652	118	10	finally	finally	ADV
cana-652	118	11	they	they	PRON
cana-652	118	12	gained	gain	VERB
cana-652	118	13	maximum	maximum	ADJ
cana-652	118	14	accuracy	accuracy	NOUN
cana-652	118	15	of	of	ADP
cana-652	118	16	91%[11	91%[11	NUM
cana-652	118	17	]	]	PUNCT
cana-652	118	18	.	.	PUNCT
cana-652	119	1	they	they	PRON
cana-652	119	2	used	use	VERB
cana-652	119	3	augmentation	augmentation	NOUN
cana-652	119	4	methods	method	NOUN
cana-652	119	5	like	like	ADP
cana-652	119	6	scaling	scaling	NOUN
cana-652	119	7	,	,	PUNCT
cana-652	119	8	rotation	rotation	NOUN
cana-652	119	9	,	,	PUNCT
cana-652	119	10	etc	etc	X
cana-652	119	11	to	to	PART
cana-652	119	12	balance	balance	VERB
cana-652	119	13	an	an	DET
cana-652	119	14	imbalanced	imbalanced	ADJ
cana-652	119	15	ham1000	ham1000	NOUN
cana-652	119	16	dataset	dataset	NOUN
cana-652	119	17	.	.	PUNCT
cana-652	120	1	also	also	ADV
cana-652	120	2	reducing	reduce	VERB
cana-652	120	3	learning	learning	NOUN
cana-652	120	4	rate	rate	NOUN
cana-652	120	5	from	from	ADP
cana-652	120	6	0.01	0.01	NUM
cana-652	120	7	to	to	PART
cana-652	120	8	0.001	0.001	NUM
cana-652	120	9	helped	help	VERB
cana-652	120	10	authors	author	NOUN
cana-652	120	11	to	to	PART
cana-652	120	12	increase	increase	VERB
cana-652	120	13	accuracy	accuracy	NOUN
cana-652	120	14	of	of	ADP
cana-652	120	15	model	model	NOUN
cana-652	120	16	regnety	regnety	PROPN
cana-652	120	17	-	-	PUNCT
cana-652	120	18	v3	v3	PROPN
cana-652	120	19	to	to	ADP
cana-652	120	20	91	91	NUM
cana-652	120	21	%	%	NOUN
cana-652	120	22	[	[	X
cana-652	120	23	11	11	NUM
cana-652	120	24	]	]	PUNCT
cana-652	120	25	.	.	PUNCT
cana-652	121	1	m.	m.	PROPN
cana-652	121	2	deepak	deepak	PROPN
cana-652	121	3	et.al	et.al	PROPN
cana-652	121	4	.	.	PUNCT
cana-652	122	1	addresses	address	VERB
cana-652	122	2	the	the	DET
cana-652	122	3	high	high	ADJ
cana-652	122	4	melanoma	melanoma	NOUN
cana-652	122	5	mortality	mortality	NOUN
cana-652	122	6	rate	rate	NOUN
cana-652	122	7	by	by	ADP
cana-652	122	8	emphasizing	emphasize	VERB
cana-652	122	9	the	the	DET
cana-652	122	10	need	need	NOUN
cana-652	122	11	for	for	ADP
cana-652	122	12	early	early	ADJ
cana-652	122	13	skin	skin	NOUN
cana-652	122	14	lesion	lesion	NOUN
cana-652	122	15	identification	identification	NOUN
cana-652	122	16	.	.	PUNCT
cana-652	123	1	it	it	PRON
cana-652	123	2	introduces	introduce	VERB
cana-652	123	3	a	a	DET
cana-652	123	4	classification	classification	NOUN
cana-652	123	5	framework	framework	NOUN
cana-652	123	6	using	use	VERB
cana-652	123	7	mobilenet	mobilenet	NOUN
cana-652	123	8	and	and	CCONJ
cana-652	123	9	transfer	transfer	VERB
cana-652	123	10	learning	learn	VERB
cana-652	123	11	to	to	PART
cana-652	123	12	accurately	accurately	ADV
cana-652	123	13	categorize	categorize	VERB
cana-652	123	14	eight	eight	NUM
cana-652	123	15	skin	skin	NOUN
cana-652	123	16	lesion	lesion	NOUN
cana-652	123	17	types	type	NOUN
cana-652	123	18	.	.	PUNCT
cana-652	124	1	the	the	DET
cana-652	124	2	approach	approach	NOUN
cana-652	124	3	,	,	PUNCT
cana-652	124	4	validated	validate	VERB
cana-652	124	5	with	with	ADP
cana-652	124	6	the	the	DET
cana-652	124	7	isic	isic	PROPN
cana-652	124	8	2019	2019	NUM
cana-652	124	9	dataset	dataset	NOUN
cana-652	124	10	,	,	PUNCT
cana-652	124	11	holds	hold	VERB
cana-652	124	12	promise	promise	NOUN
cana-652	124	13	for	for	ADP
cana-652	124	14	improving	improve	VERB
cana-652	124	15	early	early	ADJ
cana-652	124	16	diagnosis	diagnosis	NOUN
cana-652	124	17	and	and	CCONJ
cana-652	124	18	treatment	treatment	NOUN
cana-652	124	19	precision	precision	NOUN
cana-652	124	20	,	,	PUNCT
cana-652	124	21	benefitting	benefit	VERB
cana-652	124	22	both	both	DET
cana-652	124	23	patients	patient	NOUN
cana-652	124	24	and	and	CCONJ
cana-652	124	25	dermatologists	dermatologist	VERB
cana-652	124	26	[	[	X
cana-652	124	27	12	12	NUM
cana-652	124	28	]	]	PUNCT
cana-652	124	29	.	.	PUNCT
cana-652	125	1	d.	d.	PROPN
cana-652	125	2	keerthana	keerthana	PROPN
cana-652	125	3	et.al	et.al	PROPN
cana-652	125	4	.	.	PUNCT
cana-652	126	1	introduces	introduce	VERB
cana-652	126	2	two	two	NUM
cana-652	126	3	hybrid	hybrid	ADJ
cana-652	126	4	cnn	cnn	PROPN
cana-652	126	5	models	model	NOUN
cana-652	126	6	with	with	ADP
cana-652	126	7	an	an	DET
cana-652	126	8	svm	svm	ADJ
cana-652	126	9	classifier	classifier	NOUN
cana-652	126	10	for	for	ADP
cana-652	126	11	dermatoscopy	dermatoscopy	NOUN
cana-652	126	12	image	image	NOUN
cana-652	126	13	classification	classification	NOUN
cana-652	126	14	.	.	PUNCT
cana-652	127	1	by	by	ADP
cana-652	127	2	combining	combine	VERB
cana-652	127	3	features	feature	NOUN
cana-652	127	4	extracted	extract	VERB
cana-652	127	5	from	from	ADP
cana-652	127	6	these	these	DET
cana-652	127	7	models	model	NOUN
cana-652	127	8	,	,	PUNCT
cana-652	127	9	it	it	PRON
cana-652	127	10	enhances	enhance	VERB
cana-652	127	11	the	the	DET
cana-652	127	12	accuracy	accuracy	NOUN
cana-652	127	13	of	of	ADP
cana-652	127	14	benign	benign	ADJ
cana-652	127	15	and	and	CCONJ
cana-652	127	16	melanoma	melanoma	NOUN
cana-652	127	17	lesion	lesion	NOUN
cana-652	127	18	differentiation	differentiation	NOUN
cana-652	127	19	.	.	PUNCT
cana-652	128	1	expert	expert	ADJ
cana-652	128	2	dermatologist	dermatologist	NOUN
cana-652	128	3	-	-	PUNCT
cana-652	128	4	labeled	label	VERB
cana-652	128	5	data	datum	NOUN
cana-652	128	6	is	be	AUX
cana-652	128	7	utilized	utilize	VERB
cana-652	128	8	to	to	PART
cana-652	128	9	validate	validate	VERB
cana-652	128	10	the	the	DET
cana-652	128	11	model	model	NOUN
cana-652	128	12	's	's	PART
cana-652	128	13	performance	performance	NOUN
cana-652	129	1	[	[	X
cana-652	129	2	13	13	NUM
cana-652	129	3	]	]	PUNCT
cana-652	129	4	.	.	PUNCT
cana-652	130	1	k.	k.	PROPN
cana-652	130	2	mridha	mridha	PROPN
cana-652	131	1	et	et	PROPN
cana-652	131	2	.	.	PUNCT
cana-652	132	1	al	al	PROPN
cana-652	132	2	.	.	PROPN
cana-652	132	3	used	use	VERB
cana-652	132	4	an	an	DET
cana-652	132	5	optimized	optimize	VERB
cana-652	132	6	form	form	NOUN
cana-652	132	7	of	of	ADP
cana-652	132	8	cnn	cnn	PROPN
cana-652	132	9	to	to	PART
cana-652	132	10	identify	identify	VERB
cana-652	132	11	7	7	NUM
cana-652	132	12	types	type	NOUN
cana-652	132	13	of	of	ADP
cana-652	132	14	skin	skin	NOUN
cana-652	132	15	cancer	cancer	NOUN
cana-652	132	16	.	.	PUNCT
cana-652	133	1	relu	relu	NOUN
cana-652	133	2	,	,	PUNCT
cana-652	133	3	swish	swish	ADJ
cana-652	133	4	,	,	PUNCT
cana-652	133	5	tanh	tanh	NOUN
cana-652	133	6	,	,	PUNCT
cana-652	133	7	were	be	AUX
cana-652	133	8	the	the	DET
cana-652	133	9	three	three	NUM
cana-652	133	10	activation	activation	NOUN
cana-652	133	11	functions	function	NOUN
cana-652	133	12	and	and	CCONJ
cana-652	133	13	adam	adam	PROPN
cana-652	133	14	and	and	CCONJ
cana-652	133	15	rmsprop	rmsprop	VERB
cana-652	133	16	two	two	NUM
cana-652	133	17	optimization	optimization	NOUN
cana-652	133	18	functions	function	NOUN
cana-652	133	19	that	that	PRON
cana-652	133	20	were	be	AUX
cana-652	133	21	used	use	VERB
cana-652	133	22	to	to	PART
cana-652	133	23	train	train	VERB
cana-652	133	24	the	the	DET
cana-652	133	25	model	model	NOUN
cana-652	133	26	[	[	X
cana-652	133	27	14	14	NUM
cana-652	133	28	]	]	PUNCT
cana-652	133	29	.	.	PUNCT
cana-652	134	1	additionally	additionally	ADV
cana-652	134	2	,	,	PUNCT
cana-652	134	3	the	the	DET
cana-652	134	4	grad	grad	NOUN
cana-652	134	5	-	-	PUNCT
cana-652	134	6	cam	cam	NOUN
cana-652	134	7	and	and	CCONJ
cana-652	134	8	grad	grad	NOUN
cana-652	134	9	-	-	PUNCT
cana-652	134	10	cam	cam	NOUN
cana-652	134	11	+	+	ADJ
cana-652	134	12	+	+	ADJ
cana-652	134	13	skin	skin	NOUN
cana-652	134	14	lesion	lesion	NOUN
cana-652	134	15	classification	classification	NOUN
cana-652	134	16	system	system	NOUN
cana-652	134	17	based	base	VERB
cana-652	134	18	on	on	ADP
cana-652	134	19	xai	xai	PROPN
cana-652	134	20	was	be	AUX
cana-652	134	21	embedded	embed	VERB
cana-652	134	22	by	by	ADP
cana-652	134	23	the	the	DET
cana-652	134	24	authors[14	authors[14	PROPN
cana-652	134	25	]	]	PUNCT
cana-652	134	26	..	..	PUNCT
cana-652	135	1	the	the	DET
cana-652	135	2	system	system	NOUN
cana-652	135	3	was	be	AUX
cana-652	135	4	able	able	ADJ
cana-652	135	5	to	to	PART
cana-652	135	6	accurately	accurately	ADV
cana-652	135	7	detect	detect	VERB
cana-652	135	8	cancer	cancer	NOUN
cana-652	135	9	with	with	ADP
cana-652	135	10	an	an	DET
cana-652	135	11	accuracy	accuracy	NOUN
cana-652	135	12	of	of	ADP
cana-652	135	13	82	82	NUM
cana-652	135	14	%	%	NOUN
cana-652	135	15	.	.	PUNCT
cana-652	136	1	incorporating	incorporate	VERB
cana-652	136	2	ai	ai	NOUN
cana-652	136	3	models	model	NOUN
cana-652	136	4	into	into	ADP
cana-652	136	5	the	the	DET
cana-652	136	6	diagnosis	diagnosis	NOUN
cana-652	136	7	system	system	NOUN
cana-652	136	8	can	can	AUX
cana-652	136	9	help	help	VERB
cana-652	136	10	the	the	DET
cana-652	136	11	entire	entire	ADJ
cana-652	136	12	spectrum	spectrum	NOUN
cana-652	136	13	of	of	ADP
cana-652	136	14	doctors	doctor	NOUN
cana-652	136	15	to	to	PART
cana-652	136	16	identify	identify	VERB
cana-652	136	17	skin	skin	NOUN
cana-652	136	18	cancer	cancer	NOUN
cana-652	136	19	.	.	PUNCT
cana-652	137	1	but	but	CCONJ
cana-652	137	2	if	if	SCONJ
cana-652	137	3	faulty	faulty	ADJ
cana-652	137	4	ai	ai	VERB
cana-652	137	5	is	be	AUX
cana-652	137	6	incorporated	incorporate	VERB
cana-652	137	7	then	then	ADV
cana-652	137	8	it	it	PRON
cana-652	137	9	can	can	AUX
cana-652	137	10	lead	lead	VERB
cana-652	137	11	to	to	ADP
cana-652	137	12	potential	potential	ADJ
cana-652	137	13	misdiagnosis	misdiagnosis	NOUN
cana-652	137	14	and	and	CCONJ
cana-652	137	15	can	can	AUX
cana-652	137	16	cause	cause	VERB
cana-652	137	17	great	great	ADJ
cana-652	137	18	harm	harm	NOUN
cana-652	137	19	[	[	X
cana-652	137	20	15	15	NUM
cana-652	137	21	]	]	PUNCT
cana-652	137	22	.	.	PUNCT
cana-652	138	1	so	so	ADV
cana-652	138	2	,	,	PUNCT
cana-652	138	3	while	while	SCONJ
cana-652	138	4	implementing	implement	VERB
cana-652	138	5	this	this	DET
cana-652	138	6	technology	technology	NOUN
cana-652	138	7	we	we	PRON
cana-652	138	8	need	need	VERB
cana-652	138	9	to	to	PART
cana-652	138	10	be	be	AUX
cana-652	138	11	100	100	NUM
cana-652	138	12	%	%	NOUN
cana-652	138	13	sure	sure	ADJ
cana-652	138	14	that	that	SCONJ
cana-652	138	15	it	it	PRON
cana-652	138	16	works	work	VERB
cana-652	138	17	correctly	correctly	ADV
cana-652	138	18	.	.	PUNCT
cana-652	139	1	4	4	X
cana-652	139	2	.	.	X
cana-652	139	3	research	research	NOUN
cana-652	139	4	gap	gap	NOUN
cana-652	139	5	while	while	SCONJ
cana-652	139	6	the	the	DET
cana-652	139	7	potential	potential	NOUN
cana-652	139	8	of	of	ADP
cana-652	139	9	cnns	cnn	NOUN
cana-652	139	10	in	in	ADP
cana-652	139	11	skin	skin	NOUN
cana-652	139	12	cancer	cancer	NOUN
cana-652	139	13	detection	detection	NOUN
cana-652	139	14	is	be	AUX
cana-652	139	15	well	well	ADV
cana-652	139	16	-	-	PUNCT
cana-652	139	17	established	establish	VERB
cana-652	139	18	,	,	PUNCT
cana-652	139	19	comprehensive	comprehensive	ADJ
cana-652	139	20	research	research	NOUN
cana-652	139	21	focused	focus	VERB
cana-652	139	22	on	on	ADP
cana-652	139	23	the	the	DET
cana-652	139	24	development	development	NOUN
cana-652	139	25	,	,	PUNCT
cana-652	139	26	training	training	NOUN
cana-652	139	27	,	,	PUNCT
cana-652	139	28	and	and	CCONJ
cana-652	139	29	evaluation	evaluation	NOUN
cana-652	139	30	of	of	ADP
cana-652	139	31	cnn	cnn	PROPN
cana-652	139	32	-	-	PUNCT
cana-652	139	33	based	base	VERB
cana-652	139	34	models	model	NOUN
cana-652	139	35	for	for	ADP
cana-652	139	36	dermatoscopic	dermatoscopic	ADJ
cana-652	139	37	image	image	NOUN
cana-652	139	38	analysis	analysis	NOUN
cana-652	139	39	is	be	AUX
cana-652	139	40	warranted	warrant	VERB
cana-652	139	41	.	.	PUNCT
cana-652	140	1	this	this	DET
cana-652	140	2	research	research	NOUN
cana-652	140	3	paper	paper	NOUN
cana-652	140	4	addresses	address	VERB
cana-652	140	5	the	the	DET
cana-652	140	6	gaps	gap	NOUN
cana-652	140	7	by	by	ADP
cana-652	140	8	presenting	present	VERB
cana-652	140	9	a	a	DET
cana-652	140	10	detailed	detailed	ADJ
cana-652	140	11	investigation	investigation	NOUN
cana-652	140	12	into	into	ADP
cana-652	140	13	the	the	DET
cana-652	140	14	design	design	NOUN
cana-652	140	15	,	,	PUNCT
cana-652	140	16	training	training	NOUN
cana-652	140	17	,	,	PUNCT
cana-652	140	18	and	and	CCONJ
cana-652	140	19	evaluation	evaluation	NOUN
cana-652	140	20	of	of	ADP
cana-652	140	21	a	a	DET
cana-652	140	22	cnn	cnn	PROPN
cana-652	140	23	based	base	VERB
cana-652	140	24	model	model	NOUN
cana-652	140	25	for	for	ADP
cana-652	140	26	skin	skin	NOUN
cana-652	140	27	cancer	cancer	NOUN
cana-652	140	28	detection	detection	NOUN
cana-652	140	29	using	use	VERB
cana-652	140	30	dermatoscopic	dermatoscopic	NOUN
cana-652	140	31	images	image	NOUN
cana-652	140	32	.	.	PUNCT
cana-652	141	1	by	by	ADP
cana-652	141	2	harnessing	harness	VERB
cana-652	141	3	the	the	DET
cana-652	141	4	capabilities	capability	NOUN
cana-652	141	5	of	of	ADP
cana-652	141	6	deep	deep	ADJ
cana-652	141	7	learning	learning	NOUN
cana-652	141	8	and	and	CCONJ
cana-652	141	9	large	large	ADJ
cana-652	141	10	-	-	PUNCT
cana-652	141	11	scale	scale	NOUN
cana-652	141	12	image	image	NOUN
cana-652	141	13	datasets	dataset	NOUN
cana-652	141	14	,	,	PUNCT
cana-652	141	15	this	this	DET
cana-652	141	16	study	study	NOUN
cana-652	141	17	intends	intend	VERB
cana-652	141	18	to	to	PART
cana-652	141	19	support	support	VERB
cana-652	141	20	ongoing	ongoing	ADJ
cana-652	141	21	efforts	effort	NOUN
cana-652	141	22	to	to	PART
cana-652	141	23	improve	improve	VERB
cana-652	141	24	the	the	DET
cana-652	141	25	efficacy	efficacy	NOUN
cana-652	141	26	and	and	CCONJ
cana-652	141	27	accuracy	accuracy	NOUN
cana-652	141	28	of	of	ADP
cana-652	141	29	skin	skin	NOUN
cana-652	141	30	cancer	cancer	NOUN
cana-652	141	31	diagnosis	diagnosis	NOUN
cana-652	141	32	,	,	PUNCT
cana-652	141	33	eventually	eventually	ADV
cana-652	141	34	increasing	increase	VERB
cana-652	141	35	patient	patient	ADJ
cana-652	141	36	outcomes	outcome	NOUN
cana-652	141	37	and	and	CCONJ
cana-652	141	38	lowering	lower	VERB
cana-652	141	39	the	the	DET
cana-652	141	40	global	global	ADJ
cana-652	141	41	burden	burden	NOUN
cana-652	141	42	of	of	ADP
cana-652	141	43	this	this	DET
cana-652	141	44	prevalent	prevalent	ADJ
cana-652	141	45	disease	disease	NOUN
cana-652	141	46	.	.	PUNCT
cana-652	142	1	5	5	X
cana-652	142	2	.	.	X
cana-652	142	3	material	material	NOUN
cana-652	142	4	and	and	CCONJ
cana-652	142	5	methods	method	NOUN
cana-652	142	6	a.	a.	NOUN
cana-652	142	7	dataset	dataset	VERB
cana-652	142	8	the	the	DET
cana-652	142	9	research	research	NOUN
cana-652	142	10	utilized	utilize	VERB
cana-652	142	11	the	the	DET
cana-652	142	12	ham10000	ham10000	PROPN
cana-652	142	13	dataset	dataset	PROPN
cana-652	142	14	,	,	PUNCT
cana-652	142	15	known	know	VERB
cana-652	142	16	as	as	ADP
cana-652	142	17	"	"	PUNCT
cana-652	142	18	human	human	NOUN
cana-652	142	19	against	against	ADP
cana-652	142	20	machine	machine	NOUN
cana-652	142	21	with	with	ADP
cana-652	142	22	10000	10000	NUM
cana-652	142	23	training	training	NOUN
cana-652	142	24	images	image	NOUN
cana-652	142	25	.	.	PUNCT
cana-652	142	26	"	"	PUNCT
cana-652	143	1	there	there	PRON
cana-652	143	2	are	be	VERB
cana-652	143	3	10,015	10,015	NUM
cana-652	143	4	skin	skin	NOUN
cana-652	143	5	lesions	lesion	NOUN
cana-652	143	6	images	image	NOUN
cana-652	143	7	in	in	ADP
cana-652	143	8	this	this	DET
cana-652	143	9	dataset	dataset	NOUN
cana-652	143	10	.	.	PUNCT
cana-652	144	1	capturing	capture	VERB
cana-652	144	2	various	various	ADJ
cana-652	144	3	skin	skin	NOUN
cana-652	144	4	lesions	lesion	NOUN
cana-652	144	5	.	.	PUNCT
cana-652	145	1	it	it	PRON
cana-652	145	2	is	be	AUX
cana-652	145	3	divided	divide	VERB
cana-652	145	4	into	into	ADP
cana-652	145	5	two	two	NUM
cana-652	145	6	primary	primary	ADJ
cana-652	145	7	subsets	subset	NOUN
cana-652	145	8	,	,	PUNCT
cana-652	145	9	consisting	consist	VERB
cana-652	145	10	of	of	ADP
cana-652	145	11	a	a	DET
cana-652	145	12	training	training	NOUN
cana-652	145	13	set	set	VERB
cana-652	145	14	with	with	ADP
cana-652	145	15	7,039	7,039	NUM
cana-652	145	16	images	image	NOUN
cana-652	145	17	and	and	CCONJ
cana-652	145	18	a	a	DET
cana-652	145	19	test	test	NOUN
cana-652	145	20	set	set	NOUN
cana-652	145	21	containing	contain	VERB
cana-652	145	22	2,976	2,976	NUM
cana-652	145	23	images	image	NOUN
cana-652	145	24	.	.	PUNCT
cana-652	146	1	these	these	DET
cana-652	146	2	images	image	NOUN
cana-652	146	3	are	be	AUX
cana-652	146	4	commonly	commonly	ADV
cana-652	146	5	in	in	ADP
cana-652	146	6	jpeg	jpeg	NOUN
cana-652	146	7	format	format	NOUN
cana-652	146	8	,	,	PUNCT
cana-652	146	9	displaying	display	VERB
cana-652	146	10	variations	variation	NOUN
cana-652	146	11	in	in	ADP
cana-652	146	12	communications	communication	NOUN
cana-652	146	13	on	on	ADP
cana-652	146	14	applied	apply	VERB
cana-652	146	15	nonlinear	nonlinear	ADJ
cana-652	146	16	analysis	analysis	NOUN
cana-652	146	17	issn	issn	NOUN
cana-652	146	18	:	:	PUNCT
cana-652	146	19	1074	1074	NUM
cana-652	146	20	-	-	PUNCT
cana-652	146	21	133x	133x	NUM
cana-652	146	22	vol	vol	NOUN
cana-652	146	23	31	31	NUM
cana-652	146	24	no	no	NOUN
cana-652	146	25	.	.	PUNCT
cana-652	147	1	2s	2s	NUM
cana-652	147	2	(	(	PUNCT
cana-652	147	3	2024	2024	NUM
cana-652	147	4	)	)	PUNCT
cana-652	147	5	325	325	NUM
cana-652	147	6	https://internationalpubls.com	https://internationalpubls.com	NOUN
cana-652	147	7	resolution	resolution	NOUN
cana-652	147	8	,	,	PUNCT
cana-652	147	9	and	and	CCONJ
cana-652	147	10	encompass	encompass	VERB
cana-652	147	11	skin	skin	NOUN
cana-652	147	12	lesions	lesion	NOUN
cana-652	147	13	captured	capture	VERB
cana-652	147	14	from	from	ADP
cana-652	147	15	diverse	diverse	ADJ
cana-652	147	16	angles	angle	NOUN
cana-652	147	17	and	and	CCONJ
cana-652	147	18	under	under	ADP
cana-652	147	19	varying	vary	VERB
cana-652	147	20	lighting	lighting	NOUN
cana-652	147	21	conditions	condition	NOUN
cana-652	147	22	.	.	PUNCT
cana-652	148	1	it	it	PRON
cana-652	148	2	is	be	AUX
cana-652	148	3	a	a	DET
cana-652	148	4	multi	multi	ADJ
cana-652	148	5	-	-	ADJ
cana-652	148	6	class	class	ADJ
cana-652	148	7	dataset	dataset	NOUN
cana-652	148	8	which	which	PRON
cana-652	148	9	classifies	classify	VERB
cana-652	148	10	skin	skin	NOUN
cana-652	148	11	lesions	lesion	NOUN
cana-652	148	12	into	into	ADP
cana-652	148	13	seven	seven	NUM
cana-652	148	14	distinct	distinct	ADJ
cana-652	148	15	categories	category	NOUN
cana-652	148	16	based	base	VERB
cana-652	148	17	on	on	ADP
cana-652	148	18	different	different	ADJ
cana-652	148	19	types	type	NOUN
cana-652	148	20	of	of	ADP
cana-652	148	21	skin	skin	NOUN
cana-652	148	22	cancer	cancer	NOUN
cana-652	148	23	.	.	PUNCT
cana-652	149	1	these	these	DET
cana-652	149	2	categories	category	NOUN
cana-652	149	3	include	include	VERB
cana-652	149	4	:	:	PUNCT
cana-652	149	5	1	1	X
cana-652	149	6	.	.	X
cana-652	149	7	melanoma	melanoma	NOUN
cana-652	149	8	2	2	NUM
cana-652	149	9	.	.	PUNCT
cana-652	149	10	melanocytic	melanocytic	ADJ
cana-652	149	11	nevus	nevus	NOUN
cana-652	149	12	3	3	NUM
cana-652	149	13	.	.	NOUN
cana-652	149	14	basal	basal	PROPN
cana-652	149	15	cell	cell	NOUN
cana-652	149	16	carcinoma	carcinoma	NOUN
cana-652	149	17	4	4	NUM
cana-652	149	18	.	.	PUNCT
cana-652	149	19	actinic	actinic	ADJ
cana-652	149	20	keratosis	keratosis	NOUN
cana-652	149	21	5	5	NUM
cana-652	149	22	.	.	PUNCT
cana-652	149	23	benign	benign	ADJ
cana-652	149	24	keratosis	keratosis	NOUN
cana-652	149	25	6	6	NUM
cana-652	149	26	.	.	PUNCT
cana-652	150	1	dermatofibroma	dermatofibroma	ADJ
cana-652	150	2	7	7	NUM
cana-652	150	3	.	.	PUNCT
cana-652	150	4	vascular	vascular	ADJ
cana-652	150	5	lesion	lesion	NOUN
cana-652	150	6	the	the	DET
cana-652	150	7	dataset	dataset	NOUN
cana-652	150	8	features	feature	NOUN
cana-652	150	9	,	,	PUNCT
cana-652	150	10	along	along	ADP
cana-652	150	11	with	with	ADP
cana-652	150	12	their	their	PRON
cana-652	150	13	corresponding	correspond	VERB
cana-652	150	14	details	detail	NOUN
cana-652	150	15	,	,	PUNCT
cana-652	150	16	are	be	AUX
cana-652	150	17	shown	show	VERB
cana-652	150	18	in	in	ADP
cana-652	150	19	table	table	NOUN
cana-652	150	20	1	1	NUM
cana-652	150	21	.	.	PUNCT
cana-652	150	22	table	table	NOUN
cana-652	150	23	1	1	NUM
cana-652	150	24	:	:	PUNCT
cana-652	150	25	features	feature	NOUN
cana-652	150	26	of	of	ADP
cana-652	150	27	dataset	dataset	ADJ
cana-652	150	28	sr.no	sr.no	NOUN
cana-652	150	29	feature	feature	NOUN
cana-652	150	30	details	detail	NOUN
cana-652	150	31	1	1	NUM
cana-652	150	32	image	image	NOUN
cana-652	151	1	i	i	NOUN
cana-652	151	2	d	d	NOUN
cana-652	151	3	unique	unique	ADJ
cana-652	151	4	identifier	identifier	NOUN
cana-652	151	5	for	for	ADP
cana-652	151	6	each	each	DET
cana-652	151	7	image	image	NOUN
cana-652	151	8	2	2	NUM
cana-652	151	9	patient	patient	NOUN
cana-652	152	1	i	i	NOUN
cana-652	152	2	d	d	PROPN
cana-652	152	3	unique	unique	ADJ
cana-652	152	4	identifier	identifier	NOUN
cana-652	152	5	for	for	ADP
cana-652	152	6	the	the	DET
cana-652	152	7	patient	patient	NOUN
cana-652	152	8	associated	associate	VERB
cana-652	152	9	with	with	ADP
cana-652	152	10	the	the	DET
cana-652	152	11	image	image	NOUN
cana-652	152	12	3	3	NUM
cana-652	152	13	lesion	lesion	NOUN
cana-652	152	14	i	i	NOUN
cana-652	152	15	d	d	PROPN
cana-652	152	16	unique	unique	ADJ
cana-652	152	17	identifier	identifier	NOUN
cana-652	152	18	for	for	ADP
cana-652	152	19	the	the	DET
cana-652	152	20	skin	skin	NOUN
cana-652	152	21	lesion	lesion	NOUN
cana-652	152	22	4	4	NUM
cana-652	152	23	gender	gender	NOUN
cana-652	152	24	gender	gender	NOUN
cana-652	152	25	of	of	ADP
cana-652	152	26	the	the	DET
cana-652	152	27	patient	patient	ADJ
cana-652	152	28	5	5	NUM
cana-652	152	29	age	age	NOUN
cana-652	152	30	age	age	NOUN
cana-652	152	31	of	of	ADP
cana-652	152	32	the	the	DET
cana-652	152	33	patient	patient	ADJ
cana-652	152	34	6	6	NUM
cana-652	152	35	anatomical	anatomical	ADJ
cana-652	152	36	site	site	NOUN
cana-652	152	37	location	location	NOUN
cana-652	152	38	on	on	ADP
cana-652	152	39	the	the	DET
cana-652	152	40	body	body	NOUN
cana-652	152	41	where	where	SCONJ
cana-652	152	42	the	the	DET
cana-652	152	43	skin	skin	NOUN
cana-652	152	44	lesion	lesion	NOUN
cana-652	152	45	is	be	AUX
cana-652	152	46	located	locate	VERB
cana-652	152	47	7	7	NUM
cana-652	152	48	diagnosis	diagnosis	NOUN
cana-652	152	49	diagnostic	diagnostic	ADJ
cana-652	152	50	class	class	NOUN
cana-652	152	51	of	of	ADP
cana-652	152	52	the	the	DET
cana-652	152	53	skin	skin	NOUN
cana-652	152	54	lesion	lesion	NOUN
cana-652	152	55	fig.1	fig.1	PROPN
cana-652	152	56	:	:	PUNCT
cana-652	152	57	sample	sample	NOUN
cana-652	152	58	from	from	ADP
cana-652	152	59	ham10000	ham10000	PROPN
cana-652	152	60	the	the	DET
cana-652	152	61	fig.1	fig.1	PROPN
cana-652	152	62	depicts	depict	VERB
cana-652	152	63	the	the	DET
cana-652	152	64	images	image	NOUN
cana-652	152	65	sourced	source	VERB
cana-652	152	66	from	from	ADP
cana-652	152	67	'	'	PUNCT
cana-652	152	68	ham10000	ham10000	PRON
cana-652	152	69	'	'	PUNCT
cana-652	152	70	dataset	dataset	NOUN
cana-652	152	71	that	that	SCONJ
cana-652	152	72	we	we	PRON
cana-652	152	73	have	have	AUX
cana-652	152	74	used	use	VERB
cana-652	152	75	for	for	ADP
cana-652	152	76	developing	develop	VERB
cana-652	152	77	our	our	PRON
cana-652	152	78	skin	skin	NOUN
cana-652	152	79	cancer	cancer	NOUN
cana-652	152	80	detection	detection	NOUN
cana-652	152	81	system	system	NOUN
cana-652	152	82	.	.	PUNCT
cana-652	153	1	the	the	DET
cana-652	153	2	display	display	NOUN
cana-652	153	3	comprises	comprise	VERB
cana-652	153	4	5	5	NUM
cana-652	153	5	rows	row	NOUN
cana-652	153	6	and	and	CCONJ
cana-652	153	7	10	10	NUM
cana-652	153	8	columns	column	NOUN
cana-652	153	9	filled	fill	VERB
cana-652	153	10	with	with	ADP
cana-652	153	11	different	different	ADJ
cana-652	153	12	types	type	NOUN
cana-652	153	13	of	of	ADP
cana-652	153	14	skin	skin	NOUN
cana-652	153	15	lesion	lesion	NOUN
cana-652	153	16	images	image	NOUN
cana-652	153	17	.	.	PUNCT
cana-652	154	1	as	as	SCONJ
cana-652	154	2	previously	previously	ADV
cana-652	154	3	indicated	indicate	VERB
cana-652	154	4	,	,	PUNCT
cana-652	154	5	the	the	DET
cana-652	154	6	dataset	dataset	NOUN
cana-652	154	7	encompasses	encompass	VERB
cana-652	154	8	7	7	NUM
cana-652	154	9	distinct	distinct	ADJ
cana-652	154	10	categories	category	NOUN
cana-652	154	11	of	of	ADP
cana-652	154	12	skin	skin	NOUN
cana-652	154	13	lesions	lesion	NOUN
cana-652	154	14	.	.	PUNCT
cana-652	155	1	the	the	DET
cana-652	155	2	displayed	display	VERB
cana-652	155	3	images	image	NOUN
cana-652	155	4	possess	possess	VERB
cana-652	155	5	a	a	DET
cana-652	155	6	width	width	NOUN
cana-652	155	7	of	of	ADP
cana-652	155	8	12	12	NUM
cana-652	155	9	units	unit	NOUN
cana-652	155	10	and	and	CCONJ
cana-652	155	11	a	a	DET
cana-652	155	12	height	height	NOUN
cana-652	155	13	of	of	ADP
cana-652	155	14	6	6	NUM
cana-652	155	15	units	unit	NOUN
cana-652	155	16	respectively	respectively	ADV
cana-652	155	17	.	.	PUNCT
cana-652	156	1	communications	communication	NOUN
cana-652	156	2	on	on	ADP
cana-652	156	3	applied	apply	VERB
cana-652	156	4	nonlinear	nonlinear	ADJ
cana-652	156	5	analysis	analysis	NOUN
cana-652	156	6	issn	issn	NOUN
cana-652	156	7	:	:	PUNCT
cana-652	156	8	1074	1074	NUM
cana-652	156	9	-	-	PUNCT
cana-652	156	10	133x	133x	NUM
cana-652	156	11	vol	vol	NOUN
cana-652	156	12	31	31	NUM
cana-652	156	13	no	no	NOUN
cana-652	156	14	.	.	PUNCT
cana-652	157	1	2s	2s	NUM
cana-652	157	2	(	(	PUNCT
cana-652	157	3	2024	2024	NUM
cana-652	157	4	)	)	PUNCT
cana-652	157	5	326	326	NUM
cana-652	157	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-652	157	7	table	table	NOUN
cana-652	157	8	2	2	NUM
cana-652	157	9	:	:	PUNCT
cana-652	157	10	dataset	dataset	VERB
cana-652	157	11	description	description	NOUN
cana-652	157	12	by	by	ADP
cana-652	157	13	classes	class	NOUN
cana-652	157	14	cancer	cancer	NOUN
cana-652	157	15	types	type	NOUN
cana-652	157	16	number	number	NOUN
cana-652	157	17	of	of	ADP
cana-652	157	18	images	image	NOUN
cana-652	157	19	percentage	percentage	NOUN
cana-652	157	20	melanoma	melanoma	NOUN
cana-652	157	21	1113	1113	NUM
cana-652	157	22	11.11	11.11	NUM
cana-652	157	23	%	%	NOUN
cana-652	157	24	melanocytic	melanocytic	ADJ
cana-652	157	25	nevus	nevus	NOUN
cana-652	157	26	6705	6705	NUM
cana-652	157	27	66.94	66.94	NUM
cana-652	157	28	%	%	NOUN
cana-652	157	29	basal	basal	NOUN
cana-652	157	30	cell	cell	NOUN
cana-652	157	31	carcinoma	carcinoma	NOUN
cana-652	157	32	514	514	NUM
cana-652	157	33	5.13	5.13	NUM
cana-652	157	34	%	%	NOUN
cana-652	157	35	actinic	actinic	ADJ
cana-652	157	36	keratosis	keratosis	NOUN
cana-652	157	37	327	327	NUM
cana-652	157	38	3.26	3.26	NUM
cana-652	157	39	%	%	NOUN
cana-652	157	40	benign	benign	ADJ
cana-652	157	41	keratosis	keratosis	NOUN
cana-652	157	42	1099	1099	NUM
cana-652	157	43	10.97	10.97	NUM
cana-652	157	44	%	%	NOUN
cana-652	157	45	dermatofibroma	dermatofibroma	ADJ
cana-652	157	46	115	115	NUM
cana-652	157	47	1.14	1.14	NUM
cana-652	157	48	%	%	NOUN
cana-652	157	49	vascular	vascular	ADJ
cana-652	157	50	lesion	lesion	NOUN
cana-652	157	51	142	142	NUM
cana-652	157	52	1.41	1.41	NUM
cana-652	157	53	%	%	NOUN
cana-652	157	54	the	the	DET
cana-652	157	55	table	table	NOUN
cana-652	157	56	2	2	NUM
cana-652	157	57	depicts	depict	VERB
cana-652	157	58	the	the	DET
cana-652	157	59	number	number	NOUN
cana-652	157	60	of	of	ADP
cana-652	157	61	pictures	picture	NOUN
cana-652	157	62	in	in	ADP
cana-652	157	63	the	the	DET
cana-652	157	64	ham10000	ham10000	PROPN
cana-652	157	65	dataset	dataset	NOUN
cana-652	157	66	for	for	ADP
cana-652	157	67	each	each	PRON
cana-652	157	68	of	of	ADP
cana-652	157	69	the	the	DET
cana-652	157	70	7	7	NUM
cana-652	157	71	skin	skin	NOUN
cana-652	157	72	cancer	cancer	NOUN
cana-652	157	73	classes	class	NOUN
cana-652	157	74	:	:	PUNCT
cana-652	157	75	melanocytic	melanocytic	ADJ
cana-652	157	76	nevus	nevus	NOUN
cana-652	157	77	,	,	PUNCT
cana-652	157	78	melanoma	melanoma	ADJ
cana-652	157	79	,	,	PUNCT
cana-652	157	80	carcinoma	carcinoma	NOUN
cana-652	157	81	,	,	PUNCT
cana-652	157	82	basal	basal	ADJ
cana-652	157	83	cell	cell	NOUN
cana-652	157	84	actinic	actinic	ADJ
cana-652	157	85	keratosis	keratosis	NOUN
cana-652	157	86	,	,	PUNCT
cana-652	157	87	dermatofibroma	dermatofibroma	ADJ
cana-652	157	88	,	,	PUNCT
cana-652	157	89	benign	benign	ADJ
cana-652	157	90	keratosis	keratosis	NOUN
cana-652	157	91	,	,	PUNCT
cana-652	157	92	and	and	CCONJ
cana-652	157	93	vascular	vascular	ADJ
cana-652	157	94	lesion	lesion	NOUN
cana-652	157	95	.	.	PUNCT
cana-652	158	1	the	the	DET
cana-652	158	2	table	table	NOUN
cana-652	158	3	also	also	ADV
cana-652	158	4	displays	display	VERB
cana-652	158	5	the	the	DET
cana-652	158	6	percentage	percentage	NOUN
cana-652	158	7	of	of	ADP
cana-652	158	8	images	image	NOUN
cana-652	158	9	in	in	ADP
cana-652	158	10	each	each	DET
cana-652	158	11	class	class	NOUN
cana-652	158	12	.	.	PUNCT
cana-652	159	1	the	the	DET
cana-652	159	2	table	table	NOUN
cana-652	159	3	shows	show	VERB
cana-652	159	4	that	that	SCONJ
cana-652	159	5	the	the	DET
cana-652	159	6	most	most	ADV
cana-652	159	7	prevailing	prevail	VERB
cana-652	159	8	skin	skin	NOUN
cana-652	159	9	cancer	cancer	NOUN
cana-652	159	10	class	class	NOUN
cana-652	159	11	in	in	ADP
cana-652	159	12	the	the	DET
cana-652	159	13	following	follow	VERB
cana-652	159	14	dataset	dataset	NOUN
cana-652	159	15	is	be	AUX
cana-652	159	16	melanocytic	melanocytic	ADJ
cana-652	159	17	nevus	nevu	NOUN
cana-652	159	18	,	,	PUNCT
cana-652	159	19	which	which	PRON
cana-652	159	20	is	be	AUX
cana-652	159	21	a	a	DET
cana-652	159	22	benign	benign	ADJ
cana-652	159	23	mole	mole	NOUN
cana-652	159	24	.	.	PUNCT
cana-652	160	1	the	the	DET
cana-652	160	2	least	least	ADV
cana-652	160	3	common	common	ADJ
cana-652	160	4	skin	skin	NOUN
cana-652	160	5	cancer	cancer	NOUN
cana-652	160	6	class	class	NOUN
cana-652	160	7	is	be	AUX
cana-652	160	8	vascular	vascular	ADJ
cana-652	160	9	lesion	lesion	NOUN
cana-652	160	10	.	.	PUNCT
cana-652	161	1	this	this	PRON
cana-652	161	2	clearly	clearly	ADV
cana-652	161	3	indicates	indicate	VERB
cana-652	161	4	that	that	SCONJ
cana-652	161	5	the	the	DET
cana-652	161	6	dataset	dataset	NOUN
cana-652	161	7	is	be	AUX
cana-652	161	8	imbalanced	imbalance	VERB
cana-652	161	9	and	and	CCONJ
cana-652	161	10	needs	need	VERB
cana-652	161	11	oversampling	oversample	VERB
cana-652	161	12	to	to	PART
cana-652	161	13	avoid	avoid	VERB
cana-652	161	14	any	any	DET
cana-652	161	15	ambiguous	ambiguous	ADJ
cana-652	161	16	and	and	CCONJ
cana-652	161	17	biased	biased	ADJ
cana-652	161	18	results	result	NOUN
cana-652	161	19	.	.	PUNCT
cana-652	162	1	b.	b.	NOUN
cana-652	162	2	tools	tool	NOUN
cana-652	162	3	and	and	CCONJ
cana-652	162	4	libraries	library	NOUN
cana-652	162	5	:	:	PUNCT
cana-652	162	6	the	the	DET
cana-652	162	7	research	research	NOUN
cana-652	162	8	utilizes	utilize	VERB
cana-652	162	9	various	various	ADJ
cana-652	162	10	tools	tool	NOUN
cana-652	162	11	and	and	CCONJ
cana-652	162	12	libraries	library	NOUN
cana-652	162	13	for	for	ADP
cana-652	162	14	implementing	implement	VERB
cana-652	162	15	the	the	DET
cana-652	162	16	models	model	NOUN
cana-652	162	17	,	,	PUNCT
cana-652	162	18	training	training	NOUN
cana-652	162	19	,	,	PUNCT
cana-652	162	20	and	and	CCONJ
cana-652	162	21	evaluation	evaluation	NOUN
cana-652	162	22	which	which	PRON
cana-652	162	23	includes	include	VERB
cana-652	162	24	:	:	PUNCT
cana-652	162	25	(	(	PUNCT
cana-652	162	26	i	i	NOUN
cana-652	162	27	)	)	PUNCT
cana-652	162	28	python	python	PROPN
cana-652	162	29	:	:	PUNCT
cana-652	162	30	the	the	DET
cana-652	162	31	primary	primary	ADJ
cana-652	162	32	programming	programming	NOUN
cana-652	162	33	language	language	NOUN
cana-652	162	34	for	for	ADP
cana-652	162	35	its	its	PRON
cana-652	162	36	extensive	extensive	ADJ
cana-652	162	37	ecosystem	ecosystem	NOUN
cana-652	162	38	.	.	PUNCT
cana-652	163	1	(	(	PUNCT
cana-652	163	2	ii	ii	NOUN
cana-652	163	3	)	)	PUNCT
cana-652	163	4	tensorflow	tensorflow	NOUN
cana-652	163	5	and	and	CCONJ
cana-652	163	6	keras	keras	PROPN
cana-652	163	7	:	:	PUNCT
cana-652	163	8	employed	employ	VERB
cana-652	163	9	for	for	ADP
cana-652	163	10	deep	deep	ADJ
cana-652	163	11	learning	learning	NOUN
cana-652	163	12	model	model	NOUN
cana-652	163	13	development	development	NOUN
cana-652	163	14	and	and	CCONJ
cana-652	163	15	training	training	NOUN
cana-652	163	16	.	.	PUNCT
cana-652	164	1	(	(	PUNCT
cana-652	164	2	iii)scikit	iii)scikit	NOUN
cana-652	164	3	-	-	PUNCT
cana-652	164	4	learn	learn	NOUN
cana-652	164	5	:	:	PUNCT
cana-652	164	6	utilized	utilize	VERB
cana-652	164	7	for	for	ADP
cana-652	164	8	data	data	NOUN
cana-652	164	9	preprocessing	preprocessing	NOUN
cana-652	164	10	and	and	CCONJ
cana-652	164	11	evaluation	evaluation	NOUN
cana-652	164	12	.	.	PUNCT
cana-652	165	1	(	(	PUNCT
cana-652	165	2	iv	iv	X
cana-652	165	3	)	)	PUNCT
cana-652	165	4	pandas	panda	NOUN
cana-652	165	5	and	and	CCONJ
cana-652	165	6	numpy	numpy	NOUN
cana-652	165	7	:	:	PUNCT
cana-652	165	8	used	use	VERB
cana-652	165	9	for	for	ADP
cana-652	165	10	data	data	NOUN
cana-652	165	11	manipulation	manipulation	NOUN
cana-652	165	12	and	and	CCONJ
cana-652	165	13	numerical	numerical	ADJ
cana-652	165	14	computations	computation	NOUN
cana-652	165	15	.	.	PUNCT
cana-652	166	1	(	(	PUNCT
cana-652	166	2	v	v	NOUN
cana-652	166	3	)	)	PUNCT
cana-652	166	4	matplotlib	matplotlib	PROPN
cana-652	166	5	and	and	CCONJ
cana-652	166	6	seaborn	seaborn	PROPN
cana-652	166	7	:	:	PUNCT
cana-652	166	8	chosen	choose	VERB
cana-652	166	9	for	for	ADP
cana-652	166	10	data	data	NOUN
cana-652	166	11	visualization	visualization	NOUN
cana-652	166	12	.	.	PUNCT
cana-652	167	1	(	(	PUNCT
cana-652	167	2	vi	vi	NOUN
cana-652	167	3	)	)	PUNCT
cana-652	167	4	opencv	opencv	PROPN
cana-652	167	5	:	:	PUNCT
cana-652	167	6	applied	apply	VERB
cana-652	167	7	for	for	ADP
cana-652	167	8	image	image	NOUN
cana-652	167	9	preprocessing	preprocessing	NOUN
cana-652	167	10	and	and	CCONJ
cana-652	167	11	augmentation	augmentation	NOUN
cana-652	167	12	.	.	PUNCT
cana-652	168	1	(	(	PUNCT
cana-652	168	2	vii	vii	PROPN
cana-652	168	3	)	)	PUNCT
cana-652	168	4	scipy	scipy	NOUN
cana-652	168	5	:	:	PUNCT
cana-652	168	6	utilized	utilize	VERB
cana-652	168	7	for	for	ADP
cana-652	168	8	specialized	specialized	ADJ
cana-652	168	9	statistical	statistical	ADJ
cana-652	168	10	functions	function	NOUN
cana-652	168	11	.	.	PUNCT
cana-652	169	1	(	(	PUNCT
cana-652	169	2	viii	viii	NOUN
cana-652	169	3	)	)	PUNCT
cana-652	169	4	h5py	h5py	NOUN
cana-652	169	5	:	:	PUNCT
cana-652	169	6	employed	employ	VERB
cana-652	169	7	for	for	ADP
cana-652	169	8	efficient	efficient	ADJ
cana-652	169	9	dataset	dataset	NOUN
cana-652	169	10	storage	storage	NOUN
cana-652	169	11	.	.	PUNCT
cana-652	170	1	(	(	PUNCT
cana-652	170	2	ix	ix	X
cana-652	170	3	)	)	PUNCT
cana-652	170	4	google	google	PROPN
cana-652	170	5	colab	colab	PROPN
cana-652	170	6	:	:	PUNCT
cana-652	170	7	utilized	utilize	VERB
cana-652	170	8	for	for	ADP
cana-652	170	9	cloud	cloud	NOUN
cana-652	170	10	-	-	PUNCT
cana-652	170	11	based	base	VERB
cana-652	170	12	model	model	NOUN
cana-652	170	13	training	training	NOUN
cana-652	170	14	.	.	PUNCT
cana-652	171	1	(	(	PUNCT
cana-652	171	2	x	x	X
cana-652	171	3	)	)	PUNCT
cana-652	171	4	github	github	NOUN
cana-652	171	5	:	:	PUNCT
cana-652	171	6	facilitated	facilitate	VERB
cana-652	171	7	version	version	NOUN
cana-652	171	8	control	control	NOUN
cana-652	171	9	and	and	CCONJ
cana-652	171	10	collaboration	collaboration	NOUN
cana-652	171	11	.	.	PUNCT
cana-652	172	1	(	(	PUNCT
cana-652	172	2	xi	xi	X
cana-652	172	3	)	)	PUNCT
cana-652	172	4	additional	additional	ADJ
cana-652	172	5	machine	machine	NOUN
cana-652	172	6	learning	learn	VERB
cana-652	172	7	frameworks	framework	NOUN
cana-652	172	8	:	:	PUNCT
cana-652	172	9	depending	depend	VERB
cana-652	172	10	on	on	ADP
cana-652	172	11	specific	specific	ADJ
cana-652	172	12	tasks	task	NOUN
cana-652	172	13	,	,	PUNCT
cana-652	172	14	frameworks	framework	NOUN
cana-652	172	15	like	like	ADP
cana-652	172	16	pytorch	pytorch	NOUN
cana-652	172	17	and	and	CCONJ
cana-652	172	18	scikit	scikit	NOUN
cana-652	172	19	-	-	PUNCT
cana-652	172	20	learn	learn	NOUN
cana-652	172	21	were	be	AUX
cana-652	172	22	incorporated	incorporate	VERB
cana-652	172	23	as	as	SCONJ
cana-652	172	24	needed	need	VERB
cana-652	172	25	.	.	PUNCT
cana-652	173	1	c.	c.	NOUN
cana-652	173	2	algorithms	algorithms	PROPN
cana-652	173	3	:	:	PUNCT
cana-652	173	4	our	our	PRON
cana-652	173	5	approach	approach	NOUN
cana-652	173	6	involves	involve	VERB
cana-652	173	7	using	use	VERB
cana-652	173	8	traditional	traditional	ADJ
cana-652	173	9	cnn	cnn	PROPN
cana-652	173	10	and	and	CCONJ
cana-652	173	11	advanced	advanced	ADJ
cana-652	173	12	mobilenet	mobilenet	NOUN
cana-652	173	13	architectures	architecture	NOUN
cana-652	173	14	for	for	ADP
cana-652	173	15	skin	skin	NOUN
cana-652	173	16	lesion	lesion	NOUN
cana-652	173	17	detection	detection	NOUN
cana-652	173	18	and	and	CCONJ
cana-652	173	19	prediction	prediction	NOUN
cana-652	173	20	.	.	PUNCT
cana-652	174	1	1	1	X
cana-652	174	2	.	.	X
cana-652	174	3	convolutional	convolutional	ADJ
cana-652	174	4	neural	neural	ADJ
cana-652	174	5	network	network	NOUN
cana-652	174	6	(	(	PUNCT
cana-652	174	7	cnn	cnn	PROPN
cana-652	174	8	):	):	PUNCT
cana-652	174	9	cnn	cnn	PROPN
cana-652	174	10	is	be	AUX
cana-652	174	11	a	a	DET
cana-652	174	12	dl	dl	PROPN
cana-652	174	13	(	(	PUNCT
cana-652	174	14	deep	deep	ADJ
cana-652	174	15	learning	learning	NOUN
cana-652	174	16	)	)	PUNCT
cana-652	174	17	algorithm	algorithm	NOUN
cana-652	174	18	which	which	PRON
cana-652	174	19	is	be	AUX
cana-652	174	20	extensively	extensively	ADV
cana-652	174	21	used	use	VERB
cana-652	174	22	for	for	ADP
cana-652	174	23	picture	picture	NOUN
cana-652	174	24	classification	classification	NOUN
cana-652	174	25	tasks	task	NOUN
cana-652	174	26	.	.	PUNCT
cana-652	175	1	in	in	ADP
cana-652	175	2	this	this	DET
cana-652	175	3	research	research	NOUN
cana-652	175	4	,	,	PUNCT
cana-652	175	5	a	a	DET
cana-652	175	6	cnn	cnn	NOUN
cana-652	175	7	is	be	AUX
cana-652	175	8	employed	employ	VERB
cana-652	175	9	to	to	PART
cana-652	175	10	classify	classify	VERB
cana-652	175	11	skin	skin	NOUN
cana-652	175	12	lesions	lesion	NOUN
cana-652	175	13	from	from	ADP
cana-652	175	14	the	the	DET
cana-652	175	15	ham10000	ham10000	PROPN
cana-652	175	16	dataset	dataset	PROPN
cana-652	175	17	.	.	PUNCT
cana-652	176	1	the	the	DET
cana-652	176	2	cnn	cnn	PROPN
cana-652	176	3	architecture	architecture	NOUN
cana-652	176	4	is	be	AUX
cana-652	176	5	composed	compose	VERB
cana-652	176	6	of	of	ADP
cana-652	176	7	following	follow	VERB
cana-652	176	8	layers	layer	NOUN
cana-652	176	9	:	:	PUNCT
cana-652	176	10	(	(	PUNCT
cana-652	176	11	i	i	NOUN
cana-652	176	12	)	)	PUNCT
cana-652	176	13	input	input	NOUN
cana-652	176	14	layer	layer	NOUN
cana-652	176	15	:	:	PUNCT
cana-652	176	16	the	the	DET
cana-652	176	17	input	input	NOUN
cana-652	176	18	layer	layer	NOUN
cana-652	176	19	accepts	accept	VERB
cana-652	176	20	images	image	NOUN
cana-652	176	21	of	of	ADP
cana-652	176	22	size	size	NOUN
cana-652	176	23	28x28	28x28	NUM
cana-652	176	24	pixels	pixel	NOUN
cana-652	176	25	with	with	ADP
cana-652	176	26	rgb	rgb	PROPN
cana-652	176	27	channels	channel	NOUN
cana-652	176	28	.	.	PUNCT
cana-652	177	1	communications	communication	NOUN
cana-652	177	2	on	on	ADP
cana-652	177	3	applied	apply	VERB
cana-652	177	4	nonlinear	nonlinear	ADJ
cana-652	177	5	analysis	analysis	NOUN
cana-652	177	6	issn	issn	NOUN
cana-652	177	7	:	:	PUNCT
cana-652	177	8	1074	1074	NUM
cana-652	177	9	-	-	PUNCT
cana-652	177	10	133x	133x	NUM
cana-652	177	11	vol	vol	NOUN
cana-652	177	12	31	31	NUM
cana-652	177	13	no	no	NOUN
cana-652	177	14	.	.	PUNCT
cana-652	178	1	2s	2s	NUM
cana-652	178	2	(	(	PUNCT
cana-652	178	3	2024	2024	NUM
cana-652	178	4	)	)	PUNCT
cana-652	178	5	327	327	NUM
cana-652	178	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-652	178	7	(	(	PUNCT
cana-652	178	8	ii	ii	NOUN
cana-652	178	9	)	)	PUNCT
cana-652	178	10	convolutional	convolutional	ADJ
cana-652	178	11	layers	layer	NOUN
cana-652	178	12	:	:	PUNCT
cana-652	178	13	multiple	multiple	ADJ
cana-652	178	14	convolutional	convolutional	ADJ
cana-652	178	15	layers	layer	NOUN
cana-652	178	16	are	be	AUX
cana-652	178	17	employed	employ	VERB
cana-652	178	18	to	to	PART
cana-652	178	19	obtain	obtain	VERB
cana-652	178	20	features	feature	NOUN
cana-652	178	21	from	from	ADP
cana-652	178	22	the	the	DET
cana-652	178	23	input	input	NOUN
cana-652	178	24	images	image	NOUN
cana-652	178	25	.	.	PUNCT
cana-652	179	1	these	these	DET
cana-652	179	2	layers	layer	NOUN
cana-652	179	3	apply	apply	VERB
cana-652	179	4	convolution	convolution	NOUN
cana-652	179	5	operations	operation	NOUN
cana-652	179	6	to	to	PART
cana-652	179	7	learn	learn	VERB
cana-652	179	8	different	different	ADJ
cana-652	179	9	patterns	pattern	NOUN
cana-652	179	10	and	and	CCONJ
cana-652	179	11	features	feature	NOUN
cana-652	179	12	.	.	PUNCT
cana-652	180	1	(	(	PUNCT
cana-652	180	2	iii	iii	X
cana-652	180	3	)	)	PUNCT
cana-652	180	4	max	max	NOUN
cana-652	180	5	-	-	PUNCT
cana-652	180	6	pooling	pool	VERB
cana-652	180	7	layers	layer	NOUN
cana-652	180	8	:	:	PUNCT
cana-652	180	9	max	max	PROPN
cana-652	180	10	-	-	PUNCT
cana-652	180	11	pooling	pool	VERB
cana-652	180	12	layers	layer	NOUN
cana-652	180	13	reduce	reduce	VERB
cana-652	180	14	the	the	DET
cana-652	180	15	feature	feature	NOUN
cana-652	180	16	maps	map	NOUN
cana-652	180	17	'	'	PART
cana-652	180	18	spatial	spatial	ADJ
cana-652	180	19	dimensionality	dimensionality	NOUN
cana-652	180	20	,	,	PUNCT
cana-652	180	21	helping	help	VERB
cana-652	180	22	to	to	PART
cana-652	180	23	retain	retain	VERB
cana-652	180	24	essential	essential	ADJ
cana-652	180	25	information	information	NOUN
cana-652	180	26	while	while	SCONJ
cana-652	180	27	reducing	reduce	VERB
cana-652	180	28	computational	computational	ADJ
cana-652	180	29	complexity	complexity	NOUN
cana-652	180	30	.	.	PUNCT
cana-652	181	1	(	(	PUNCT
cana-652	181	2	iv	iv	X
cana-652	181	3	)	)	PUNCT
cana-652	181	4	batch	batch	NOUN
cana-652	181	5	normalization	normalization	NOUN
cana-652	181	6	layers	layer	NOUN
cana-652	181	7	:	:	PUNCT
cana-652	181	8	batch	batch	NOUN
cana-652	181	9	normalization	normalization	NOUN
cana-652	181	10	is	be	AUX
cana-652	181	11	utilized	utilize	VERB
cana-652	181	12	to	to	PART
cana-652	181	13	improve	improve	VERB
cana-652	181	14	the	the	DET
cana-652	181	15	convergence	convergence	NOUN
cana-652	181	16	of	of	ADP
cana-652	181	17	the	the	DET
cana-652	181	18	model	model	NOUN
cana-652	181	19	during	during	ADP
cana-652	181	20	training	training	NOUN
cana-652	181	21	.	.	PUNCT
cana-652	182	1	(	(	PUNCT
cana-652	182	2	v	v	NOUN
cana-652	182	3	)	)	PUNCT
cana-652	182	4	fully	fully	ADV
cana-652	182	5	connected	connected	ADJ
cana-652	182	6	layers	layer	NOUN
cana-652	182	7	:	:	PUNCT
cana-652	182	8	after	after	ADP
cana-652	182	9	feature	feature	NOUN
cana-652	182	10	extraction	extraction	NOUN
cana-652	182	11	,	,	PUNCT
cana-652	182	12	fully	fully	ADV
cana-652	182	13	connected	connected	ADJ
cana-652	182	14	layers	layer	NOUN
cana-652	182	15	are	be	AUX
cana-652	182	16	employed	employ	VERB
cana-652	182	17	to	to	PART
cana-652	182	18	make	make	VERB
cana-652	182	19	predictions	prediction	NOUN
cana-652	182	20	.	.	PUNCT
cana-652	183	1	these	these	DET
cana-652	183	2	layers	layer	NOUN
cana-652	183	3	consist	consist	VERB
cana-652	183	4	of	of	ADP
cana-652	183	5	densely	densely	ADV
cana-652	183	6	connected	connected	ADJ
cana-652	183	7	neurons	neuron	NOUN
cana-652	183	8	that	that	PRON
cana-652	183	9	produce	produce	VERB
cana-652	183	10	output	output	NOUN
cana-652	183	11	probabilities	probability	NOUN
cana-652	183	12	for	for	ADP
cana-652	183	13	each	each	DET
cana-652	183	14	class	class	NOUN
cana-652	183	15	.	.	PUNCT
cana-652	184	1	(	(	PUNCT
cana-652	184	2	vi	vi	NOUN
cana-652	184	3	)	)	PUNCT
cana-652	184	4	flattened	flatten	VERB
cana-652	184	5	layers	layer	NOUN
cana-652	184	6	:	:	PUNCT
cana-652	184	7	the	the	DET
cana-652	184	8	flattened	flatten	VERB
cana-652	184	9	layer	layer	NOUN
cana-652	184	10	in	in	ADP
cana-652	184	11	a	a	DET
cana-652	184	12	cnn	cnn	NOUN
cana-652	184	13	plays	play	VERB
cana-652	184	14	a	a	DET
cana-652	184	15	pivotal	pivotal	ADJ
cana-652	184	16	role	role	NOUN
cana-652	184	17	in	in	ADP
cana-652	184	18	transforming	transform	VERB
cana-652	184	19	the	the	DET
cana-652	184	20	multidimensional	multidimensional	ADJ
cana-652	184	21	feature	feature	NOUN
cana-652	184	22	maps	map	NOUN
cana-652	184	23	from	from	ADP
cana-652	184	24	the	the	DET
cana-652	184	25	preceding	precede	VERB
cana-652	184	26	convolutional	convolutional	ADJ
cana-652	184	27	and	and	CCONJ
cana-652	184	28	pooling	pool	VERB
cana-652	184	29	layers	layer	NOUN
cana-652	184	30	into	into	ADP
cana-652	184	31	a	a	DET
cana-652	184	32	onedimensional	onedimensional	ADJ
cana-652	184	33	vector	vector	NOUN
cana-652	184	34	.	.	PUNCT
cana-652	185	1	this	this	DET
cana-652	185	2	vector	vector	NOUN
cana-652	185	3	acts	act	VERB
cana-652	185	4	as	as	ADP
cana-652	185	5	the	the	DET
cana-652	185	6	input	input	NOUN
cana-652	185	7	for	for	ADP
cana-652	185	8	the	the	DET
cana-652	185	9	subsequent	subsequent	ADJ
cana-652	185	10	fully	fully	ADV
cana-652	185	11	connected	connected	ADJ
cana-652	185	12	layers	layer	NOUN
cana-652	185	13	,	,	PUNCT
cana-652	185	14	enabling	enable	VERB
cana-652	185	15	the	the	DET
cana-652	185	16	network	network	NOUN
cana-652	185	17	to	to	PART
cana-652	185	18	learn	learn	VERB
cana-652	185	19	hierarchical	hierarchical	ADJ
cana-652	185	20	patterns	pattern	NOUN
cana-652	185	21	in	in	ADP
cana-652	185	22	the	the	DET
cana-652	185	23	data	datum	NOUN
cana-652	185	24	.	.	PUNCT
cana-652	186	1	(	(	PUNCT
cana-652	186	2	vii	vii	PROPN
cana-652	186	3	)	)	PUNCT
cana-652	186	4	dropout	dropout	NOUN
cana-652	186	5	layers	layer	NOUN
cana-652	186	6	:	:	PUNCT
cana-652	186	7	dropout	dropout	NOUN
cana-652	186	8	layers	layer	NOUN
cana-652	186	9	are	be	AUX
cana-652	186	10	introduced	introduce	VERB
cana-652	186	11	to	to	PART
cana-652	186	12	prevent	prevent	VERB
cana-652	186	13	overfitting	overfitting	NOUN
cana-652	186	14	by	by	ADP
cana-652	186	15	randomly	randomly	ADV
cana-652	186	16	deactivating	deactivate	VERB
cana-652	186	17	a	a	DET
cana-652	186	18	fraction	fraction	NOUN
cana-652	186	19	of	of	ADP
cana-652	186	20	neurons	neuron	NOUN
cana-652	186	21	during	during	ADP
cana-652	186	22	training	training	NOUN
cana-652	186	23	.	.	PUNCT
cana-652	187	1	(	(	PUNCT
cana-652	187	2	viii	viii	NOUN
cana-652	187	3	)	)	PUNCT
cana-652	187	4	output	output	NOUN
cana-652	187	5	layer	layer	NOUN
cana-652	187	6	:	:	PUNCT
cana-652	187	7	the	the	DET
cana-652	187	8	output	output	NOUN
cana-652	187	9	layer	layer	NOUN
cana-652	187	10	consists	consist	VERB
cana-652	187	11	of	of	ADP
cana-652	187	12	seven	seven	NUM
cana-652	187	13	neurons	neuron	NOUN
cana-652	187	14	(	(	PUNCT
cana-652	187	15	one	one	NUM
cana-652	187	16	for	for	ADP
cana-652	187	17	each	each	DET
cana-652	187	18	class	class	NOUN
cana-652	187	19	)	)	PUNCT
cana-652	187	20	,	,	PUNCT
cana-652	187	21	and	and	CCONJ
cana-652	187	22	it	it	PRON
cana-652	187	23	uses	use	VERB
cana-652	187	24	softmax	softmax	ADJ
cana-652	187	25	activation	activation	NOUN
cana-652	187	26	to	to	PART
cana-652	187	27	produce	produce	VERB
cana-652	187	28	class	class	NOUN
cana-652	187	29	probabilities	probability	NOUN
cana-652	187	30	.	.	PUNCT
cana-652	188	1	2	2	X
cana-652	188	2	.	.	X
cana-652	188	3	mobilenet	mobilenet	NOUN
cana-652	188	4	:	:	PUNCT
cana-652	188	5	mobilenet	mobilenet	PROPN
cana-652	188	6	is	be	AUX
cana-652	188	7	a	a	DET
cana-652	188	8	lightweight	lightweight	ADJ
cana-652	188	9	deep	deep	ADJ
cana-652	188	10	learning	learning	NOUN
cana-652	188	11	architecture	architecture	NOUN
cana-652	188	12	developed	develop	VERB
cana-652	188	13	for	for	ADP
cana-652	188	14	embedded	embed	VERB
cana-652	188	15	devices	device	NOUN
cana-652	188	16	and	and	CCONJ
cana-652	188	17	mobile	mobile	NOUN
cana-652	188	18	.	.	PUNCT
cana-652	189	1	in	in	ADP
cana-652	189	2	this	this	DET
cana-652	189	3	research	research	NOUN
cana-652	189	4	,	,	PUNCT
cana-652	189	5	a	a	DET
cana-652	189	6	pre	pre	ADJ
cana-652	189	7	-	-	ADJ
cana-652	189	8	trained	train	VERB
cana-652	189	9	mobilenet	mobilenet	NOUN
cana-652	189	10	model	model	NOUN
cana-652	189	11	is	be	AUX
cana-652	189	12	employed	employ	VERB
cana-652	189	13	as	as	ADP
cana-652	189	14	an	an	DET
cana-652	189	15	extractor	extractor	NOUN
cana-652	189	16	of	of	ADP
cana-652	189	17	features	feature	NOUN
cana-652	189	18	from	from	ADP
cana-652	189	19	images	image	NOUN
cana-652	189	20	for	for	ADP
cana-652	189	21	skin	skin	NOUN
cana-652	189	22	lesion	lesion	NOUN
cana-652	189	23	classification	classification	NOUN
cana-652	189	24	.	.	PUNCT
cana-652	190	1	the	the	DET
cana-652	190	2	model	model	NOUN
cana-652	190	3	's	's	PART
cana-652	190	4	architecture	architecture	NOUN
cana-652	190	5	is	be	AUX
cana-652	190	6	modified	modify	VERB
cana-652	190	7	to	to	PART
cana-652	190	8	remove	remove	VERB
cana-652	190	9	some	some	DET
cana-652	190	10	layers	layer	NOUN
cana-652	190	11	and	and	CCONJ
cana-652	190	12	include	include	VERB
cana-652	190	13	additional	additional	ADJ
cana-652	190	14	layers	layer	NOUN
cana-652	190	15	for	for	ADP
cana-652	190	16	classification	classification	NOUN
cana-652	190	17	.	.	PUNCT
cana-652	191	1	here	here	ADV
cana-652	191	2	is	be	AUX
cana-652	191	3	an	an	DET
cana-652	191	4	overview	overview	NOUN
cana-652	191	5	of	of	ADP
cana-652	191	6	the	the	DET
cana-652	191	7	mobilenet	mobilenet	NOUN
cana-652	191	8	-	-	PUNCT
cana-652	191	9	based	base	VERB
cana-652	191	10	model	model	NOUN
cana-652	191	11	:	:	PUNCT
cana-652	191	12	(	(	PUNCT
cana-652	191	13	i	i	NOUN
cana-652	191	14	)	)	PUNCT
cana-652	191	15	mobilenet	mobilenet	NOUN
cana-652	191	16	feature	feature	NOUN
cana-652	191	17	extractor	extractor	NOUN
cana-652	191	18	:	:	PUNCT
cana-652	191	19	the	the	DET
cana-652	191	20	mobilenet	mobilenet	NOUN
cana-652	191	21	design	design	NOUN
cana-652	191	22	is	be	AUX
cana-652	191	23	employed	employ	VERB
cana-652	191	24	to	to	PART
cana-652	191	25	extract	extract	VERB
cana-652	191	26	relevant	relevant	ADJ
cana-652	191	27	features	feature	NOUN
cana-652	191	28	from	from	ADP
cana-652	191	29	skin	skin	NOUN
cana-652	191	30	cancer	cancer	NOUN
cana-652	191	31	images	image	NOUN
cana-652	191	32	.	.	PUNCT
cana-652	192	1	the	the	DET
cana-652	192	2	pre	pre	ADJ
cana-652	192	3	-	-	ADJ
cana-652	192	4	trained	train	VERB
cana-652	192	5	mobilenet	mobilenet	NOUN
cana-652	192	6	model	model	NOUN
cana-652	192	7	has	have	AUX
cana-652	192	8	already	already	ADV
cana-652	192	9	learned	learn	VERB
cana-652	192	10	a	a	DET
cana-652	192	11	wide	wide	ADJ
cana-652	192	12	range	range	NOUN
cana-652	192	13	of	of	ADP
cana-652	192	14	features	feature	NOUN
cana-652	192	15	from	from	ADP
cana-652	192	16	various	various	ADJ
cana-652	192	17	images	image	NOUN
cana-652	192	18	.	.	PUNCT
cana-652	193	1	(	(	PUNCT
cana-652	193	2	ii	ii	NOUN
cana-652	193	3	)	)	PUNCT
cana-652	193	4	additional	additional	ADJ
cana-652	193	5	layers	layer	NOUN
cana-652	193	6	:	:	PUNCT
cana-652	193	7	on	on	ADP
cana-652	193	8	top	top	NOUN
cana-652	193	9	of	of	ADP
cana-652	193	10	the	the	DET
cana-652	193	11	mobilenet	mobilenet	NOUN
cana-652	193	12	feature	feature	NOUN
cana-652	193	13	extractor	extractor	NOUN
cana-652	193	14	,	,	PUNCT
cana-652	193	15	new	new	ADJ
cana-652	193	16	layers	layer	NOUN
cana-652	193	17	are	be	AUX
cana-652	193	18	added	add	VERB
cana-652	193	19	for	for	ADP
cana-652	193	20	classification	classification	NOUN
cana-652	193	21	purposes	purpose	NOUN
cana-652	193	22	.	.	PUNCT
cana-652	194	1	these	these	DET
cana-652	194	2	layers	layer	NOUN
cana-652	194	3	include	include	VERB
cana-652	194	4	a	a	DET
cana-652	194	5	dense	dense	ADJ
cana-652	194	6	layer	layer	NOUN
cana-652	194	7	,	,	PUNCT
cana-652	194	8	flatten	flatten	VERB
cana-652	194	9	layer	layer	NOUN
cana-652	194	10	,	,	PUNCT
cana-652	194	11	dropout	dropout	NOUN
cana-652	194	12	layer	layer	NOUN
cana-652	194	13	,	,	PUNCT
cana-652	194	14	and	and	CCONJ
cana-652	194	15	an	an	DET
cana-652	194	16	output	output	NOUN
cana-652	194	17	layer	layer	NOUN
cana-652	194	18	.	.	PUNCT
cana-652	195	1	the	the	DET
cana-652	195	2	mobilenet	mobilenet	NOUN
cana-652	195	3	-	-	PUNCT
cana-652	195	4	based	base	VERB
cana-652	195	5	model	model	NOUN
cana-652	195	6	is	be	AUX
cana-652	195	7	fine	fine	ADV
cana-652	195	8	-	-	PUNCT
cana-652	195	9	tuned	tune	VERB
cana-652	195	10	using	use	VERB
cana-652	195	11	transfer	transfer	NOUN
cana-652	195	12	learning	learning	NOUN
cana-652	195	13	,	,	PUNCT
cana-652	195	14	where	where	SCONJ
cana-652	195	15	the	the	DET
cana-652	195	16	weights	weight	NOUN
cana-652	195	17	of	of	ADP
cana-652	195	18	the	the	DET
cana-652	195	19	mobilenet	mobilenet	NOUN
cana-652	195	20	layers	layer	NOUN
cana-652	195	21	are	be	AUX
cana-652	195	22	frozen	freeze	VERB
cana-652	195	23	,	,	PUNCT
cana-652	195	24	and	and	CCONJ
cana-652	195	25	only	only	ADV
cana-652	195	26	the	the	DET
cana-652	195	27	additional	additional	ADJ
cana-652	195	28	layers	layer	NOUN
cana-652	195	29	are	be	AUX
cana-652	195	30	trained	train	VERB
cana-652	195	31	.	.	PUNCT
cana-652	196	1	the	the	DET
cana-652	196	2	model	model	NOUN
cana-652	196	3	is	be	AUX
cana-652	196	4	improved	improve	VERB
cana-652	196	5	using	use	VERB
cana-652	196	6	the	the	DET
cana-652	196	7	adam	adam	PROPN
cana-652	196	8	optimizer	optimizer	NOUN
cana-652	196	9	,	,	PUNCT
cana-652	196	10	and	and	CCONJ
cana-652	196	11	categorical	categorical	ADJ
cana-652	196	12	cross	cross	ADJ
cana-652	196	13	-	-	ADJ
cana-652	196	14	entropy	entropy	ADJ
cana-652	196	15	loss	loss	NOUN
cana-652	196	16	is	be	AUX
cana-652	196	17	used	use	VERB
cana-652	196	18	for	for	ADP
cana-652	196	19	training	training	NOUN
cana-652	196	20	.	.	PUNCT
cana-652	197	1	various	various	ADJ
cana-652	197	2	metrics	metric	NOUN
cana-652	197	3	,	,	PUNCT
cana-652	197	4	such	such	ADJ
cana-652	197	5	as	as	ADP
cana-652	197	6	categorical	categorical	ADJ
cana-652	197	7	accuracy	accuracy	NOUN
cana-652	197	8	and	and	CCONJ
cana-652	197	9	top	top	ADJ
cana-652	197	10	-	-	PUNCT
cana-652	197	11	k	k	NOUN
cana-652	197	12	accuracy	accuracy	NOUN
cana-652	197	13	,	,	PUNCT
cana-652	197	14	are	be	AUX
cana-652	197	15	monitored	monitor	VERB
cana-652	197	16	during	during	ADP
cana-652	197	17	training	training	NOUN
cana-652	197	18	.	.	PUNCT
cana-652	198	1	d.	d.	PROPN
cana-652	198	2	mathematical	mathematical	PROPN
cana-652	198	3	operation	operation	NOUN
cana-652	198	4	used	use	VERB
cana-652	198	5	:	:	PUNCT
cana-652	198	6	in	in	ADP
cana-652	198	7	the	the	DET
cana-652	198	8	course	course	NOUN
cana-652	198	9	of	of	ADP
cana-652	198	10	this	this	DET
cana-652	198	11	research	research	NOUN
cana-652	198	12	,	,	PUNCT
cana-652	198	13	several	several	ADJ
cana-652	198	14	fundamental	fundamental	ADJ
cana-652	198	15	mathematical	mathematical	ADJ
cana-652	198	16	operations	operation	NOUN
cana-652	198	17	were	be	AUX
cana-652	198	18	applied	apply	VERB
cana-652	198	19	to	to	PART
cana-652	198	20	facilitate	facilitate	VERB
cana-652	198	21	the	the	DET
cana-652	198	22	training	training	NOUN
cana-652	198	23	and	and	CCONJ
cana-652	198	24	optimization	optimization	NOUN
cana-652	198	25	of	of	ADP
cana-652	198	26	deep	deep	ADJ
cana-652	198	27	learning	learning	NOUN
cana-652	198	28	models	model	NOUN
cana-652	198	29	.	.	PUNCT
cana-652	199	1	these	these	DET
cana-652	199	2	operations	operation	NOUN
cana-652	199	3	are	be	AUX
cana-652	199	4	integral	integral	ADJ
cana-652	199	5	to	to	ADP
cana-652	199	6	convolutional	convolutional	ADJ
cana-652	199	7	neural	neural	ADJ
cana-652	199	8	networks	network	NOUN
cana-652	199	9	(	(	PUNCT
cana-652	199	10	cnns	cnns	PROPN
cana-652	199	11	)	)	PUNCT
cana-652	199	12	and	and	CCONJ
cana-652	199	13	are	be	AUX
cana-652	199	14	outlined	outline	VERB
cana-652	199	15	below	below	ADP
cana-652	199	16	:	:	PUNCT
cana-652	199	17	1	1	X
cana-652	199	18	.	.	X
cana-652	199	19	convolution	convolution	NOUN
cana-652	199	20	operation	operation	NOUN
cana-652	199	21	:	:	PUNCT
cana-652	199	22	the	the	DET
cana-652	199	23	convolution	convolution	NOUN
cana-652	199	24	operation	operation	NOUN
cana-652	199	25	,	,	PUNCT
cana-652	199	26	a	a	DET
cana-652	199	27	cornerstone	cornerstone	NOUN
cana-652	199	28	of	of	ADP
cana-652	199	29	cnns	cnn	NOUN
cana-652	199	30	,	,	PUNCT
cana-652	199	31	is	be	AUX
cana-652	199	32	at	at	ADP
cana-652	199	33	the	the	DET
cana-652	199	34	core	core	NOUN
cana-652	199	35	of	of	ADP
cana-652	199	36	feature	feature	NOUN
cana-652	199	37	extraction	extraction	NOUN
cana-652	199	38	from	from	ADP
cana-652	199	39	images	image	NOUN
cana-652	199	40	.	.	PUNCT
cana-652	200	1	it	it	PRON
cana-652	200	2	entails	entail	VERB
cana-652	200	3	computing	compute	VERB
cana-652	200	4	the	the	DET
cana-652	200	5	dot	dot	NOUN
cana-652	200	6	product	product	NOUN
cana-652	200	7	between	between	ADP
cana-652	200	8	weights	weight	NOUN
cana-652	200	9	(	(	PUNCT
cana-652	200	10	filters	filter	NOUN
cana-652	200	11	)	)	PUNCT
cana-652	200	12	and	and	CCONJ
cana-652	200	13	local	local	ADJ
cana-652	200	14	regions	region	NOUN
cana-652	200	15	of	of	ADP
cana-652	200	16	the	the	DET
cana-652	200	17	input	input	NOUN
cana-652	200	18	image	image	NOUN
cana-652	200	19	.	.	PUNCT
cana-652	201	1	the	the	DET
cana-652	201	2	output	output	NOUN
cana-652	201	3	of	of	ADP
cana-652	201	4	a	a	DET
cana-652	201	5	convolutional	convolutional	ADJ
cana-652	201	6	layer	layer	NOUN
cana-652	201	7	is	be	AUX
cana-652	201	8	expressed	express	VERB
cana-652	201	9	as	as	ADP
cana-652	201	10	:	:	PUNCT
cana-652	201	11	communications	communication	NOUN
cana-652	201	12	on	on	ADP
cana-652	201	13	applied	apply	VERB
cana-652	201	14	nonlinear	nonlinear	ADJ
cana-652	201	15	analysis	analysis	NOUN
cana-652	201	16	issn	issn	NOUN
cana-652	201	17	:	:	PUNCT
cana-652	201	18	1074	1074	NUM
cana-652	201	19	-	-	PUNCT
cana-652	201	20	133x	133x	NUM
cana-652	201	21	vol	vol	NOUN
cana-652	201	22	31	31	NUM
cana-652	201	23	no	no	NOUN
cana-652	201	24	.	.	PUNCT
cana-652	202	1	2s	2s	NUM
cana-652	202	2	(	(	PUNCT
cana-652	202	3	2024	2024	NUM
cana-652	202	4	)	)	PUNCT
cana-652	202	5	328	328	NUM
cana-652	202	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-652	202	7	(	(	PUNCT
cana-652	202	8	𝐼	𝐼	PROPN
cana-652	202	9	∗	∗	NOUN
cana-652	202	10	𝐾)(𝑥	𝐾)(𝑥	PROPN
cana-652	202	11	,	,	PUNCT
cana-652	202	12	𝑦	𝑦	NOUN
cana-652	202	13	)	)	PUNCT
cana-652	202	14	=	=	SYM
cana-652	202	15	∑	∑	PUNCT
cana-652	202	16	⬚	⬚	PROPN
cana-652	202	17	𝑁−1	𝑁−1	NOUN
cana-652	202	18	𝑖=0	𝑖=0	PUNCT
cana-652	202	19	∑	∑	PUNCT
cana-652	202	20	⬚	⬚	PROPN
cana-652	202	21	𝑁−1	𝑁−1	NOUN
cana-652	202	22	𝑗=0	𝑗=0	ADP
cana-652	203	1	𝐼(𝑥	𝐼(𝑥	PUNCT
cana-652	203	2	+	+	CCONJ
cana-652	203	3	𝑖	𝑖	SYM
cana-652	203	4	,	,	PUNCT
cana-652	203	5	𝑦	𝑦	NOUN
cana-652	203	6	+	+	X
cana-652	203	7	𝑗	𝑗	NOUN
cana-652	203	8	)	)	PUNCT
cana-652	203	9	⋅	⋅	PROPN
cana-652	203	10	𝑘(𝑖	𝑘(𝑖	PROPN
cana-652	203	11	,	,	PUNCT
cana-652	203	12	𝑗	𝑗	NOUN
cana-652	203	13	)	)	PUNCT
cana-652	203	14	(	(	PUNCT
cana-652	203	15	1	1	X
cana-652	203	16	)	)	PUNCT
cana-652	203	17	here	here	ADV
cana-652	203	18	,	,	PUNCT
cana-652	203	19	i	i	PRON
cana-652	203	20	represents	represent	VERB
cana-652	203	21	the	the	DET
cana-652	203	22	input	input	NOUN
cana-652	203	23	image	image	NOUN
cana-652	203	24	,	,	PUNCT
cana-652	203	25	k	k	PROPN
cana-652	203	26	denotes	denote	VERB
cana-652	203	27	the	the	DET
cana-652	203	28	filter	filter	NOUN
cana-652	203	29	,	,	PUNCT
cana-652	203	30	and	and	CCONJ
cana-652	203	31	(	(	PUNCT
cana-652	203	32	x	x	X
cana-652	203	33	,	,	PUNCT
cana-652	203	34	y	y	NOUN
cana-652	203	35	)	)	PUNCT
cana-652	203	36	signifies	signify	VERB
cana-652	203	37	the	the	DET
cana-652	203	38	spatial	spatial	ADJ
cana-652	203	39	location	location	NOUN
cana-652	203	40	.	.	PUNCT
cana-652	204	1	2	2	X
cana-652	204	2	.	.	X
cana-652	204	3	activation	activation	NOUN
cana-652	204	4	function	function	NOUN
cana-652	204	5	(	(	PUNCT
cana-652	204	6	relu	relu	NOUN
cana-652	204	7	):	):	PUNCT
cana-652	204	8	the	the	DET
cana-652	204	9	rectified	rectified	ADJ
cana-652	204	10	linear	linear	NOUN
cana-652	204	11	unit	unit	NOUN
cana-652	204	12	(	(	PUNCT
cana-652	204	13	relu	relu	NOUN
cana-652	204	14	)	)	PUNCT
cana-652	204	15	serves	serve	VERB
cana-652	204	16	as	as	ADP
cana-652	204	17	a	a	DET
cana-652	204	18	widely	widely	ADV
cana-652	204	19	adopted	adopt	VERB
cana-652	204	20	activation	activation	NOUN
cana-652	204	21	function	function	NOUN
cana-652	204	22	in	in	ADP
cana-652	204	23	cnns	cnns	PROPN
cana-652	204	24	.	.	PUNCT
cana-652	205	1	its	its	PRON
cana-652	205	2	primary	primary	ADJ
cana-652	205	3	role	role	NOUN
cana-652	205	4	is	be	AUX
cana-652	205	5	to	to	PART
cana-652	205	6	acquaint	acquaint	VERB
cana-652	205	7	nonlinearity	nonlinearity	NOUN
cana-652	205	8	into	into	ADP
cana-652	205	9	the	the	DET
cana-652	205	10	architecture	architecture	NOUN
cana-652	205	11	,	,	PUNCT
cana-652	205	12	enabling	enable	VERB
cana-652	205	13	it	it	PRON
cana-652	205	14	to	to	PART
cana-652	205	15	capture	capture	VERB
cana-652	205	16	complex	complex	ADJ
cana-652	205	17	patterns	pattern	NOUN
cana-652	205	18	.	.	PUNCT
cana-652	206	1	mathematically	mathematically	ADV
cana-652	206	2	,	,	PUNCT
cana-652	206	3	the	the	DET
cana-652	206	4	relu	relu	NOUN
cana-652	206	5	activation	activation	NOUN
cana-652	206	6	is	be	AUX
cana-652	206	7	defined	define	VERB
cana-652	206	8	as	as	ADP
cana-652	206	9	:	:	PUNCT
cana-652	206	10	𝑓(𝑥	𝑓(𝑥	NOUN
cana-652	206	11	)	)	PUNCT
cana-652	206	12	=	=	PUNCT
cana-652	206	13	𝑚𝑎𝑥(0	𝑚𝑎𝑥(0	NOUN
cana-652	206	14	,	,	PUNCT
cana-652	206	15	𝑥)(2	𝑥)(2	NOUN
cana-652	206	16	)	)	PUNCT
cana-652	207	1	3	3	NUM
cana-652	207	2	.	.	PUNCT
cana-652	207	3	max	max	PROPN
cana-652	207	4	-	-	PUNCT
cana-652	207	5	pooling	pool	VERB
cana-652	207	6	operation	operation	NOUN
cana-652	207	7	:	:	PUNCT
cana-652	207	8	the	the	DET
cana-652	207	9	mathematical	mathematical	ADJ
cana-652	207	10	equation	equation	NOUN
cana-652	207	11	for	for	ADP
cana-652	207	12	a	a	DET
cana-652	207	13	max	max	PROPN
cana-652	207	14	-	-	PUNCT
cana-652	207	15	pooling	pool	VERB
cana-652	207	16	operation	operation	NOUN
cana-652	207	17	on	on	ADP
cana-652	207	18	an	an	DET
cana-652	207	19	input	input	NOUN
cana-652	207	20	feature	feature	NOUN
cana-652	207	21	map	map	NOUN
cana-652	207	22	"	"	PUNCT
cana-652	207	23	𝑋	𝑋	PROPN
cana-652	207	24	"	"	PUNCT
cana-652	207	25	with	with	ADP
cana-652	207	26	dimensions	dimension	NOUN
cana-652	207	27	𝐻𝑥𝑊𝑥𝐶	𝐻𝑥𝑊𝑥𝐶	ADJ
cana-652	207	28	,	,	PUNCT
cana-652	207	29	using	use	VERB
cana-652	207	30	a	a	DET
cana-652	207	31	window	window	NOUN
cana-652	207	32	size	size	NOUN
cana-652	207	33	of	of	ADP
cana-652	207	34	"	"	PUNCT
cana-652	207	35	𝐾𝑥𝐾	𝐾𝑥𝐾	PROPN
cana-652	207	36	"	"	PUNCT
cana-652	207	37	and	and	CCONJ
cana-652	207	38	a	a	DET
cana-652	207	39	stride	stride	NOUN
cana-652	207	40	of	of	ADP
cana-652	207	41	"	"	PUNCT
cana-652	207	42	𝑆	𝑆	PROPN
cana-652	207	43	"	"	PUNCT
cana-652	207	44	to	to	PART
cana-652	207	45	produce	produce	VERB
cana-652	207	46	an	an	DET
cana-652	207	47	output	output	NOUN
cana-652	207	48	feature	feature	NOUN
cana-652	207	49	map	map	NOUN
cana-652	207	50	"	"	PUNCT
cana-652	207	51	𝑌	𝑌	PROPN
cana-652	207	52	"	"	PUNCT
cana-652	207	53	with	with	ADP
cana-652	207	54	dimensions	dimension	NOUN
cana-652	208	1	𝐻𝑜𝑢𝑡𝑥	𝐻𝑜𝑢𝑡𝑥	PROPN
cana-652	209	1	𝑊𝑜𝑢𝑡	𝑊𝑜𝑢𝑡	PROPN
cana-652	209	2	𝑥	𝑥	PROPN
cana-652	209	3	𝐶	𝐶	PROPN
cana-652	209	4	is	be	AUX
cana-652	209	5	:	:	PUNCT
cana-652	209	6	𝐻𝑜𝑢𝑡	𝐻𝑜𝑢𝑡	PROPN
cana-652	209	7	=	=	PUNCT
cana-652	209	8	𝑓𝑙𝑜𝑜𝑟((𝐻	𝑓𝑙𝑜𝑜𝑟((𝐻	PROPN
cana-652	209	9	−	−	PROPN
cana-652	209	10	𝐾)/𝑆	𝐾)/𝑆	NOUN
cana-652	209	11	)	)	PUNCT
cana-652	210	1	+	+	CCONJ
cana-652	210	2	1	1	NUM
cana-652	210	3	(	(	PUNCT
cana-652	210	4	3	3	NUM
cana-652	210	5	)	)	PUNCT
cana-652	210	6	𝑊𝑜𝑢𝑡	𝑊𝑜𝑢𝑡	PROPN
cana-652	210	7	=	=	PUNCT
cana-652	210	8	𝑓𝑙𝑜𝑜𝑟((𝑊	𝑓𝑙𝑜𝑜𝑟((𝑊	PROPN
cana-652	210	9	−	−	PROPN
cana-652	210	10	𝐾)/𝑆	𝐾)/𝑆	NOUN
cana-652	210	11	)	)	PUNCT
cana-652	210	12	+	+	CCONJ
cana-652	210	13	1	1	NUM
cana-652	210	14	(	(	PUNCT
cana-652	210	15	4	4	NUM
cana-652	210	16	)	)	PUNCT
cana-652	210	17	for	for	ADP
cana-652	210	18	each	each	DET
cana-652	210	19	element	element	NOUN
cana-652	210	20	𝑌[𝑖	𝑌[𝑖	PROPN
cana-652	210	21	,	,	PUNCT
cana-652	210	22	𝑗	𝑗	NOUN
cana-652	210	23	,	,	PUNCT
cana-652	210	24	𝑐	𝑐	X
cana-652	210	25	]	]	X
cana-652	210	26	in	in	ADP
cana-652	210	27	the	the	DET
cana-652	210	28	output	output	NOUN
cana-652	210	29	feature	feature	NOUN
cana-652	210	30	map	map	NOUN
cana-652	210	31	,	,	PUNCT
cana-652	210	32	you	you	PRON
cana-652	210	33	take	take	VERB
cana-652	210	34	the	the	DET
cana-652	210	35	maximum	maximum	ADJ
cana-652	210	36	value	value	NOUN
cana-652	210	37	from	from	ADP
cana-652	210	38	the	the	DET
cana-652	210	39	corresponding	corresponding	ADJ
cana-652	210	40	window	window	NOUN
cana-652	210	41	in	in	ADP
cana-652	210	42	the	the	DET
cana-652	210	43	input	input	NOUN
cana-652	210	44	feature	feature	NOUN
cana-652	210	45	map	map	NOUN
cana-652	210	46	𝑋	𝑋	NOUN
cana-652	210	47	:	:	PUNCT
cana-652	210	48	𝑌[𝑖	𝑌[𝑖	PROPN
cana-652	210	49	,	,	PUNCT
cana-652	210	50	𝑗	𝑗	INTJ
cana-652	210	51	,	,	PUNCT
cana-652	210	52	𝑐	𝑐	NOUN
cana-652	210	53	]	]	X
cana-652	210	54	=	=	SYM
cana-652	210	55	𝑚𝑎𝑥(𝑋𝑤𝑖𝑛𝑑𝑜𝑤[𝑖,𝑗,𝑐	𝑚𝑎𝑥(𝑋𝑤𝑖𝑛𝑑𝑜𝑤[𝑖,𝑗,𝑐	NOUN
cana-652	210	56	]	]	X
cana-652	210	57	)	)	PUNCT
cana-652	210	58	(	(	PUNCT
cana-652	210	59	5	5	X
cana-652	210	60	)	)	PUNCT
cana-652	210	61	here	here	ADV
cana-652	210	62	,	,	PUNCT
cana-652	210	63	"	"	PUNCT
cana-652	210	64	𝑋𝑤𝑖𝑛𝑑𝑜𝑤	𝑋𝑤𝑖𝑛𝑑𝑜𝑤	NOUN
cana-652	210	65	"	"	PUNCT
cana-652	210	66	is	be	AUX
cana-652	210	67	a	a	DET
cana-652	210	68	sub	sub	NOUN
cana-652	210	69	-	-	NOUN
cana-652	210	70	matrix	matrix	NOUN
cana-652	210	71	of	of	ADP
cana-652	210	72	"	"	PUNCT
cana-652	210	73	𝑋	𝑋	PROPN
cana-652	210	74	"	"	PUNCT
cana-652	210	75	determined	determine	VERB
cana-652	210	76	by	by	ADP
cana-652	210	77	the	the	DET
cana-652	210	78	window	window	NOUN
cana-652	210	79	parameters	parameter	NOUN
cana-652	210	80	.	.	PUNCT
cana-652	211	1	this	this	DET
cana-652	211	2	equation	equation	NOUN
cana-652	211	3	describes	describe	VERB
cana-652	211	4	how	how	SCONJ
cana-652	211	5	each	each	DET
cana-652	211	6	element	element	NOUN
cana-652	211	7	in	in	ADP
cana-652	211	8	the	the	DET
cana-652	211	9	output	output	NOUN
cana-652	211	10	feature	feature	NOUN
cana-652	211	11	map	map	NOUN
cana-652	211	12	"	"	PUNCT
cana-652	211	13	𝑌	𝑌	PROPN
cana-652	211	14	"	"	PUNCT
cana-652	211	15	is	be	AUX
cana-652	211	16	computed	compute	VERB
cana-652	211	17	by	by	ADP
cana-652	211	18	finding	find	VERB
cana-652	211	19	the	the	DET
cana-652	211	20	maximum	maximum	ADJ
cana-652	211	21	value	value	NOUN
cana-652	211	22	within	within	ADP
cana-652	211	23	the	the	DET
cana-652	211	24	specified	specified	ADJ
cana-652	211	25	window	window	NOUN
cana-652	211	26	in	in	ADP
cana-652	211	27	the	the	DET
cana-652	211	28	input	input	NOUN
cana-652	211	29	feature	feature	NOUN
cana-652	211	30	map	map	NOUN
cana-652	211	31	"	"	PUNCT
cana-652	211	32	𝑋	𝑋	PROPN
cana-652	211	33	"	"	PUNCT
cana-652	211	34	.	.	PUNCT
cana-652	212	1	4	4	X
cana-652	212	2	.	.	NOUN
cana-652	212	3	batch	batch	NOUN
cana-652	212	4	normalization	normalization	NOUN
cana-652	212	5	:	:	PUNCT
cana-652	212	6	batch	batch	NOUN
cana-652	212	7	normalization	normalization	NOUN
cana-652	212	8	plays	play	VERB
cana-652	212	9	a	a	DET
cana-652	212	10	pivotal	pivotal	ADJ
cana-652	212	11	role	role	NOUN
cana-652	212	12	in	in	ADP
cana-652	212	13	normalizing	normalize	VERB
cana-652	212	14	layer	layer	NOUN
cana-652	212	15	activations	activation	NOUN
cana-652	212	16	,	,	PUNCT
cana-652	212	17	fostering	foster	VERB
cana-652	212	18	training	training	NOUN
cana-652	212	19	stability	stability	NOUN
cana-652	212	20	and	and	CCONJ
cana-652	212	21	acceleration	acceleration	NOUN
cana-652	212	22	.	.	PUNCT
cana-652	213	1	the	the	DET
cana-652	213	2	batch	batch	NOUN
cana-652	213	3	normalization	normalization	NOUN
cana-652	213	4	formula	formula	NOUN
cana-652	213	5	for	for	ADP
cana-652	213	6	a	a	DET
cana-652	213	7	specific	specific	ADJ
cana-652	213	8	feature	feature	NOUN
cana-652	213	9	x	x	NOUN
cana-652	213	10	in	in	ADP
cana-652	213	11	a	a	DET
cana-652	213	12	mini	mini	NOUN
cana-652	213	13	-	-	NOUN
cana-652	213	14	batch	batch	NOUN
cana-652	213	15	is	be	AUX
cana-652	213	16	:	:	PUNCT
cana-652	213	17	𝐵𝑁(𝑥	𝐵𝑁(𝑥	X
cana-652	213	18	)	)	PUNCT
cana-652	214	1	=	=	SYM
cana-652	214	2	𝛾	𝛾	PROPN
cana-652	214	3	(	(	PUNCT
cana-652	214	4	𝑥−𝜇	𝑥−𝜇	X
cana-652	214	5	𝜎	𝜎	NOUN
cana-652	214	6	)	)	PUNCT
cana-652	215	1	+	+	PUNCT
cana-652	215	2	𝛽	𝛽	NOUN
cana-652	215	3	(	(	PUNCT
cana-652	215	4	6	6	NUM
cana-652	215	5	)	)	PUNCT
cana-652	215	6	here	here	ADV
cana-652	215	7	,	,	PUNCT
cana-652	215	8	μ	μ	PROPN
cana-652	215	9	represents	represent	VERB
cana-652	215	10	the	the	DET
cana-652	215	11	mean	mean	NOUN
cana-652	215	12	,	,	PUNCT
cana-652	215	13	σ	σ	PROPN
cana-652	215	14	stands	stand	VERB
cana-652	215	15	for	for	ADP
cana-652	215	16	the	the	DET
cana-652	215	17	standard	standard	ADJ
cana-652	215	18	deviation	deviation	NOUN
cana-652	215	19	,	,	PUNCT
cana-652	215	20	γ	γ	PROPN
cana-652	215	21	signifies	signify	VERB
cana-652	215	22	a	a	DET
cana-652	215	23	learned	learn	VERB
cana-652	215	24	scale	scale	NOUN
cana-652	215	25	parameter	parameter	NOUN
cana-652	215	26	,	,	PUNCT
cana-652	215	27	β	β	PROPN
cana-652	215	28	denotes	denote	VERB
cana-652	215	29	a	a	DET
cana-652	215	30	learned	learn	VERB
cana-652	215	31	shift	shift	NOUN
cana-652	215	32	parameter	parameter	NOUN
cana-652	215	33	.	.	PUNCT
cana-652	216	1	5	5	NUM
cana-652	216	2	.	.	X
cana-652	216	3	softmax	softmax	NOUN
cana-652	216	4	activation	activation	NOUN
cana-652	216	5	:	:	PUNCT
cana-652	216	6	in	in	ADP
cana-652	216	7	multi	multi	ADJ
cana-652	216	8	-	-	ADJ
cana-652	216	9	class	class	ADJ
cana-652	216	10	classification	classification	NOUN
cana-652	216	11	,	,	PUNCT
cana-652	216	12	the	the	DET
cana-652	216	13	softmax	softmax	NOUN
cana-652	216	14	activation	activation	NOUN
cana-652	216	15	function	function	NOUN
cana-652	216	16	is	be	AUX
cana-652	216	17	instrumental	instrumental	ADJ
cana-652	216	18	in	in	ADP
cana-652	216	19	converting	convert	VERB
cana-652	216	20	raw	raw	ADJ
cana-652	216	21	scores	score	NOUN
cana-652	216	22	(	(	PUNCT
cana-652	216	23	logits	logit	NOUN
cana-652	216	24	)	)	PUNCT
cana-652	216	25	into	into	ADP
cana-652	216	26	class	class	NOUN
cana-652	216	27	probabilities	probability	NOUN
cana-652	216	28	.	.	PUNCT
cana-652	217	1	the	the	DET
cana-652	217	2	softmax	softmax	NOUN
cana-652	217	3	function	function	NOUN
cana-652	217	4	for	for	ADP
cana-652	217	5	k	k	PROPN
cana-652	217	6	classes	class	NOUN
cana-652	217	7	is	be	AUX
cana-652	217	8	articulated	articulate	VERB
cana-652	217	9	as	as	ADP
cana-652	217	10	:	:	PUNCT
cana-652	217	11	𝑃(𝑦	𝑃(𝑦	X
cana-652	217	12	=	=	SYM
cana-652	217	13	𝑘/𝑧	𝑘/𝑧	PROPN
cana-652	217	14	)	)	PUNCT
cana-652	218	1	=	=	SYM
cana-652	218	2	𝑒𝑧𝑘	𝑒𝑧𝑘	NOUN
cana-652	218	3	∑	∑	PUNCT
cana-652	218	4	⬚	⬚	VERB
cana-652	218	5	𝑘	𝑘	DET
cana-652	218	6	𝑗=1	𝑗=1	NOUN
cana-652	218	7	𝑒𝑧𝑗	𝑒𝑧𝑗	X
cana-652	218	8	(	(	PUNCT
cana-652	218	9	𝑓𝑜𝑟	𝑓𝑜𝑟	ADV
cana-652	218	10	𝑗	𝑗	INTJ
cana-652	218	11	𝑖𝑛	𝑖𝑛	X
cana-652	218	12	𝑎𝑙𝑙	𝑎𝑙𝑙	PROPN
cana-652	218	13	𝑐𝑙𝑎𝑠𝑠𝑒𝑠	𝑐𝑙𝑎𝑠𝑠𝑒𝑠	PROPN
cana-652	218	14	)	)	PUNCT
cana-652	218	15	(	(	PUNCT
cana-652	218	16	7	7	X
cana-652	218	17	)	)	PUNCT
cana-652	218	18	where	where	SCONJ
cana-652	218	19	z_k	z_k	NUM
cana-652	218	20	signifies	signify	VERB
cana-652	218	21	the	the	DET
cana-652	218	22	logit	logit	NOUN
cana-652	218	23	for	for	ADP
cana-652	218	24	class	class	NOUN
cana-652	218	25	k.	k.	PROPN
cana-652	218	26	6	6	NUM
cana-652	218	27	.	.	PUNCT
cana-652	219	1	categorical	categorical	ADJ
cana-652	219	2	cross	cross	ADJ
cana-652	219	3	-	-	ADJ
cana-652	219	4	entropy	entropy	ADJ
cana-652	219	5	loss	loss	NOUN
cana-652	219	6	:	:	PUNCT
cana-652	219	7	categorical	categorical	ADJ
cana-652	219	8	cross	cross	NOUN
cana-652	219	9	-	-	ADJ
cana-652	219	10	entropy	entropy	NOUN
cana-652	219	11	serves	serve	VERB
cana-652	219	12	as	as	ADP
cana-652	219	13	a	a	DET
cana-652	219	14	prevailing	prevail	VERB
cana-652	219	15	loss	loss	NOUN
cana-652	219	16	function	function	NOUN
cana-652	219	17	for	for	ADP
cana-652	219	18	multi	multi	ADJ
cana-652	219	19	-	-	ADJ
cana-652	219	20	class	class	ADJ
cana-652	219	21	classification	classification	NOUN
cana-652	219	22	tasks	task	NOUN
cana-652	219	23	.	.	PUNCT
cana-652	220	1	it	it	PRON
cana-652	220	2	quantifies	quantify	VERB
cana-652	220	3	the	the	DET
cana-652	220	4	dissimilarity	dissimilarity	NOUN
cana-652	220	5	between	between	ADP
cana-652	220	6	predicted	predict	VERB
cana-652	220	7	probabilities	probability	NOUN
cana-652	220	8	and	and	CCONJ
cana-652	220	9	actual	actual	ADJ
cana-652	220	10	one	one	NUM
cana-652	220	11	-	-	PUNCT
cana-652	220	12	hot	hot	ADJ
cana-652	220	13	encoded	encode	VERB
cana-652	220	14	class	class	NOUN
cana-652	220	15	labels	label	NOUN
cana-652	220	16	.	.	PUNCT
cana-652	221	1	the	the	DET
cana-652	221	2	categorical	categorical	ADJ
cana-652	221	3	cross	cross	ADJ
cana-652	221	4	-	-	ADJ
cana-652	221	5	entropy	entropy	ADJ
cana-652	221	6	loss	loss	NOUN
cana-652	221	7	for	for	ADP
cana-652	221	8	a	a	DET
cana-652	221	9	single	single	ADJ
cana-652	221	10	example	example	NOUN
cana-652	221	11	is	be	AUX
cana-652	221	12	calculated	calculate	VERB
cana-652	221	13	as	as	ADP
cana-652	221	14	:	:	PUNCT
cana-652	221	15	𝐿(𝑦	𝐿(𝑦	NUM
cana-652	221	16	,	,	PUNCT
cana-652	221	17	𝑝	𝑝	NOUN
cana-652	221	18	)	)	PUNCT
cana-652	221	19	=	=	SYM
cana-652	222	1	−	−	PROPN
cana-652	222	2	∑	∑	PUNCT
cana-652	222	3	⬚	⬚	VERB
cana-652	222	4	𝑘	𝑘	DET
cana-652	222	5	𝑖=1	𝑖=1	PROPN
cana-652	222	6	𝑦𝑖	𝑦𝑖	PROPN
cana-652	222	7	⬚	⬚	PROPN
cana-652	222	8	𝑙𝑜𝑔(𝑝𝑖	𝑙𝑜𝑔(𝑝𝑖	PROPN
cana-652	222	9	)	)	PUNCT
cana-652	222	10	(	(	PUNCT
cana-652	222	11	8)	8)	NUM
cana-652	222	12	communications	communication	NOUN
cana-652	222	13	on	on	ADP
cana-652	222	14	applied	apply	VERB
cana-652	222	15	nonlinear	nonlinear	ADJ
cana-652	222	16	analysis	analysis	NOUN
cana-652	222	17	issn	issn	NOUN
cana-652	222	18	:	:	PUNCT
cana-652	222	19	1074	1074	NUM
cana-652	222	20	-	-	PUNCT
cana-652	222	21	133x	133x	NUM
cana-652	222	22	vol	vol	NOUN
cana-652	222	23	31	31	NUM
cana-652	222	24	no	no	NOUN
cana-652	222	25	.	.	PUNCT
cana-652	223	1	2s	2s	NUM
cana-652	223	2	(	(	PUNCT
cana-652	223	3	2024	2024	NUM
cana-652	223	4	)	)	PUNCT
cana-652	223	5	329	329	NUM
cana-652	223	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-652	223	7	here	here	ADV
cana-652	223	8	,	,	PUNCT
cana-652	223	9	y	y	PROPN
cana-652	223	10	represents	represent	VERB
cana-652	223	11	the	the	DET
cana-652	223	12	one	one	NUM
cana-652	223	13	-	-	PUNCT
cana-652	223	14	hot	hot	ADJ
cana-652	223	15	encoded	encode	VERB
cana-652	223	16	true	true	ADJ
cana-652	223	17	label	label	NOUN
cana-652	223	18	,	,	PUNCT
cana-652	223	19	while	while	SCONJ
cana-652	223	20	p	p	NOUN
cana-652	223	21	signifies	signify	VERB
cana-652	223	22	the	the	DET
cana-652	223	23	predicted	predict	VERB
cana-652	223	24	probability	probability	NOUN
cana-652	223	25	distribution	distribution	NOUN
cana-652	223	26	.	.	PUNCT
cana-652	224	1	7	7	X
cana-652	224	2	.	.	X
cana-652	224	3	learning	learn	VERB
cana-652	224	4	rate	rate	NOUN
cana-652	224	5	(	(	PUNCT
cana-652	224	6	optimizer	optimizer	NOUN
cana-652	224	7	):	):	PUNCT
cana-652	224	8	the	the	DET
cana-652	224	9	learning	learning	NOUN
cana-652	224	10	rate	rate	NOUN
cana-652	224	11	(	(	PUNCT
cana-652	224	12	α	α	NOUN
cana-652	224	13	)	)	PUNCT
cana-652	224	14	emerges	emerge	VERB
cana-652	224	15	as	as	ADP
cana-652	224	16	a	a	DET
cana-652	224	17	pivotal	pivotal	ADJ
cana-652	224	18	hyperparameter	hyperparameter	NOUN
cana-652	224	19	governing	govern	VERB
cana-652	224	20	the	the	DET
cana-652	224	21	step	step	NOUN
cana-652	224	22	size	size	NOUN
cana-652	224	23	during	during	ADP
cana-652	224	24	optimization	optimization	NOUN
cana-652	224	25	,	,	PUNCT
cana-652	224	26	such	such	ADJ
cana-652	224	27	as	as	ADP
cana-652	224	28	gradient	gradient	ADJ
cana-652	224	29	descent	descent	NOUN
cana-652	224	30	.	.	PUNCT
cana-652	225	1	its	its	PRON
cana-652	225	2	selection	selection	NOUN
cana-652	225	3	often	often	ADV
cana-652	225	4	entails	entail	VERB
cana-652	225	5	rigorous	rigorous	ADJ
cana-652	225	6	hyperparameter	hyperparameter	NOUN
cana-652	225	7	tuning	tune	VERB
cana-652	225	8	to	to	PART
cana-652	225	9	achieve	achieve	VERB
cana-652	225	10	optimal	optimal	ADJ
cana-652	225	11	model	model	NOUN
cana-652	225	12	convergence	convergence	NOUN
cana-652	225	13	.	.	PUNCT
cana-652	226	1	6	6	NUM
cana-652	226	2	.	.	NUM
cana-652	226	3	proposed	propose	VERB
cana-652	226	4	system	system	NOUN
cana-652	226	5	fig.2	fig.2	PROPN
cana-652	226	6	:	:	PUNCT
cana-652	226	7	proposed	propose	VERB
cana-652	226	8	methodology	methodology	NOUN
cana-652	226	9	several	several	ADJ
cana-652	226	10	researchers	researcher	NOUN
cana-652	226	11	have	have	AUX
cana-652	226	12	previously	previously	ADV
cana-652	226	13	attempted	attempt	VERB
cana-652	226	14	to	to	PART
cana-652	226	15	utilize	utilize	VERB
cana-652	226	16	various	various	ADJ
cana-652	226	17	machine	machine	NOUN
cana-652	226	18	learning	learn	VERB
cana-652	226	19	techniques	technique	NOUN
cana-652	226	20	for	for	ADP
cana-652	226	21	detecting	detect	VERB
cana-652	226	22	skin	skin	NOUN
cana-652	226	23	cancer	cancer	NOUN
cana-652	226	24	on	on	ADP
cana-652	226	25	the	the	DET
cana-652	226	26	ham10000	ham10000	PROPN
cana-652	226	27	dataset	dataset	PROPN
cana-652	226	28	.	.	PUNCT
cana-652	227	1	however	however	ADV
cana-652	227	2	,	,	PUNCT
cana-652	227	3	their	their	PRON
cana-652	227	4	results	result	NOUN
cana-652	227	5	have	have	AUX
cana-652	227	6	fallen	fall	VERB
cana-652	227	7	short	short	ADJ
cana-652	227	8	when	when	SCONJ
cana-652	227	9	compared	compare	VERB
cana-652	227	10	to	to	ADP
cana-652	227	11	the	the	DET
cana-652	227	12	achievements	achievement	NOUN
cana-652	227	13	of	of	ADP
cana-652	227	14	our	our	PRON
cana-652	227	15	study	study	NOUN
cana-652	227	16	.	.	PUNCT
cana-652	228	1	in	in	ADP
cana-652	228	2	our	our	PRON
cana-652	228	3	methodology	methodology	NOUN
cana-652	228	4	,	,	PUNCT
cana-652	228	5	we	we	PRON
cana-652	228	6	have	have	AUX
cana-652	228	7	structured	structure	VERB
cana-652	228	8	the	the	DET
cana-652	228	9	process	process	NOUN
cana-652	228	10	into	into	ADP
cana-652	228	11	four	four	NUM
cana-652	228	12	distinct	distinct	ADJ
cana-652	228	13	phases	phase	NOUN
cana-652	228	14	,	,	PUNCT
cana-652	228	15	as	as	SCONJ
cana-652	228	16	illustrated	illustrate	VERB
cana-652	228	17	in	in	ADP
cana-652	228	18	fig.2	fig.2	PROPN
cana-652	228	19	,	,	PUNCT
cana-652	228	20	where	where	SCONJ
cana-652	228	21	we	we	PRON
cana-652	228	22	implemented	implement	VERB
cana-652	228	23	customized	customize	VERB
cana-652	228	24	cnn	cnn	PROPN
cana-652	228	25	and	and	CCONJ
cana-652	228	26	mobilenet	mobilenet	NOUN
cana-652	228	27	models	model	NOUN
cana-652	228	28	.	.	PUNCT
cana-652	229	1	the	the	DET
cana-652	229	2	primary	primary	ADJ
cana-652	229	3	focus	focus	NOUN
cana-652	229	4	of	of	ADP
cana-652	229	5	our	our	PRON
cana-652	229	6	approach	approach	NOUN
cana-652	229	7	is	be	AUX
cana-652	229	8	to	to	PART
cana-652	229	9	enhance	enhance	VERB
cana-652	229	10	accuracy	accuracy	NOUN
cana-652	229	11	and	and	CCONJ
cana-652	229	12	other	other	ADJ
cana-652	229	13	performance	performance	NOUN
cana-652	229	14	parameters	parameter	NOUN
cana-652	229	15	significantly	significantly	ADV
cana-652	229	16	,	,	PUNCT
cana-652	229	17	outperforming	outperform	VERB
cana-652	229	18	previous	previous	ADJ
cana-652	229	19	studies	study	NOUN
cana-652	229	20	.	.	PUNCT
cana-652	230	1	for	for	ADP
cana-652	230	2	our	our	PRON
cana-652	230	3	customized	customize	VERB
cana-652	230	4	mobilenet	mobilenet	NOUN
cana-652	230	5	model	model	NOUN
cana-652	230	6	,	,	PUNCT
cana-652	230	7	we	we	PRON
cana-652	230	8	adopted	adopt	VERB
cana-652	230	9	a	a	DET
cana-652	230	10	specific	specific	ADJ
cana-652	230	11	strategy	strategy	NOUN
cana-652	230	12	.	.	PUNCT
cana-652	231	1	we	we	PRON
cana-652	231	2	excluded	exclude	VERB
cana-652	231	3	the	the	DET
cana-652	231	4	last	last	ADJ
cana-652	231	5	five	five	NUM
cana-652	231	6	layers	layer	NOUN
cana-652	231	7	of	of	ADP
cana-652	231	8	the	the	DET
cana-652	231	9	original	original	ADJ
cana-652	231	10	mobilenet	mobilenet	NOUN
cana-652	231	11	architecture	architecture	NOUN
cana-652	231	12	,	,	PUNCT
cana-652	231	13	retaining	retain	VERB
cana-652	231	14	layers	layer	NOUN
cana-652	231	15	up	up	ADP
cana-652	231	16	to	to	ADP
cana-652	231	17	'	'	PUNCT
cana-652	231	18	global_average_pooling2d_1	global_average_pooling2d_1	NOUN
cana-652	231	19	'	'	PUNCT
cana-652	231	20	to	to	PART
cana-652	231	21	capture	capture	VERB
cana-652	231	22	essential	essential	ADJ
cana-652	231	23	features	feature	NOUN
cana-652	231	24	.	.	PUNCT
cana-652	232	1	also	also	ADV
cana-652	232	2	we	we	PRON
cana-652	232	3	introduced	introduce	VERB
cana-652	232	4	a	a	DET
cana-652	232	5	'	'	PUNCT
cana-652	232	6	flatten	flatten	ADJ
cana-652	232	7	'	'	PUNCT
cana-652	232	8	layer	layer	NOUN
cana-652	232	9	to	to	PART
cana-652	232	10	convert	convert	VERB
cana-652	232	11	the	the	DET
cana-652	232	12	model	model	NOUN
cana-652	232	13	's	's	PART
cana-652	232	14	output	output	NOUN
cana-652	232	15	into	into	ADP
cana-652	232	16	a	a	DET
cana-652	232	17	one	one	NUM
cana-652	232	18	-	-	PUNCT
cana-652	232	19	dimensional	dimensional	ADJ
cana-652	232	20	tensor	tensor	NOUN
cana-652	232	21	.	.	PUNCT
cana-652	233	1	following	follow	VERB
cana-652	233	2	this	this	PRON
cana-652	233	3	,	,	PUNCT
cana-652	233	4	we	we	PRON
cana-652	233	5	added	add	VERB
cana-652	233	6	a	a	DET
cana-652	233	7	dense	dense	ADJ
cana-652	233	8	layer	layer	NOUN
cana-652	233	9	with	with	ADP
cana-652	233	10	1024	1024	NUM
cana-652	233	11	units	unit	NOUN
cana-652	233	12	,	,	PUNCT
cana-652	233	13	applying	apply	VERB
cana-652	233	14	relu	relu	NOUN
cana-652	233	15	activation	activation	NOUN
cana-652	233	16	to	to	PART
cana-652	233	17	enhance	enhance	VERB
cana-652	233	18	feature	feature	NOUN
cana-652	233	19	extraction	extraction	NOUN
cana-652	233	20	.	.	PUNCT
cana-652	234	1	to	to	PART
cana-652	234	2	mitigate	mitigate	VERB
cana-652	234	3	overfitting	overfitte	VERB
cana-652	234	4	concerns	concern	NOUN
cana-652	234	5	,	,	PUNCT
cana-652	234	6	a	a	DET
cana-652	234	7	dropout	dropout	NOUN
cana-652	234	8	layer	layer	NOUN
cana-652	234	9	with	with	ADP
cana-652	234	10	a	a	DET
cana-652	234	11	rate	rate	NOUN
cana-652	234	12	of	of	ADP
cana-652	234	13	0.25	0.25	NUM
cana-652	234	14	was	be	AUX
cana-652	234	15	incorporated	incorporate	VERB
cana-652	234	16	.	.	PUNCT
cana-652	235	1	the	the	DET
cana-652	235	2	final	final	ADJ
cana-652	235	3	dense	dense	ADJ
cana-652	235	4	layer	layer	NOUN
cana-652	235	5	consisted	consist	VERB
cana-652	235	6	of	of	ADP
cana-652	235	7	seven	seven	NUM
cana-652	235	8	units	unit	NOUN
cana-652	235	9	,	,	PUNCT
cana-652	235	10	each	each	PRON
cana-652	235	11	corresponding	correspond	VERB
cana-652	235	12	to	to	ADP
cana-652	235	13	one	one	NUM
cana-652	235	14	of	of	ADP
cana-652	235	15	the	the	DET
cana-652	235	16	skin	skin	NOUN
cana-652	235	17	cancer	cancer	NOUN
cana-652	235	18	classes	class	NOUN
cana-652	235	19	,	,	PUNCT
cana-652	235	20	and	and	CCONJ
cana-652	235	21	was	be	AUX
cana-652	235	22	equipped	equip	VERB
cana-652	235	23	with	with	ADP
cana-652	235	24	a	a	DET
cana-652	235	25	'	'	PUNCT
cana-652	235	26	softmax	softmax	NOUN
cana-652	235	27	'	'	PUNCT
cana-652	235	28	activation	activation	NOUN
cana-652	235	29	function	function	NOUN
cana-652	235	30	.	.	PUNCT
cana-652	236	1	the	the	DET
cana-652	236	2	success	success	NOUN
cana-652	236	3	of	of	ADP
cana-652	236	4	this	this	DET
cana-652	236	5	approach	approach	NOUN
cana-652	236	6	is	be	AUX
cana-652	236	7	demonstrated	demonstrate	VERB
cana-652	236	8	through	through	ADP
cana-652	236	9	a	a	DET
cana-652	236	10	comprehensive	comprehensive	ADJ
cana-652	236	11	performance	performance	NOUN
cana-652	236	12	evaluation	evaluation	NOUN
cana-652	236	13	,	,	PUNCT
cana-652	236	14	where	where	SCONJ
cana-652	236	15	we	we	PRON
cana-652	236	16	compared	compare	VERB
cana-652	236	17	the	the	DET
cana-652	236	18	communications	communication	NOUN
cana-652	236	19	on	on	ADP
cana-652	236	20	applied	apply	VERB
cana-652	236	21	nonlinear	nonlinear	ADJ
cana-652	236	22	analysis	analysis	NOUN
cana-652	236	23	issn	issn	NOUN
cana-652	236	24	:	:	PUNCT
cana-652	236	25	1074	1074	NUM
cana-652	236	26	-	-	PUNCT
cana-652	236	27	133x	133x	NUM
cana-652	236	28	vol	vol	NOUN
cana-652	236	29	31	31	NUM
cana-652	236	30	no	no	NOUN
cana-652	236	31	.	.	PUNCT
cana-652	237	1	2s	2s	NUM
cana-652	237	2	(	(	PUNCT
cana-652	237	3	2024	2024	NUM
cana-652	237	4	)	)	PUNCT
cana-652	237	5	330	330	NUM
cana-652	237	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-652	237	7	outcomes	outcome	NOUN
cana-652	237	8	with	with	ADP
cana-652	237	9	existing	exist	VERB
cana-652	237	10	methods	method	NOUN
cana-652	237	11	.	.	PUNCT
cana-652	238	1	the	the	DET
cana-652	238	2	detailed	detailed	ADJ
cana-652	238	3	results	result	NOUN
cana-652	238	4	and	and	CCONJ
cana-652	238	5	findings	finding	NOUN
cana-652	238	6	are	be	AUX
cana-652	238	7	extensively	extensively	ADV
cana-652	238	8	covered	cover	VERB
cana-652	238	9	in	in	ADP
cana-652	238	10	our	our	PRON
cana-652	238	11	research	research	NOUN
cana-652	238	12	paper	paper	NOUN
cana-652	238	13	.	.	PUNCT
cana-652	239	1	the	the	DET
cana-652	239	2	detailed	detailed	ADJ
cana-652	239	3	methodology	methodology	NOUN
cana-652	239	4	is	be	AUX
cana-652	239	5	as	as	SCONJ
cana-652	239	6	follows	follow	VERB
cana-652	239	7	:	:	PUNCT
cana-652	239	8	1	1	X
cana-652	239	9	.	.	PUNCT
cana-652	239	10	data	datum	NOUN
cana-652	239	11	preprocessing	preprocessing	NOUN
cana-652	239	12	and	and	CCONJ
cana-652	239	13	splitting	splitting	NOUN
cana-652	239	14	:	:	PUNCT
cana-652	239	15	(	(	PUNCT
cana-652	239	16	i	i	NOUN
cana-652	239	17	)	)	PUNCT
cana-652	239	18	dataset	dataset	NOUN
cana-652	239	19	loading	loading	NOUN
cana-652	239	20	:	:	PUNCT
cana-652	239	21	the	the	DET
cana-652	239	22	initial	initial	ADJ
cana-652	239	23	step	step	NOUN
cana-652	239	24	was	be	AUX
cana-652	239	25	to	to	PART
cana-652	239	26	load	load	VERB
cana-652	239	27	the	the	DET
cana-652	239	28	dataset	dataset	NOUN
cana-652	239	29	,	,	PUNCT
cana-652	239	30	which	which	PRON
cana-652	239	31	accommodates	accommodate	VERB
cana-652	239	32	images	image	NOUN
cana-652	239	33	of	of	ADP
cana-652	239	34	various	various	ADJ
cana-652	239	35	skin	skin	NOUN
cana-652	239	36	lesions	lesion	NOUN
cana-652	239	37	associated	associate	VERB
cana-652	239	38	with	with	ADP
cana-652	239	39	various	various	ADJ
cana-652	239	40	types	type	NOUN
cana-652	239	41	of	of	ADP
cana-652	239	42	skin	skin	NOUN
cana-652	239	43	cancer	cancer	NOUN
cana-652	239	44	.	.	PUNCT
cana-652	240	1	(	(	PUNCT
cana-652	240	2	ii	ii	NOUN
cana-652	240	3	)	)	PUNCT
cana-652	240	4	addressing	address	VERB
cana-652	240	5	class	class	NOUN
cana-652	240	6	imbalance	imbalance	NOUN
cana-652	240	7	:	:	PUNCT
cana-652	240	8	then	then	ADV
cana-652	240	9	the	the	DET
cana-652	240	10	issue	issue	NOUN
cana-652	240	11	of	of	ADP
cana-652	240	12	class	class	NOUN
cana-652	240	13	imbalance	imbalance	NOUN
cana-652	240	14	was	be	AUX
cana-652	240	15	resolved	resolve	VERB
cana-652	240	16	by	by	ADP
cana-652	240	17	oversampling	oversample	VERB
cana-652	240	18	the	the	DET
cana-652	240	19	minority	minority	NOUN
cana-652	240	20	classes	class	NOUN
cana-652	240	21	using	use	VERB
cana-652	240	22	the	the	DET
cana-652	240	23	randomoversampler	randomoversampler	NOUN
cana-652	240	24	from	from	ADP
cana-652	240	25	the	the	DET
cana-652	240	26	imbalanced	imbalance	VERB
cana-652	240	27	-	-	PUNCT
cana-652	240	28	learn	learn	NOUN
cana-652	240	29	library	library	NOUN
cana-652	240	30	.	.	PUNCT
cana-652	241	1	this	this	PRON
cana-652	241	2	helped	help	VERB
cana-652	241	3	ensure	ensure	VERB
cana-652	241	4	that	that	SCONJ
cana-652	241	5	each	each	DET
cana-652	241	6	class	class	NOUN
cana-652	241	7	had	have	VERB
cana-652	241	8	a	a	DET
cana-652	241	9	balanced	balanced	ADJ
cana-652	241	10	representation	representation	NOUN
cana-652	241	11	in	in	ADP
cana-652	241	12	the	the	DET
cana-652	241	13	training	training	NOUN
cana-652	241	14	data	datum	NOUN
cana-652	241	15	.	.	PUNCT
cana-652	242	1	(	(	PUNCT
cana-652	242	2	iii)data	iii)data	ADJ
cana-652	242	3	augmentation	augmentation	NOUN
cana-652	242	4	:	:	PUNCT
cana-652	242	5	to	to	PART
cana-652	242	6	expand	expand	VERB
cana-652	242	7	the	the	DET
cana-652	242	8	diversity	diversity	NOUN
cana-652	242	9	of	of	ADP
cana-652	242	10	the	the	DET
cana-652	242	11	training	training	NOUN
cana-652	242	12	data	datum	NOUN
cana-652	242	13	and	and	CCONJ
cana-652	242	14	improve	improve	VERB
cana-652	242	15	model	model	NOUN
cana-652	242	16	generalization	generalization	NOUN
cana-652	242	17	,	,	PUNCT
cana-652	242	18	we	we	PRON
cana-652	242	19	applied	apply	VERB
cana-652	242	20	data	datum	NOUN
cana-652	242	21	augmentation	augmentation	NOUN
cana-652	242	22	techniques	technique	NOUN
cana-652	242	23	to	to	ADP
cana-652	242	24	the	the	DET
cana-652	242	25	training	training	NOUN
cana-652	242	26	images	image	NOUN
cana-652	242	27	.	.	PUNCT
cana-652	243	1	this	this	PRON
cana-652	243	2	included	include	VERB
cana-652	243	3	random	random	ADJ
cana-652	243	4	rotations	rotation	NOUN
cana-652	243	5	,	,	PUNCT
cana-652	243	6	zooming	zooming	NOUN
cana-652	243	7	,	,	PUNCT
cana-652	243	8	shifting	shift	VERB
cana-652	243	9	,	,	PUNCT
cana-652	243	10	and	and	CCONJ
cana-652	243	11	flipping	flipping	NOUN
cana-652	243	12	of	of	ADP
cana-652	243	13	images	image	NOUN
cana-652	243	14	.	.	PUNCT
cana-652	244	1	we	we	PRON
cana-652	244	2	also	also	ADV
cana-652	244	3	augmented	augment	VERB
cana-652	244	4	the	the	DET
cana-652	244	5	dataset	dataset	NOUN
cana-652	244	6	to	to	PART
cana-652	244	7	achieve	achieve	VERB
cana-652	244	8	a	a	DET
cana-652	244	9	total	total	NOUN
cana-652	244	10	of	of	ADP
cana-652	244	11	6,000	6,000	NUM
cana-652	244	12	images	image	NOUN
cana-652	244	13	for	for	ADP
cana-652	244	14	each	each	DET
cana-652	244	15	class	class	NOUN
cana-652	244	16	.	.	PUNCT
cana-652	245	1	(	(	PUNCT
cana-652	245	2	iv	iv	X
cana-652	245	3	)	)	PUNCT
cana-652	245	4	dataset	dataset	NOUN
cana-652	245	5	splitting	splitting	NOUN
cana-652	245	6	:	:	PUNCT
cana-652	245	7	the	the	DET
cana-652	245	8	dataset	dataset	NOUN
cana-652	245	9	was	be	AUX
cana-652	245	10	splitted	splitte	VERB
cana-652	245	11	into	into	ADP
cana-652	245	12	training	training	NOUN
cana-652	245	13	and	and	CCONJ
cana-652	245	14	validation	validation	NOUN
cana-652	245	15	sets	set	NOUN
cana-652	245	16	,	,	PUNCT
cana-652	245	17	with	with	ADP
cana-652	245	18	75	75	NUM
cana-652	245	19	%	%	NOUN
cana-652	245	20	of	of	ADP
cana-652	245	21	the	the	DET
cana-652	245	22	data	datum	NOUN
cana-652	245	23	used	use	VERB
cana-652	245	24	for	for	ADP
cana-652	245	25	training	training	NOUN
cana-652	245	26	and	and	CCONJ
cana-652	245	27	25	25	NUM
cana-652	245	28	%	%	NOUN
cana-652	245	29	for	for	ADP
cana-652	245	30	validation	validation	NOUN
cana-652	245	31	.	.	PUNCT
cana-652	246	1	before	before	ADP
cana-652	246	2	splitting	splitting	NOUN
cana-652	246	3	,	,	PUNCT
cana-652	246	4	we	we	PRON
cana-652	246	5	converted	convert	VERB
cana-652	246	6	the	the	DET
cana-652	246	7	labels	label	NOUN
cana-652	246	8	from	from	ADP
cana-652	246	9	numerical	numerical	ADJ
cana-652	246	10	abbreviations	abbreviation	NOUN
cana-652	246	11	to	to	ADP
cana-652	246	12	their	their	PRON
cana-652	246	13	corresponding	corresponding	ADJ
cana-652	246	14	class	class	NOUN
cana-652	246	15	names	name	NOUN
cana-652	246	16	for	for	ADP
cana-652	246	17	better	well	ADJ
cana-652	246	18	interpretability	interpretability	NOUN
cana-652	246	19	.	.	PUNCT
cana-652	247	1	2	2	X
cana-652	247	2	.	.	X
cana-652	247	3	convolutional	convolutional	ADJ
cana-652	247	4	neural	neural	ADJ
cana-652	247	5	network	network	NOUN
cana-652	247	6	(	(	PUNCT
cana-652	247	7	cnn	cnn	PROPN
cana-652	247	8	)	)	PUNCT
cana-652	247	9	(	(	PUNCT
cana-652	247	10	i	i	NOUN
cana-652	247	11	)	)	PUNCT
cana-652	247	12	model	model	NOUN
cana-652	247	13	architecture	architecture	NOUN
cana-652	247	14	:	:	PUNCT
cana-652	247	15	for	for	ADP
cana-652	247	16	the	the	DET
cana-652	247	17	cnn	cnn	PROPN
cana-652	247	18	model	model	NOUN
cana-652	247	19	,	,	PUNCT
cana-652	247	20	multiple	multiple	ADJ
cana-652	247	21	convolutional	convolutional	ADJ
cana-652	247	22	layers	layer	NOUN
cana-652	247	23	were	be	AUX
cana-652	247	24	included	include	VERB
cana-652	247	25	in	in	ADP
cana-652	247	26	the	the	DET
cana-652	247	27	deep	deep	ADJ
cana-652	247	28	convolutional	convolutional	ADJ
cana-652	247	29	neural	neural	ADJ
cana-652	247	30	network	network	NOUN
cana-652	247	31	developed	develop	VERB
cana-652	247	32	by	by	ADP
cana-652	247	33	us	we	PRON
cana-652	247	34	,	,	PUNCT
cana-652	247	35	which	which	PRON
cana-652	247	36	was	be	AUX
cana-652	247	37	then	then	ADV
cana-652	247	38	subjected	subject	VERB
cana-652	247	39	to	to	PART
cana-652	247	40	batch	batch	VERB
cana-652	247	41	normalization	normalization	NOUN
cana-652	247	42	and	and	CCONJ
cana-652	247	43	max	max	PROPN
cana-652	247	44	-	-	PUNCT
cana-652	247	45	pooling	pooling	NOUN
cana-652	247	46	..	..	PUNCT
cana-652	248	1	the	the	DET
cana-652	248	2	architecture	architecture	NOUN
cana-652	248	3	included	include	VERB
cana-652	248	4	3	3	NUM
cana-652	248	5	convolutional	convolutional	ADJ
cana-652	248	6	blocks	block	NOUN
cana-652	248	7	,	,	PUNCT
cana-652	248	8	each	each	PRON
cana-652	248	9	consisting	consist	VERB
cana-652	248	10	of	of	ADP
cana-652	248	11	2	2	NUM
cana-652	248	12	convolutional	convolutional	ADJ
cana-652	248	13	layers	layer	NOUN
cana-652	248	14	with	with	ADP
cana-652	248	15	batch	batch	NOUN
cana-652	248	16	normalization	normalization	NOUN
cana-652	248	17	and	and	CCONJ
cana-652	248	18	relu	relu	NOUN
cana-652	248	19	activation	activation	NOUN
cana-652	248	20	,	,	PUNCT
cana-652	248	21	followed	follow	VERB
cana-652	248	22	by	by	ADP
cana-652	248	23	max	max	PROPN
cana-652	248	24	-	-	PUNCT
cana-652	248	25	pooling	pooling	NOUN
cana-652	248	26	.	.	PUNCT
cana-652	249	1	after	after	ADP
cana-652	249	2	the	the	DET
cana-652	249	3	convolutional	convolutional	ADJ
cana-652	249	4	blocks	block	NOUN
cana-652	249	5	,	,	PUNCT
cana-652	249	6	we	we	PRON
cana-652	249	7	added	add	VERB
cana-652	249	8	fully	fully	ADV
cana-652	249	9	connected	connected	ADJ
cana-652	249	10	layers	layer	NOUN
cana-652	249	11	with	with	ADP
cana-652	249	12	dropout	dropout	NOUN
cana-652	249	13	and	and	CCONJ
cana-652	249	14	batch	batch	VERB
cana-652	249	15	normalization	normalization	NOUN
cana-652	249	16	for	for	ADP
cana-652	249	17	regularization	regularization	NOUN
cana-652	249	18	.	.	PUNCT
cana-652	250	1	the	the	DET
cana-652	250	2	output	output	NOUN
cana-652	250	3	layer	layer	NOUN
cana-652	250	4	had	have	VERB
cana-652	250	5	seven	seven	NUM
cana-652	250	6	units	unit	NOUN
cana-652	250	7	,	,	PUNCT
cana-652	250	8	one	one	NUM
cana-652	250	9	for	for	ADP
cana-652	250	10	each	each	DET
cana-652	250	11	skin	skin	NOUN
cana-652	250	12	cancer	cancer	NOUN
cana-652	250	13	class	class	NOUN
cana-652	250	14	,	,	PUNCT
cana-652	250	15	with	with	ADP
cana-652	250	16	a	a	DET
cana-652	250	17	softmax	softmax	NOUN
cana-652	250	18	activation	activation	NOUN
cana-652	250	19	function	function	NOUN
cana-652	250	20	.	.	PUNCT
cana-652	251	1	fig	fig	NOUN
cana-652	251	2	.	.	PUNCT
cana-652	252	1	3	3	NUM
cana-652	252	2	:	:	PUNCT
cana-652	252	3	model	model	PROPN
cana-652	252	4	summary	summary	PROPN
cana-652	252	5	cnn	cnn	PROPN
cana-652	252	6	communications	communication	NOUN
cana-652	252	7	on	on	ADP
cana-652	252	8	applied	apply	VERB
cana-652	252	9	nonlinear	nonlinear	ADJ
cana-652	252	10	analysis	analysis	NOUN
cana-652	252	11	issn	issn	NOUN
cana-652	252	12	:	:	PUNCT
cana-652	252	13	1074	1074	NUM
cana-652	252	14	-	-	PUNCT
cana-652	252	15	133x	133x	NUM
cana-652	252	16	vol	vol	NOUN
cana-652	252	17	31	31	NUM
cana-652	252	18	no	no	NOUN
cana-652	252	19	.	.	PUNCT
cana-652	253	1	2s	2s	NUM
cana-652	253	2	(	(	PUNCT
cana-652	253	3	2024	2024	NUM
cana-652	253	4	)	)	PUNCT
cana-652	253	5	331	331	NUM
cana-652	253	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-652	253	7	fig	fig	NOUN
cana-652	253	8	.	.	PUNCT
cana-652	254	1	3	3	NUM
cana-652	255	1	provides	provide	VERB
cana-652	255	2	a	a	DET
cana-652	255	3	short	short	ADJ
cana-652	255	4	overview	overview	NOUN
cana-652	255	5	of	of	ADP
cana-652	255	6	the	the	DET
cana-652	255	7	cnn	cnn	PROPN
cana-652	255	8	-	-	PUNCT
cana-652	255	9	based	base	VERB
cana-652	255	10	model	model	NOUN
cana-652	255	11	,	,	PUNCT
cana-652	255	12	generated	generate	VERB
cana-652	255	13	using	use	VERB
cana-652	255	14	the	the	DET
cana-652	255	15	model.summary	model.summary	PROPN
cana-652	255	16	(	(	PUNCT
cana-652	255	17	)	)	PUNCT
cana-652	255	18	function	function	NOUN
cana-652	255	19	.	.	PUNCT
cana-652	256	1	in	in	ADP
cana-652	256	2	our	our	PRON
cana-652	256	3	approach	approach	NOUN
cana-652	256	4	,	,	PUNCT
cana-652	256	5	we	we	PRON
cana-652	256	6	employed	employ	VERB
cana-652	256	7	a	a	DET
cana-652	256	8	sequential	sequential	ADJ
cana-652	256	9	keras	keras	PROPN
cana-652	256	10	model	model	NOUN
cana-652	256	11	,	,	PUNCT
cana-652	256	12	meticulously	meticulously	ADV
cana-652	256	13	incorporating	incorporate	VERB
cana-652	256	14	all	all	DET
cana-652	256	15	the	the	DET
cana-652	256	16	above	above	ADJ
cana-652	256	17	specified	specified	ADJ
cana-652	256	18	layers	layer	NOUN
cana-652	256	19	.	.	PUNCT
cana-652	257	1	this	this	DET
cana-652	257	2	model	model	NOUN
cana-652	257	3	architecture	architecture	NOUN
cana-652	257	4	comprises	comprise	VERB
cana-652	257	5	1	1	NUM
cana-652	257	6	input	input	NOUN
cana-652	257	7	layer	layer	NOUN
cana-652	257	8	,	,	PUNCT
cana-652	257	9	7	7	NUM
cana-652	257	10	convolutional	convolutional	ADJ
cana-652	257	11	layers	layer	NOUN
cana-652	257	12	,	,	PUNCT
cana-652	257	13	4	4	NUM
cana-652	257	14	max	max	NOUN
cana-652	257	15	-	-	PUNCT
cana-652	257	16	pooling	pool	VERB
cana-652	257	17	layers	layer	NOUN
cana-652	257	18	,	,	PUNCT
cana-652	257	19	8	8	NUM
cana-652	257	20	batch	batch	NOUN
cana-652	257	21	normalization	normalization	NOUN
cana-652	257	22	layers	layer	NOUN
cana-652	257	23	,	,	PUNCT
cana-652	257	24	1	1	NUM
cana-652	257	25	flattened	flatten	VERB
cana-652	257	26	layer	layer	NOUN
cana-652	257	27	and	and	CCONJ
cana-652	257	28	dropout	dropout	NOUN
cana-652	257	29	layer	layer	NOUN
cana-652	257	30	,	,	PUNCT
cana-652	257	31	5	5	NUM
cana-652	257	32	dense	dense	ADJ
cana-652	257	33	layers	layer	NOUN
cana-652	257	34	.	.	PUNCT
cana-652	258	1	we	we	PRON
cana-652	258	2	consistently	consistently	ADV
cana-652	258	3	applied	apply	VERB
cana-652	258	4	the	the	DET
cana-652	258	5	‘	'	PUNCT
cana-652	258	6	relu	relu	NOUN
cana-652	258	7	’	'	PUNCT
cana-652	258	8	activation	activation	NOUN
cana-652	258	9	function	function	NOUN
cana-652	258	10	across	across	ADP
cana-652	258	11	all	all	DET
cana-652	258	12	layers	layer	NOUN
cana-652	258	13	,	,	PUNCT
cana-652	258	14	except	except	SCONJ
cana-652	258	15	for	for	ADP
cana-652	258	16	the	the	DET
cana-652	258	17	final	final	ADJ
cana-652	258	18	layer	layer	NOUN
cana-652	258	19	,	,	PUNCT
cana-652	258	20	where	where	SCONJ
cana-652	258	21	we	we	PRON
cana-652	258	22	utilized	utilize	VERB
cana-652	258	23	the	the	DET
cana-652	258	24	‘	'	PUNCT
cana-652	258	25	softmax	softmax	NOUN
cana-652	258	26	’	'	PUNCT
cana-652	258	27	activation	activation	NOUN
cana-652	258	28	function	function	NOUN
cana-652	258	29	.	.	PUNCT
cana-652	259	1	for	for	ADP
cana-652	259	2	initializing	initialize	VERB
cana-652	259	3	the	the	DET
cana-652	259	4	model	model	NOUN
cana-652	259	5	's	's	PART
cana-652	259	6	kernel	kernel	PROPN
cana-652	259	7	weights	weights	PROPN
cana-652	259	8	,	,	PUNCT
cana-652	259	9	we	we	PRON
cana-652	259	10	opted	opt	VERB
cana-652	259	11	for	for	ADP
cana-652	259	12	the	the	DET
cana-652	259	13	'	'	PUNCT
cana-652	259	14	he_normal	he_normal	NOUN
cana-652	259	15	'	'	PUNCT
cana-652	259	16	initializer	initializer	NOUN
cana-652	259	17	,	,	PUNCT
cana-652	259	18	and	and	CCONJ
cana-652	259	19	we	we	PRON
cana-652	259	20	maintained	maintain	VERB
cana-652	259	21	'	'	PUNCT
cana-652	259	22	same	same	ADJ
cana-652	259	23	'	'	PUNCT
cana-652	259	24	padding	padding	NOUN
cana-652	259	25	throughout	throughout	ADP
cana-652	259	26	the	the	DET
cana-652	259	27	model	model	NOUN
cana-652	259	28	.	.	PUNCT
cana-652	260	1	in	in	ADP
cana-652	260	2	terms	term	NOUN
cana-652	260	3	of	of	ADP
cana-652	260	4	optimization	optimization	NOUN
cana-652	260	5	,	,	PUNCT
cana-652	260	6	we	we	PRON
cana-652	260	7	selected	select	VERB
cana-652	260	8	the	the	DET
cana-652	260	9	‘	'	PUNCT
cana-652	260	10	adamax	adamax	ADJ
cana-652	260	11	’	'	PUNCT
cana-652	260	12	optimizer	optimizer	NOUN
cana-652	260	13	,	,	PUNCT
cana-652	260	14	with	with	ADP
cana-652	260	15	a	a	DET
cana-652	260	16	learning	learn	VERB
cana-652	260	17	rate	rate	NOUN
cana-652	260	18	capped	cap	VERB
cana-652	260	19	at	at	ADP
cana-652	260	20	0.001	0.001	NUM
cana-652	260	21	,	,	PUNCT
cana-652	260	22	to	to	PART
cana-652	260	23	enhance	enhance	VERB
cana-652	260	24	learning	learn	VERB
cana-652	260	25	efficiency	efficiency	NOUN
cana-652	260	26	,	,	PUNCT
cana-652	260	27	the	the	DET
cana-652	260	28	loss	loss	NOUN
cana-652	260	29	function	function	NOUN
cana-652	260	30	was	be	AUX
cana-652	260	31	set	set	VERB
cana-652	260	32	to	to	ADP
cana-652	260	33	‘	'	PUNCT
cana-652	260	34	crossentropy	crossentropy	NOUN
cana-652	260	35	’	'	PUNCT
cana-652	260	36	and	and	CCONJ
cana-652	260	37	metrics	metric	NOUN
cana-652	260	38	used	use	VERB
cana-652	260	39	was	be	AUX
cana-652	260	40	‘	'	PUNCT
cana-652	260	41	accuracy	accuracy	NOUN
cana-652	260	42	’	'	PUNCT
cana-652	260	43	.	.	PUNCT
cana-652	261	1	the	the	DET
cana-652	261	2	fig	fig	NOUN
cana-652	261	3	3	3	NUM
cana-652	261	4	provides	provide	VERB
cana-652	261	5	insights	insight	NOUN
cana-652	261	6	into	into	ADP
cana-652	261	7	the	the	DET
cana-652	261	8	trainable	trainable	ADJ
cana-652	261	9	parameters	parameter	NOUN
cana-652	261	10	,	,	PUNCT
cana-652	261	11	total	total	ADJ
cana-652	261	12	parameters	parameter	NOUN
cana-652	261	13	and	and	CCONJ
cana-652	261	14	non	non	ADJ
cana-652	261	15	-	-	ADJ
cana-652	261	16	trainable	trainable	ADJ
cana-652	261	17	parameters	parameter	NOUN
cana-652	261	18	employed	employ	VERB
cana-652	261	19	in	in	ADP
cana-652	261	20	our	our	PRON
cana-652	261	21	model	model	NOUN
cana-652	261	22	.	.	PUNCT
cana-652	262	1	this	this	DET
cana-652	262	2	detailed	detailed	ADJ
cana-652	262	3	description	description	NOUN
cana-652	262	4	of	of	ADP
cana-652	262	5	the	the	DET
cana-652	262	6	model	model	NOUN
cana-652	262	7	architecture	architecture	NOUN
cana-652	262	8	is	be	AUX
cana-652	262	9	crucial	crucial	ADJ
cana-652	262	10	for	for	ADP
cana-652	262	11	comprehending	comprehend	VERB
cana-652	262	12	the	the	DET
cana-652	262	13	methodology	methodology	NOUN
cana-652	262	14	employed	employ	VERB
cana-652	262	15	in	in	ADP
cana-652	262	16	our	our	PRON
cana-652	262	17	research	research	NOUN
cana-652	262	18	work	work	NOUN
cana-652	262	19	.	.	PUNCT
cana-652	263	1	(	(	PUNCT
cana-652	263	2	ii	ii	NOUN
cana-652	263	3	)	)	PUNCT
cana-652	263	4	training	training	NOUN
cana-652	263	5	:	:	PUNCT
cana-652	263	6	with	with	ADP
cana-652	263	7	a	a	DET
cana-652	263	8	learning	learning	NOUN
cana-652	263	9	rate	rate	NOUN
cana-652	263	10	of	of	ADP
cana-652	263	11	0.001	0.001	NUM
cana-652	263	12	,	,	PUNCT
cana-652	263	13	we	we	PRON
cana-652	263	14	used	use	VERB
cana-652	263	15	the	the	DET
cana-652	263	16	adamax	adamax	NOUN
cana-652	263	17	optimizer	optimizer	NOUN
cana-652	263	18	to	to	PART
cana-652	263	19	train	train	VERB
cana-652	263	20	the	the	DET
cana-652	263	21	cnn	cnn	PROPN
cana-652	263	22	model	model	NOUN
cana-652	263	23	across	across	ADP
cana-652	263	24	25	25	NUM
cana-652	263	25	epochs	epoch	NOUN
cana-652	263	26	.	.	PUNCT
cana-652	264	1	learning	learn	VERB
cana-652	264	2	rate	rate	NOUN
cana-652	264	3	reduction	reduction	NOUN
cana-652	264	4	is	be	AUX
cana-652	264	5	employed	employ	VERB
cana-652	264	6	on	on	ADP
cana-652	264	7	a	a	DET
cana-652	264	8	plateau	plateau	NOUN
cana-652	264	9	,	,	PUNCT
cana-652	264	10	monitoring	monitor	VERB
cana-652	264	11	validation	validation	NOUN
cana-652	264	12	accuracy	accuracy	NOUN
cana-652	264	13	,	,	PUNCT
cana-652	264	14	to	to	ADP
cana-652	264	15	fine	fine	ADJ
cana-652	264	16	-	-	PUNCT
cana-652	264	17	tune	tune	NOUN
cana-652	264	18	the	the	DET
cana-652	264	19	learning	learning	NOUN
cana-652	264	20	rate	rate	NOUN
cana-652	264	21	during	during	ADP
cana-652	264	22	training	training	NOUN
cana-652	264	23	.	.	PUNCT
cana-652	265	1	the	the	DET
cana-652	265	2	training	training	NOUN
cana-652	265	3	history	history	NOUN
cana-652	265	4	was	be	AUX
cana-652	265	5	visualized	visualize	VERB
cana-652	265	6	with	with	ADP
cana-652	265	7	plots	plot	NOUN
cana-652	265	8	showing	show	VERB
cana-652	265	9	training	training	NOUN
cana-652	265	10	vs	vs	ADP
cana-652	265	11	validation	validation	NOUN
cana-652	265	12	loss	loss	NOUN
cana-652	265	13	,	,	PUNCT
cana-652	265	14	accuracy	accuracy	NOUN
cana-652	265	15	,	,	PUNCT
cana-652	265	16	top2	top2	NOUN
cana-652	265	17	and	and	CCONJ
cana-652	265	18	top3	top3	NOUN
cana-652	265	19	accuracy	accuracy	NOUN
cana-652	265	20	.	.	PUNCT
cana-652	266	1	(	(	PUNCT
cana-652	266	2	iii)model	iii)model	NOUN
cana-652	266	3	evaluation	evaluation	NOUN
cana-652	266	4	:	:	PUNCT
cana-652	266	5	we	we	PRON
cana-652	266	6	evaluated	evaluate	VERB
cana-652	266	7	the	the	DET
cana-652	266	8	cnn	cnn	PROPN
cana-652	266	9	model	model	NOUN
cana-652	266	10	on	on	ADP
cana-652	266	11	the	the	DET
cana-652	266	12	validation	validation	NOUN
cana-652	266	13	set	set	VERB
cana-652	266	14	and	and	CCONJ
cana-652	266	15	calculated	calculate	VERB
cana-652	266	16	metrics	metric	NOUN
cana-652	266	17	such	such	ADJ
cana-652	266	18	as	as	ADP
cana-652	266	19	categorical	categorical	ADJ
cana-652	266	20	,	,	PUNCT
cana-652	266	21	top2	top2	NOUN
cana-652	266	22	and	and	CCONJ
cana-652	266	23	top3	top3	NOUN
cana-652	266	24	accuracy	accuracy	NOUN
cana-652	266	25	.	.	PUNCT
cana-652	267	1	the	the	DET
cana-652	267	2	best	well	ADV
cana-652	267	3	-	-	PUNCT
cana-652	267	4	performing	perform	VERB
cana-652	267	5	model	model	NOUN
cana-652	267	6	was	be	AUX
cana-652	267	7	selected	select	VERB
cana-652	267	8	based	base	VERB
cana-652	267	9	on	on	ADP
cana-652	267	10	the	the	DET
cana-652	267	11	validation	validation	NOUN
cana-652	267	12	top-3	top-3	NUM
cana-652	267	13	accuracy	accuracy	NOUN
cana-652	267	14	.	.	PUNCT
cana-652	268	1	additionally	additionally	ADV
cana-652	268	2	,	,	PUNCT
cana-652	268	3	we	we	PRON
cana-652	268	4	created	create	VERB
cana-652	268	5	a	a	DET
cana-652	268	6	confusion	confusion	NOUN
cana-652	268	7	matrix	matrix	NOUN
cana-652	268	8	to	to	PART
cana-652	268	9	visualize	visualize	VERB
cana-652	268	10	the	the	DET
cana-652	268	11	model	model	NOUN
cana-652	268	12	's	's	PART
cana-652	268	13	performance	performance	NOUN
cana-652	268	14	in	in	ADP
cana-652	268	15	classifying	classify	VERB
cana-652	268	16	different	different	ADJ
cana-652	268	17	types	type	NOUN
cana-652	268	18	of	of	ADP
cana-652	268	19	skin	skin	NOUN
cana-652	268	20	lesions	lesion	NOUN
cana-652	268	21	.	.	PUNCT
cana-652	269	1	3	3	X
cana-652	269	2	.	.	X
cana-652	269	3	mobilenet	mobilenet	NOUN
cana-652	269	4	(	(	PUNCT
cana-652	269	5	i	i	NOUN
cana-652	269	6	)	)	PUNCT
cana-652	269	7	model	model	NOUN
cana-652	269	8	architecture	architecture	NOUN
cana-652	269	9	:	:	PUNCT
cana-652	269	10	for	for	ADP
cana-652	269	11	the	the	DET
cana-652	269	12	mobilenet	mobilenet	NOUN
cana-652	269	13	model	model	NOUN
cana-652	269	14	,	,	PUNCT
cana-652	269	15	we	we	PRON
cana-652	269	16	utilized	utilize	VERB
cana-652	269	17	a	a	DET
cana-652	269	18	pre	pre	ADJ
cana-652	269	19	-	-	ADJ
cana-652	269	20	trained	trained	ADJ
cana-652	269	21	mobilenet	mobilenet	NOUN
cana-652	269	22	architecture	architecture	NOUN
cana-652	269	23	,	,	PUNCT
cana-652	269	24	excluding	exclude	VERB
cana-652	269	25	the	the	DET
cana-652	269	26	last	last	ADJ
cana-652	269	27	five	five	NUM
cana-652	269	28	layers	layer	NOUN
cana-652	269	29	.	.	PUNCT
cana-652	270	1	we	we	PRON
cana-652	270	2	added	add	VERB
cana-652	270	3	our	our	PRON
cana-652	270	4	custom	custom	NOUN
cana-652	270	5	layers	layer	NOUN
cana-652	270	6	,	,	PUNCT
cana-652	270	7	including	include	VERB
cana-652	270	8	a	a	DET
cana-652	270	9	dense	dense	ADJ
cana-652	270	10	layer	layer	NOUN
cana-652	270	11	with	with	ADP
cana-652	270	12	relu	relu	NOUN
cana-652	270	13	activation	activation	NOUN
cana-652	270	14	and	and	CCONJ
cana-652	270	15	dropout	dropout	NOUN
cana-652	270	16	,	,	PUNCT
cana-652	270	17	followed	follow	VERB
cana-652	270	18	by	by	ADP
cana-652	270	19	the	the	DET
cana-652	270	20	output	output	NOUN
cana-652	270	21	layer	layer	NOUN
cana-652	270	22	with	with	ADP
cana-652	270	23	seven	seven	NUM
cana-652	270	24	units	unit	NOUN
cana-652	270	25	for	for	ADP
cana-652	270	26	the	the	DET
cana-652	270	27	skin	skin	NOUN
cana-652	270	28	lesion	lesion	NOUN
cana-652	270	29	classes	class	NOUN
cana-652	270	30	.	.	PUNCT
cana-652	271	1	fig	fig	NOUN
cana-652	271	2	.	.	PUNCT
cana-652	272	1	4	4	NUM
cana-652	272	2	:	:	PUNCT
cana-652	272	3	model	model	NOUN
cana-652	272	4	summary	summary	NOUN
cana-652	272	5	mobilenet	mobilenet	NOUN
cana-652	272	6	communications	communication	NOUN
cana-652	272	7	on	on	ADP
cana-652	272	8	applied	apply	VERB
cana-652	272	9	nonlinear	nonlinear	ADJ
cana-652	272	10	analysis	analysis	NOUN
cana-652	272	11	issn	issn	NOUN
cana-652	272	12	:	:	PUNCT
cana-652	272	13	1074	1074	NUM
cana-652	272	14	-	-	PUNCT
cana-652	272	15	133x	133x	NUM
cana-652	272	16	vol	vol	NOUN
cana-652	272	17	31	31	NUM
cana-652	272	18	no	no	NOUN
cana-652	272	19	.	.	PUNCT
cana-652	273	1	2s	2s	NUM
cana-652	273	2	(	(	PUNCT
cana-652	273	3	2024	2024	NUM
cana-652	273	4	)	)	PUNCT
cana-652	273	5	332	332	NUM
cana-652	273	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-652	273	7	fig.4	fig.4	PROPN
cana-652	273	8	shows	show	VERB
cana-652	273	9	some	some	DET
cana-652	273	10	glimpse	glimpse	NOUN
cana-652	273	11	of	of	ADP
cana-652	273	12	the	the	DET
cana-652	273	13	customized	customize	VERB
cana-652	273	14	mobilenet	mobilenet	NOUN
cana-652	273	15	model	model	NOUN
cana-652	273	16	summary	summary	NOUN
cana-652	273	17	and	and	CCONJ
cana-652	273	18	architecture	architecture	NOUN
cana-652	273	19	used	use	VERB
cana-652	273	20	in	in	ADP
cana-652	273	21	this	this	DET
cana-652	273	22	research	research	NOUN
cana-652	273	23	work	work	NOUN
cana-652	273	24	.	.	PUNCT
cana-652	274	1	the	the	DET
cana-652	274	2	model	model	NOUN
cana-652	274	3	consists	consist	VERB
cana-652	274	4	of	of	ADP
cana-652	274	5	a	a	DET
cana-652	274	6	series	series	NOUN
cana-652	274	7	of	of	ADP
cana-652	274	8	convolutional	convolutional	ADJ
cana-652	274	9	layers	layer	NOUN
cana-652	274	10	,	,	PUNCT
cana-652	274	11	depthwise	depthwise	NOUN
cana-652	274	12	separable	separable	ADJ
cana-652	274	13	convolutions	convolution	NOUN
cana-652	274	14	,	,	PUNCT
cana-652	274	15	and	and	CCONJ
cana-652	274	16	pointwise	pointwise	PROPN
cana-652	274	17	convolutions	convolution	NOUN
cana-652	274	18	,	,	PUNCT
cana-652	274	19	resulting	result	VERB
cana-652	274	20	in	in	ADP
cana-652	274	21	a	a	DET
cana-652	274	22	lightweight	lightweight	ADJ
cana-652	274	23	yet	yet	CCONJ
cana-652	274	24	powerful	powerful	ADJ
cana-652	274	25	architecture	architecture	NOUN
cana-652	274	26	.	.	PUNCT
cana-652	275	1	the	the	DET
cana-652	275	2	model	model	NOUN
cana-652	275	3	is	be	AUX
cana-652	275	4	designed	design	VERB
cana-652	275	5	for	for	ADP
cana-652	275	6	image	image	NOUN
cana-652	275	7	classification	classification	NOUN
cana-652	275	8	tasks	task	NOUN
cana-652	275	9	and	and	CCONJ
cana-652	275	10	has	have	VERB
cana-652	275	11	a	a	DET
cana-652	275	12	total	total	NOUN
cana-652	275	13	of	of	ADP
cana-652	275	14	28,923,079	28,923,079	NUM
cana-652	275	15	parameters	parameter	NOUN
cana-652	275	16	.	.	PUNCT
cana-652	276	1	it	it	PRON
cana-652	276	2	includes	include	VERB
cana-652	276	3	7	7	NUM
cana-652	276	4	class	class	NOUN
cana-652	276	5	output	output	NOUN
cana-652	276	6	neurons	neuron	NOUN
cana-652	276	7	and	and	CCONJ
cana-652	276	8	is	be	AUX
cana-652	276	9	trained	train	VERB
cana-652	276	10	to	to	PART
cana-652	276	11	achieve	achieve	VERB
cana-652	276	12	high	high	ADJ
cana-652	276	13	accuracy	accuracy	NOUN
cana-652	276	14	on	on	ADP
cana-652	276	15	a	a	DET
cana-652	276	16	specific	specific	ADJ
cana-652	276	17	classification	classification	NOUN
cana-652	276	18	task	task	NOUN
cana-652	276	19	.	.	PUNCT
cana-652	277	1	this	this	DET
cana-652	277	2	compact	compact	ADJ
cana-652	277	3	model	model	NOUN
cana-652	277	4	is	be	AUX
cana-652	277	5	suitable	suitable	ADJ
cana-652	277	6	for	for	ADP
cana-652	277	7	resource	resource	NOUN
cana-652	277	8	-	-	PUNCT
cana-652	277	9	constrained	constrain	VERB
cana-652	277	10	environments	environment	NOUN
cana-652	277	11	and	and	CCONJ
cana-652	277	12	offers	offer	VERB
cana-652	277	13	competitive	competitive	ADJ
cana-652	277	14	performance	performance	NOUN
cana-652	277	15	for	for	ADP
cana-652	277	16	the	the	DET
cana-652	277	17	given	give	VERB
cana-652	277	18	task	task	NOUN
cana-652	277	19	.	.	PUNCT
cana-652	278	1	(	(	PUNCT
cana-652	278	2	ii	ii	NOUN
cana-652	278	3	)	)	PUNCT
cana-652	278	4	training	training	NOUN
cana-652	278	5	:	:	PUNCT
cana-652	278	6	we	we	PRON
cana-652	278	7	fine	fine	ADV
cana-652	278	8	-	-	PUNCT
cana-652	278	9	tuned	tune	VERB
cana-652	278	10	the	the	DET
cana-652	278	11	mobilenet	mobilenet	NOUN
cana-652	278	12	model	model	NOUN
cana-652	278	13	by	by	ADP
cana-652	278	14	freezing	freeze	VERB
cana-652	278	15	all	all	DET
cana-652	278	16	layers	layer	NOUN
cana-652	278	17	except	except	SCONJ
cana-652	278	18	the	the	DET
cana-652	278	19	last	last	ADJ
cana-652	278	20	23	23	NUM
cana-652	278	21	layers	layer	NOUN
cana-652	278	22	.	.	PUNCT
cana-652	279	1	with	with	ADP
cana-652	279	2	a	a	DET
cana-652	279	3	0.01	0.01	NUM
cana-652	279	4	learning	learning	NOUN
cana-652	279	5	rate	rate	NOUN
cana-652	279	6	,	,	PUNCT
cana-652	279	7	we	we	PRON
cana-652	279	8	used	use	VERB
cana-652	279	9	the	the	DET
cana-652	279	10	adam	adam	PROPN
cana-652	279	11	optimizer	optimizer	NOUN
cana-652	279	12	to	to	PART
cana-652	279	13	construct	construct	VERB
cana-652	279	14	the	the	DET
cana-652	279	15	suggested	suggest	VERB
cana-652	279	16	architecture	architecture	NOUN
cana-652	279	17	and	and	CCONJ
cana-652	279	18	applied	apply	VERB
cana-652	279	19	class	class	NOUN
cana-652	279	20	weights	weight	NOUN
cana-652	279	21	which	which	PRON
cana-652	279	22	made	make	VERB
cana-652	279	23	the	the	DET
cana-652	279	24	architecture	architecture	NOUN
cana-652	279	25	more	more	ADV
cana-652	279	26	sensitive	sensitive	ADJ
cana-652	279	27	to	to	ADP
cana-652	279	28	melanoma	melanoma	NOUN
cana-652	279	29	,	,	PUNCT
cana-652	279	30	which	which	PRON
cana-652	279	31	is	be	AUX
cana-652	279	32	an	an	DET
cana-652	279	33	important	important	ADJ
cana-652	279	34	skin	skin	NOUN
cana-652	279	35	cancer	cancer	NOUN
cana-652	279	36	type	type	NOUN
cana-652	279	37	.	.	PUNCT
cana-652	280	1	we	we	PRON
cana-652	280	2	trained	train	VERB
cana-652	280	3	the	the	DET
cana-652	280	4	model	model	NOUN
cana-652	280	5	for	for	ADP
cana-652	280	6	10	10	NUM
cana-652	280	7	epochs	epoch	NOUN
cana-652	280	8	and	and	CCONJ
cana-652	280	9	used	use	VERB
cana-652	280	10	callbacks	callback	NOUN
cana-652	280	11	to	to	PART
cana-652	280	12	save	save	VERB
cana-652	280	13	the	the	DET
cana-652	280	14	best	good	ADJ
cana-652	280	15	model	model	NOUN
cana-652	280	16	based	base	VERB
cana-652	280	17	on	on	ADP
cana-652	280	18	validation	validation	NOUN
cana-652	280	19	top-3	top-3	NUM
cana-652	280	20	accuracy	accuracy	NOUN
cana-652	280	21	.	.	PUNCT
cana-652	281	1	(	(	PUNCT
cana-652	281	2	iii)model	iii)model	NOUN
cana-652	281	3	evaluation	evaluation	NOUN
cana-652	281	4	:	:	PUNCT
cana-652	281	5	mobilenet	mobilenet	NOUN
cana-652	281	6	evaluation	evaluation	NOUN
cana-652	281	7	was	be	AUX
cana-652	281	8	done	do	VERB
cana-652	281	9	on	on	ADP
cana-652	281	10	the	the	DET
cana-652	281	11	basis	basis	NOUN
cana-652	281	12	of	of	ADP
cana-652	281	13	the	the	DET
cana-652	281	14	validation	validation	NOUN
cana-652	281	15	set	set	NOUN
cana-652	281	16	and	and	CCONJ
cana-652	281	17	reported	report	VERB
cana-652	281	18	metrics	metric	NOUN
cana-652	281	19	including	include	VERB
cana-652	281	20	categorical	categorical	ADJ
cana-652	281	21	,	,	PUNCT
cana-652	281	22	top2	top2	NOUN
cana-652	281	23	and	and	CCONJ
cana-652	281	24	top3	top3	NOUN
cana-652	281	25	accuracy	accuracy	NOUN
cana-652	281	26	.	.	PUNCT
cana-652	282	1	4	4	X
cana-652	282	2	.	.	X
cana-652	282	3	model	model	NOUN
cana-652	282	4	comparison	comparison	NOUN
cana-652	282	5	and	and	CCONJ
cana-652	282	6	visualization	visualization	NOUN
cana-652	282	7	:	:	PUNCT
cana-652	282	8	we	we	PRON
cana-652	282	9	performed	perform	VERB
cana-652	282	10	a	a	DET
cana-652	282	11	comparison	comparison	NOUN
cana-652	282	12	of	of	ADP
cana-652	282	13	the	the	DET
cana-652	282	14	performance	performance	NOUN
cana-652	282	15	of	of	ADP
cana-652	282	16	both	both	CCONJ
cana-652	282	17	the	the	DET
cana-652	282	18	cnn	cnn	PROPN
cana-652	282	19	and	and	CCONJ
cana-652	282	20	mobilenet	mobilenet	NOUN
cana-652	282	21	models	model	NOUN
cana-652	282	22	using	use	VERB
cana-652	282	23	metrics	metric	NOUN
cana-652	282	24	such	such	ADJ
cana-652	282	25	as	as	ADP
cana-652	282	26	validation	validation	NOUN
cana-652	282	27	loss	loss	NOUN
cana-652	282	28	and	and	CCONJ
cana-652	282	29	accuracy	accuracy	NOUN
cana-652	282	30	.	.	PUNCT
cana-652	283	1	we	we	PRON
cana-652	283	2	also	also	ADV
cana-652	283	3	visualized	visualize	VERB
cana-652	283	4	the	the	DET
cana-652	283	5	training	training	NOUN
cana-652	283	6	curves	curve	NOUN
cana-652	283	7	to	to	PART
cana-652	283	8	understand	understand	VERB
cana-652	283	9	how	how	SCONJ
cana-652	283	10	the	the	DET
cana-652	283	11	models	model	NOUN
cana-652	283	12	learned	learn	VERB
cana-652	283	13	over	over	ADP
cana-652	283	14	epochs	epoch	NOUN
cana-652	283	15	.	.	PUNCT
cana-652	284	1	additionally	additionally	ADV
cana-652	284	2	,	,	PUNCT
cana-652	284	3	we	we	PRON
cana-652	284	4	created	create	VERB
cana-652	284	5	confusion	confusion	NOUN
cana-652	284	6	matrices	matrix	NOUN
cana-652	284	7	to	to	PART
cana-652	284	8	visualize	visualize	VERB
cana-652	284	9	the	the	DET
cana-652	284	10	models	model	NOUN
cana-652	284	11	'	'	PART
cana-652	284	12	performance	performance	NOUN
cana-652	284	13	in	in	ADP
cana-652	284	14	classifying	classify	VERB
cana-652	284	15	different	different	ADJ
cana-652	284	16	skin	skin	NOUN
cana-652	284	17	cancer	cancer	NOUN
cana-652	284	18	types	type	NOUN
cana-652	284	19	.	.	PUNCT
cana-652	285	1	7	7	X
cana-652	285	2	.	.	NOUN
cana-652	285	3	results	result	NOUN
cana-652	285	4	and	and	CCONJ
cana-652	285	5	discussions	discussion	NOUN
cana-652	285	6	year	year	NOUN
cana-652	285	7	&	&	CCONJ
cana-652	285	8	reference	reference	PROPN
cana-652	285	9	method	method	NOUN
cana-652	285	10	accuracy	accuracy	NOUN
cana-652	285	11	aug	aug	PROPN
cana-652	285	12	2022	2022	NUM
cana-652	285	13	[	[	X
cana-652	285	14	11	11	NUM
cana-652	285	15	]	]	PUNCT
cana-652	285	16	alexnet	alexnet	NOUN
cana-652	285	17	76	76	NUM
cana-652	285	18	%	%	NOUN
cana-652	285	19	aug	aug	PROPN
cana-652	285	20	2022	2022	NUM
cana-652	286	1	[	[	X
cana-652	286	2	11	11	NUM
cana-652	286	3	]	]	PUNCT
cana-652	286	4	inceptionv3	inceptionv3	NOUN
cana-652	287	1	77	77	NUM
cana-652	287	2	%	%	NOUN
cana-652	287	3	dec	dec	PROPN
cana-652	287	4	2019	2019	NUM
cana-652	287	5	[	[	X
cana-652	287	6	9	9	NUM
cana-652	287	7	]	]	PUNCT
cana-652	287	8	cnn	cnn	NOUN
cana-652	287	9	78	78	NUM
cana-652	287	10	%	%	NOUN
cana-652	287	11	aug	aug	PROPN
cana-652	287	12	2022	2022	NUM
cana-652	287	13	[	[	X
cana-652	287	14	11	11	NUM
cana-652	287	15	]	]	PUNCT
cana-652	287	16	regnety-320	regnety-320	NOUN
cana-652	287	17	85	85	NUM
cana-652	287	18	%	%	NOUN
cana-652	287	19	july	july	NOUN
cana-652	287	20	2022	2022	NUM
cana-652	287	21	[	[	X
cana-652	287	22	12	12	NUM
cana-652	287	23	]	]	X
cana-652	287	24	mobilenet	mobilenet	NOUN
cana-652	287	25	83	83	NUM
cana-652	287	26	%	%	NOUN
cana-652	287	27	dec	dec	PROPN
cana-652	287	28	2021	2021	NUM
cana-652	287	29	[	[	X
cana-652	287	30	8	8	NUM
cana-652	287	31	]	]	PUNCT
cana-652	287	32	cnn	cnn	PROPN
cana-652	287	33	87.9	87.9	NUM
cana-652	287	34	%	%	NOUN
cana-652	287	35	april	april	PROPN
cana-652	287	36	2023	2023	NUM
cana-652	287	37	[	[	X
cana-652	287	38	10	10	NUM
cana-652	287	39	]	]	PUNCT
cana-652	287	40	cnn	cnn	PROPN
cana-652	287	41	91.77	91.77	NUM
cana-652	287	42	%	%	NOUN
cana-652	287	43	2023	2023	NUM
cana-652	287	44	(	(	PUNCT
cana-652	287	45	proposed	propose	VERB
cana-652	287	46	methodology	methodology	NOUN
cana-652	287	47	)	)	PUNCT
cana-652	287	48	mobilenet	mobilenet	NOUN
cana-652	287	49	92.21	92.21	NUM
cana-652	287	50	%	%	NOUN
cana-652	287	51	2023	2023	NUM
cana-652	287	52	(	(	PUNCT
cana-652	287	53	proposed	propose	VERB
cana-652	287	54	methodology	methodology	NOUN
cana-652	287	55	)	)	PUNCT
cana-652	288	1	cnn	cnn	PROPN
cana-652	288	2	98.52	98.52	NUM
cana-652	288	3	%	%	NOUN
cana-652	288	4	table	table	NOUN
cana-652	288	5	3	3	NUM
cana-652	288	6	:	:	PUNCT
cana-652	288	7	comparison	comparison	NOUN
cana-652	288	8	with	with	ADP
cana-652	288	9	existing	exist	VERB
cana-652	288	10	system	system	NOUN
cana-652	288	11	the	the	DET
cana-652	288	12	comparison	comparison	NOUN
cana-652	288	13	between	between	ADP
cana-652	288	14	the	the	DET
cana-652	288	15	outcomes	outcome	NOUN
cana-652	288	16	of	of	ADP
cana-652	288	17	previous	previous	ADJ
cana-652	288	18	methods	method	NOUN
cana-652	288	19	and	and	CCONJ
cana-652	288	20	the	the	DET
cana-652	288	21	proposed	propose	VERB
cana-652	288	22	methodology	methodology	NOUN
cana-652	288	23	presented	present	VERB
cana-652	288	24	in	in	ADP
cana-652	288	25	table	table	NOUN
cana-652	288	26	3	3	NUM
cana-652	288	27	shows	show	VERB
cana-652	288	28	a	a	DET
cana-652	288	29	clear	clear	ADJ
cana-652	288	30	trend	trend	NOUN
cana-652	288	31	of	of	ADP
cana-652	288	32	improving	improve	VERB
cana-652	288	33	image	image	NOUN
cana-652	288	34	classification	classification	NOUN
cana-652	288	35	accuracy	accuracy	NOUN
cana-652	288	36	over	over	ADP
cana-652	288	37	the	the	DET
cana-652	288	38	time	time	NOUN
cana-652	288	39	.	.	PUNCT
cana-652	289	1	in	in	ADP
cana-652	289	2	previous	previous	ADJ
cana-652	289	3	methodologies	methodology	NOUN
cana-652	289	4	,	,	PUNCT
cana-652	289	5	up	up	ADV
cana-652	289	6	until	until	ADP
cana-652	289	7	2022	2022	NUM
cana-652	289	8	,	,	PUNCT
cana-652	289	9	models	model	NOUN
cana-652	289	10	such	such	ADJ
cana-652	289	11	as	as	ADP
cana-652	289	12	alexnet	alexnet	ADJ
cana-652	289	13	,	,	PUNCT
cana-652	289	14	inceptionv3	inceptionv3	PROPN
cana-652	289	15	,	,	PUNCT
cana-652	289	16	regnety-320	regnety-320	NOUN
cana-652	289	17	,	,	PUNCT
cana-652	289	18	and	and	CCONJ
cana-652	289	19	mobilenet	mobilenet	NOUN
cana-652	289	20	achieved	achieve	VERB
cana-652	289	21	accuracy	accuracy	NOUN
cana-652	289	22	scores	score	NOUN
cana-652	289	23	ranging	range	VERB
cana-652	289	24	from	from	ADP
cana-652	289	25	76	76	NUM
cana-652	289	26	%	%	NOUN
cana-652	289	27	to	to	PART
cana-652	289	28	85	85	NUM
cana-652	289	29	%	%	NOUN
cana-652	289	30	.	.	PUNCT
cana-652	290	1	in	in	ADP
cana-652	290	2	the	the	DET
cana-652	290	3	same	same	ADJ
cana-652	290	4	period	period	NOUN
cana-652	290	5	,	,	PUNCT
cana-652	290	6	cnns	cnn	NOUN
cana-652	290	7	achieved	achieve	VERB
cana-652	290	8	an	an	DET
cana-652	290	9	accuracy	accuracy	NOUN
cana-652	290	10	of	of	ADP
cana-652	290	11	78	78	NUM
cana-652	290	12	%	%	NOUN
cana-652	290	13	in	in	ADP
cana-652	290	14	december	december	PROPN
cana-652	290	15	2019	2019	NUM
cana-652	290	16	and	and	CCONJ
cana-652	290	17	87.9	87.9	NUM
cana-652	290	18	%	%	NOUN
cana-652	290	19	in	in	ADP
cana-652	290	20	december	december	PROPN
cana-652	290	21	2021	2021	NUM
cana-652	290	22	.	.	PUNCT
cana-652	291	1	these	these	DET
cana-652	291	2	results	result	NOUN
cana-652	291	3	indicate	indicate	VERB
cana-652	291	4	a	a	DET
cana-652	291	5	steady	steady	ADJ
cana-652	291	6	but	but	CCONJ
cana-652	291	7	relatively	relatively	ADV
cana-652	291	8	moderate	moderate	ADJ
cana-652	291	9	progression	progression	NOUN
cana-652	291	10	in	in	ADP
cana-652	291	11	accuracy	accuracy	NOUN
cana-652	291	12	over	over	ADP
cana-652	291	13	time	time	NOUN
cana-652	291	14	.	.	PUNCT
cana-652	292	1	however	however	ADV
cana-652	292	2	,	,	PUNCT
cana-652	292	3	the	the	DET
cana-652	292	4	proposed	propose	VERB
cana-652	292	5	methodology	methodology	NOUN
cana-652	292	6	,	,	PUNCT
cana-652	292	7	as	as	ADP
cana-652	292	8	of	of	ADP
cana-652	292	9	2023	2023	NUM
cana-652	292	10	,	,	PUNCT
cana-652	292	11	has	have	AUX
cana-652	292	12	demonstrated	demonstrate	VERB
cana-652	292	13	a	a	DET
cana-652	292	14	substantial	substantial	ADJ
cana-652	292	15	leap	leap	NOUN
cana-652	292	16	in	in	ADP
cana-652	292	17	accuracy	accuracy	NOUN
cana-652	292	18	.	.	PUNCT
cana-652	293	1	mobilenet	mobilenet	PROPN
cana-652	293	2	,	,	PUNCT
cana-652	293	3	attained	attain	VERB
cana-652	293	4	an	an	DET
cana-652	293	5	amazing	amazing	ADJ
cana-652	293	6	accuracy	accuracy	NOUN
cana-652	293	7	of	of	ADP
cana-652	293	8	92.21	92.21	NUM
cana-652	293	9	%	%	NOUN
cana-652	293	10	under	under	ADP
cana-652	293	11	the	the	DET
cana-652	293	12	suggested	suggest	VERB
cana-652	293	13	methodology	methodology	NOUN
cana-652	293	14	,	,	PUNCT
cana-652	293	15	while	while	SCONJ
cana-652	293	16	cnn	cnn	PROPN
cana-652	293	17	achieved	achieve	VERB
cana-652	293	18	an	an	DET
cana-652	293	19	even	even	ADV
cana-652	293	20	higher	high	ADJ
cana-652	293	21	accuracy	accuracy	NOUN
cana-652	293	22	of	of	ADP
cana-652	293	23	98.52	98.52	NUM
cana-652	293	24	%	%	NOUN
cana-652	293	25	.	.	PUNCT
cana-652	294	1	this	this	PRON
cana-652	294	2	suggests	suggest	VERB
cana-652	294	3	that	that	SCONJ
cana-652	294	4	the	the	DET
cana-652	294	5	proposed	propose	VERB
cana-652	294	6	methodology	methodology	NOUN
cana-652	294	7	has	have	AUX
cana-652	294	8	made	make	VERB
cana-652	294	9	communications	communication	NOUN
cana-652	294	10	on	on	ADP
cana-652	294	11	applied	apply	VERB
cana-652	294	12	nonlinear	nonlinear	ADJ
cana-652	294	13	analysis	analysis	NOUN
cana-652	294	14	issn	issn	NOUN
cana-652	294	15	:	:	PUNCT
cana-652	294	16	1074	1074	NUM
cana-652	294	17	-	-	PUNCT
cana-652	294	18	133x	133x	NUM
cana-652	294	19	vol	vol	NOUN
cana-652	294	20	31	31	NUM
cana-652	294	21	no	no	NOUN
cana-652	294	22	.	.	PUNCT
cana-652	295	1	2s	2s	NUM
cana-652	295	2	(	(	PUNCT
cana-652	295	3	2024	2024	NUM
cana-652	295	4	)	)	PUNCT
cana-652	295	5	333	333	NUM
cana-652	295	6	https://internationalpubls.com	https://internationalpubls.com	SYM
cana-652	295	7	significant	significant	ADJ
cana-652	295	8	advancements	advancement	NOUN
cana-652	295	9	in	in	ADP
cana-652	295	10	image	image	NOUN
cana-652	295	11	classification	classification	NOUN
cana-652	295	12	accuracy	accuracy	NOUN
cana-652	295	13	,	,	PUNCT
cana-652	295	14	outperforming	outperform	VERB
cana-652	295	15	the	the	DET
cana-652	295	16	earlier	early	ADJ
cana-652	295	17	models	model	NOUN
cana-652	295	18	by	by	ADP
cana-652	295	19	a	a	DET
cana-652	295	20	considerable	considerable	ADJ
cana-652	295	21	margin	margin	NOUN
cana-652	295	22	.	.	PUNCT
cana-652	296	1	skin	skin	NOUN
cana-652	296	2	cancer	cancer	NOUN
cana-652	296	3	classes	class	NOUN
cana-652	296	4	precision	precision	NOUN
cana-652	296	5	recall	recall	VERB
cana-652	296	6	f1	f1	NOUN
cana-652	296	7	-	-	PUNCT
cana-652	296	8	score	score	NOUN
cana-652	296	9	no	no	NOUN
cana-652	296	10	.	.	PUNCT
cana-652	297	1	of	of	ADP
cana-652	297	2	image	image	NOUN
cana-652	297	3	melanocytic	melanocytic	ADJ
cana-652	297	4	nevi	nevi	NOUN
cana-652	297	5	(	(	PUNCT
cana-652	297	6	nv	nv	PROPN
cana-652	297	7	)	)	PUNCT
cana-652	297	8	1	1	NUM
cana-652	297	9	1	1	NUM
cana-652	297	10	1	1	NUM
cana-652	297	11	1667	1667	NUM
cana-652	297	12	melanoma	melanoma	NOUN
cana-652	297	13	(	(	PUNCT
cana-652	297	14	mel	mel	PROPN
cana-652	297	15	)	)	PUNCT
cana-652	297	16	1	1	NUM
cana-652	297	17	1	1	NUM
cana-652	297	18	1	1	NUM
cana-652	297	19	1689	1689	NUM
cana-652	297	20	benign	benign	ADJ
cana-652	297	21	keratosis	keratosis	NOUN
cana-652	297	22	-	-	PUNCT
cana-652	297	23	like	like	ADJ
cana-652	297	24	lesions	lesion	NOUN
cana-652	297	25	(	(	PUNCT
cana-652	297	26	bkl	bkl	NOUN
cana-652	297	27	)	)	PUNCT
cana-652	297	28	0.97	0.97	NUM
cana-652	297	29	1	1	NUM
cana-652	297	30	0.98	0.98	NUM
cana-652	297	31	1651	1651	NUM
cana-652	297	32	basal	basal	NOUN
cana-652	297	33	cell	cell	NOUN
cana-652	297	34	carcinoma	carcinoma	NOUN
cana-652	297	35	(	(	PUNCT
cana-652	297	36	bcc	bcc	PROPN
cana-652	297	37	)	)	PUNCT
cana-652	297	38	1	1	NUM
cana-652	297	39	1	1	NUM
cana-652	297	40	1	1	NUM
cana-652	297	41	1629	1629	NUM
cana-652	297	42	pyogenic	pyogenic	ADJ
cana-652	297	43	granulomas	granuloma	NOUN
cana-652	297	44	(	(	PUNCT
cana-652	297	45	vasc	vasc	ADJ
cana-652	297	46	)	)	PUNCT
cana-652	297	47	0.99	0.99	NUM
cana-652	297	48	0.91	0.91	NUM
cana-652	297	49	0.95	0.95	NUM
cana-652	297	50	1663	1663	NUM
cana-652	297	51	actinic	actinic	ADJ
cana-652	297	52	keratoses	keratose	NOUN
cana-652	297	53	(	(	PUNCT
cana-652	297	54	akiec	akiec	NOUN
cana-652	297	55	)	)	PUNCT
cana-652	297	56	1	1	NUM
cana-652	297	57	1	1	NUM
cana-652	297	58	1	1	NUM
cana-652	297	59	1680	1680	NUM
cana-652	297	60	dermatofibroma	dermatofibroma	ADJ
cana-652	297	61	(	(	PUNCT
cana-652	297	62	df	df	PROPN
cana-652	297	63	)	)	PUNCT
cana-652	297	64	0.95	0.95	NUM
cana-652	297	65	0.99	0.99	NUM
cana-652	297	66	0.97	0.97	NUM
cana-652	297	67	1755	1755	NUM
cana-652	297	68	table	table	NOUN
cana-652	297	69	4	4	NUM
cana-652	297	70	:	:	PUNCT
cana-652	297	71	classification	classification	NOUN
cana-652	297	72	report	report	NOUN
cana-652	297	73	table	table	NOUN
cana-652	297	74	4	4	NUM
cana-652	297	75	presents	present	VERB
cana-652	297	76	the	the	DET
cana-652	297	77	classification	classification	NOUN
cana-652	297	78	report	report	NOUN
cana-652	297	79	for	for	ADP
cana-652	297	80	individual	individual	ADJ
cana-652	297	81	skin	skin	NOUN
cana-652	297	82	cancer	cancer	NOUN
cana-652	297	83	classes	class	NOUN
cana-652	297	84	,	,	PUNCT
cana-652	297	85	showcasing	showcase	VERB
cana-652	297	86	the	the	DET
cana-652	297	87	performance	performance	NOUN
cana-652	297	88	of	of	ADP
cana-652	297	89	our	our	PRON
cana-652	297	90	cnn	cnn	PROPN
cana-652	297	91	-	-	PUNCT
cana-652	297	92	based	base	VERB
cana-652	297	93	skin	skin	NOUN
cana-652	297	94	cancer	cancer	NOUN
cana-652	297	95	prediction	prediction	NOUN
cana-652	297	96	model	model	NOUN
cana-652	297	97	across	across	ADP
cana-652	297	98	diverse	diverse	ADJ
cana-652	297	99	categories	category	NOUN
cana-652	297	100	.	.	PUNCT
cana-652	298	1	it	it	PRON
cana-652	298	2	indicates	indicate	VERB
cana-652	298	3	that	that	SCONJ
cana-652	298	4	the	the	DET
cana-652	298	5	model	model	NOUN
cana-652	298	6	demonstrates	demonstrate	VERB
cana-652	298	7	exceptional	exceptional	ADJ
cana-652	298	8	accuracy	accuracy	NOUN
cana-652	298	9	in	in	ADP
cana-652	298	10	classifying	classify	VERB
cana-652	298	11	various	various	ADJ
cana-652	298	12	skin	skin	NOUN
cana-652	298	13	cancer	cancer	NOUN
cana-652	298	14	types	type	NOUN
cana-652	298	15	,	,	PUNCT
cana-652	298	16	with	with	ADP
cana-652	298	17	high	high	ADJ
cana-652	298	18	precision	precision	NOUN
cana-652	298	19	,	,	PUNCT
cana-652	298	20	recall	recall	NOUN
cana-652	298	21	,	,	PUNCT
cana-652	298	22	and	and	CCONJ
cana-652	298	23	f1	f1	NOUN
cana-652	298	24	-	-	PUNCT
cana-652	298	25	scores	score	NOUN
cana-652	298	26	.	.	PUNCT
cana-652	299	1	this	this	DET
cana-652	299	2	outcome	outcome	NOUN
cana-652	299	3	is	be	AUX
cana-652	299	4	highly	highly	ADV
cana-652	299	5	promising	promising	ADJ
cana-652	299	6	,	,	PUNCT
cana-652	299	7	as	as	SCONJ
cana-652	299	8	it	it	PRON
cana-652	299	9	suggests	suggest	VERB
cana-652	299	10	that	that	SCONJ
cana-652	299	11	the	the	DET
cana-652	299	12	model	model	NOUN
cana-652	299	13	accurately	accurately	ADV
cana-652	299	14	identifies	identify	VERB
cana-652	299	15	different	different	ADJ
cana-652	299	16	types	type	NOUN
cana-652	299	17	of	of	ADP
cana-652	299	18	skin	skin	NOUN
cana-652	299	19	lesions	lesion	NOUN
cana-652	299	20	,	,	PUNCT
cana-652	299	21	acritical	acritical	ADJ
cana-652	299	22	step	step	NOUN
cana-652	299	23	in	in	ADP
cana-652	299	24	early	early	ADJ
cana-652	299	25	skin	skin	NOUN
cana-652	299	26	cancer	cancer	NOUN
cana-652	299	27	detection	detection	NOUN
cana-652	299	28	.	.	PUNCT
cana-652	300	1	the	the	DET
cana-652	300	2	overall	overall	ADJ
cana-652	300	3	performance	performance	NOUN
cana-652	300	4	metrics	metric	NOUN
cana-652	300	5	of	of	ADP
cana-652	300	6	the	the	DET
cana-652	300	7	presented	present	VERB
cana-652	300	8	system	system	NOUN
cana-652	300	9	:	:	PUNCT
cana-652	300	10	1	1	X
cana-652	300	11	.	.	X
cana-652	300	12	f1	f1	NOUN
cana-652	300	13	score	score	NOUN
cana-652	300	14	0.9846	0.9846	NUM
cana-652	300	15	:	:	PUNCT
cana-652	300	16	this	this	DET
cana-652	300	17	score	score	NOUN
cana-652	300	18	indicates	indicate	VERB
cana-652	300	19	a	a	DET
cana-652	300	20	high	high	ADJ
cana-652	300	21	balance	balance	NOUN
cana-652	300	22	between	between	ADP
cana-652	300	23	precision	precision	NOUN
cana-652	300	24	and	and	CCONJ
cana-652	300	25	recall	recall	NOUN
cana-652	300	26	,	,	PUNCT
cana-652	300	27	highlighting	highlight	VERB
cana-652	300	28	the	the	DET
cana-652	300	29	system	system	NOUN
cana-652	300	30	's	's	PART
cana-652	300	31	strong	strong	ADJ
cana-652	300	32	accuracy	accuracy	NOUN
cana-652	300	33	in	in	ADP
cana-652	300	34	classification	classification	NOUN
cana-652	300	35	.	.	PUNCT
cana-652	301	1	2	2	X
cana-652	301	2	.	.	X
cana-652	301	3	recall	recall	NOUN
cana-652	301	4	0.9847	0.9847	NUM
cana-652	301	5	:	:	PUNCT
cana-652	301	6	the	the	DET
cana-652	301	7	system	system	NOUN
cana-652	301	8	effectively	effectively	ADV
cana-652	301	9	identifies	identify	VERB
cana-652	301	10	a	a	DET
cana-652	301	11	large	large	ADJ
cana-652	301	12	portion	portion	NOUN
cana-652	301	13	of	of	ADP
cana-652	301	14	relevant	relevant	ADJ
cana-652	301	15	instances	instance	NOUN
cana-652	301	16	,	,	PUNCT
cana-652	301	17	making	make	VERB
cana-652	301	18	it	it	PRON
cana-652	301	19	highly	highly	ADV
cana-652	301	20	reliable	reliable	ADJ
cana-652	301	21	.	.	PUNCT
cana-652	302	1	3	3	X
cana-652	302	2	.	.	X
cana-652	302	3	precision	precision	NOUN
cana-652	302	4	0.9850	0.9850	NUM
cana-652	302	5	:	:	PUNCT
cana-652	302	6	this	this	PRON
cana-652	302	7	reflects	reflect	VERB
cana-652	302	8	the	the	DET
cana-652	302	9	system	system	NOUN
cana-652	302	10	's	's	PART
cana-652	302	11	ability	ability	NOUN
cana-652	302	12	to	to	PART
cana-652	302	13	make	make	VERB
cana-652	302	14	accurate	accurate	ADJ
cana-652	302	15	positive	positive	ADJ
cana-652	302	16	predictions	prediction	NOUN
cana-652	302	17	,	,	PUNCT
cana-652	302	18	showing	show	VERB
cana-652	302	19	a	a	DET
cana-652	302	20	low	low	ADJ
cana-652	302	21	rate	rate	NOUN
cana-652	302	22	of	of	ADP
cana-652	302	23	false	false	ADJ
cana-652	302	24	positives	positive	NOUN
cana-652	302	25	.	.	PUNCT
cana-652	303	1	4	4	X
cana-652	303	2	.	.	X
cana-652	303	3	accuracy	accuracy	NOUN
cana-652	303	4	0.9850	0.9850	NUM
cana-652	303	5	:	:	PUNCT
cana-652	303	6	the	the	DET
cana-652	303	7	system	system	NOUN
cana-652	303	8	's	's	PART
cana-652	303	9	overall	overall	ADJ
cana-652	303	10	correctness	correctness	NOUN
cana-652	303	11	in	in	ADP
cana-652	303	12	classification	classification	NOUN
cana-652	303	13	is	be	AUX
cana-652	303	14	exceptional	exceptional	ADJ
cana-652	303	15	,	,	PUNCT
cana-652	303	16	making	make	VERB
cana-652	303	17	it	it	PRON
cana-652	303	18	a	a	DET
cana-652	303	19	highly	highly	ADV
cana-652	303	20	dependable	dependable	ADJ
cana-652	303	21	tool	tool	NOUN
cana-652	303	22	.	.	PUNCT
cana-652	304	1	further	further	ADJ
cana-652	304	2	details	detail	NOUN
cana-652	304	3	on	on	ADP
cana-652	304	4	the	the	DET
cana-652	304	5	results	result	NOUN
cana-652	304	6	and	and	CCONJ
cana-652	304	7	discussions	discussion	NOUN
cana-652	304	8	are	be	AUX
cana-652	304	9	as	as	SCONJ
cana-652	304	10	follows	follow	VERB
cana-652	304	11	:	:	PUNCT
cana-652	305	1	1	1	X
cana-652	305	2	.	.	X
cana-652	305	3	cnn	cnn	PROPN
cana-652	305	4	:	:	PUNCT
cana-652	305	5	the	the	DET
cana-652	305	6	cnn	cnn	PROPN
cana-652	305	7	model	model	NOUN
cana-652	305	8	's	's	PART
cana-652	305	9	accuracy	accuracy	NOUN
cana-652	305	10	is	be	AUX
cana-652	305	11	98.52	98.52	NUM
cana-652	305	12	%	%	NOUN
cana-652	305	13	.	.	PUNCT
cana-652	306	1	the	the	DET
cana-652	306	2	model	model	NOUN
cana-652	306	3	is	be	AUX
cana-652	306	4	trained	train	VERB
cana-652	306	5	using	use	VERB
cana-652	306	6	a	a	DET
cana-652	306	7	128	128	NUM
cana-652	306	8	-	-	PUNCT
cana-652	306	9	batch	batch	NOUN
cana-652	306	10	size	size	NOUN
cana-652	306	11	across	across	ADP
cana-652	306	12	25	25	NUM
cana-652	306	13	epochs	epoch	NOUN
cana-652	306	14	.	.	PUNCT
cana-652	307	1	in	in	ADP
cana-652	307	2	order	order	NOUN
cana-652	307	3	to	to	PART
cana-652	307	4	improve	improve	VERB
cana-652	307	5	the	the	DET
cana-652	307	6	model	model	NOUN
cana-652	307	7	's	's	PART
cana-652	307	8	correctness	correctness	NOUN
cana-652	307	9	,	,	PUNCT
cana-652	307	10	we	we	PRON
cana-652	307	11	are	be	AUX
cana-652	307	12	here	here	ADV
cana-652	307	13	decreasing	decrease	VERB
cana-652	307	14	the	the	DET
cana-652	307	15	learning	learning	NOUN
cana-652	307	16	rate	rate	NOUN
cana-652	307	17	in	in	ADP
cana-652	307	18	comparison	comparison	NOUN
cana-652	307	19	to	to	ADP
cana-652	307	20	the	the	DET
cana-652	307	21	current	current	ADJ
cana-652	307	22	system	system	NOUN
cana-652	307	23	.	.	PUNCT
cana-652	308	1	accuracy	accuracy	NOUN
cana-652	308	2	was	be	AUX
cana-652	308	3	employed	employ	VERB
cana-652	308	4	as	as	ADP
cana-652	308	5	the	the	DET
cana-652	308	6	performance	performance	NOUN
cana-652	308	7	evaluation	evaluation	NOUN
cana-652	308	8	parameter	parameter	NOUN
cana-652	308	9	during	during	ADP
cana-652	308	10	testing	testing	NOUN
cana-652	308	11	on	on	ADP
cana-652	308	12	the	the	DET
cana-652	308	13	test	test	NOUN
cana-652	308	14	dataset	dataset	NOUN
cana-652	308	15	,	,	PUNCT
cana-652	308	16	where	where	SCONJ
cana-652	308	17	11734	11734	NUM
cana-652	308	18	pictures	picture	NOUN
cana-652	308	19	were	be	AUX
cana-652	308	20	utilized	utilize	VERB
cana-652	308	21	for	for	ADP
cana-652	308	22	testing	testing	NOUN
cana-652	308	23	from	from	ADP
cana-652	308	24	35201	35201	NUM
cana-652	308	25	photos	photo	NOUN
cana-652	308	26	from	from	ADP
cana-652	308	27	the	the	DET
cana-652	308	28	skin	skin	NOUN
cana-652	308	29	cancer	cancer	NOUN
cana-652	308	30	dataset	dataset	NOUN
cana-652	308	31	were	be	AUX
cana-652	308	32	used	use	VERB
cana-652	308	33	for	for	ADP
cana-652	308	34	training	training	NOUN
cana-652	308	35	after	after	ADP
cana-652	308	36	employing	employ	VERB
cana-652	308	37	oversampling	oversample	VERB
cana-652	308	38	on	on	ADP
cana-652	308	39	the	the	DET
cana-652	308	40	dataset	dataset	NOUN
cana-652	308	41	.	.	PUNCT
cana-652	309	1	communications	communication	NOUN
cana-652	309	2	on	on	ADP
cana-652	309	3	applied	apply	VERB
cana-652	309	4	nonlinear	nonlinear	ADJ
cana-652	309	5	analysis	analysis	NOUN
cana-652	309	6	issn	issn	NOUN
cana-652	309	7	:	:	PUNCT
cana-652	309	8	1074	1074	NUM
cana-652	309	9	-	-	PUNCT
cana-652	309	10	133x	133x	NUM
cana-652	309	11	vol	vol	NOUN
cana-652	309	12	31	31	NUM
cana-652	309	13	no	no	NOUN
cana-652	309	14	.	.	PUNCT
cana-652	310	1	2s	2s	NUM
cana-652	310	2	(	(	PUNCT
cana-652	310	3	2024	2024	NUM
cana-652	310	4	)	)	PUNCT
cana-652	310	5	334	334	NUM
cana-652	310	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-652	310	7	fig	fig	NOUN
cana-652	310	8	.	.	PUNCT
cana-652	311	1	5	5	NUM
cana-652	311	2	(	(	PUNCT
cana-652	311	3	a	a	NOUN
cana-652	311	4	)	)	PUNCT
cana-652	311	5	training	training	NOUN
cana-652	311	6	and	and	CCONJ
cana-652	311	7	validation	validation	NOUN
cana-652	311	8	loss	loss	NOUN
cana-652	311	9	fig	fig	NOUN
cana-652	311	10	.	.	PUNCT
cana-652	312	1	5	5	NUM
cana-652	312	2	(	(	PUNCT
cana-652	312	3	a	a	PRON
cana-652	312	4	)	)	PUNCT
cana-652	312	5	illustrates	illustrate	VERB
cana-652	312	6	the	the	DET
cana-652	312	7	loss	loss	NOUN
cana-652	312	8	vs	vs	ADP
cana-652	312	9	epoch	epoch	NOUN
cana-652	312	10	graph	graph	NOUN
cana-652	312	11	of	of	ADP
cana-652	312	12	the	the	DET
cana-652	312	13	system	system	NOUN
cana-652	312	14	which	which	PRON
cana-652	312	15	shows	show	VERB
cana-652	312	16	that	that	SCONJ
cana-652	312	17	the	the	DET
cana-652	312	18	model	model	NOUN
cana-652	312	19	is	be	AUX
cana-652	312	20	generalized	generalize	VERB
cana-652	312	21	effectively	effectively	ADV
cana-652	312	22	to	to	ADP
cana-652	312	23	new	new	ADJ
cana-652	312	24	data	datum	NOUN
cana-652	312	25	with	with	ADP
cana-652	312	26	a	a	DET
cana-652	312	27	high	high	ADJ
cana-652	312	28	level	level	NOUN
cana-652	312	29	of	of	ADP
cana-652	312	30	accuracy	accuracy	NOUN
cana-652	312	31	.	.	PUNCT
cana-652	313	1	the	the	DET
cana-652	313	2	best	good	ADJ
cana-652	313	3	epoch	epoch	NOUN
cana-652	313	4	of	of	ADP
cana-652	313	5	the	the	DET
cana-652	313	6	model	model	NOUN
cana-652	313	7	is	be	AUX
cana-652	313	8	epoch	epoch	NOUN
cana-652	313	9	12	12	NUM
cana-652	313	10	,	,	PUNCT
cana-652	313	11	with	with	ADP
cana-652	313	12	a	a	DET
cana-652	313	13	validation	validation	NOUN
cana-652	313	14	loss	loss	NOUN
cana-652	313	15	of	of	ADP
cana-652	313	16	0.25	0.25	NUM
cana-652	313	17	.	.	PUNCT
cana-652	314	1	fig	fig	NOUN
cana-652	314	2	.	.	PUNCT
cana-652	315	1	5	5	NUM
cana-652	315	2	(	(	PUNCT
cana-652	315	3	b	b	NOUN
cana-652	315	4	)	)	PUNCT
cana-652	315	5	training	training	NOUN
cana-652	315	6	and	and	CCONJ
cana-652	315	7	validation	validation	NOUN
cana-652	315	8	accuracy	accuracy	NOUN
cana-652	315	9	fig	fig	NOUN
cana-652	315	10	.	.	PUNCT
cana-652	316	1	5	5	NUM
cana-652	316	2	(	(	PUNCT
cana-652	316	3	b	b	NOUN
cana-652	316	4	)	)	PUNCT
cana-652	316	5	illustrates	illustrate	VERB
cana-652	316	6	the	the	DET
cana-652	316	7	accuracy	accuracy	NOUN
cana-652	316	8	vs	vs	ADP
cana-652	316	9	epoch	epoch	NOUN
cana-652	316	10	graph	graph	NOUN
cana-652	316	11	of	of	ADP
cana-652	316	12	the	the	DET
cana-652	316	13	system	system	NOUN
cana-652	316	14	which	which	PRON
cana-652	316	15	shows	show	VERB
cana-652	316	16	that	that	SCONJ
cana-652	316	17	the	the	DET
cana-652	316	18	model	model	NOUN
cana-652	316	19	is	be	AUX
cana-652	316	20	generalized	generalize	VERB
cana-652	316	21	effectively	effectively	ADV
cana-652	316	22	to	to	ADP
cana-652	316	23	new	new	ADJ
cana-652	316	24	data	datum	NOUN
cana-652	316	25	with	with	ADP
cana-652	316	26	a	a	DET
cana-652	316	27	high	high	ADJ
cana-652	316	28	level	level	NOUN
cana-652	316	29	of	of	ADP
cana-652	316	30	accuracy	accuracy	NOUN
cana-652	316	31	(	(	PUNCT
cana-652	316	32	98.5	98.5	NUM
cana-652	316	33	%	%	NOUN
cana-652	316	34	validation	validation	NOUN
cana-652	316	35	accuracy	accuracy	NOUN
cana-652	316	36	)	)	PUNCT
cana-652	316	37	.	.	PUNCT
cana-652	317	1	the	the	DET
cana-652	317	2	best	good	ADJ
cana-652	317	3	epoch	epoch	NOUN
cana-652	317	4	of	of	ADP
cana-652	317	5	the	the	DET
cana-652	317	6	model	model	NOUN
cana-652	317	7	is	be	AUX
cana-652	317	8	epoch	epoch	PROPN
cana-652	317	9	19	19	NUM
cana-652	317	10	.	.	PUNCT
cana-652	318	1	communications	communication	NOUN
cana-652	318	2	on	on	ADP
cana-652	318	3	applied	apply	VERB
cana-652	318	4	nonlinear	nonlinear	ADJ
cana-652	318	5	analysis	analysis	NOUN
cana-652	318	6	issn	issn	NOUN
cana-652	318	7	:	:	PUNCT
cana-652	318	8	1074	1074	NUM
cana-652	318	9	-	-	PUNCT
cana-652	318	10	133x	133x	NUM
cana-652	318	11	vol	vol	NOUN
cana-652	318	12	31	31	NUM
cana-652	318	13	no	no	NOUN
cana-652	318	14	.	.	PUNCT
cana-652	319	1	2s	2s	NUM
cana-652	319	2	(	(	PUNCT
cana-652	319	3	2024	2024	NUM
cana-652	319	4	)	)	PUNCT
cana-652	319	5	335	335	NUM
cana-652	319	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-652	319	7	fig	fig	NOUN
cana-652	319	8	.	.	PUNCT
cana-652	320	1	5	5	NUM
cana-652	320	2	(	(	PUNCT
cana-652	320	3	c	c	NOUN
cana-652	320	4	)	)	PUNCT
cana-652	320	5	confusion	confusion	NOUN
cana-652	320	6	matrix	matrix	NOUN
cana-652	320	7	fig	fig	NOUN
cana-652	320	8	.	.	PUNCT
cana-652	321	1	5	5	NUM
cana-652	321	2	(	(	PUNCT
cana-652	321	3	c	c	NOUN
cana-652	321	4	)	)	PUNCT
cana-652	321	5	depicts	depict	VERB
cana-652	321	6	the	the	DET
cana-652	321	7	confusion	confusion	NOUN
cana-652	321	8	matrix	matrix	NOUN
cana-652	321	9	of	of	ADP
cana-652	321	10	the	the	DET
cana-652	321	11	represented	represent	VERB
cana-652	321	12	system	system	NOUN
cana-652	321	13	which	which	PRON
cana-652	321	14	shows	show	VERB
cana-652	321	15	that	that	SCONJ
cana-652	321	16	the	the	DET
cana-652	321	17	system	system	NOUN
cana-652	321	18	has	have	VERB
cana-652	321	19	a	a	DET
cana-652	321	20	high	high	ADJ
cana-652	321	21	level	level	NOUN
cana-652	321	22	of	of	ADP
cana-652	321	23	accuracy	accuracy	NOUN
cana-652	321	24	in	in	ADP
cana-652	321	25	detecting	detect	VERB
cana-652	321	26	skin	skin	NOUN
cana-652	321	27	cancer	cancer	NOUN
cana-652	321	28	(	(	PUNCT
cana-652	321	29	98.5	98.5	NUM
cana-652	321	30	%	%	NOUN
cana-652	321	31	)	)	PUNCT
cana-652	321	32	.	.	PUNCT
cana-652	322	1	the	the	DET
cana-652	322	2	system	system	NOUN
cana-652	322	3	is	be	AUX
cana-652	322	4	particularly	particularly	ADV
cana-652	322	5	good	good	ADJ
cana-652	322	6	at	at	ADP
cana-652	322	7	detecting	detect	VERB
cana-652	322	8	malignant	malignant	ADJ
cana-652	322	9	lesions	lesion	NOUN
cana-652	322	10	(	(	PUNCT
cana-652	322	11	precision	precision	NOUN
cana-652	322	12	:	:	PUNCT
cana-652	322	13	99.2	99.2	NUM
cana-652	322	14	%	%	NOUN
cana-652	322	15	,	,	PUNCT
cana-652	322	16	recall	recall	NOUN
cana-652	322	17	:	:	PUNCT
cana-652	322	18	98.6	98.6	NUM
cana-652	322	19	%	%	NOUN
cana-652	322	20	)	)	PUNCT
cana-652	322	21	,	,	PUNCT
cana-652	322	22	and	and	CCONJ
cana-652	322	23	also	also	ADV
cana-652	322	24	good	good	ADJ
cana-652	322	25	at	at	ADP
cana-652	322	26	detecting	detect	VERB
cana-652	322	27	benign	benign	ADJ
cana-652	322	28	lesions	lesion	NOUN
cana-652	322	29	(	(	PUNCT
cana-652	322	30	precision	precision	NOUN
cana-652	322	31	:	:	PUNCT
cana-652	322	32	97.6	97.6	NUM
cana-652	322	33	%	%	NOUN
cana-652	322	34	,	,	PUNCT
cana-652	322	35	recall	recall	NOUN
cana-652	322	36	:	:	PUNCT
cana-652	322	37	97.8	97.8	NUM
cana-652	322	38	%	%	NOUN
cana-652	322	39	)	)	PUNCT
cana-652	322	40	.	.	PUNCT
cana-652	323	1	overall	overall	ADJ
cana-652	323	2	f1	f1	ADJ
cana-652	323	3	-	-	PUNCT
cana-652	323	4	score	score	NOUN
cana-652	323	5	,	,	PUNCT
cana-652	323	6	recall	recall	NOUN
cana-652	323	7	and	and	CCONJ
cana-652	323	8	precision	precision	NOUN
cana-652	323	9	of	of	ADP
cana-652	323	10	the	the	DET
cana-652	323	11	system	system	NOUN
cana-652	323	12	is	be	AUX
cana-652	323	13	0.9851	0.9851	NOUN
cana-652	323	14	,	,	PUNCT
cana-652	323	15	0.9853	0.9853	NUM
cana-652	323	16	,	,	PUNCT
cana-652	323	17	0.9855overall	0.9855overall	PROPN
cana-652	323	18	,	,	PUNCT
cana-652	323	19	the	the	DET
cana-652	323	20	skin	skin	NOUN
cana-652	323	21	cancer	cancer	NOUN
cana-652	323	22	detection	detection	NOUN
cana-652	323	23	system	system	NOUN
cana-652	323	24	is	be	AUX
cana-652	323	25	a	a	DET
cana-652	323	26	highly	highly	ADV
cana-652	323	27	accurate	accurate	ADJ
cana-652	323	28	and	and	CCONJ
cana-652	323	29	reliable	reliable	ADJ
cana-652	323	30	system	system	NOUN
cana-652	323	31	.	.	PUNCT
cana-652	324	1	1	1	X
cana-652	324	2	.	.	X
cana-652	324	3	mobilenet	mobilenet	NOUN
cana-652	324	4	:	:	PUNCT
cana-652	324	5	fig	fig	NOUN
cana-652	324	6	6.(a	6.(a	NOUN
cana-652	324	7	)	)	PUNCT
cana-652	324	8	training	training	NOUN
cana-652	324	9	vs	vs	ADP
cana-652	324	10	validation	validation	NOUN
cana-652	324	11	loss	loss	NOUN
cana-652	324	12	fig	fig	NOUN
cana-652	324	13	6.(a	6.(a	NOUN
cana-652	324	14	)	)	PUNCT
cana-652	324	15	depicts	depict	VERB
cana-652	324	16	the	the	DET
cana-652	324	17	training	training	NOUN
cana-652	324	18	vs	vs	ADP
cana-652	324	19	validation	validation	NOUN
cana-652	324	20	loss	loss	NOUN
cana-652	324	21	graph	graph	NOUN
cana-652	324	22	of	of	ADP
cana-652	324	23	the	the	DET
cana-652	324	24	mobilenet	mobilenet	NOUN
cana-652	324	25	model	model	NOUN
cana-652	324	26	architecture	architecture	NOUN
cana-652	324	27	of	of	ADP
cana-652	324	28	the	the	DET
cana-652	324	29	system	system	NOUN
cana-652	324	30	.	.	PUNCT
cana-652	325	1	it	it	PRON
cana-652	325	2	shows	show	VERB
cana-652	325	3	that	that	SCONJ
cana-652	325	4	the	the	DET
cana-652	325	5	model	model	NOUN
cana-652	325	6	is	be	AUX
cana-652	325	7	well	well	ADV
cana-652	325	8	-	-	PUNCT
cana-652	325	9	trained	train	VERB
cana-652	325	10	and	and	CCONJ
cana-652	325	11	has	have	VERB
cana-652	325	12	good	good	ADJ
cana-652	325	13	generalization	generalization	NOUN
cana-652	325	14	performance	performance	NOUN
cana-652	325	15	.	.	PUNCT
cana-652	326	1	the	the	DET
cana-652	326	2	validation	validation	NOUN
cana-652	326	3	loss	loss	NOUN
cana-652	326	4	decreases	decrease	VERB
cana-652	326	5	rapidly	rapidly	ADV
cana-652	326	6	over	over	ADP
cana-652	326	7	the	the	DET
cana-652	326	8	first	first	ADJ
cana-652	326	9	few	few	ADJ
cana-652	326	10	epochs	epoch	NOUN
cana-652	326	11	and	and	CCONJ
cana-652	326	12	then	then	ADV
cana-652	326	13	plateaus	plateaus	NOUN
cana-652	326	14	at	at	ADP
cana-652	326	15	a	a	DET
cana-652	326	16	relatively	relatively	ADV
cana-652	326	17	low	low	ADJ
cana-652	326	18	level	level	NOUN
cana-652	326	19	and	and	CCONJ
cana-652	326	20	then	then	ADV
cana-652	326	21	again	again	ADV
cana-652	326	22	decreases	decrease	VERB
cana-652	326	23	.	.	PUNCT
cana-652	327	1	nevertheless	nevertheless	ADV
cana-652	327	2	,	,	PUNCT
cana-652	327	3	the	the	DET
cana-652	327	4	overall	overall	ADJ
cana-652	327	5	reduction	reduction	NOUN
cana-652	327	6	between	between	ADP
cana-652	327	7	training	training	NOUN
cana-652	327	8	and	and	CCONJ
cana-652	327	9	validation	validation	NOUN
cana-652	327	10	loss	loss	NOUN
cana-652	327	11	is	be	AUX
cana-652	327	12	relatively	relatively	ADV
cana-652	327	13	small	small	ADJ
cana-652	327	14	,	,	PUNCT
cana-652	327	15	indicating	indicate	VERB
cana-652	327	16	that	that	SCONJ
cana-652	327	17	the	the	DET
cana-652	327	18	model	model	NOUN
cana-652	327	19	is	be	AUX
cana-652	327	20	able	able	ADJ
cana-652	327	21	to	to	PART
cana-652	327	22	generalize	generalize	VERB
cana-652	327	23	well	well	ADV
cana-652	327	24	to	to	ADP
cana-652	327	25	new	new	ADJ
cana-652	327	26	data	datum	NOUN
cana-652	327	27	.	.	PUNCT
cana-652	328	1	communications	communication	NOUN
cana-652	328	2	on	on	ADP
cana-652	328	3	applied	apply	VERB
cana-652	328	4	nonlinear	nonlinear	ADJ
cana-652	328	5	analysis	analysis	NOUN
cana-652	328	6	issn	issn	NOUN
cana-652	328	7	:	:	PUNCT
cana-652	328	8	1074	1074	NUM
cana-652	328	9	-	-	PUNCT
cana-652	328	10	133x	133x	NUM
cana-652	328	11	vol	vol	NOUN
cana-652	328	12	31	31	NUM
cana-652	328	13	no	no	NOUN
cana-652	328	14	.	.	PUNCT
cana-652	329	1	2s	2s	NUM
cana-652	329	2	(	(	PUNCT
cana-652	329	3	2024	2024	NUM
cana-652	329	4	)	)	PUNCT
cana-652	329	5	336	336	NUM
cana-652	329	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-652	329	7	fig	fig	NOUN
cana-652	329	8	6.(b	6.(b	NOUN
cana-652	329	9	)	)	PUNCT
cana-652	329	10	training	training	NOUN
cana-652	329	11	vs	vs	ADP
cana-652	329	12	validation	validation	NOUN
cana-652	329	13	cat	cat	NOUN
cana-652	329	14	accuracy	accuracy	NOUN
cana-652	329	15	fig	fig	NOUN
cana-652	329	16	6	6	NUM
cana-652	329	17	.	.	PUNCT
cana-652	330	1	(	(	PUNCT
cana-652	330	2	b	b	X
cana-652	330	3	)	)	PUNCT
cana-652	330	4	depicts	depict	VERB
cana-652	330	5	the	the	DET
cana-652	330	6	training	training	NOUN
cana-652	330	7	vs	vs	ADP
cana-652	330	8	validation	validation	NOUN
cana-652	330	9	categorical	categorical	ADJ
cana-652	330	10	accuracy	accuracy	NOUN
cana-652	330	11	of	of	ADP
cana-652	330	12	the	the	DET
cana-652	330	13	mobilenet	mobilenet	NOUN
cana-652	330	14	model	model	NOUN
cana-652	330	15	.	.	PUNCT
cana-652	331	1	the	the	DET
cana-652	331	2	graph	graph	NOUN
cana-652	331	3	shows	show	VERB
cana-652	331	4	that	that	SCONJ
cana-652	331	5	the	the	DET
cana-652	331	6	mobilenet	mobilenet	NOUN
cana-652	331	7	model	model	NOUN
cana-652	331	8	is	be	AUX
cana-652	331	9	learning	learn	VERB
cana-652	331	10	to	to	PART
cana-652	331	11	classify	classify	VERB
cana-652	331	12	cat	cat	NOUN
cana-652	331	13	images	image	NOUN
cana-652	331	14	with	with	ADP
cana-652	331	15	a	a	DET
cana-652	331	16	high	high	ADJ
cana-652	331	17	degree	degree	NOUN
cana-652	331	18	of	of	ADP
cana-652	331	19	accuracy	accuracy	NOUN
cana-652	331	20	,	,	PUNCT
cana-652	331	21	as	as	SCONJ
cana-652	331	22	the	the	DET
cana-652	331	23	validation	validation	NOUN
cana-652	331	24	cat	cat	NOUN
cana-652	331	25	accuracy	accuracy	NOUN
cana-652	331	26	reaches	reach	VERB
cana-652	331	27	a	a	DET
cana-652	331	28	high	high	ADJ
cana-652	331	29	percentage	percentage	NOUN
cana-652	331	30	.	.	PUNCT
cana-652	332	1	the	the	DET
cana-652	332	2	model	model	NOUN
cana-652	332	3	could	could	AUX
cana-652	332	4	be	be	AUX
cana-652	332	5	used	use	VERB
cana-652	332	6	to	to	PART
cana-652	332	7	develop	develop	VERB
cana-652	332	8	reliable	reliable	ADJ
cana-652	332	9	cat	cat	NOUN
cana-652	332	10	classification	classification	NOUN
cana-652	332	11	applications	application	NOUN
cana-652	332	12	.	.	PUNCT
cana-652	333	1	fig	fig	NOUN
cana-652	333	2	6.(c)training	6.(c)training	NUM
cana-652	333	3	vs	vs	ADP
cana-652	333	4	validation	validation	NOUN
cana-652	333	5	top2	top2	NOUN
cana-652	333	6	accuracy	accuracy	NOUN
cana-652	333	7	communications	communication	NOUN
cana-652	333	8	on	on	ADP
cana-652	333	9	applied	apply	VERB
cana-652	333	10	nonlinear	nonlinear	ADJ
cana-652	333	11	analysis	analysis	NOUN
cana-652	333	12	issn	issn	NOUN
cana-652	333	13	:	:	PUNCT
cana-652	333	14	1074	1074	NUM
cana-652	333	15	-	-	PUNCT
cana-652	333	16	133x	133x	NUM
cana-652	333	17	vol	vol	NOUN
cana-652	333	18	31	31	NUM
cana-652	333	19	no	no	NOUN
cana-652	333	20	.	.	PUNCT
cana-652	334	1	2s	2s	NUM
cana-652	334	2	(	(	PUNCT
cana-652	334	3	2024	2024	NUM
cana-652	334	4	)	)	PUNCT
cana-652	334	5	337	337	NUM
cana-652	334	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-652	334	7	fig	fig	NOUN
cana-652	334	8	6.(d	6.(d	NOUN
cana-652	334	9	)	)	PUNCT
cana-652	334	10	training	training	NOUN
cana-652	334	11	vs	vs	ADP
cana-652	334	12	validation	validation	NOUN
cana-652	334	13	top3	top3	NOUN
cana-652	334	14	accuracy	accuracy	NOUN
cana-652	334	15	fig	fig	NOUN
cana-652	334	16	6.(c	6.(c	NUM
cana-652	334	17	)	)	PUNCT
cana-652	334	18	and	and	CCONJ
cana-652	334	19	fig	fig	NOUN
cana-652	334	20	6.(d	6.(d	NUM
cana-652	334	21	)	)	PUNCT
cana-652	334	22	illustrate	illustrate	VERB
cana-652	334	23	the	the	DET
cana-652	334	24	mobilenet	mobilenet	NOUN
cana-652	334	25	model	model	NOUN
cana-652	334	26	's	's	PART
cana-652	334	27	training	training	NOUN
cana-652	334	28	vs	vs	ADP
cana-652	334	29	validation	validation	NOUN
cana-652	334	30	top2	top2	NOUN
cana-652	334	31	and	and	CCONJ
cana-652	334	32	top3	top3	NOUN
cana-652	334	33	accuracy	accuracy	NOUN
cana-652	334	34	throughout	throughout	ADP
cana-652	334	35	the	the	DET
cana-652	334	36	epochs	epoch	NOUN
cana-652	334	37	.	.	PUNCT
cana-652	335	1	the	the	DET
cana-652	335	2	validation	validation	NOUN
cana-652	335	3	accuracy	accuracy	NOUN
cana-652	335	4	is	be	AUX
cana-652	335	5	always	always	ADV
cana-652	335	6	greater	great	ADJ
cana-652	335	7	than	than	ADP
cana-652	335	8	the	the	DET
cana-652	335	9	training	training	NOUN
cana-652	335	10	accuracy	accuracy	NOUN
cana-652	335	11	,	,	PUNCT
cana-652	335	12	showing	show	VERB
cana-652	335	13	that	that	SCONJ
cana-652	335	14	the	the	DET
cana-652	335	15	model	model	NOUN
cana-652	335	16	is	be	AUX
cana-652	335	17	learning	learn	VERB
cana-652	335	18	to	to	PART
cana-652	335	19	perform	perform	VERB
cana-652	335	20	well	well	ADV
cana-652	335	21	on	on	ADP
cana-652	335	22	validation	validation	NOUN
cana-652	335	23	data	datum	NOUN
cana-652	335	24	.	.	PUNCT
cana-652	336	1	this	this	PRON
cana-652	336	2	is	be	AUX
cana-652	336	3	a	a	DET
cana-652	336	4	good	good	ADJ
cana-652	336	5	indicator	indicator	NOUN
cana-652	336	6	because	because	SCONJ
cana-652	336	7	it	it	PRON
cana-652	336	8	indicates	indicate	VERB
cana-652	336	9	that	that	SCONJ
cana-652	336	10	the	the	DET
cana-652	336	11	model	model	NOUN
cana-652	336	12	can	can	AUX
cana-652	336	13	learn	learn	VERB
cana-652	336	14	the	the	DET
cana-652	336	15	patterns	pattern	NOUN
cana-652	336	16	in	in	ADP
cana-652	336	17	the	the	DET
cana-652	336	18	data	datum	NOUN
cana-652	336	19	.	.	PUNCT
cana-652	337	1	8	8	X
cana-652	337	2	.	.	X
cana-652	337	3	conclusion	conclusion	NOUN
cana-652	337	4	and	and	CCONJ
cana-652	337	5	future	future	ADJ
cana-652	337	6	scope	scope	NOUN
cana-652	337	7	in	in	ADP
cana-652	337	8	this	this	DET
cana-652	337	9	study	study	NOUN
cana-652	337	10	,	,	PUNCT
cana-652	337	11	we	we	PRON
cana-652	337	12	developed	develop	VERB
cana-652	337	13	an	an	DET
cana-652	337	14	effective	effective	ADJ
cana-652	337	15	skin	skin	NOUN
cana-652	337	16	lesion	lesion	NOUN
cana-652	337	17	detection	detection	NOUN
cana-652	337	18	system	system	NOUN
cana-652	337	19	using	use	VERB
cana-652	337	20	cnn	cnn	PROPN
cana-652	337	21	and	and	CCONJ
cana-652	337	22	mobilenet	mobilenet	PROPN
cana-652	337	23	.	.	PUNCT
cana-652	338	1	our	our	PRON
cana-652	338	2	customized	customized	ADJ
cana-652	338	3	cnn	cnn	PROPN
cana-652	338	4	achieved	achieve	VERB
cana-652	338	5	a	a	DET
cana-652	338	6	remarkable	remarkable	ADJ
cana-652	338	7	accuracy	accuracy	NOUN
cana-652	338	8	of	of	ADP
cana-652	338	9	98.52	98.52	NUM
cana-652	338	10	%	%	NOUN
cana-652	338	11	,	,	PUNCT
cana-652	338	12	showcasing	showcase	VERB
cana-652	338	13	its	its	PRON
cana-652	338	14	capability	capability	NOUN
cana-652	338	15	to	to	PART
cana-652	338	16	generalize	generalize	VERB
cana-652	338	17	well	well	ADV
cana-652	338	18	to	to	ADP
cana-652	338	19	new	new	ADJ
cana-652	338	20	data	datum	NOUN
cana-652	338	21	.	.	PUNCT
cana-652	339	1	the	the	DET
cana-652	339	2	fine	fine	ADV
cana-652	339	3	-	-	PUNCT
cana-652	339	4	tuned	tune	VERB
cana-652	339	5	mobilenet	mobilenet	NOUN
cana-652	339	6	model	model	NOUN
cana-652	339	7	also	also	ADV
cana-652	339	8	displayed	display	VERB
cana-652	339	9	promising	promising	ADJ
cana-652	339	10	results	result	NOUN
cana-652	339	11	,	,	PUNCT
cana-652	339	12	further	far	ADV
cana-652	339	13	expanding	expand	VERB
cana-652	339	14	the	the	DET
cana-652	339	15	horizons	horizon	NOUN
cana-652	339	16	of	of	ADP
cana-652	339	17	dermatoscopic	dermatoscopic	ADJ
cana-652	339	18	image	image	NOUN
cana-652	339	19	analysis	analysis	NOUN
cana-652	339	20	for	for	ADP
cana-652	339	21	skin	skin	NOUN
cana-652	339	22	cancer	cancer	NOUN
cana-652	339	23	detection	detection	NOUN
cana-652	339	24	.	.	PUNCT
cana-652	340	1	our	our	PRON
cana-652	340	2	findings	finding	NOUN
cana-652	340	3	reflect	reflect	VERB
cana-652	340	4	a	a	DET
cana-652	340	5	significant	significant	ADJ
cana-652	340	6	potential	potential	NOUN
cana-652	340	7	in	in	ADP
cana-652	340	8	the	the	DET
cana-652	340	9	efficiency	efficiency	NOUN
cana-652	340	10	of	of	ADP
cana-652	340	11	the	the	DET
cana-652	340	12	skin	skin	NOUN
cana-652	340	13	lesion	lesion	NOUN
cana-652	340	14	diagnosis	diagnosis	NOUN
cana-652	340	15	by	by	ADP
cana-652	340	16	increasing	increase	VERB
cana-652	340	17	the	the	DET
cana-652	340	18	accuracy	accuracy	NOUN
cana-652	340	19	of	of	ADP
cana-652	340	20	the	the	DET
cana-652	340	21	system	system	NOUN
cana-652	340	22	.	.	PUNCT
cana-652	341	1	the	the	DET
cana-652	341	2	future	future	NOUN
cana-652	341	3	may	may	AUX
cana-652	341	4	hold	hold	VERB
cana-652	341	5	some	some	DET
cana-652	341	6	exciting	exciting	ADJ
cana-652	341	7	possibilities	possibility	NOUN
cana-652	341	8	for	for	ADP
cana-652	341	9	the	the	DET
cana-652	341	10	advancement	advancement	NOUN
cana-652	341	11	of	of	ADP
cana-652	341	12	skin	skin	NOUN
cana-652	341	13	cancer	cancer	NOUN
cana-652	341	14	detection	detection	NOUN
cana-652	341	15	:	:	PUNCT
cana-652	341	16	(	(	PUNCT
cana-652	341	17	i	i	NOUN
cana-652	341	18	)	)	PUNCT
cana-652	341	19	real	real	ADJ
cana-652	341	20	-	-	PUNCT
cana-652	341	21	time	time	NOUN
cana-652	341	22	detection	detection	NOUN
cana-652	341	23	:	:	PUNCT
cana-652	341	24	developing	develop	VERB
cana-652	341	25	mobile	mobile	ADJ
cana-652	341	26	applications	application	NOUN
cana-652	341	27	with	with	ADP
cana-652	341	28	these	these	DET
cana-652	341	29	models	model	NOUN
cana-652	341	30	can	can	AUX
cana-652	341	31	provide	provide	VERB
cana-652	341	32	quick	quick	ADJ
cana-652	341	33	and	and	CCONJ
cana-652	341	34	accurate	accurate	ADJ
cana-652	341	35	skin	skin	NOUN
cana-652	341	36	cancer	cancer	NOUN
cana-652	341	37	assessments	assessment	NOUN
cana-652	341	38	,	,	PUNCT
cana-652	341	39	benefiting	benefit	VERB
cana-652	341	40	both	both	DET
cana-652	341	41	patients	patient	NOUN
cana-652	341	42	and	and	CCONJ
cana-652	341	43	medical	medical	ADJ
cana-652	341	44	professionals	professional	NOUN
cana-652	341	45	.	.	PUNCT
cana-652	342	1	(	(	PUNCT
cana-652	342	2	ii	ii	NOUN
cana-652	342	3	)	)	PUNCT
cana-652	342	4	cross	cross	ADJ
cana-652	342	5	-	-	ADJ
cana-652	342	6	domain	domain	ADJ
cana-652	342	7	applications	application	NOUN
cana-652	342	8	:	:	PUNCT
cana-652	342	9	the	the	DET
cana-652	342	10	techniques	technique	NOUN
cana-652	342	11	and	and	CCONJ
cana-652	342	12	methodologies	methodology	NOUN
cana-652	342	13	developed	develop	VERB
cana-652	342	14	for	for	ADP
cana-652	342	15	skin	skin	NOUN
cana-652	342	16	cancer	cancer	NOUN
cana-652	342	17	detection	detection	NOUN
cana-652	342	18	can	can	AUX
cana-652	342	19	potentially	potentially	ADV
cana-652	342	20	extend	extend	VERB
cana-652	342	21	to	to	ADP
cana-652	342	22	other	other	ADJ
cana-652	342	23	medical	medical	ADJ
cana-652	342	24	image	image	NOUN
cana-652	342	25	analysis	analysis	NOUN
cana-652	342	26	tasks	task	NOUN
cana-652	342	27	,	,	PUNCT
cana-652	342	28	broadening	broaden	VERB
cana-652	342	29	the	the	DET
cana-652	342	30	impact	impact	NOUN
cana-652	342	31	of	of	ADP
cana-652	342	32	this	this	DET
cana-652	342	33	research	research	NOUN
cana-652	342	34	.	.	PUNCT
cana-652	343	1	reference	reference	NOUN
cana-652	343	2	[	[	X
cana-652	343	3	1	1	NUM
cana-652	343	4	]	]	X
cana-652	343	5	mohommad	mohommad	NOUN
cana-652	343	6	kadampur	kadampur	NOUN
cana-652	343	7	,	,	PUNCT
cana-652	343	8	"	"	PUNCT
cana-652	343	9	skin	skin	NOUN
cana-652	343	10	cancer	cancer	NOUN
cana-652	343	11	detection	detection	NOUN
cana-652	343	12	applying	apply	VERB
cana-652	343	13	a	a	DET
cana-652	343	14	deep	deep	ADJ
cana-652	343	15	learning	learning	NOUN
cana-652	343	16	based	base	VERB
cana-652	343	17	model	model	NOUN
cana-652	343	18	driven	drive	VERB
cana-652	343	19	architecture	architecture	NOUN
cana-652	343	20	in	in	ADP
cana-652	343	21	the	the	DET
cana-652	343	22	cloud	cloud	NOUN
cana-652	343	23	for	for	ADP
cana-652	343	24	classifying	classify	VERB
cana-652	343	25	dermal	dermal	ADJ
cana-652	343	26	cell	cell	NOUN
cana-652	343	27	images	image	NOUN
cana-652	343	28	,	,	PUNCT
cana-652	343	29	"	"	PUNCT
cana-652	343	30	elsevier	elsevier	NOUN
cana-652	343	31	enhanced	enhanced	ADJ
cana-652	343	32	reader	reader	NOUN
cana-652	343	33	.	.	PUNCT
cana-652	344	1	informatics	informatic	NOUN
cana-652	344	2	in	in	ADP
cana-652	344	3	medicine	medicine	NOUN
cana-652	344	4	unlocked	unlock	VERB
cana-652	344	5	,	,	PUNCT
cana-652	344	6	18	18	NUM
cana-652	344	7	,	,	PUNCT
cana-652	344	8	2020	2020	NUM
cana-652	344	9	.	.	PUNCT
cana-652	345	1	doi	doi	NOUN
cana-652	345	2	:	:	PUNCT
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cana-652	345	4	/	/	SYM
cana-652	345	5	j.imu.2019.100282	j.imu.2019.100282	PROPN
cana-652	345	6	.	.	PUNCT
cana-652	346	1	[	[	X
cana-652	346	2	2	2	NUM
cana-652	346	3	]	]	X
cana-652	346	4	m.f	m.f	PROPN
cana-652	346	5	.	.	PUNCT
cana-652	347	1	jojoa	jojoa	PROPN
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cana-652	347	3	,	,	PUNCT
cana-652	347	4	l.y	l.y	PROPN
cana-652	347	5	.	.	PROPN
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cana-652	347	7	tovar	tovar	PROPN
cana-652	347	8	,	,	PUNCT
cana-652	347	9	m.b	m.b	PROPN
cana-652	347	10	.	.	PROPN
cana-652	347	11	garcia	garcia	PROPN
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cana-652	347	13	zapirain	zapirain	PROPN
cana-652	347	14	,	,	PUNCT
cana-652	347	15	"	"	PUNCT
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cana-652	347	19	deep	deep	ADJ
cana-652	347	20	learning	learning	NOUN
cana-652	347	21	techniques	technique	NOUN
cana-652	347	22	on	on	ADP
cana-652	347	23	dermatoscopic	dermatoscopic	NOUN
cana-652	347	24	images	image	NOUN
cana-652	347	25	,	,	PUNCT
cana-652	347	26	"	"	PUNCT
cana-652	347	27	bmc	bmc	ADJ
cana-652	347	28	med	med	ADJ
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cana-652	347	30	,	,	PUNCT
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cana-652	347	32	,	,	PUNCT
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cana-652	347	34	,	,	PUNCT
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cana-652	347	36	.	.	PUNCT
cana-652	348	1	doi	doi	NOUN
cana-652	348	2	:	:	PUNCT
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cana-652	348	4	/	/	SYM
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cana-652	348	8	-	-	PUNCT
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cana-652	348	10	-	-	SYM
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cana-652	349	3	]	]	PUNCT
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cana-652	349	38	.	.	PUNCT
cana-652	350	1	doi	doi	NOUN
cana-652	350	2	:	:	PUNCT
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cana-652	350	4	/	/	SYM
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cana-652	350	6	.	.	PUNCT
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cana-652	351	7	:	:	PUNCT
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cana-652	353	1	[	[	X
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cana-652	353	3	]	]	X
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cana-652	355	1	doi	doi	NOUN
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cana-652	355	4	/	/	SYM
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cana-652	355	8	-	-	PUNCT
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cana-652	355	10	-	-	PUNCT
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cana-652	356	2	5	5	X
cana-652	356	3	]	]	PUNCT
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cana-652	356	42	"	"	PUNCT
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cana-652	357	1	doi	doi	NOUN
cana-652	357	2	:	:	PUNCT
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cana-652	357	4	/	/	SYM
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cana-652	358	3	]	]	PUNCT
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cana-652	359	2	,	,	PUNCT
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cana-652	359	5	,	,	PUNCT
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cana-652	359	9	.	.	PUNCT
cana-652	360	1	doi	doi	NOUN
cana-652	360	2	:	:	PUNCT
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cana-652	360	4	/	/	SYM
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cana-652	360	6	.	.	PUNCT
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cana-652	361	2	7	7	X
cana-652	361	3	]	]	X
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cana-652	361	12	,	,	PUNCT
cana-652	361	13	"	"	PUNCT
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cana-652	362	23	efficientnets	efficientnet	NOUN
cana-652	362	24	-a	-a	X
cana-652	362	25	first	first	ADJ
cana-652	362	26	step	step	NOUN
cana-652	362	27	towards	towards	ADP
cana-652	362	28	preventing	prevent	VERB
cana-652	362	29	skin	skin	NOUN
cana-652	362	30	cancer	cancer	NOUN
cana-652	362	31	,	,	PUNCT
cana-652	362	32	"	"	PUNCT
cana-652	362	33	neuroscience	neuroscience	NOUN
cana-652	362	34	informatics	informatic	NOUN
cana-652	362	35	,	,	PUNCT
cana-652	362	36	2	2	NUM
cana-652	362	37	,	,	PUNCT
cana-652	362	38	2021	2021	NUM
cana-652	362	39	.	.	PUNCT
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cana-652	363	2	9	9	NUM
cana-652	363	3	]	]	SYM
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cana-652	363	7	,	,	PUNCT
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cana-652	363	9	,	,	PUNCT
cana-652	363	10	"	"	PUNCT
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cana-652	363	12	cancer	cancer	NOUN
cana-652	363	13	identification	identification	NOUN
cana-652	363	14	system	system	NOUN
cana-652	363	15	of	of	ADP
cana-652	363	16	haml0000	haml0000	PROPN
cana-652	363	17	skin	skin	NOUN
cana-652	363	18	cancer	cancer	NOUN
cana-652	363	19	dataset	dataset	NOUN
cana-652	363	20	using	use	VERB
cana-652	363	21	convolutional	convolutional	ADJ
cana-652	363	22	neural	neural	ADJ
cana-652	363	23	network	network	NOUN
cana-652	363	24	,	,	PUNCT
cana-652	363	25	"	"	PUNCT
cana-652	363	26	aip	aip	PROPN
cana-652	363	27	conf	conf	PROPN
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cana-652	364	2	.	.	PROPN
cana-652	364	3	,	,	PUNCT
cana-652	364	4	2202	2202	NUM
cana-652	364	5	(	(	PUNCT
cana-652	364	6	1	1	NUM
cana-652	364	7	)	)	PUNCT
cana-652	364	8	,	,	PUNCT
cana-652	364	9	020039	020039	NUM
cana-652	364	10	,	,	PUNCT
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cana-652	364	12	.	.	PUNCT
cana-652	365	1	[	[	X
cana-652	365	2	10	10	NUM
cana-652	365	3	]	]	PUNCT
cana-652	365	4	a.	a.	NOUN
cana-652	365	5	tajerian	tajerian	PROPN
cana-652	365	6	,	,	PUNCT
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cana-652	365	8	kazemian	kazemian	PROPN
cana-652	365	9	,	,	PUNCT
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cana-652	365	14	akhavanmalayeri	akhavanmalayeri	NOUN
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cana-652	365	16	"	"	PUNCT
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cana-652	365	21	a	a	DET
cana-652	365	22	new	new	ADJ
cana-652	365	23	machine	machine	NOUN
cana-652	365	24	-	-	PUNCT
cana-652	365	25	learningbased	learningbase	VERB
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cana-652	365	27	tool	tool	NOUN
cana-652	365	28	for	for	ADP
cana-652	365	29	the	the	DET
cana-652	365	30	differentiation	differentiation	NOUN
cana-652	365	31	of	of	ADP
cana-652	365	32	dermatoscopic	dermatoscopic	ADJ
cana-652	365	33	skin	skin	NOUN
cana-652	365	34	cancer	cancer	NOUN
cana-652	365	35	images	image	NOUN
cana-652	365	36	,	,	PUNCT
cana-652	365	37	"	"	PUNCT
cana-652	365	38	plos	plos	PROPN
cana-652	365	39	one	one	NUM
cana-652	365	40	,	,	PUNCT
cana-652	365	41	18(4	18(4	NUM
cana-652	365	42	)	)	PUNCT
cana-652	365	43	,	,	PUNCT
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cana-652	365	45	,	,	PUNCT
cana-652	365	46	2023	2023	NUM
cana-652	365	47	.	.	PUNCT
cana-652	366	1	doi	doi	NOUN
cana-652	366	2	:	:	PUNCT
cana-652	366	3	10.1371	10.1371	NUM
cana-652	366	4	/	/	SYM
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cana-652	366	6	.	.	PUNCT
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cana-652	367	2	11	11	NUM
cana-652	367	3	]	]	X
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cana-652	367	5	.	.	PROPN
cana-652	367	6	alam	alam	PROPN
cana-652	367	7	,	,	PUNCT
cana-652	367	8	k.	k.	PROPN
cana-652	367	9	shaukat	shaukat	PROPN
cana-652	367	10	,	,	PUNCT
cana-652	367	11	w.a	w.a	PROPN
cana-652	367	12	.	.	PROPN
cana-652	367	13	khan	khan	PROPN
cana-652	367	14	,	,	PUNCT
cana-652	367	15	i.a	i.a	PROPN
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cana-652	367	30	s.	s.	PROPN
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cana-652	367	32	,	,	PUNCT
cana-652	367	33	"	"	PUNCT
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cana-652	367	36	deep	deep	ADJ
cana-652	367	37	learning	learning	NOUN
cana-652	367	38	-	-	PUNCT
cana-652	367	39	based	base	VERB
cana-652	367	40	skin	skin	NOUN
cana-652	367	41	cancer	cancer	NOUN
cana-652	367	42	classifier	classifier	NOUN
cana-652	367	43	for	for	ADP
cana-652	367	44	an	an	DET
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cana-652	367	46	dataset	dataset	NOUN
cana-652	367	47	,	,	PUNCT
cana-652	367	48	"	"	PUNCT
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cana-652	367	50	,	,	PUNCT
cana-652	367	51	12(9	12(9	NUM
cana-652	367	52	)	)	PUNCT
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cana-652	367	55	,	,	PUNCT
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cana-652	367	57	.	.	PUNCT
cana-652	368	1	doi	doi	NOUN
cana-652	368	2	:	:	PUNCT
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cana-652	368	4	/	/	SYM
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cana-652	368	6	.	.	PUNCT
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cana-652	369	3	]	]	X
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cana-652	369	6	,	,	PUNCT
cana-652	369	7	rashmi	rashmi	PROPN
cana-652	369	8	ashtagi	ashtagi	PROPN
cana-652	369	9	,	,	PUNCT
cana-652	369	10	prashant	prashant	PROPN
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cana-652	369	12	,	,	PUNCT
cana-652	369	13	sandeep	sandeep	PROPN
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cana-652	369	15	,	,	PUNCT
cana-652	369	16	dipmalasalunke	dipmalasalunke	PROPN
cana-652	369	17	,	,	PUNCT
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cana-652	369	19	upadhye	upadhye	NOUN
cana-652	369	20	,	,	PUNCT
cana-652	369	21	"	"	PUNCT
cana-652	369	22	an	an	DET
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cana-652	369	25	learning	learn	VERB
cana-652	369	26	approach	approach	NOUN
cana-652	369	27	for	for	ADP
cana-652	369	28	classification	classification	NOUN
cana-652	369	29	of	of	ADP
cana-652	369	30	types	type	NOUN
cana-652	369	31	of	of	ADP
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cana-652	369	33	,	,	PUNCT
cana-652	369	34	"	"	PUNCT
cana-652	369	35	traitement	traitement	ADJ
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cana-652	369	38	,	,	PUNCT
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cana-652	369	40	,	,	PUNCT
cana-652	369	41	20952101	20952101	NUM
cana-652	369	42	,	,	PUNCT
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cana-652	369	44	/	/	SYM
cana-652	369	45	ts.390622	ts.390622	PROPN
cana-652	369	46	.	.	PUNCT
cana-652	370	1	[	[	X
cana-652	370	2	13	13	NUM
cana-652	370	3	]	]	SYM
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cana-652	370	5	,	,	PUNCT
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cana-652	370	7	,	,	PUNCT
cana-652	370	8	malaya	malaya	PROPN
cana-652	370	9	kumar	kumar	PROPN
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cana-652	370	11	,	,	PUNCT
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cana-652	370	13	mishra	mishra	PROPN
cana-652	370	14	,	,	PUNCT
cana-652	370	15	"	"	PUNCT
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cana-652	370	18	neural	neural	ADJ
cana-652	370	19	networks	network	NOUN
cana-652	370	20	with	with	ADP
cana-652	370	21	svm	svm	ADJ
cana-652	370	22	classifier	classifier	NOUN
cana-652	370	23	for	for	ADP
cana-652	370	24	classification	classification	NOUN
cana-652	370	25	of	of	ADP
cana-652	370	26	skin	skin	NOUN
cana-652	370	27	cancer	cancer	NOUN
cana-652	370	28	,	,	PUNCT
cana-652	370	29	"	"	PUNCT
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cana-652	370	33	,	,	PUNCT
cana-652	370	34	5	5	NUM
cana-652	370	35	,	,	PUNCT
cana-652	370	36	100069	100069	NUM
cana-652	370	37	,	,	PUNCT
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cana-652	370	39	:	:	PUNCT
cana-652	370	40	10.1016	10.1016	NUM
cana-652	370	41	/	/	SYM
cana-652	370	42	j.bea.2022.100069	j.bea.2022.100069	PROPN
cana-652	370	43	.	.	PUNCT
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cana-652	371	2	14	14	NUM
cana-652	371	3	]	]	X
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cana-652	371	13	susan	susan	PROPN
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cana-652	371	16	"	"	PUNCT
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cana-652	371	19	skin	skin	NOUN
cana-652	371	20	cancer	cancer	NOUN
cana-652	371	21	classification	classification	NOUN
cana-652	371	22	using	use	VERB
cana-652	371	23	optimized	optimize	VERB
cana-652	371	24	convolutional	convolutional	ADJ
cana-652	371	25	neural	neural	ADJ
cana-652	371	26	network	network	NOUN
cana-652	371	27	for	for	ADP
cana-652	371	28	a	a	DET
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cana-652	371	30	healthcare	healthcare	NOUN
cana-652	371	31	system	system	NOUN
cana-652	371	32	,	,	PUNCT
cana-652	371	33	"	"	PUNCT
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cana-652	371	35	.	.	PUNCT
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cana-652	372	2	15	15	NUM
cana-652	372	3	]	]	X
cana-652	372	4	philipp	philipp	PROPN
cana-652	372	5	tschandl	tschandl	PROPN
cana-652	372	6	,	,	PUNCT
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cana-652	372	8	codella	codella	PROPN
cana-652	372	9	,	,	PUNCT
cana-652	372	10	allan	allan	PROPN
cana-652	372	11	halpern	halpern	PROPN
cana-652	372	12	,	,	PUNCT
cana-652	372	13	susana	susana	PROPN
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cana-652	372	23	,	,	PUNCT
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cana-652	372	25	rosendahl	rosendahl	NOUN
cana-652	372	26	,	,	PUNCT
cana-652	372	27	josepmalvehy	josepmalvehy	NOUN
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cana-652	372	41	"	"	PUNCT
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cana-652	372	49	recognition	recognition	NOUN
cana-652	372	50	,	,	PUNCT
cana-652	372	51	"	"	PUNCT
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cana-652	372	55	26	26	NUM
cana-652	372	56	,	,	PUNCT
cana-652	372	57	2020	2020	NUM
cana-652	372	58	.	.	PUNCT
cana-652	373	1	doi	doi	NOUN
cana-652	373	2	:	:	PUNCT
cana-652	373	3	10.1038	10.1038	NUM
cana-652	373	4	/	/	SYM
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cana-652	373	6	-	-	PUNCT
cana-652	373	7	020	020	NUM
cana-652	373	8	-	-	PUNCT
cana-652	373	9	0942	0942	NUM
cana-652	373	10	-	-	PUNCT
cana-652	373	11	0	0	NUM
cana-652	373	12	.	.	PUNCT
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cana-652	374	2	16	16	NUM
cana-652	374	3	]	]	PUNCT
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cana-652	374	5	,	,	PUNCT
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cana-652	374	20	)	)	PUNCT
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cana-652	374	35	)	)	PUNCT
cana-652	374	36	,	,	PUNCT
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cana-652	374	38	.	.	PUNCT
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cana-652	375	2	-	-	SYM
cana-652	375	3	480	480	NUM
cana-652	375	4	.	.	PUNCT
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cana-652	376	3	]	]	X
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cana-652	376	6	a.	a.	PROPN
cana-652	376	7	,	,	PUNCT
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cana-652	376	9	,	,	PUNCT
cana-652	376	10	s.i	s.i	PROPN
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cana-652	376	31	functions	function	NOUN
cana-652	376	32	using	use	VERB
cana-652	376	33	homotopy	homotopy	NOUN
cana-652	376	34	continuation	continuation	NOUN
cana-652	376	35	method	method	NOUN
cana-652	376	36	"	"	PUNCT
cana-652	376	37	(	(	PUNCT
cana-652	376	38	2022	2022	NUM
cana-652	376	39	)	)	PUNCT
cana-652	376	40	advances	advance	NOUN
cana-652	376	41	in	in	ADP
cana-652	376	42	the	the	DET
cana-652	376	43	theory	theory	NOUN
cana-652	376	44	of	of	ADP
cana-652	376	45	nonlinear	nonlinear	ADJ
cana-652	376	46	analysis	analysis	NOUN
cana-652	376	47	and	and	CCONJ
cana-652	376	48	its	its	PRON
cana-652	376	49	applications	application	NOUN
cana-652	376	50	,	,	PUNCT
cana-652	376	51	6	6	NUM
cana-652	376	52	(	(	PUNCT
cana-652	376	53	3	3	NUM
cana-652	376	54	)	)	PUNCT
cana-652	376	55	,	,	PUNCT
cana-652	376	56	pp	pp	PROPN
cana-652	376	57	.	.	PUNCT
cana-652	377	1	354	354	NUM
cana-652	377	2	-	-	SYM
cana-652	377	3	363	363	NUM
cana-652	377	4	.	.	PUNCT
cana-652	378	1	[	[	X
cana-652	378	2	18	18	NUM
cana-652	378	3	]	]	X
cana-652	378	4	p.	p.	NOUN
cana-652	378	5	deshpande	deshpande	PROPN
cana-652	378	6	,	,	PUNCT
cana-652	378	7	r.	r.	PROPN
cana-652	378	8	dhabliya	dhabliya	PROPN
cana-652	378	9	,	,	PUNCT
cana-652	378	10	d.	d.	PROPN
cana-652	378	11	khubalkar	khubalkar	PROPN
cana-652	378	12	,	,	PUNCT
cana-652	378	13	p.	p.	NOUN
cana-652	378	14	a.	a.	NOUN
cana-652	378	15	upadhye	upadhye	NOUN
cana-652	378	16	,	,	PUNCT
cana-652	378	17	k.	k.	PROPN
cana-652	378	18	a.	a.	PROPN
cana-652	378	19	wagh	wagh	PROPN
cana-652	378	20	and	and	CCONJ
cana-652	378	21	v.	v.	ADP
cana-652	378	22	khetani	khetani	X
cana-652	378	23	,	,	PUNCT
cana-652	378	24	"	"	PUNCT
cana-652	378	25	alzheimer	alzheimer	NOUN
cana-652	378	26	disease	disease	NOUN
cana-652	378	27	progression	progression	NOUN
cana-652	378	28	forecasting	forecasting	NOUN
cana-652	378	29	:	:	PUNCT
cana-652	378	30	empowering	empower	VERB
cana-652	378	31	models	model	NOUN
cana-652	378	32	through	through	ADP
cana-652	378	33	hybrid	hybrid	NOUN
cana-652	378	34	of	of	ADP
cana-652	378	35	cnn	cnn	PROPN
cana-652	378	36	and	and	CCONJ
cana-652	378	37	lstm	lstm	NOUN
cana-652	378	38	with	with	ADP
cana-652	378	39	pso	pso	NOUN
cana-652	378	40	op	op	NOUN
cana-652	378	41	-	-	PUNCT
cana-652	378	42	timization	timization	NOUN
cana-652	378	43	,	,	PUNCT
cana-652	378	44	"	"	PUNCT
cana-652	378	45	2024	2024	NUM
cana-652	378	46	international	international	ADJ
cana-652	378	47	conference	conference	NOUN
cana-652	378	48	on	on	ADP
cana-652	378	49	emerging	emerge	VERB
cana-652	378	50	smart	smart	ADJ
cana-652	378	51	computing	computing	NOUN
cana-652	378	52	and	and	CCONJ
cana-652	378	53	informatics	informatics	PROPN
cana-652	378	54	(	(	PUNCT
cana-652	378	55	esci	esci	PROPN
cana-652	378	56	)	)	PUNCT
cana-652	378	57	,	,	PUNCT
cana-652	378	58	pune	pune	NOUN
cana-652	378	59	,	,	PUNCT
cana-652	378	60	india	india	PROPN
cana-652	378	61	,	,	PUNCT
cana-652	378	62	2024	2024	NUM
cana-652	378	63	,	,	PUNCT
cana-652	378	64	pp	pp	ADJ
cana-652	378	65	.	.	PUNCT
cana-652	379	1	1	1	NUM
cana-652	379	2	-	-	SYM
cana-652	379	3	5	5	NUM
cana-652	379	4	,	,	PUNCT
cana-652	379	5	doi	doi	NOUN
cana-652	379	6	:	:	PUNCT
cana-652	379	7	10.1109	10.1109	NUM
cana-652	379	8	/	/	SYM
cana-652	379	9	esci59607.2024.10497309	esci59607.2024.10497309	PROPN
cana-652	379	10	.	.	PUNCT