id	sid	tid	token	lemma	pos
cana-2940	1	1	communications	communication	NOUN
cana-2940	1	2	on	on	ADP
cana-2940	1	3	applied	apply	VERB
cana-2940	1	4	nonlinear	nonlinear	ADJ
cana-2940	1	5	analysis	analysis	NOUN
cana-2940	1	6	issn	issn	NOUN
cana-2940	1	7	:	:	PUNCT
cana-2940	1	8	1074	1074	NUM
cana-2940	1	9	-	-	PUNCT
cana-2940	1	10	133x	133x	NUM
cana-2940	1	11	vol	vol	NOUN
cana-2940	1	12	32	32	NUM
cana-2940	1	13	no	no	NOUN
cana-2940	1	14	.	.	PUNCT
cana-2940	2	1	5s	5s	NUM
cana-2940	2	2	(	(	PUNCT
cana-2940	2	3	2025	2025	NUM
cana-2940	2	4	)	)	PUNCT
cana-2940	2	5	26	26	NUM
cana-2940	2	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2940	2	7	multi	multi	ADJ
cana-2940	2	8	-	-	ADJ
cana-2940	2	9	class	class	ADJ
cana-2940	2	10	deep	deep	ADJ
cana-2940	2	11	learning	learning	NOUN
cana-2940	2	12	-	-	PUNCT
cana-2940	2	13	based	base	VERB
cana-2940	2	14	enhanced	enhanced	ADJ
cana-2940	2	15	image	image	NOUN
cana-2940	2	16	segmentation	segmentation	NOUN
cana-2940	2	17	and	and	CCONJ
cana-2940	2	18	feature	feature	NOUN
cana-2940	2	19	extraction	extraction	NOUN
cana-2940	2	20	technique	technique	NOUN
cana-2940	2	21	for	for	ADP
cana-2940	2	22	early	early	ADJ
cana-2940	2	23	classification	classification	NOUN
cana-2940	2	24	of	of	ADP
cana-2940	2	25	skin	skin	NOUN
cana-2940	2	26	diseases	disease	NOUN
cana-2940	2	27	ritika	ritika	X
cana-2940	2	28	sharma1,sushil	sharma1,sushil	PUNCT
cana-2940	3	1	kumar	kumar	PROPN
cana-2940	3	2	bansal2,amit	bansal2,amit	PROPN
cana-2940	3	3	verma3	verma3	PROPN
cana-2940	3	4	,	,	PUNCT
cana-2940	3	5	gulshan	gulshan	PROPN
cana-2940	3	6	goyal4,mukesh	goyal4,mukesh	PUNCT
cana-2940	4	1	singla5	singla5	NOUN
cana-2940	4	2	1,2,3	1,2,3	NUM
cana-2940	4	3	department	department	NOUN
cana-2940	4	4	of	of	ADP
cana-2940	4	5	computer	computer	NOUN
cana-2940	4	6	science	science	NOUN
cana-2940	4	7	and	and	CCONJ
cana-2940	4	8	engineering	engineering	NOUN
cana-2940	4	9	,	,	PUNCT
cana-2940	4	10	mait	mait	PROPN
cana-2940	4	11	,	,	PUNCT
cana-2940	4	12	maharaja	maharaja	PROPN
cana-2940	4	13	agrasen	agrasen	PROPN
cana-2940	4	14	university	university	PROPN
cana-2940	4	15	,	,	PUNCT
cana-2940	4	16	baddi,174103	baddi,174103	PROPN
cana-2940	4	17	,	,	PUNCT
cana-2940	4	18	hp	hp	PROPN
cana-2940	4	19	,	,	PUNCT
cana-2940	4	20	india	india	PROPN
cana-2940	4	21	1rmoudgil16@gmail.com	1rmoudgil16@gmail.com	PROPN
cana-2940	4	22	,	,	PUNCT
cana-2940	4	23	2sk93recj@gmail.com,3verma0152@gmail.com	2sk93recj@gmail.com,3verma0152@gmail.com	NUM
cana-2940	4	24	4department	4department	NUM
cana-2940	4	25	of	of	ADP
cana-2940	4	26	computer	computer	NOUN
cana-2940	4	27	science	science	NOUN
cana-2940	4	28	and	and	CCONJ
cana-2940	4	29	engineering	engineering	NOUN
cana-2940	4	30	,	,	PUNCT
cana-2940	4	31	chandigarh	chandigarh	NOUN
cana-2940	4	32	college	college	NOUN
cana-2940	4	33	of	of	ADP
cana-2940	4	34	engineering	engineering	PROPN
cana-2940	4	35	&	&	CCONJ
cana-2940	4	36	technology	technology	NOUN
cana-2940	4	37	,	,	PUNCT
cana-2940	4	38	chandigarh	chandigarh	VERB
cana-2940	4	39	4gulshangoyal@ccet.ac.in	4gulshangoyal@ccet.ac.in	NUM
cana-2940	4	40	5department	5department	NUM
cana-2940	4	41	of	of	ADP
cana-2940	4	42	computer	computer	NOUN
cana-2940	4	43	science	science	NOUN
cana-2940	4	44	and	and	CCONJ
cana-2940	4	45	engineering	engineering	NOUN
cana-2940	4	46	,	,	PUNCT
cana-2940	4	47	baba	baba	PROPN
cana-2940	4	48	masth	masth	PROPN
cana-2940	4	49	nath	nath	PROPN
cana-2940	4	50	university	university	PROPN
cana-2940	4	51	,	,	PUNCT
cana-2940	4	52	rohtak	rohtak	PROPN
cana-2940	4	53	.	.	PUNCT
cana-2940	5	1	5mukesh27singla@yahoo.co.in	5mukesh27singla@yahoo.co.in	PROPN
cana-2940	5	2	article	article	NOUN
cana-2940	5	3	history	history	NOUN
cana-2940	5	4	:	:	PUNCT
cana-2940	5	5	received	receive	VERB
cana-2940	5	6	:	:	PUNCT
cana-2940	5	7	02	02	NUM
cana-2940	5	8	-	-	SYM
cana-2940	5	9	10	10	NUM
cana-2940	5	10	-	-	PUNCT
cana-2940	5	11	2024	2024	NUM
cana-2940	5	12	revised	revise	VERB
cana-2940	5	13	:	:	PUNCT
cana-2940	5	14	22	22	NUM
cana-2940	5	15	-	-	SYM
cana-2940	5	16	11	11	NUM
cana-2940	5	17	-	-	PUNCT
cana-2940	5	18	2024	2024	NUM
cana-2940	5	19	accepted	accept	VERB
cana-2940	5	20	:	:	PUNCT
cana-2940	5	21	02	02	NUM
cana-2940	5	22	-	-	SYM
cana-2940	5	23	12	12	NUM
cana-2940	5	24	-	-	PUNCT
cana-2940	5	25	2024	2024	NUM
cana-2940	5	26	abstract	abstract	NOUN
cana-2940	5	27	:	:	PUNCT
cana-2940	5	28	skin	skin	NOUN
cana-2940	5	29	disorders	disorder	NOUN
cana-2940	5	30	pose	pose	VERB
cana-2940	5	31	great	great	ADJ
cana-2940	5	32	health	health	NOUN
cana-2940	5	33	issues	issue	NOUN
cana-2940	5	34	among	among	ADP
cana-2940	5	35	millions	million	NOUN
cana-2940	5	36	of	of	ADP
cana-2940	5	37	people	people	NOUN
cana-2940	5	38	across	across	ADP
cana-2940	5	39	the	the	DET
cana-2940	5	40	globe	globe	NOUN
cana-2940	5	41	and	and	CCONJ
cana-2940	5	42	thus	thus	ADV
cana-2940	5	43	require	require	VERB
cana-2940	5	44	accurate	accurate	ADJ
cana-2940	5	45	diagnosis	diagnosis	NOUN
cana-2940	5	46	and	and	CCONJ
cana-2940	5	47	treatment	treatment	NOUN
cana-2940	5	48	.	.	PUNCT
cana-2940	6	1	this	this	DET
cana-2940	6	2	research	research	NOUN
cana-2940	6	3	article	article	NOUN
cana-2940	6	4	proposed	propose	VERB
cana-2940	6	5	an	an	DET
cana-2940	6	6	enhanced	enhanced	ADJ
cana-2940	6	7	deep	deep	ADJ
cana-2940	6	8	learning	learning	NOUN
cana-2940	6	9	-	-	PUNCT
cana-2940	6	10	based	base	VERB
cana-2940	6	11	image	image	NOUN
cana-2940	6	12	segmentation	segmentation	NOUN
cana-2940	6	13	and	and	CCONJ
cana-2940	6	14	feature	feature	NOUN
cana-2940	6	15	extraction	extraction	NOUN
cana-2940	6	16	approach	approach	NOUN
cana-2940	6	17	for	for	ADP
cana-2940	6	18	the	the	DET
cana-2940	6	19	early	early	ADJ
cana-2940	6	20	categorization	categorization	NOUN
cana-2940	6	21	of	of	ADP
cana-2940	6	22	human	human	ADJ
cana-2940	6	23	skin	skin	NOUN
cana-2940	6	24	diseases	disease	NOUN
cana-2940	6	25	utilizing	utilize	VERB
cana-2940	6	26	the	the	DET
cana-2940	6	27	dermnet	dermnet	NOUN
cana-2940	6	28	dataset	dataset	NOUN
cana-2940	6	29	of	of	ADP
cana-2940	6	30	image	image	NOUN
cana-2940	6	31	samples	sample	NOUN
cana-2940	6	32	classified	classify	VERB
cana-2940	6	33	under	under	ADP
cana-2940	6	34	23	23	NUM
cana-2940	6	35	categories	category	NOUN
cana-2940	6	36	.	.	PUNCT
cana-2940	7	1	input	input	NOUN
cana-2940	7	2	images	image	NOUN
cana-2940	7	3	are	be	AUX
cana-2940	7	4	applied	apply	VERB
cana-2940	7	5	during	during	ADP
cana-2940	7	6	the	the	DET
cana-2940	7	7	preprocessing	preprocessing	NOUN
cana-2940	7	8	stage	stage	NOUN
cana-2940	7	9	to	to	ADP
cana-2940	7	10	an	an	DET
cana-2940	7	11	enhanced	enhanced	ADJ
cana-2940	7	12	image	image	NOUN
cana-2940	7	13	segmentation	segmentation	NOUN
cana-2940	7	14	technique	technique	NOUN
cana-2940	7	15	known	know	VERB
cana-2940	7	16	as	as	ADP
cana-2940	7	17	enhanced	enhance	VERB
cana-2940	7	18	holistically	holistically	ADV
cana-2940	7	19	nested	nest	VERB
cana-2940	7	20	edge	edge	NOUN
cana-2940	7	21	detection	detection	NOUN
cana-2940	7	22	(	(	PUNCT
cana-2940	7	23	ehned	ehne	VERB
cana-2940	7	24	)	)	PUNCT
cana-2940	7	25	toward	toward	ADP
cana-2940	7	26	detail	detail	NOUN
cana-2940	7	27	enrichment	enrichment	NOUN
cana-2940	7	28	in	in	ADP
cana-2940	7	29	edges	edge	NOUN
cana-2940	7	30	.	.	PUNCT
cana-2940	8	1	further	far	ADV
cana-2940	8	2	,	,	PUNCT
cana-2940	8	3	for	for	ADP
cana-2940	8	4	multiple	multiple	ADJ
cana-2940	8	5	feature	feature	NOUN
cana-2940	8	6	extraction	extraction	NOUN
cana-2940	8	7	,	,	PUNCT
cana-2940	8	8	the	the	DET
cana-2940	8	9	model	model	NOUN
cana-2940	8	10	used	use	VERB
cana-2940	8	11	efficientnet	efficientnet	NOUN
cana-2940	8	12	-	-	PUNCT
cana-2940	8	13	b0	b0	NOUN
cana-2940	8	14	and	and	CCONJ
cana-2940	8	15	extracted	extract	VERB
cana-2940	8	16	1280	1280	NUM
cana-2940	8	17	features	feature	NOUN
cana-2940	8	18	from	from	ADP
cana-2940	8	19	an	an	DET
cana-2940	8	20	image	image	NOUN
cana-2940	8	21	represented	represent	VERB
cana-2940	8	22	as	as	ADP
cana-2940	8	23	a	a	DET
cana-2940	8	24	1d	1d	NUM
cana-2940	8	25	array	array	NOUN
cana-2940	8	26	.	.	PUNCT
cana-2940	9	1	the	the	DET
cana-2940	9	2	extracted	extract	VERB
cana-2940	9	3	features	feature	NOUN
cana-2940	9	4	form	form	VERB
cana-2940	9	5	a	a	DET
cana-2940	9	6	baseline	baseline	NOUN
cana-2940	9	7	for	for	ADP
cana-2940	9	8	training	training	NOUN
cana-2940	9	9	and	and	CCONJ
cana-2940	9	10	testing	testing	NOUN
cana-2940	9	11	cnns	cnn	NOUN
cana-2940	9	12	on	on	ADP
cana-2940	9	13	the	the	DET
cana-2940	9	14	diagnosis	diagnosis	NOUN
cana-2940	9	15	of	of	ADP
cana-2940	9	16	skin	skin	NOUN
cana-2940	9	17	diseases	disease	NOUN
cana-2940	9	18	.	.	PUNCT
cana-2940	10	1	it	it	PRON
cana-2940	10	2	tries	try	VERB
cana-2940	10	3	to	to	PART
cana-2940	10	4	bridge	bridge	VERB
cana-2940	10	5	all	all	DET
cana-2940	10	6	the	the	DET
cana-2940	10	7	loopholes	loophole	NOUN
cana-2940	10	8	from	from	ADP
cana-2940	10	9	previous	previous	ADJ
cana-2940	10	10	research	research	NOUN
cana-2940	10	11	by	by	ADP
cana-2940	10	12	observing	observe	VERB
cana-2940	10	13	an	an	DET
cana-2940	10	14	extremely	extremely	ADV
cana-2940	10	15	vast	vast	ADJ
cana-2940	10	16	and	and	CCONJ
cana-2940	10	17	heterogeneous	heterogeneous	ADJ
cana-2940	10	18	dataset	dataset	NOUN
cana-2940	10	19	and	and	CCONJ
cana-2940	10	20	uses	use	VERB
cana-2940	10	21	better	well	ADJ
cana-2940	10	22	techniques	technique	NOUN
cana-2940	10	23	for	for	ADP
cana-2940	10	24	segmentation	segmentation	NOUN
cana-2940	10	25	and	and	CCONJ
cana-2940	10	26	feature	feature	NOUN
cana-2940	10	27	extraction	extraction	NOUN
cana-2940	10	28	.	.	PUNCT
cana-2940	11	1	the	the	DET
cana-2940	11	2	proposed	propose	VERB
cana-2940	11	3	framework	framework	NOUN
cana-2940	11	4	is	be	AUX
cana-2940	11	5	likely	likely	ADJ
cana-2940	11	6	to	to	PART
cana-2940	11	7	increase	increase	VERB
cana-2940	11	8	the	the	DET
cana-2940	11	9	recall	recall	NOUN
cana-2940	11	10	performance	performance	NOUN
cana-2940	11	11	metric	metric	ADJ
cana-2940	11	12	to	to	ADP
cana-2940	11	13	91	91	NUM
cana-2940	11	14	%	%	NOUN
cana-2940	11	15	,	,	PUNCT
cana-2940	11	16	specificity	specificity	NOUN
cana-2940	11	17	to	to	ADP
cana-2940	11	18	96	96	NUM
cana-2940	11	19	%	%	NOUN
cana-2940	11	20	,	,	PUNCT
cana-2940	11	21	and	and	CCONJ
cana-2940	11	22	ability	ability	NOUN
cana-2940	11	23	to	to	PART
cana-2940	11	24	enable	enable	VERB
cana-2940	11	25	early	early	ADJ
cana-2940	11	26	diagnosis	diagnosis	NOUN
cana-2940	11	27	in	in	ADP
cana-2940	11	28	case	case	NOUN
cana-2940	11	29	of	of	ADP
cana-2940	11	30	skin	skin	NOUN
cana-2940	11	31	diseases	disease	NOUN
cana-2940	11	32	.	.	PUNCT
cana-2940	12	1	this	this	DET
cana-2940	12	2	enhancement	enhancement	NOUN
cana-2940	12	3	is	be	AUX
cana-2940	12	4	supposed	suppose	VERB
cana-2940	12	5	to	to	PART
cana-2940	12	6	follow	follow	VERB
cana-2940	12	7	more	more	ADV
cana-2940	12	8	effective	effective	ADJ
cana-2940	12	9	management	management	NOUN
cana-2940	12	10	and	and	CCONJ
cana-2940	12	11	better	well	ADJ
cana-2940	12	12	patient	patient	ADJ
cana-2940	12	13	outcomes	outcome	NOUN
cana-2940	12	14	.	.	PUNCT
cana-2940	13	1	findings	finding	NOUN
cana-2940	13	2	in	in	ADP
cana-2940	13	3	this	this	DET
cana-2940	13	4	study	study	NOUN
cana-2940	13	5	are	be	AUX
cana-2940	13	6	likely	likely	ADJ
cana-2940	13	7	to	to	PART
cana-2940	13	8	contribute	contribute	VERB
cana-2940	13	9	toward	toward	ADP
cana-2940	13	10	dermatology	dermatology	NOUN
cana-2940	13	11	research	research	NOUN
cana-2940	13	12	and	and	CCONJ
cana-2940	13	13	promote	promote	VERB
cana-2940	13	14	intelligent	intelligent	ADJ
cana-2940	13	15	systems	system	NOUN
cana-2940	13	16	in	in	ADP
cana-2940	13	17	diagnostics	diagnostic	NOUN
cana-2940	13	18	for	for	ADP
cana-2940	13	19	automatic	automatic	ADJ
cana-2940	13	20	classification	classification	NOUN
cana-2940	13	21	of	of	ADP
cana-2940	13	22	skin	skin	NOUN
cana-2940	13	23	diseases	disease	NOUN
cana-2940	13	24	.	.	PUNCT
cana-2940	14	1	keywords	keyword	NOUN
cana-2940	14	2	:	:	PUNCT
cana-2940	14	3	skin	skin	NOUN
cana-2940	14	4	disease	disease	NOUN
cana-2940	14	5	detection	detection	NOUN
cana-2940	14	6	,	,	PUNCT
cana-2940	14	7	classification	classification	NOUN
cana-2940	14	8	model	model	NOUN
cana-2940	14	9	,	,	PUNCT
cana-2940	14	10	deep	deep	ADJ
cana-2940	14	11	learning	learning	NOUN
cana-2940	14	12	,	,	PUNCT
cana-2940	14	13	enhanced	enhance	VERB
cana-2940	14	14	image	image	NOUN
cana-2940	14	15	segmentation	segmentation	NOUN
cana-2940	14	16	,	,	PUNCT
cana-2940	14	17	feature	feature	NOUN
cana-2940	14	18	extraction	extraction	NOUN
cana-2940	14	19	,	,	PUNCT
cana-2940	14	20	cnn	cnn	PROPN
cana-2940	14	21	,	,	PUNCT
cana-2940	14	22	multiclass	multiclass	ADJ
cana-2940	14	23	,	,	PUNCT
cana-2940	14	24	dataset	dataset	NOUN
cana-2940	14	25	,	,	PUNCT
cana-2940	14	26	dermnet	dermnet	NOUN
cana-2940	14	27	1	1	NUM
cana-2940	14	28	.	.	PUNCT
cana-2940	15	1	introduction	introduction	NOUN
cana-2940	15	2	:	:	PUNCT
cana-2940	15	3	the	the	DET
cana-2940	15	4	body	body	NOUN
cana-2940	15	5	of	of	ADP
cana-2940	15	6	an	an	DET
cana-2940	15	7	individual	individual	NOUN
cana-2940	15	8	includes	include	VERB
cana-2940	15	9	multiple	multiple	ADJ
cana-2940	15	10	organs	organ	NOUN
cana-2940	15	11	.	.	PUNCT
cana-2940	16	1	the	the	DET
cana-2940	16	2	largest	large	ADJ
cana-2940	16	3	organ	organ	NOUN
cana-2940	16	4	represents	represent	VERB
cana-2940	16	5	as	as	ADP
cana-2940	16	6	the	the	DET
cana-2940	16	7	human	human	ADJ
cana-2940	16	8	skin	skin	NOUN
cana-2940	16	9	.	.	PUNCT
cana-2940	17	1	skin	skin	NOUN
cana-2940	17	2	protects	protect	VERB
cana-2940	17	3	us	we	PRON
cana-2940	17	4	in	in	ADP
cana-2940	17	5	many	many	ADJ
cana-2940	17	6	ways	way	NOUN
cana-2940	17	7	and	and	CCONJ
cana-2940	17	8	serves	serve	VERB
cana-2940	17	9	as	as	ADP
cana-2940	17	10	a	a	DET
cana-2940	17	11	crucial	crucial	ADJ
cana-2940	17	12	barrier	barrier	NOUN
cana-2940	17	13	against	against	ADP
cana-2940	17	14	pathogens	pathogen	NOUN
cana-2940	17	15	,	,	PUNCT
cana-2940	17	16	ultraviolet	ultraviolet	ADJ
cana-2940	17	17	rays	ray	NOUN
cana-2940	17	18	,	,	PUNCT
cana-2940	17	19	and	and	CCONJ
cana-2940	17	20	other	other	ADJ
cana-2940	17	21	harmful	harmful	ADJ
cana-2940	17	22	agents	agent	NOUN
cana-2940	17	23	found	find	VERB
cana-2940	17	24	in	in	ADP
cana-2940	17	25	the	the	DET
cana-2940	17	26	external	external	ADJ
cana-2940	17	27	environment(ahammed	environment(ahammed	PROPN
cana-2940	17	28	et	et	PROPN
cana-2940	17	29	al	al	PROPN
cana-2940	17	30	.	.	PROPN
cana-2940	17	31	,	,	PUNCT
cana-2940	17	32	2022a	2022a	NUM
cana-2940	17	33	)	)	PUNCT
cana-2940	17	34	.	.	PUNCT
cana-2940	18	1	as	as	SCONJ
cana-2940	18	2	the	the	DET
cana-2940	18	3	skin	skin	NOUN
cana-2940	18	4	acts	act	VERB
cana-2940	18	5	as	as	ADP
cana-2940	18	6	a	a	DET
cana-2940	18	7	protector	protector	NOUN
cana-2940	18	8	,	,	PUNCT
cana-2940	18	9	it	it	PRON
cana-2940	18	10	also	also	ADV
cana-2940	18	11	requires	require	VERB
cana-2940	18	12	much	much	ADJ
cana-2940	18	13	attention	attention	NOUN
cana-2940	18	14	and	and	CCONJ
cana-2940	18	15	care	care	NOUN
cana-2940	18	16	.	.	PUNCT
cana-2940	19	1	a	a	DET
cana-2940	19	2	skin	skin	NOUN
cana-2940	19	3	disorder	disorder	NOUN
cana-2940	19	4	refers	refer	VERB
cana-2940	19	5	to	to	ADP
cana-2940	19	6	any	any	DET
cana-2940	19	7	ailment	ailment	NOUN
cana-2940	19	8	which	which	PRON
cana-2940	19	9	impacts	impact	VERB
cana-2940	19	10	the	the	DET
cana-2940	19	11	human	human	ADJ
cana-2940	19	12	skin	skin	NOUN
cana-2940	19	13	.	.	PUNCT
cana-2940	20	1	the	the	DET
cana-2940	20	2	disorders	disorder	NOUN
cana-2940	20	3	pertaining	pertain	VERB
cana-2940	20	4	to	to	ADP
cana-2940	20	5	skin	skin	NOUN
cana-2940	20	6	are	be	AUX
cana-2940	20	7	common	common	ADJ
cana-2940	20	8	and	and	CCONJ
cana-2940	20	9	become	become	VERB
cana-2940	20	10	more	more	ADV
cana-2940	20	11	dangerous	dangerous	ADJ
cana-2940	20	12	health	health	NOUN
cana-2940	20	13	problems	problem	NOUN
cana-2940	20	14	that	that	PRON
cana-2940	20	15	impact	impact	VERB
cana-2940	20	16	millions	million	NOUN
cana-2940	20	17	of	of	ADP
cana-2940	20	18	people	people	NOUN
cana-2940	20	19	around	around	ADP
cana-2940	20	20	the	the	DET
cana-2940	20	21	globe	globe	NOUN
cana-2940	20	22	.	.	PUNCT
cana-2940	21	1	these	these	DET
cana-2940	21	2	problems	problem	NOUN
cana-2940	21	3	can	can	AUX
cana-2940	21	4	take	take	VERB
cana-2940	21	5	many	many	ADJ
cana-2940	21	6	different	different	ADJ
cana-2940	21	7	forms	form	NOUN
cana-2940	21	8	,	,	PUNCT
cana-2940	21	9	including	include	VERB
cana-2940	21	10	rashes	rash	NOUN
cana-2940	21	11	,	,	PUNCT
cana-2940	21	12	lesions	lesion	NOUN
cana-2940	21	13	,	,	PUNCT
cana-2940	21	14	and	and	CCONJ
cana-2940	21	15	long	long	ADJ
cana-2940	21	16	-	-	PUNCT
cana-2940	21	17	term	term	NOUN
cana-2940	21	18	ailments	ailment	NOUN
cana-2940	21	19	like	like	ADP
cana-2940	21	20	psoriasis	psoriasis	NOUN
cana-2940	21	21	and	and	CCONJ
cana-2940	21	22	eczema	eczema	NOUN
cana-2940	21	23	,	,	PUNCT
cana-2940	21	24	therefore	therefore	ADV
cana-2940	21	25	a	a	DET
cana-2940	21	26	precise	precise	ADJ
cana-2940	21	27	diagnosis	diagnosis	NOUN
cana-2940	21	28	and	and	CCONJ
cana-2940	21	29	course	course	NOUN
cana-2940	21	30	of	of	ADP
cana-2940	21	31	treatment	treatment	NOUN
cana-2940	21	32	are	be	AUX
cana-2940	21	33	essential(y	essential(y	NUM
cana-2940	21	34	.	.	PUNCT
cana-2940	22	1	sharma	sharma	PROPN
cana-2940	22	2	et	et	PROPN
cana-2940	22	3	al	al	PROPN
cana-2940	22	4	.	.	PROPN
cana-2940	22	5	,	,	PUNCT
cana-2940	22	6	2024	2024	NUM
cana-2940	22	7	)	)	PUNCT
cana-2940	22	8	.	.	PUNCT
cana-2940	23	1	mailto:rmoudgil16@gmail.com	mailto:rmoudgil16@gmail.com	PROPN
cana-2940	23	2	mailto:sk93recj@gmail.com	mailto:sk93recj@gmail.com	X
cana-2940	23	3	communications	communication	NOUN
cana-2940	23	4	on	on	ADP
cana-2940	23	5	applied	apply	VERB
cana-2940	23	6	nonlinear	nonlinear	ADJ
cana-2940	23	7	analysis	analysis	NOUN
cana-2940	23	8	issn	issn	NOUN
cana-2940	23	9	:	:	PUNCT
cana-2940	23	10	1074	1074	NUM
cana-2940	23	11	-	-	PUNCT
cana-2940	23	12	133x	133x	NUM
cana-2940	23	13	vol	vol	NOUN
cana-2940	23	14	32	32	NUM
cana-2940	23	15	no	no	NOUN
cana-2940	23	16	.	.	PUNCT
cana-2940	24	1	5s	5s	NUM
cana-2940	24	2	(	(	PUNCT
cana-2940	24	3	2025	2025	NUM
cana-2940	24	4	)	)	PUNCT
cana-2940	24	5	27	27	NUM
cana-2940	24	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2940	24	7	the	the	DET
cana-2940	24	8	branch	branch	NOUN
cana-2940	24	9	that	that	PRON
cana-2940	24	10	deals	deal	VERB
cana-2940	24	11	with	with	ADP
cana-2940	24	12	human	human	ADJ
cana-2940	24	13	skin	skin	NOUN
cana-2940	24	14	conditions	condition	NOUN
cana-2940	24	15	is	be	AUX
cana-2940	24	16	known	know	VERB
cana-2940	24	17	as	as	ADP
cana-2940	24	18	dermatology	dermatology	NOUN
cana-2940	24	19	,	,	PUNCT
cana-2940	24	20	several	several	ADJ
cana-2940	24	21	advancements	advancement	NOUN
cana-2940	24	22	have	have	AUX
cana-2940	24	23	been	be	AUX
cana-2940	24	24	made	make	VERB
cana-2940	24	25	in	in	ADP
cana-2940	24	26	the	the	DET
cana-2940	24	27	dermatological	dermatological	ADJ
cana-2940	24	28	research	research	NOUN
cana-2940	24	29	field	field	NOUN
cana-2940	24	30	which	which	PRON
cana-2940	24	31	led	lead	VERB
cana-2940	24	32	to	to	ADP
cana-2940	24	33	an	an	DET
cana-2940	24	34	improvised	improvised	ADJ
cana-2940	24	35	understanding	understanding	NOUN
cana-2940	24	36	of	of	ADP
cana-2940	24	37	skin	skin	NOUN
cana-2940	24	38	problems	problem	NOUN
cana-2940	24	39	and	and	CCONJ
cana-2940	24	40	innovative	innovative	ADJ
cana-2940	24	41	treatments(jeong	treatments(jeong	PROPN
cana-2940	24	42	et	et	PROPN
cana-2940	24	43	al	al	PROPN
cana-2940	24	44	.	.	PROPN
cana-2940	24	45	,	,	PUNCT
cana-2940	24	46	2023	2023	NUM
cana-2940	24	47	)	)	PUNCT
cana-2940	24	48	.	.	PUNCT
cana-2940	25	1	however	however	ADV
cana-2940	25	2	,	,	PUNCT
cana-2940	25	3	dermatology	dermatology	NOUN
cana-2940	25	4	faces	face	VERB
cana-2940	25	5	problems	problem	NOUN
cana-2940	25	6	in	in	ADP
cana-2940	25	7	the	the	DET
cana-2940	25	8	diagnosis	diagnosis	NOUN
cana-2940	25	9	process	process	NOUN
cana-2940	25	10	due	due	ADP
cana-2940	25	11	to	to	ADP
cana-2940	25	12	the	the	DET
cana-2940	25	13	variability	variability	NOUN
cana-2940	25	14	of	of	ADP
cana-2940	25	15	skin	skin	NOUN
cana-2940	25	16	conditions	condition	NOUN
cana-2940	25	17	and	and	CCONJ
cana-2940	25	18	their	their	PRON
cana-2940	25	19	appearance	appearance	NOUN
cana-2940	25	20	with	with	ADP
cana-2940	25	21	an	an	DET
cana-2940	25	22	increase	increase	NOUN
cana-2940	25	23	in	in	ADP
cana-2940	25	24	time	time	NOUN
cana-2940	25	25	.	.	PUNCT
cana-2940	26	1	due	due	ADP
cana-2940	26	2	to	to	ADP
cana-2940	26	3	this	this	PRON
cana-2940	26	4	,	,	PUNCT
cana-2940	26	5	the	the	DET
cana-2940	26	6	examination	examination	NOUN
cana-2940	26	7	process	process	NOUN
cana-2940	26	8	of	of	ADP
cana-2940	26	9	skin	skin	NOUN
cana-2940	26	10	infections	infection	NOUN
cana-2940	26	11	becomes	become	VERB
cana-2940	26	12	more	more	ADV
cana-2940	26	13	complicated	complicated	ADJ
cana-2940	26	14	and	and	CCONJ
cana-2940	26	15	hence	hence	ADV
cana-2940	26	16	delay	delay	VERB
cana-2940	26	17	in	in	ADP
cana-2940	26	18	treatment	treatment	NOUN
cana-2940	26	19	.	.	PUNCT
cana-2940	27	1	so	so	ADV
cana-2940	27	2	,	,	PUNCT
cana-2940	27	3	the	the	DET
cana-2940	27	4	intervention	intervention	NOUN
cana-2940	27	5	of	of	ADP
cana-2940	27	6	machine	machine	NOUN
cana-2940	27	7	learning	learning	NOUN
cana-2940	27	8	(	(	PUNCT
cana-2940	27	9	ml	ml	NOUN
cana-2940	27	10	)	)	PUNCT
cana-2940	27	11	and	and	CCONJ
cana-2940	27	12	deep	deep	ADJ
cana-2940	27	13	learning	learning	NOUN
cana-2940	27	14	(	(	PUNCT
cana-2940	27	15	dl	dl	INTJ
cana-2940	27	16	)	)	PUNCT
cana-2940	27	17	is	be	AUX
cana-2940	27	18	more	more	ADV
cana-2940	27	19	important	important	ADJ
cana-2940	27	20	for	for	ADP
cana-2940	27	21	early	early	ADJ
cana-2940	27	22	diagnosis	diagnosis	NOUN
cana-2940	27	23	and	and	CCONJ
cana-2940	27	24	categorization	categorization	NOUN
cana-2940	27	25	of	of	ADP
cana-2940	27	26	skin	skin	NOUN
cana-2940	27	27	diseases	disease	NOUN
cana-2940	27	28	,	,	PUNCT
cana-2940	27	29	which	which	PRON
cana-2940	27	30	is	be	AUX
cana-2940	27	31	important	important	ADJ
cana-2940	27	32	for	for	ADP
cana-2940	27	33	better	well	ADJ
cana-2940	27	34	management	management	NOUN
cana-2940	27	35	and	and	CCONJ
cana-2940	27	36	effective	effective	ADJ
cana-2940	27	37	patient	patient	ADJ
cana-2940	27	38	experiences(reddy	experiences(reddy	PROPN
cana-2940	27	39	et	et	NOUN
cana-2940	27	40	al	al	PROPN
cana-2940	27	41	.	.	PROPN
cana-2940	27	42	,	,	PUNCT
cana-2940	27	43	2024)(ahammed	2024)(ahammed	NUM
cana-2940	27	44	et	et	NOUN
cana-2940	27	45	al	al	PROPN
cana-2940	27	46	.	.	PROPN
cana-2940	27	47	,	,	PUNCT
cana-2940	27	48	2022b	2022b	NUM
cana-2940	27	49	)	)	PUNCT
cana-2940	27	50	.	.	PUNCT
cana-2940	28	1	over	over	ADP
cana-2940	28	2	the	the	DET
cana-2940	28	3	last	last	ADJ
cana-2940	28	4	two	two	NUM
cana-2940	28	5	decades	decade	NOUN
cana-2940	28	6	,	,	PUNCT
cana-2940	28	7	due	due	ADP
cana-2940	28	8	to	to	ADP
cana-2940	28	9	the	the	DET
cana-2940	28	10	widespread	widespread	ADJ
cana-2940	28	11	use	use	NOUN
cana-2940	28	12	of	of	ADP
cana-2940	28	13	artificial	artificial	ADJ
cana-2940	28	14	intelligence	intelligence	NOUN
cana-2940	28	15	(	(	PUNCT
cana-2940	28	16	ai	ai	PROPN
cana-2940	28	17	)	)	PUNCT
cana-2940	28	18	(	(	PUNCT
cana-2940	28	19	putatunda	putatunda	NOUN
cana-2940	28	20	,	,	PUNCT
cana-2940	28	21	2019	2019	NUM
cana-2940	28	22	)	)	PUNCT
cana-2940	28	23	,	,	PUNCT
cana-2940	28	24	machine	machine	NOUN
cana-2940	28	25	-	-	PUNCT
cana-2940	28	26	learning	learning	NOUN
cana-2940	28	27	(	(	PUNCT
cana-2940	28	28	ml	ml	NOUN
cana-2940	28	29	)	)	PUNCT
cana-2940	28	30	(	(	PUNCT
cana-2940	28	31	ahammed	ahamme	VERB
cana-2940	28	32	et	et	PROPN
cana-2940	28	33	al	al	PROPN
cana-2940	28	34	.	.	PROPN
cana-2940	28	35	,	,	PUNCT
cana-2940	28	36	2022a	2022a	NUM
cana-2940	28	37	)	)	PUNCT
cana-2940	28	38	,	,	PUNCT
cana-2940	28	39	and	and	CCONJ
cana-2940	28	40	deep	deep	ADV
cana-2940	28	41	-	-	PUNCT
cana-2940	28	42	learning	learn	VERB
cana-2940	28	43	(	(	PUNCT
cana-2940	28	44	dl	dl	NOUN
cana-2940	28	45	)	)	PUNCT
cana-2940	28	46	(	(	PUNCT
cana-2940	28	47	sazzadul	sazzadul	INTJ
cana-2940	28	48	islam	islam	PROPN
cana-2940	28	49	prottasha	prottasha	PROPN
cana-2940	28	50	et	et	PROPN
cana-2940	28	51	al	al	PROPN
cana-2940	28	52	.	.	PROPN
cana-2940	28	53	,	,	PUNCT
cana-2940	28	54	2023	2023	NUM
cana-2940	28	55	)	)	PUNCT
cana-2940	28	56	,	,	PUNCT
cana-2940	28	57	the	the	DET
cana-2940	28	58	skin	skin	NOUN
cana-2940	28	59	care	care	NOUN
cana-2940	28	60	sector	sector	NOUN
cana-2940	28	61	has	have	AUX
cana-2940	28	62	achieved	achieve	VERB
cana-2940	28	63	promising	promising	ADJ
cana-2940	28	64	results	result	NOUN
cana-2940	28	65	in	in	ADP
cana-2940	28	66	medicine	medicine	NOUN
cana-2940	28	67	,	,	PUNCT
cana-2940	28	68	manufacturing	manufacturing	NOUN
cana-2940	28	69	,	,	PUNCT
cana-2940	28	70	cosmetics	cosmetic	NOUN
cana-2940	28	71	,	,	PUNCT
cana-2940	28	72	and	and	CCONJ
cana-2940	28	73	personal	personal	ADJ
cana-2940	28	74	care	care	NOUN
cana-2940	28	75	.	.	PUNCT
cana-2940	29	1	deep	deep	ADJ
cana-2940	29	2	learning(saiwaeo	learning(saiwaeo	PROPN
cana-2940	29	3	et	et	PROPN
cana-2940	29	4	al	al	PROPN
cana-2940	29	5	.	.	PROPN
cana-2940	29	6	,	,	PUNCT
cana-2940	29	7	2023	2023	NUM
cana-2940	29	8	)	)	PUNCT
cana-2940	29	9	,	,	PUNCT
cana-2940	29	10	ml	ml	VERB
cana-2940	29	11	-	-	PUNCT
cana-2940	29	12	based	base	VERB
cana-2940	29	13	techniques	technique	NOUN
cana-2940	29	14	are	be	AUX
cana-2940	29	15	growing	grow	VERB
cana-2940	29	16	very	very	ADV
cana-2940	29	17	fast	fast	ADV
cana-2940	29	18	and	and	CCONJ
cana-2940	29	19	have	have	AUX
cana-2940	29	20	taken	take	VERB
cana-2940	29	21	the	the	DET
cana-2940	29	22	skin	skin	NOUN
cana-2940	29	23	diagnosis	diagnosis	NOUN
cana-2940	29	24	process	process	NOUN
cana-2940	29	25	to	to	ADP
cana-2940	29	26	the	the	DET
cana-2940	29	27	next	next	ADJ
cana-2940	29	28	level	level	NOUN
cana-2940	29	29	.	.	PUNCT
cana-2940	30	1	dl	dl	PROPN
cana-2940	30	2	has	have	AUX
cana-2940	30	3	enabled	enable	VERB
cana-2940	30	4	algorithms	algorithm	NOUN
cana-2940	30	5	that	that	PRON
cana-2940	30	6	can	can	AUX
cana-2940	30	7	analyze	analyze	VERB
cana-2940	30	8	images	image	NOUN
cana-2940	30	9	of	of	ADP
cana-2940	30	10	skin	skin	NOUN
cana-2940	30	11	diseases	disease	NOUN
cana-2940	30	12	with	with	ADP
cana-2940	30	13	remarkable	remarkable	ADJ
cana-2940	30	14	accuracy	accuracy	NOUN
cana-2940	30	15	,	,	PUNCT
cana-2940	30	16	early	early	ADJ
cana-2940	30	17	identification	identification	NOUN
cana-2940	30	18	,	,	PUNCT
cana-2940	30	19	and	and	CCONJ
cana-2940	30	20	more	more	ADV
cana-2940	30	21	individualized	individualized	ADJ
cana-2940	30	22	treatment	treatment	NOUN
cana-2940	30	23	approaches(rao	approaches(rao	NOUN
cana-2940	30	24	,	,	PUNCT
cana-2940	30	25	2021	2021	NUM
cana-2940	30	26	)	)	PUNCT
cana-2940	30	27	.	.	PUNCT
cana-2940	31	1	1.1	1.1	NUM
cana-2940	31	2	motivation	motivation	NOUN
cana-2940	31	3	and	and	CCONJ
cana-2940	31	4	objectives	objective	NOUN
cana-2940	31	5	although	although	SCONJ
cana-2940	31	6	several	several	ADJ
cana-2940	31	7	advances	advance	NOUN
cana-2940	31	8	have	have	AUX
cana-2940	31	9	been	be	AUX
cana-2940	31	10	made	make	VERB
cana-2940	31	11	in	in	ADP
cana-2940	31	12	the	the	DET
cana-2940	31	13	realm	realm	NOUN
cana-2940	31	14	of	of	ADP
cana-2940	31	15	deep	deep	ADJ
cana-2940	31	16	learning	learning	NOUN
cana-2940	31	17	-	-	PUNCT
cana-2940	31	18	based	base	VERB
cana-2940	31	19	skin	skin	NOUN
cana-2940	31	20	disease	disease	NOUN
cana-2940	31	21	diagnosis	diagnosis	NOUN
cana-2940	31	22	,	,	PUNCT
cana-2940	31	23	obstacles	obstacle	NOUN
cana-2940	31	24	still	still	ADV
cana-2940	31	25	impede	impede	VERB
cana-2940	31	26	the	the	DET
cana-2940	31	27	successful	successful	ADJ
cana-2940	31	28	implementation	implementation	NOUN
cana-2940	31	29	of	of	ADP
cana-2940	31	30	these	these	DET
cana-2940	31	31	innovations	innovation	NOUN
cana-2940	31	32	.	.	PUNCT
cana-2940	32	1	the	the	DET
cana-2940	32	2	motivation	motivation	NOUN
cana-2940	32	3	behind	behind	ADP
cana-2940	32	4	this	this	DET
cana-2940	32	5	study	study	NOUN
cana-2940	32	6	is	be	AUX
cana-2940	32	7	to	to	PART
cana-2940	32	8	enhance	enhance	VERB
cana-2940	32	9	the	the	DET
cana-2940	32	10	diagnosis	diagnosis	NOUN
cana-2940	32	11	process	process	NOUN
cana-2940	32	12	of	of	ADP
cana-2940	32	13	dermatological	dermatological	ADJ
cana-2940	32	14	conditions	condition	NOUN
cana-2940	32	15	using	use	VERB
cana-2940	32	16	dl	dl	PROPN
cana-2940	32	17	approaches	approach	NOUN
cana-2940	32	18	.	.	PUNCT
cana-2940	33	1	this	this	DET
cana-2940	33	2	work	work	NOUN
cana-2940	33	3	aims	aim	VERB
cana-2940	33	4	to	to	PART
cana-2940	33	5	improve	improve	VERB
cana-2940	33	6	image	image	NOUN
cana-2940	33	7	segmentation	segmentation	NOUN
cana-2940	33	8	and	and	CCONJ
cana-2940	33	9	feature	feature	NOUN
cana-2940	33	10	extraction	extraction	NOUN
cana-2940	33	11	techniques	technique	NOUN
cana-2940	33	12	.	.	PUNCT
cana-2940	34	1	this	this	DET
cana-2940	34	2	study	study	NOUN
cana-2940	34	3	introduces	introduce	VERB
cana-2940	34	4	a	a	DET
cana-2940	34	5	holistically	holistically	ADV
cana-2940	34	6	nested	nested	ADJ
cana-2940	34	7	edge	edge	NOUN
cana-2940	34	8	detection	detection	NOUN
cana-2940	34	9	(	(	PUNCT
cana-2940	34	10	hned	hne	VERB
cana-2940	34	11	)	)	PUNCT
cana-2940	34	12	technique	technique	NOUN
cana-2940	34	13	to	to	PART
cana-2940	34	14	improve	improve	VERB
cana-2940	34	15	the	the	DET
cana-2940	34	16	edge	edge	NOUN
cana-2940	34	17	details	detail	NOUN
cana-2940	34	18	of	of	ADP
cana-2940	34	19	images	image	NOUN
cana-2940	34	20	during	during	ADP
cana-2940	34	21	segmentation	segmentation	NOUN
cana-2940	34	22	.	.	PUNCT
cana-2940	35	1	this	this	DET
cana-2940	35	2	step	step	NOUN
cana-2940	35	3	ensures	ensure	VERB
cana-2940	35	4	high	high	ADJ
cana-2940	35	5	-	-	PUNCT
cana-2940	35	6	quality	quality	NOUN
cana-2940	35	7	preprocessing	preprocessing	NOUN
cana-2940	35	8	,	,	PUNCT
cana-2940	35	9	capturing	capture	VERB
cana-2940	35	10	critical	critical	ADJ
cana-2940	35	11	information	information	NOUN
cana-2940	35	12	essential	essential	ADJ
cana-2940	35	13	for	for	ADP
cana-2940	35	14	accurate	accurate	ADJ
cana-2940	35	15	classification	classification	NOUN
cana-2940	35	16	.	.	PUNCT
cana-2940	36	1	the	the	DET
cana-2940	36	2	study	study	NOUN
cana-2940	36	3	employs	employ	VERB
cana-2940	36	4	efficientnet	efficientnet	NOUN
cana-2940	36	5	-	-	PUNCT
cana-2940	36	6	b0	b0	NOUN
cana-2940	36	7	,	,	PUNCT
cana-2940	36	8	a	a	DET
cana-2940	36	9	powerful	powerful	ADJ
cana-2940	36	10	dl	dl	PROPN
cana-2940	36	11	model	model	NOUN
cana-2940	36	12	proficient	proficient	ADJ
cana-2940	36	13	in	in	ADP
cana-2940	36	14	feature	feature	NOUN
cana-2940	36	15	extraction	extraction	NOUN
cana-2940	36	16	from	from	ADP
cana-2940	36	17	an	an	DET
cana-2940	36	18	image	image	NOUN
cana-2940	36	19	and	and	CCONJ
cana-2940	36	20	representing	represent	VERB
cana-2940	36	21	them	they	PRON
cana-2940	36	22	in	in	ADP
cana-2940	36	23	a	a	DET
cana-2940	36	24	1d	1d	NUM
cana-2940	36	25	array	array	NOUN
cana-2940	36	26	to	to	PART
cana-2940	36	27	extract	extract	VERB
cana-2940	36	28	comprehensive	comprehensive	ADJ
cana-2940	36	29	and	and	CCONJ
cana-2940	36	30	high	high	ADV
cana-2940	36	31	-	-	PUNCT
cana-2940	36	32	dimensional	dimensional	ADJ
cana-2940	36	33	features	feature	NOUN
cana-2940	36	34	.	.	PUNCT
cana-2940	37	1	this	this	DET
cana-2940	37	2	rich	rich	ADJ
cana-2940	37	3	feature	feature	NOUN
cana-2940	37	4	set	set	NOUN
cana-2940	37	5	is	be	AUX
cana-2940	37	6	instrumental	instrumental	ADJ
cana-2940	37	7	in	in	ADP
cana-2940	37	8	capturing	capture	VERB
cana-2940	37	9	the	the	DET
cana-2940	37	10	diverse	diverse	ADJ
cana-2940	37	11	characteristics	characteristic	NOUN
cana-2940	37	12	of	of	ADP
cana-2940	37	13	skin	skin	NOUN
cana-2940	37	14	lesions	lesion	NOUN
cana-2940	37	15	,	,	PUNCT
cana-2940	37	16	enabling	enable	VERB
cana-2940	37	17	a	a	DET
cana-2940	37	18	more	more	ADV
cana-2940	37	19	detailed	detailed	ADJ
cana-2940	37	20	analysis	analysis	NOUN
cana-2940	37	21	of	of	ADP
cana-2940	37	22	disease	disease	NOUN
cana-2940	37	23	patterns	pattern	NOUN
cana-2940	37	24	.	.	PUNCT
cana-2940	38	1	furthermore	furthermore	ADV
cana-2940	38	2	,	,	PUNCT
cana-2940	38	3	the	the	DET
cana-2940	38	4	research	research	NOUN
cana-2940	38	5	evaluates	evaluate	VERB
cana-2940	38	6	the	the	DET
cana-2940	38	7	performance	performance	NOUN
cana-2940	38	8	of	of	ADP
cana-2940	38	9	convolutional	convolutional	ADJ
cana-2940	38	10	deep	deep	ADJ
cana-2940	38	11	neural	neural	ADJ
cana-2940	38	12	networks	network	NOUN
cana-2940	38	13	(	(	PUNCT
cana-2940	38	14	cnns	cnns	PROPN
cana-2940	38	15	)	)	PUNCT
cana-2940	38	16	trained	train	VERB
cana-2940	38	17	on	on	ADP
cana-2940	38	18	dermnet	dermnet	NOUN
cana-2940	38	19	dataset	dataset	NOUN
cana-2940	38	20	encompassing	encompass	VERB
cana-2940	38	21	23	23	NUM
cana-2940	38	22	different	different	ADJ
cana-2940	38	23	skin	skin	NOUN
cana-2940	38	24	disease	disease	NOUN
cana-2940	38	25	classes	class	NOUN
cana-2940	38	26	.	.	PUNCT
cana-2940	39	1	this	this	DET
cana-2940	39	2	method	method	NOUN
cana-2940	39	3	seeks	seek	VERB
cana-2940	39	4	to	to	PART
cana-2940	39	5	improve	improve	VERB
cana-2940	39	6	performance	performance	NOUN
cana-2940	39	7	measures	measure	NOUN
cana-2940	39	8	,	,	PUNCT
cana-2940	39	9	including	include	VERB
cana-2940	39	10	precision	precision	NOUN
cana-2940	39	11	,	,	PUNCT
cana-2940	39	12	recall	recall	NOUN
cana-2940	39	13	,	,	PUNCT
cana-2940	39	14	accuracy	accuracy	NOUN
cana-2940	39	15	,	,	PUNCT
cana-2940	39	16	and	and	CCONJ
cana-2940	39	17	f1	f1	NOUN
cana-2940	39	18	-	-	PUNCT
cana-2940	39	19	score	score	NOUN
cana-2940	39	20	,	,	PUNCT
cana-2940	39	21	by	by	ADP
cana-2940	39	22	integrating	integrate	VERB
cana-2940	39	23	the	the	DET
cana-2940	39	24	feature	feature	NOUN
cana-2940	39	25	extraction	extraction	NOUN
cana-2940	39	26	abilities	ability	NOUN
cana-2940	39	27	of	of	ADP
cana-2940	39	28	efficientnet	efficientnet	NOUN
cana-2940	39	29	-	-	PUNCT
cana-2940	39	30	b0	b0	NOUN
cana-2940	39	31	with	with	ADP
cana-2940	39	32	the	the	DET
cana-2940	39	33	categorization	categorization	NOUN
cana-2940	39	34	strength	strength	NOUN
cana-2940	39	35	of	of	ADP
cana-2940	39	36	cnns	cnns	PROPN
cana-2940	39	37	.	.	PUNCT
cana-2940	40	1	in	in	ADP
cana-2940	40	2	consort	consort	NOUN
cana-2940	40	3	with	with	ADP
cana-2940	40	4	improving	improve	VERB
cana-2940	40	5	the	the	DET
cana-2940	40	6	metrics	metric	NOUN
cana-2940	40	7	of	of	ADP
cana-2940	40	8	the	the	DET
cana-2940	40	9	designed	design	VERB
cana-2940	40	10	model	model	NOUN
cana-2940	40	11	,	,	PUNCT
cana-2940	40	12	dl	dl	NOUN
cana-2940	40	13	-	-	PUNCT
cana-2940	40	14	based	base	VERB
cana-2940	40	15	segmentation	segmentation	NOUN
cana-2940	40	16	models	model	NOUN
cana-2940	40	17	have	have	AUX
cana-2940	40	18	surged	surge	VERB
cana-2940	40	19	in	in	ADP
cana-2940	40	20	popularity	popularity	NOUN
cana-2940	40	21	owing	owe	VERB
cana-2940	40	22	to	to	ADP
cana-2940	40	23	their	their	PRON
cana-2940	40	24	capability	capability	NOUN
cana-2940	40	25	to	to	PART
cana-2940	40	26	produce	produce	VERB
cana-2940	40	27	highly	highly	ADV
cana-2940	40	28	accurate	accurate	ADJ
cana-2940	40	29	and	and	CCONJ
cana-2940	40	30	precise	precise	ADJ
cana-2940	40	31	segmentation	segmentation	NOUN
cana-2940	40	32	results(ahammed	results(ahamme	VERB
cana-2940	40	33	et	et	PROPN
cana-2940	40	34	al	al	PROPN
cana-2940	40	35	.	.	PROPN
cana-2940	40	36	,	,	PUNCT
cana-2940	40	37	2022a	2022a	NUM
cana-2940	40	38	)	)	PUNCT
cana-2940	40	39	.	.	PUNCT
cana-2940	41	1	in	in	ADP
cana-2940	41	2	the	the	DET
cana-2940	41	3	skin	skin	NOUN
cana-2940	41	4	disease	disease	NOUN
cana-2940	41	5	diagnostic	diagnostic	ADJ
cana-2940	41	6	process	process	NOUN
cana-2940	41	7	,	,	PUNCT
cana-2940	41	8	these	these	DET
cana-2940	41	9	models	model	NOUN
cana-2940	41	10	are	be	AUX
cana-2940	41	11	very	very	ADV
cana-2940	41	12	useful	useful	ADJ
cana-2940	41	13	since	since	SCONJ
cana-2940	41	14	they	they	PRON
cana-2940	41	15	can	can	AUX
cana-2940	41	16	delineate	delineate	VERB
cana-2940	41	17	the	the	DET
cana-2940	41	18	contours	contours	NOUN
cana-2940	41	19	of	of	ADP
cana-2940	41	20	skin	skin	NOUN
cana-2940	41	21	diseases	disease	NOUN
cana-2940	41	22	even	even	ADV
cana-2940	41	23	in	in	ADP
cana-2940	41	24	difficult	difficult	ADJ
cana-2940	41	25	situations	situation	NOUN
cana-2940	41	26	and	and	CCONJ
cana-2940	41	27	improve	improve	VERB
cana-2940	41	28	the	the	DET
cana-2940	41	29	results(manerkar	results(manerkar	PROPN
cana-2940	41	30	et	et	NOUN
cana-2940	41	31	al	al	PROPN
cana-2940	41	32	.	.	PROPN
cana-2940	41	33	,	,	PUNCT
cana-2940	41	34	2016	2016	NUM
cana-2940	41	35	)	)	PUNCT
cana-2940	41	36	.	.	PUNCT
cana-2940	42	1	similarly	similarly	ADV
cana-2940	42	2	,	,	PUNCT
cana-2940	42	3	numerous	numerous	ADJ
cana-2940	42	4	dl	dl	PROPN
cana-2940	42	5	approaches	approach	NOUN
cana-2940	42	6	offer	offer	VERB
cana-2940	42	7	the	the	DET
cana-2940	42	8	benefit	benefit	NOUN
cana-2940	42	9	of	of	ADP
cana-2940	42	10	autonomous	autonomous	ADJ
cana-2940	42	11	communications	communication	NOUN
cana-2940	42	12	on	on	ADP
cana-2940	42	13	applied	apply	VERB
cana-2940	42	14	nonlinear	nonlinear	ADJ
cana-2940	42	15	analysis	analysis	NOUN
cana-2940	42	16	issn	issn	NOUN
cana-2940	42	17	:	:	PUNCT
cana-2940	42	18	1074	1074	NUM
cana-2940	42	19	-	-	PUNCT
cana-2940	42	20	133x	133x	NUM
cana-2940	42	21	vol	vol	NOUN
cana-2940	42	22	32	32	NUM
cana-2940	42	23	no	no	NOUN
cana-2940	42	24	.	.	PUNCT
cana-2940	43	1	5s	5s	NUM
cana-2940	43	2	(	(	PUNCT
cana-2940	43	3	2025	2025	NUM
cana-2940	43	4	)	)	PUNCT
cana-2940	43	5	28	28	NUM
cana-2940	43	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2940	43	7	feature	feature	NOUN
cana-2940	43	8	extraction	extraction	NOUN
cana-2940	43	9	from	from	ADP
cana-2940	43	10	the	the	DET
cana-2940	43	11	pre	pre	ADJ
cana-2940	43	12	-	-	ADJ
cana-2940	43	13	trained	train	VERB
cana-2940	43	14	type	type	NOUN
cana-2940	43	15	of	of	ADP
cana-2940	43	16	cnns	cnn	NOUN
cana-2940	43	17	(	(	PUNCT
cana-2940	43	18	shetty	shetty	PROPN
cana-2940	43	19	et	et	PROPN
cana-2940	43	20	al	al	PROPN
cana-2940	43	21	.	.	PROPN
cana-2940	43	22	,	,	PUNCT
cana-2940	43	23	2022)(benyahia	2022)(benyahia	NUM
cana-2940	43	24	et	et	NOUN
cana-2940	43	25	al	al	PROPN
cana-2940	43	26	.	.	PROPN
cana-2940	43	27	,	,	PUNCT
cana-2940	43	28	2022	2022	NUM
cana-2940	43	29	)	)	PUNCT
cana-2940	43	30	.	.	PUNCT
cana-2940	44	1	following	follow	VERB
cana-2940	44	2	these	these	DET
cana-2940	44	3	advancements	advancement	NOUN
cana-2940	44	4	,	,	PUNCT
cana-2940	44	5	this	this	DET
cana-2940	44	6	research	research	NOUN
cana-2940	44	7	paper	paper	NOUN
cana-2940	44	8	has	have	VERB
cana-2940	44	9	some	some	DET
cana-2940	44	10	key	key	ADJ
cana-2940	44	11	contributions	contribution	NOUN
cana-2940	44	12	as	as	SCONJ
cana-2940	44	13	follows	follow	VERB
cana-2940	44	14	:	:	PUNCT
cana-2940	44	15	(	(	PUNCT
cana-2940	44	16	1	1	X
cana-2940	44	17	)	)	PUNCT
cana-2940	44	18	an	an	DET
cana-2940	44	19	advanced	advanced	ADJ
cana-2940	44	20	image	image	NOUN
cana-2940	44	21	segmentation	segmentation	NOUN
cana-2940	44	22	approach	approach	NOUN
cana-2940	44	23	using	use	VERB
cana-2940	44	24	dl	dl	PROPN
cana-2940	44	25	holistically	holistically	ADV
cana-2940	44	26	nested	nest	VERB
cana-2940	44	27	edge	edge	NOUN
cana-2940	44	28	detection	detection	NOUN
cana-2940	44	29	(	(	PUNCT
cana-2940	44	30	hned	hne	VERB
cana-2940	44	31	)	)	PUNCT
cana-2940	44	32	enhances	enhance	VERB
cana-2940	44	33	the	the	DET
cana-2940	44	34	edge	edge	NOUN
cana-2940	44	35	features	feature	NOUN
cana-2940	44	36	of	of	ADP
cana-2940	44	37	the	the	DET
cana-2940	44	38	source	source	NOUN
cana-2940	44	39	image	image	NOUN
cana-2940	44	40	during	during	ADP
cana-2940	44	41	the	the	DET
cana-2940	44	42	image	image	NOUN
cana-2940	44	43	segmentation	segmentation	NOUN
cana-2940	44	44	process	process	NOUN
cana-2940	44	45	.	.	PUNCT
cana-2940	45	1	(	(	PUNCT
cana-2940	45	2	2	2	X
cana-2940	45	3	)	)	PUNCT
cana-2940	45	4	for	for	ADP
cana-2940	45	5	the	the	DET
cana-2940	45	6	multiple	multiple	ADJ
cana-2940	45	7	feature	feature	NOUN
cana-2940	45	8	extraction	extraction	NOUN
cana-2940	45	9	,	,	PUNCT
cana-2940	45	10	a	a	DET
cana-2940	45	11	dl	dl	NOUN
cana-2940	45	12	-	-	PUNCT
cana-2940	45	13	based	base	VERB
cana-2940	45	14	model	model	NOUN
cana-2940	45	15	efficientnet	efficientnet	NOUN
cana-2940	45	16	-	-	PUNCT
cana-2940	45	17	b0	b0	NOUN
cana-2940	45	18	is	be	AUX
cana-2940	45	19	used	use	VERB
cana-2940	45	20	,	,	PUNCT
cana-2940	45	21	which	which	PRON
cana-2940	45	22	extracts	extract	VERB
cana-2940	45	23	1280	1280	NUM
cana-2940	45	24	features	feature	NOUN
cana-2940	45	25	from	from	ADP
cana-2940	45	26	an	an	DET
cana-2940	45	27	image	image	NOUN
cana-2940	45	28	and	and	CCONJ
cana-2940	45	29	represents	represent	VERB
cana-2940	45	30	them	they	PRON
cana-2940	45	31	into	into	ADP
cana-2940	45	32	a	a	DET
cana-2940	45	33	1d	1d	NUM
cana-2940	45	34	array	array	NOUN
cana-2940	45	35	.	.	PUNCT
cana-2940	46	1	(	(	PUNCT
cana-2940	46	2	3	3	X
cana-2940	46	3	)	)	PUNCT
cana-2940	46	4	evaluate	evaluate	VERB
cana-2940	46	5	convolution	convolution	NOUN
cana-2940	46	6	deep	deep	ADJ
cana-2940	46	7	neural	neural	ADJ
cana-2940	46	8	networks	network	NOUN
cana-2940	46	9	(	(	PUNCT
cana-2940	46	10	cnns	cnns	PROPN
cana-2940	46	11	)	)	PUNCT
cana-2940	46	12	for	for	ADP
cana-2940	46	13	skin	skin	NOUN
cana-2940	46	14	disease	disease	NOUN
cana-2940	46	15	diagnosis	diagnosis	NOUN
cana-2940	46	16	by	by	ADP
cana-2940	46	17	training	train	VERB
cana-2940	46	18	23	23	NUM
cana-2940	46	19	different	different	ADJ
cana-2940	46	20	classes	class	NOUN
cana-2940	46	21	of	of	ADP
cana-2940	46	22	the	the	DET
cana-2940	46	23	dermnet	dermnet	NOUN
cana-2940	46	24	dataset	dataset	NOUN
cana-2940	46	25	and	and	CCONJ
cana-2940	46	26	on	on	ADP
cana-2940	46	27	the	the	DET
cana-2940	46	28	features	feature	NOUN
cana-2940	46	29	extracted	extract	VERB
cana-2940	46	30	using	use	VERB
cana-2940	46	31	efficintnet	efficintnet	NOUN
cana-2940	46	32	-	-	PUNCT
cana-2940	46	33	b0	b0	NOUN
cana-2940	46	34	to	to	PART
cana-2940	46	35	enhance	enhance	VERB
cana-2940	46	36	the	the	DET
cana-2940	46	37	categorization	categorization	NOUN
cana-2940	46	38	performance	performance	NOUN
cana-2940	46	39	metrics	metric	NOUN
cana-2940	46	40	.	.	PUNCT
cana-2940	47	1	the	the	DET
cana-2940	47	2	remnant	remnant	ADJ
cana-2940	47	3	part	part	NOUN
cana-2940	47	4	of	of	ADP
cana-2940	47	5	this	this	DET
cana-2940	47	6	research	research	NOUN
cana-2940	47	7	paper	paper	NOUN
cana-2940	47	8	is	be	AUX
cana-2940	47	9	outlined	outline	VERB
cana-2940	47	10	as	as	ADP
cana-2940	47	11	:	:	PUNCT
cana-2940	47	12	section	section	NOUN
cana-2940	47	13	2	2	NUM
cana-2940	47	14	covers	cover	VERB
cana-2940	47	15	the	the	DET
cana-2940	47	16	related	relate	VERB
cana-2940	47	17	works	work	NOUN
cana-2940	47	18	done	do	VERB
cana-2940	47	19	in	in	ADP
cana-2940	47	20	skin	skin	NOUN
cana-2940	47	21	disease	disease	NOUN
cana-2940	47	22	diagnosis	diagnosis	NOUN
cana-2940	47	23	using	use	VERB
cana-2940	47	24	dl	dl	NOUN
cana-2940	47	25	-	-	PUNCT
cana-2940	47	26	based	base	VERB
cana-2940	47	27	techniques	technique	NOUN
cana-2940	47	28	.	.	PUNCT
cana-2940	48	1	section	section	NOUN
cana-2940	48	2	3	3	NUM
cana-2940	48	3	highlights	highlight	NOUN
cana-2940	48	4	the	the	DET
cana-2940	48	5	materials	material	NOUN
cana-2940	48	6	and	and	CCONJ
cana-2940	48	7	methodology	methodology	NOUN
cana-2940	48	8	including	include	VERB
cana-2940	48	9	the	the	DET
cana-2940	48	10	system	system	NOUN
cana-2940	48	11	design	design	NOUN
cana-2940	48	12	workflow	workflow	NOUN
cana-2940	48	13	of	of	ADP
cana-2940	48	14	the	the	DET
cana-2940	48	15	proposed	propose	VERB
cana-2940	48	16	methodology	methodology	NOUN
cana-2940	48	17	,	,	PUNCT
cana-2940	48	18	dataset	dataset	NOUN
cana-2940	48	19	,	,	PUNCT
cana-2940	48	20	image	image	NOUN
cana-2940	48	21	pre	pre	ADJ
cana-2940	48	22	-	-	ADJ
cana-2940	48	23	processing	processing	ADJ
cana-2940	48	24	techniques	technique	NOUN
cana-2940	48	25	,	,	PUNCT
cana-2940	48	26	enhanced	enhance	VERB
cana-2940	48	27	cnn	cnn	PROPN
cana-2940	48	28	-	-	PUNCT
cana-2940	48	29	based	base	VERB
cana-2940	48	30	image	image	NOUN
cana-2940	48	31	segmentation	segmentation	NOUN
cana-2940	48	32	technique	technique	NOUN
cana-2940	48	33	,	,	PUNCT
cana-2940	48	34	and	and	CCONJ
cana-2940	48	35	feature	feature	NOUN
cana-2940	48	36	extraction	extraction	NOUN
cana-2940	48	37	technique	technique	NOUN
cana-2940	48	38	.	.	PUNCT
cana-2940	49	1	section	section	NOUN
cana-2940	49	2	4	4	NUM
cana-2940	49	3	expounds	expound	VERB
cana-2940	49	4	on	on	ADP
cana-2940	49	5	the	the	DET
cana-2940	49	6	classification	classification	NOUN
cana-2940	49	7	outcomes	outcome	NOUN
cana-2940	49	8	.	.	PUNCT
cana-2940	50	1	section	section	NOUN
cana-2940	50	2	5	5	NUM
cana-2940	50	3	delineates	delineate	VERB
cana-2940	50	4	the	the	DET
cana-2940	50	5	experimental	experimental	ADJ
cana-2940	50	6	configuration	configuration	NOUN
cana-2940	50	7	and	and	CCONJ
cana-2940	50	8	results	result	NOUN
cana-2940	50	9	of	of	ADP
cana-2940	50	10	the	the	DET
cana-2940	50	11	proposed	propose	VERB
cana-2940	50	12	approach	approach	NOUN
cana-2940	50	13	while	while	SCONJ
cana-2940	50	14	assessing	assess	VERB
cana-2940	50	15	its	its	PRON
cana-2940	50	16	performance	performance	NOUN
cana-2940	50	17	.	.	PUNCT
cana-2940	51	1	the	the	DET
cana-2940	51	2	research	research	NOUN
cana-2940	51	3	study	study	NOUN
cana-2940	51	4	concludes	conclude	VERB
cana-2940	51	5	in	in	ADP
cana-2940	51	6	section	section	NOUN
cana-2940	51	7	6	6	NUM
cana-2940	51	8	.	.	NOUN
cana-2940	51	9	2	2	NUM
cana-2940	51	10	.	.	NUM
cana-2940	52	1	related	relate	VERB
cana-2940	52	2	works	work	NOUN
cana-2940	52	3	researchers	researcher	NOUN
cana-2940	52	4	have	have	AUX
cana-2940	52	5	developed	develop	VERB
cana-2940	52	6	many	many	ADJ
cana-2940	52	7	ways	way	NOUN
cana-2940	52	8	to	to	PART
cana-2940	52	9	facilitate	facilitate	VERB
cana-2940	52	10	intelligent	intelligent	ADJ
cana-2940	52	11	diagnostic	diagnostic	ADJ
cana-2940	52	12	tools	tool	NOUN
cana-2940	52	13	for	for	ADP
cana-2940	52	14	automated	automate	VERB
cana-2940	52	15	identification	identification	NOUN
cana-2940	52	16	of	of	ADP
cana-2940	52	17	skin	skin	NOUN
cana-2940	52	18	problems	problem	NOUN
cana-2940	52	19	.	.	PUNCT
cana-2940	53	1	according	accord	VERB
cana-2940	53	2	to	to	ADP
cana-2940	53	3	the	the	DET
cana-2940	53	4	many	many	ADJ
cana-2940	53	5	categories	category	NOUN
cana-2940	53	6	of	of	ADP
cana-2940	53	7	dermatological	dermatological	ADJ
cana-2940	53	8	conditions	condition	NOUN
cana-2940	53	9	,	,	PUNCT
cana-2940	53	10	image	image	NOUN
cana-2940	53	11	pre	pre	ADJ
cana-2940	53	12	-	-	ADJ
cana-2940	53	13	processing	processing	ADJ
cana-2940	53	14	,	,	PUNCT
cana-2940	53	15	extraction	extraction	NOUN
cana-2940	53	16	and	and	CCONJ
cana-2940	53	17	selection	selection	NOUN
cana-2940	53	18	of	of	ADP
cana-2940	53	19	features	feature	NOUN
cana-2940	53	20	methodologies	methodology	NOUN
cana-2940	53	21	,	,	PUNCT
cana-2940	53	22	and	and	CCONJ
cana-2940	53	23	classification	classification	NOUN
cana-2940	53	24	methods	method	NOUN
cana-2940	53	25	related	relate	VERB
cana-2940	53	26	studies	study	NOUN
cana-2940	53	27	are	be	AUX
cana-2940	53	28	categorized	categorize	VERB
cana-2940	53	29	into	into	ADP
cana-2940	53	30	various	various	ADJ
cana-2940	53	31	types(r	types(r	NOUN
cana-2940	53	32	.	.	PUNCT
cana-2940	54	1	sharma	sharma	PROPN
cana-2940	54	2	&	&	CCONJ
cana-2940	54	3	mehan	mehan	PROPN
cana-2940	54	4	,	,	PUNCT
cana-2940	54	5	2022	2022	NUM
cana-2940	54	6	)	)	PUNCT
cana-2940	54	7	.	.	PUNCT
cana-2940	55	1	initially	initially	ADV
cana-2940	55	2	,	,	PUNCT
cana-2940	55	3	classification	classification	NOUN
cana-2940	55	4	problems	problem	NOUN
cana-2940	55	5	were	be	AUX
cana-2940	55	6	evaluated	evaluate	VERB
cana-2940	55	7	using	use	VERB
cana-2940	55	8	several	several	ADJ
cana-2940	55	9	standard	standard	ADJ
cana-2940	55	10	ml	ml	NOUN
cana-2940	55	11	based	base	VERB
cana-2940	55	12	algorithms	algorithm	NOUN
cana-2940	55	13	such	such	ADJ
cana-2940	55	14	as	as	ADP
cana-2940	55	15	naïve	naïve	ADJ
cana-2940	55	16	bayes	bayes	NOUN
cana-2940	55	17	(	(	PUNCT
cana-2940	55	18	nbs)(balaji	nbs)(balaji	NUM
cana-2940	55	19	et	et	NOUN
cana-2940	55	20	al	al	PROPN
cana-2940	55	21	.	.	PROPN
cana-2940	55	22	,	,	PUNCT
cana-2940	55	23	2020)(hegde	2020)(hegde	NUM
cana-2940	55	24	et	et	NOUN
cana-2940	55	25	al	al	PROPN
cana-2940	55	26	.	.	PROPN
cana-2940	55	27	,	,	PUNCT
cana-2940	55	28	2018	2018	NUM
cana-2940	55	29	)	)	PUNCT
cana-2940	55	30	,	,	PUNCT
cana-2940	55	31	linear	linear	VERB
cana-2940	55	32	discriminant	discriminant	ADJ
cana-2940	55	33	analysis	analysis	NOUN
cana-2940	55	34	(	(	PUNCT
cana-2940	55	35	lda)(hegde	lda)(hegde	NOUN
cana-2940	55	36	et	et	PROPN
cana-2940	55	37	al	al	PROPN
cana-2940	55	38	.	.	PROPN
cana-2940	55	39	,	,	PUNCT
cana-2940	55	40	2018	2018	NUM
cana-2940	55	41	)	)	PUNCT
cana-2940	55	42	,	,	PUNCT
cana-2940	55	43	k	k	X
cana-2940	55	44	-	-	PUNCT
cana-2940	55	45	nearest	near	ADJ
cana-2940	55	46	neighbourhood	neighbourhood	NOUN
cana-2940	55	47	(	(	PUNCT
cana-2940	55	48	knn)(ballerini	knn)(ballerini	PROPN
cana-2940	55	49	et	et	PROPN
cana-2940	55	50	al	al	PROPN
cana-2940	55	51	.	.	PROPN
cana-2940	55	52	,	,	PUNCT
cana-2940	55	53	2013)(rahman	2013)(rahman	NUM
cana-2940	55	54	et	et	NOUN
cana-2940	55	55	al	al	PROPN
cana-2940	55	56	.	.	PROPN
cana-2940	55	57	,	,	PUNCT
cana-2940	55	58	n.d	n.d	PROPN
cana-2940	55	59	.	.	PROPN
cana-2940	55	60	)	)	PUNCT
cana-2940	55	61	,	,	PUNCT
cana-2940	55	62	and	and	CCONJ
cana-2940	55	63	support	support	VERB
cana-2940	55	64	vector	vector	NOUN
cana-2940	55	65	machine	machine	NOUN
cana-2940	55	66	(	(	PUNCT
cana-2940	55	67	svm)(hameed	svm)(hameed	X
cana-2940	55	68	et	et	NOUN
cana-2940	55	69	al	al	PROPN
cana-2940	55	70	.	.	PROPN
cana-2940	55	71	,	,	PUNCT
cana-2940	55	72	2020	2020	NUM
cana-2940	55	73	)	)	PUNCT
cana-2940	55	74	were	be	AUX
cana-2940	55	75	the	the	DET
cana-2940	55	76	preferences	preference	NOUN
cana-2940	55	77	of	of	ADP
cana-2940	55	78	researchers	researcher	NOUN
cana-2940	55	79	.	.	PUNCT
cana-2940	56	1	however	however	ADV
cana-2940	56	2	,	,	PUNCT
cana-2940	56	3	these	these	DET
cana-2940	56	4	methodologies	methodology	NOUN
cana-2940	56	5	have	have	VERB
cana-2940	56	6	a	a	DET
cana-2940	56	7	problem	problem	NOUN
cana-2940	56	8	of	of	ADP
cana-2940	56	9	less	less	ADJ
cana-2940	56	10	data	datum	NOUN
cana-2940	56	11	which	which	PRON
cana-2940	56	12	is	be	AUX
cana-2940	56	13	further	far	ADV
cana-2940	56	14	solved	solve	VERB
cana-2940	56	15	by	by	ADP
cana-2940	56	16	dl	dl	PROPN
cana-2940	56	17	techniques	technique	NOUN
cana-2940	56	18	.	.	PUNCT
cana-2940	57	1	in	in	ADP
cana-2940	57	2	(	(	PUNCT
cana-2940	57	3	jagdish	jagdish	PROPN
cana-2940	57	4	et	et	PROPN
cana-2940	57	5	al	al	PROPN
cana-2940	57	6	.	.	PROPN
cana-2940	57	7	,	,	PUNCT
cana-2940	57	8	2022	2022	NUM
cana-2940	57	9	)	)	PUNCT
cana-2940	57	10	authors	author	NOUN
cana-2940	57	11	suggested	suggest	VERB
cana-2940	57	12	a	a	DET
cana-2940	57	13	methodology	methodology	NOUN
cana-2940	57	14	for	for	ADP
cana-2940	57	15	the	the	DET
cana-2940	57	16	categorization	categorization	NOUN
cana-2940	57	17	of	of	ADP
cana-2940	57	18	skin	skin	NOUN
cana-2940	57	19	diseases	disease	NOUN
cana-2940	57	20	using	use	VERB
cana-2940	57	21	image	image	NOUN
cana-2940	57	22	processing	processing	NOUN
cana-2940	57	23	methods	method	NOUN
cana-2940	57	24	.	.	PUNCT
cana-2940	58	1	fuzzy	fuzzy	ADJ
cana-2940	58	2	clustering	clustering	NOUN
cana-2940	58	3	is	be	AUX
cana-2940	58	4	conducted	conduct	VERB
cana-2940	58	5	on	on	ADP
cana-2940	58	6	50	50	NUM
cana-2940	58	7	sample	sample	NOUN
cana-2940	58	8	images	image	NOUN
cana-2940	58	9	using	use	VERB
cana-2940	58	10	knn	knn	PROPN
cana-2940	58	11	and	and	CCONJ
cana-2940	58	12	svm	svm	VERB
cana-2940	58	13	in	in	ADP
cana-2940	58	14	conjunction	conjunction	NOUN
cana-2940	58	15	with	with	ADP
cana-2940	58	16	wavelet	wavelet	NOUN
cana-2940	58	17	analysis	analysis	NOUN
cana-2940	58	18	.	.	PUNCT
cana-2940	59	1	the	the	DET
cana-2940	59	2	proposed	propose	VERB
cana-2940	59	3	method	method	NOUN
cana-2940	59	4	achieved	achieve	VERB
cana-2940	59	5	an	an	DET
cana-2940	59	6	accuracy	accuracy	NOUN
cana-2940	59	7	of	of	ADP
cana-2940	59	8	91.2	91.2	NUM
cana-2940	59	9	%	%	NOUN
cana-2940	59	10	in	in	ADP
cana-2940	59	11	identifying	identify	VERB
cana-2940	59	12	skin	skin	NOUN
cana-2940	59	13	disease	disease	NOUN
cana-2940	59	14	types	type	NOUN
cana-2940	59	15	.	.	PUNCT
cana-2940	60	1	in	in	ADP
cana-2940	60	2	addition	addition	NOUN
cana-2940	60	3	,	,	PUNCT
cana-2940	60	4	the	the	DET
cana-2940	60	5	problem	problem	NOUN
cana-2940	60	6	was	be	AUX
cana-2940	60	7	the	the	DET
cana-2940	60	8	model	model	NOUN
cana-2940	60	9	worked	work	VERB
cana-2940	60	10	only	only	ADV
cana-2940	60	11	for	for	ADP
cana-2940	60	12	50	50	NUM
cana-2940	60	13	sample	sample	NOUN
cana-2940	60	14	images	image	NOUN
cana-2940	60	15	of	of	ADP
cana-2940	60	16	two	two	NUM
cana-2940	60	17	classes	class	NOUN
cana-2940	60	18	.	.	PUNCT
cana-2940	61	1	in(bandyopadhyay	in(bandyopadhyay	NOUN
cana-2940	61	2	et	et	PROPN
cana-2940	61	3	al	al	PROPN
cana-2940	61	4	.	.	PROPN
cana-2940	61	5	,	,	PUNCT
cana-2940	61	6	2022	2022	NUM
cana-2940	61	7	)	)	PUNCT
cana-2940	61	8	authors	author	NOUN
cana-2940	61	9	suggested	suggest	VERB
cana-2940	61	10	a	a	DET
cana-2940	61	11	hybrid	hybrid	ADJ
cana-2940	61	12	approach	approach	NOUN
cana-2940	61	13	that	that	PRON
cana-2940	61	14	integrates	integrate	VERB
cana-2940	61	15	ml	ml	X
cana-2940	61	16	with	with	ADP
cana-2940	61	17	dl	dl	PRON
cana-2940	61	18	for	for	ADP
cana-2940	61	19	choosing	choose	VERB
cana-2940	61	20	features	feature	NOUN
cana-2940	61	21	and	and	CCONJ
cana-2940	61	22	categorization	categorization	NOUN
cana-2940	61	23	.	.	PUNCT
cana-2940	62	1	deep	deep	ADJ
cana-2940	62	2	neural	neural	ADJ
cana-2940	62	3	network	network	NOUN
cana-2940	62	4	techniques	technique	NOUN
cana-2940	62	5	such	such	ADJ
cana-2940	62	6	as	as	ADP
cana-2940	62	7	alexnet	alexnet	ADJ
cana-2940	62	8	,	,	PUNCT
cana-2940	62	9	resnet50	resnet50	NOUN
cana-2940	62	10	,	,	PUNCT
cana-2940	62	11	googlenet	googlenet	NOUN
cana-2940	62	12	,	,	PUNCT
cana-2940	62	13	and	and	CCONJ
cana-2940	62	14	vgg16	vgg16	NOUN
cana-2940	62	15	were	be	AUX
cana-2940	62	16	used	use	VERB
cana-2940	62	17	for	for	ADP
cana-2940	62	18	feature	feature	NOUN
cana-2940	62	19	selection	selection	NOUN
cana-2940	62	20	,	,	PUNCT
cana-2940	62	21	and	and	CCONJ
cana-2940	62	22	ml	ml	ADV
cana-2940	62	23	based	base	VERB
cana-2940	62	24	techniques	technique	NOUN
cana-2940	62	25	corresponding	correspond	VERB
cana-2940	62	26	to	to	AUX
cana-2940	62	27	svm	svm	VERB
cana-2940	62	28	,	,	PUNCT
cana-2940	62	29	ensemble	ensemble	ADJ
cana-2940	62	30	adaboost	adaboost	ADV
cana-2940	62	31	,	,	PUNCT
cana-2940	62	32	and	and	CCONJ
cana-2940	62	33	decision	decision	NOUN
cana-2940	62	34	tree	tree	NOUN
cana-2940	62	35	(	(	PUNCT
cana-2940	62	36	dt	dt	NOUN
cana-2940	62	37	)	)	PUNCT
cana-2940	62	38	were	be	AUX
cana-2940	62	39	used	use	VERB
cana-2940	62	40	for	for	ADP
cana-2940	62	41	classification	classification	NOUN
cana-2940	62	42	.	.	PUNCT
cana-2940	63	1	further	further	PROPN
cana-2940	63	2	literature	literature	PROPN
cana-2940	63	3	relevant	relevant	ADJ
cana-2940	63	4	to	to	ADP
cana-2940	63	5	the	the	DET
cana-2940	63	6	study	study	NOUN
cana-2940	63	7	is	be	AUX
cana-2940	63	8	summarized	summarize	VERB
cana-2940	63	9	in	in	ADP
cana-2940	63	10	table	table	NOUN
cana-2940	63	11	1	1	NUM
cana-2940	63	12	.	.	PUNCT
cana-2940	64	1	communications	communication	NOUN
cana-2940	64	2	on	on	ADP
cana-2940	64	3	applied	apply	VERB
cana-2940	64	4	nonlinear	nonlinear	ADJ
cana-2940	64	5	analysis	analysis	NOUN
cana-2940	64	6	issn	issn	NOUN
cana-2940	64	7	:	:	PUNCT
cana-2940	64	8	1074	1074	NUM
cana-2940	64	9	-	-	PUNCT
cana-2940	64	10	133x	133x	NUM
cana-2940	64	11	vol	vol	NOUN
cana-2940	64	12	32	32	NUM
cana-2940	64	13	no	no	NOUN
cana-2940	64	14	.	.	PUNCT
cana-2940	65	1	5s	5s	NUM
cana-2940	65	2	(	(	PUNCT
cana-2940	65	3	2025	2025	NUM
cana-2940	65	4	)	)	PUNCT
cana-2940	65	5	29	29	NUM
cana-2940	65	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2940	65	7	table	table	NOUN
cana-2940	65	8	1	1	NUM
cana-2940	65	9	:	:	PUNCT
cana-2940	65	10	depicts	depict	VERB
cana-2940	65	11	the	the	DET
cana-2940	65	12	comparison	comparison	NOUN
cana-2940	65	13	based	base	VERB
cana-2940	65	14	on	on	ADP
cana-2940	65	15	image	image	NOUN
cana-2940	65	16	segmentation	segmentation	NOUN
cana-2940	65	17	and	and	CCONJ
cana-2940	65	18	feature	feature	NOUN
cana-2940	65	19	extraction	extraction	NOUN
cana-2940	65	20	reference	reference	NOUN
cana-2940	65	21	research	research	NOUN
cana-2940	65	22	aim	aim	VERB
cana-2940	65	23	techniques	technique	NOUN
cana-2940	65	24	used	use	VERB
cana-2940	65	25	feature	feature	NOUN
cana-2940	65	26	extracted	extract	VERB
cana-2940	65	27	sample	sample	NOUN
cana-2940	65	28	size	size	NOUN
cana-2940	65	29	classification	classification	NOUN
cana-2940	65	30	metrics	metric	NOUN
cana-2940	65	31	(	(	PUNCT
cana-2940	65	32	kashyap	kashyap	PROPN
cana-2940	65	33	&	&	CCONJ
cana-2940	65	34	kashyap	kashyap	PROPN
cana-2940	65	35	,	,	PUNCT
cana-2940	65	36	2024	2024	NUM
cana-2940	65	37	;	;	PUNCT
cana-2940	65	38	wei	wei	PROPN
cana-2940	65	39	et	et	PROPN
cana-2940	65	40	al	al	PROPN
cana-2940	65	41	.	.	PROPN
cana-2940	65	42	,	,	PUNCT
cana-2940	65	43	2018	2018	NUM
cana-2940	65	44	)	)	PUNCT
cana-2940	65	45	skin	skin	NOUN
cana-2940	65	46	disease	disease	NOUN
cana-2940	65	47	identification	identification	NOUN
cana-2940	65	48	using	use	VERB
cana-2940	65	49	image	image	NOUN
cana-2940	65	50	segmentation	segmentation	NOUN
cana-2940	65	51	and	and	CCONJ
cana-2940	65	52	feature	feature	NOUN
cana-2940	65	53	extraction	extraction	NOUN
cana-2940	65	54	median	median	NOUN
cana-2940	65	55	filter	filter	NOUN
cana-2940	65	56	,	,	PUNCT
cana-2940	65	57	glcm	glcm	PROPN
cana-2940	65	58	&	&	CCONJ
cana-2940	65	59	svm	svm	PROPN
cana-2940	65	60	color	color	NOUN
cana-2940	65	61	,	,	PUNCT
cana-2940	65	62	texture	texture	NOUN
cana-2940	65	63	90	90	NUM
cana-2940	65	64	images	image	NOUN
cana-2940	65	65	~	~	PUNCT
cana-2940	65	66	90	90	NUM
cana-2940	65	67	%	%	NOUN
cana-2940	65	68	(	(	PUNCT
cana-2940	65	69	roy	roy	PROPN
cana-2940	65	70	et	et	PROPN
cana-2940	65	71	al	al	PROPN
cana-2940	65	72	.	.	PROPN
cana-2940	65	73	,	,	PUNCT
cana-2940	65	74	2019	2019	NUM
cana-2940	65	75	)	)	PUNCT
cana-2940	65	76	various	various	ADJ
cana-2940	65	77	segmentation	segmentation	NOUN
cana-2940	65	78	techniques	technique	NOUN
cana-2940	65	79	for	for	ADP
cana-2940	65	80	skin	skin	NOUN
cana-2940	65	81	disease	disease	NOUN
cana-2940	65	82	detection	detection	NOUN
cana-2940	65	83	adaptive	adaptive	ADJ
cana-2940	65	84	thresholding	thresholding	NOUN
cana-2940	65	85	,	,	PUNCT
cana-2940	65	86	edge	edge	NOUN
cana-2940	65	87	detection	detection	NOUN
cana-2940	65	88	,	,	PUNCT
cana-2940	65	89	morphologybased	morphologybase	VERB
cana-2940	65	90	segmentation	segmentation	NOUN
cana-2940	65	91	and	and	CCONJ
cana-2940	65	92	k	k	NOUN
cana-2940	65	93	-	-	PUNCT
cana-2940	65	94	means	mean	VERB
cana-2940	65	95	clustering	cluster	VERB
cana-2940	65	96	not	not	PART
cana-2940	65	97	defined	define	VERB
cana-2940	65	98	5	5	NUM
cana-2940	65	99	not	not	PART
cana-2940	65	100	defined	define	VERB
cana-2940	65	101	(	(	PUNCT
cana-2940	65	102	gede	gede	PROPN
cana-2940	65	103	et	et	PROPN
cana-2940	65	104	al	al	PROPN
cana-2940	65	105	.	.	PROPN
cana-2940	65	106	,	,	PUNCT
cana-2940	65	107	2020	2020	NUM
cana-2940	65	108	)	)	PUNCT
cana-2940	65	109	detection	detection	NOUN
cana-2940	65	110	of	of	ADP
cana-2940	65	111	dermatological	dermatological	ADJ
cana-2940	65	112	conditions	condition	NOUN
cana-2940	65	113	using	use	VERB
cana-2940	65	114	k	k	ADJ
cana-2940	65	115	-	-	PUNCT
cana-2940	65	116	means	mean	VERB
cana-2940	65	117	clustering	clustering	NOUN
cana-2940	65	118	separation	separation	NOUN
cana-2940	65	119	and	and	CCONJ
cana-2940	65	120	feature	feature	NOUN
cana-2940	65	121	extraction	extraction	NOUN
cana-2940	65	122	techniques	technique	NOUN
cana-2940	65	123	k	k	NOUN
cana-2940	65	124	-	-	PUNCT
cana-2940	65	125	means	mean	VERB
cana-2940	65	126	clustering	clustering	ADJ
cana-2940	65	127	segmentation	segmentation	NOUN
cana-2940	65	128	,	,	PUNCT
cana-2940	65	129	discrete	discrete	ADJ
cana-2940	65	130	wavelet	wavelet	NOUN
cana-2940	65	131	transform	transform	NOUN
cana-2940	65	132	(	(	PUNCT
cana-2940	65	133	dwt	dwt	NOUN
cana-2940	65	134	)	)	PUNCT
cana-2940	65	135	and	and	CCONJ
cana-2940	65	136	svm	svm	VERB
cana-2940	65	137	color	color	NOUN
cana-2940	65	138	131	131	NUM
cana-2940	65	139	images	image	NOUN
cana-2940	65	140	(	(	PUNCT
cana-2940	65	141	4	4	NUM
cana-2940	65	142	classes	class	NOUN
cana-2940	65	143	of	of	ADP
cana-2940	65	144	skin	skin	NOUN
cana-2940	65	145	diseases	disease	NOUN
cana-2940	65	146	)	)	PUNCT
cana-2940	65	147	sensitivity	sensitivity	NOUN
cana-2940	65	148	95	95	NUM
cana-2940	65	149	%	%	NOUN
cana-2940	65	150	specificity	specificity	NOUN
cana-2940	65	151	97.9	97.9	NUM
cana-2940	65	152	%	%	NOUN
cana-2940	65	153	accuracy	accuracy	NOUN
cana-2940	65	154	97.1	97.1	NUM
cana-2940	65	155	%	%	NOUN
cana-2940	65	156	(	(	PUNCT
cana-2940	65	157	sinthura	sinthura	NOUN
cana-2940	65	158	et	et	PROPN
cana-2940	65	159	al	al	PROPN
cana-2940	65	160	.	.	PROPN
cana-2940	65	161	,	,	PUNCT
cana-2940	65	162	2020	2020	NUM
cana-2940	65	163	)	)	PUNCT
cana-2940	65	164	leveraging	leverage	VERB
cana-2940	65	165	technologies	technology	NOUN
cana-2940	65	166	for	for	ADP
cana-2940	65	167	skin	skin	NOUN
cana-2940	65	168	diseases	disease	NOUN
cana-2940	65	169	using	use	VERB
cana-2940	65	170	image	image	NOUN
cana-2940	65	171	processing	processing	NOUN
cana-2940	65	172	otsu	otsu	NOUN
cana-2940	65	173	’s	’s	PART
cana-2940	65	174	thresholding	thresholding	NOUN
cana-2940	65	175	,	,	PUNCT
cana-2940	65	176	glcm	glcm	NOUN
cana-2940	65	177	,	,	PUNCT
cana-2940	65	178	and	and	CCONJ
cana-2940	65	179	svm	svm	ADJ
cana-2940	65	180	autocorrelation	autocorrelation	NOUN
cana-2940	65	181	,	,	PUNCT
cana-2940	65	182	homogeneity	homogeneity	NOUN
cana-2940	65	183	,	,	PUNCT
cana-2940	65	184	entropy	entropy	NOUN
cana-2940	65	185	,	,	PUNCT
cana-2940	65	186	and	and	CCONJ
cana-2940	65	187	energy	energy	NOUN
cana-2940	65	188	100	100	NUM
cana-2940	65	189	images	image	NOUN
cana-2940	65	190	svm89	svm89	VERB
cana-2940	65	191	%	%	NOUN
cana-2940	65	192	(	(	PUNCT
cana-2940	65	193	aldera	aldera	PROPN
cana-2940	65	194	&	&	CCONJ
cana-2940	65	195	othman	othman	PROPN
cana-2940	65	196	,	,	PUNCT
cana-2940	65	197	2022	2022	NUM
cana-2940	65	198	)	)	PUNCT
cana-2940	65	199	categorization	categorization	NOUN
cana-2940	65	200	and	and	CCONJ
cana-2940	65	201	identification	identification	NOUN
cana-2940	65	202	of	of	ADP
cana-2940	65	203	dermatological	dermatological	ADJ
cana-2940	65	204	conditions	condition	NOUN
cana-2940	65	205	with	with	ADP
cana-2940	65	206	ml	ml	NOUN
cana-2940	65	207	and	and	CCONJ
cana-2940	65	208	image	image	NOUN
cana-2940	65	209	processing	processing	NOUN
cana-2940	65	210	techniques	technique	NOUN
cana-2940	65	211	otsu	otsu	NOUN
cana-2940	65	212	’s	’s	PART
cana-2940	65	213	thresholding	thresholding	NOUN
cana-2940	65	214	,	,	PUNCT
cana-2940	65	215	gabor	gabor	NOUN
cana-2940	65	216	and	and	CCONJ
cana-2940	65	217	entropy	entropy	VERB
cana-2940	65	218	for	for	ADP
cana-2940	65	219	texture	texture	NOUN
cana-2940	65	220	,	,	PUNCT
cana-2940	65	221	and	and	CCONJ
cana-2940	65	222	sobel	sobel	NOUN
cana-2940	65	223	for	for	ADP
cana-2940	65	224	edge	edge	NOUN
cana-2940	65	225	texture	texture	NOUN
cana-2940	65	226	and	and	CCONJ
cana-2940	65	227	edge	edge	VERB
cana-2940	65	228	dermnet	dermnet	PROPN
cana-2940	65	229	nz	nz	PROPN
cana-2940	65	230	&	&	CCONJ
cana-2940	65	231	atlas	atlas	PROPN
cana-2940	65	232	dermatologic	dermatologic	PROPN
cana-2940	65	233	(	(	PUNCT
cana-2940	65	234	377	377	NUM
cana-2940	65	235	images	image	NOUN
cana-2940	65	236	&	&	CCONJ
cana-2940	65	237	5	5	NUM
cana-2940	65	238	types	type	NOUN
cana-2940	65	239	of	of	ADP
cana-2940	65	240	diseases	disease	NOUN
cana-2940	65	241	)	)	PUNCT
cana-2940	65	242	accuracy	accuracy	NOUN
cana-2940	65	243	:	:	PUNCT
cana-2940	65	244	svm90.7	svm90.7	NOUN
cana-2940	65	245	%	%	NOUN
cana-2940	65	246	rf84.2	rf84.2	NOUN
cana-2940	65	247	%	%	NOUN
cana-2940	65	248	knn67.1	knn67.1	INTJ
cana-2940	65	249	%	%	NOUN
cana-2940	65	250	moreover	moreover	ADV
cana-2940	65	251	,	,	PUNCT
cana-2940	65	252	extensive	extensive	ADJ
cana-2940	65	253	literature	literature	NOUN
cana-2940	65	254	exists	exist	VERB
cana-2940	65	255	detailing	detail	VERB
cana-2940	65	256	the	the	DET
cana-2940	65	257	development	development	NOUN
cana-2940	65	258	of	of	ADP
cana-2940	65	259	several	several	ADJ
cana-2940	65	260	algorithms	algorithm	NOUN
cana-2940	65	261	designed	design	VERB
cana-2940	65	262	to	to	PART
cana-2940	65	263	classify	classify	VERB
cana-2940	65	264	skin	skin	NOUN
cana-2940	65	265	illnesses	illness	NOUN
cana-2940	65	266	with	with	ADP
cana-2940	65	267	accuracy	accuracy	NOUN
cana-2940	65	268	and	and	CCONJ
cana-2940	65	269	efficiency	efficiency	NOUN
cana-2940	65	270	,	,	PUNCT
cana-2940	65	271	enabling	enable	VERB
cana-2940	65	272	the	the	DET
cana-2940	65	273	fast	fast	ADJ
cana-2940	65	274	identification	identification	NOUN
cana-2940	65	275	of	of	ADP
cana-2940	65	276	many	many	ADJ
cana-2940	65	277	skincommunications	skincommunication	NOUN
cana-2940	65	278	on	on	ADP
cana-2940	65	279	applied	apply	VERB
cana-2940	65	280	nonlinear	nonlinear	ADJ
cana-2940	65	281	analysis	analysis	NOUN
cana-2940	65	282	issn	issn	NOUN
cana-2940	65	283	:	:	PUNCT
cana-2940	65	284	1074	1074	NUM
cana-2940	65	285	-	-	PUNCT
cana-2940	65	286	133x	133x	NUM
cana-2940	65	287	vol	vol	NOUN
cana-2940	65	288	32	32	NUM
cana-2940	65	289	no	no	NOUN
cana-2940	65	290	.	.	PUNCT
cana-2940	66	1	5s	5s	NUM
cana-2940	66	2	(	(	PUNCT
cana-2940	66	3	2025	2025	NUM
cana-2940	66	4	)	)	PUNCT
cana-2940	66	5	30	30	NUM
cana-2940	66	6	https://internationalpubls.com	https://internationalpubls.com	NUM
cana-2940	66	7	related	relate	VERB
cana-2940	66	8	conditions	condition	NOUN
cana-2940	66	9	by	by	ADP
cana-2940	66	10	these	these	DET
cana-2940	66	11	classification	classification	NOUN
cana-2940	66	12	methods	method	NOUN
cana-2940	66	13	.	.	PUNCT
cana-2940	67	1	from	from	ADP
cana-2940	67	2	the	the	DET
cana-2940	67	3	literature	literature	NOUN
cana-2940	67	4	,	,	PUNCT
cana-2940	67	5	it	it	PRON
cana-2940	67	6	is	be	AUX
cana-2940	67	7	observed	observe	VERB
cana-2940	67	8	that	that	SCONJ
cana-2940	67	9	most	most	ADJ
cana-2940	67	10	of	of	ADP
cana-2940	67	11	the	the	DET
cana-2940	67	12	research	research	NOUN
cana-2940	67	13	has	have	AUX
cana-2940	67	14	been	be	AUX
cana-2940	67	15	conducted	conduct	VERB
cana-2940	67	16	on	on	ADP
cana-2940	67	17	a	a	DET
cana-2940	67	18	few	few	ADJ
cana-2940	67	19	classes	class	NOUN
cana-2940	67	20	of	of	ADP
cana-2940	67	21	skin	skin	NOUN
cana-2940	67	22	diseases	disease	NOUN
cana-2940	67	23	.	.	PUNCT
cana-2940	68	1	it	it	PRON
cana-2940	68	2	is	be	AUX
cana-2940	68	3	also	also	ADV
cana-2940	68	4	detected	detect	VERB
cana-2940	68	5	that	that	SCONJ
cana-2940	68	6	most	most	ADJ
cana-2940	68	7	of	of	ADP
cana-2940	68	8	the	the	DET
cana-2940	68	9	research	research	NOUN
cana-2940	68	10	has	have	AUX
cana-2940	68	11	been	be	AUX
cana-2940	68	12	done	do	VERB
cana-2940	68	13	on	on	ADP
cana-2940	68	14	small	small	ADJ
cana-2940	68	15	sample	sample	NOUN
cana-2940	68	16	-	-	PUNCT
cana-2940	68	17	size	size	NOUN
cana-2940	68	18	datasets	dataset	NOUN
cana-2940	68	19	and	and	CCONJ
cana-2940	68	20	the	the	DET
cana-2940	68	21	count	count	NOUN
cana-2940	68	22	of	of	ADP
cana-2940	68	23	features	feature	NOUN
cana-2940	68	24	extracted	extract	VERB
cana-2940	68	25	is	be	AUX
cana-2940	68	26	also	also	ADV
cana-2940	68	27	less	less	ADJ
cana-2940	68	28	.	.	PUNCT
cana-2940	69	1	furthermore	furthermore	ADV
cana-2940	69	2	,	,	PUNCT
cana-2940	69	3	it	it	PRON
cana-2940	69	4	is	be	AUX
cana-2940	69	5	noticed	notice	VERB
cana-2940	69	6	that	that	SCONJ
cana-2940	69	7	much	much	ADJ
cana-2940	69	8	literature	literature	NOUN
cana-2940	69	9	is	be	AUX
cana-2940	69	10	available	available	ADJ
cana-2940	69	11	for	for	ADP
cana-2940	69	12	multiple	multiple	ADJ
cana-2940	69	13	classes	class	NOUN
cana-2940	69	14	but	but	CCONJ
cana-2940	69	15	not	not	PART
cana-2940	69	16	more	more	ADJ
cana-2940	69	17	than	than	ADP
cana-2940	69	18	20	20	NUM
cana-2940	69	19	classes	class	NOUN
cana-2940	69	20	of	of	ADP
cana-2940	69	21	skin	skin	NOUN
cana-2940	69	22	diseases	disease	NOUN
cana-2940	69	23	this	this	DET
cana-2940	69	24	work	work	NOUN
cana-2940	69	25	seeks	seek	VERB
cana-2940	69	26	to	to	PART
cana-2940	69	27	address	address	VERB
cana-2940	69	28	the	the	DET
cana-2940	69	29	constraint	constraint	NOUN
cana-2940	69	30	by	by	ADP
cana-2940	69	31	analysing	analyse	VERB
cana-2940	69	32	the	the	DET
cana-2940	69	33	dermnet	dermnet	NOUN
cana-2940	69	34	dataset	dataset	NOUN
cana-2940	69	35	and	and	CCONJ
cana-2940	69	36	applying	apply	VERB
cana-2940	69	37	an	an	DET
cana-2940	69	38	enhanced	enhanced	ADJ
cana-2940	69	39	image	image	NOUN
cana-2940	69	40	segmentation	segmentation	NOUN
cana-2940	69	41	and	and	CCONJ
cana-2940	69	42	feature	feature	NOUN
cana-2940	69	43	extraction	extraction	NOUN
cana-2940	69	44	technique	technique	NOUN
cana-2940	69	45	.	.	PUNCT
cana-2940	70	1	3	3	X
cana-2940	70	2	.	.	X
cana-2940	70	3	materials	material	NOUN
cana-2940	70	4	and	and	CCONJ
cana-2940	70	5	methods	method	NOUN
cana-2940	70	6	3.1	3.1	NUM
cana-2940	70	7	proposed	propose	VERB
cana-2940	70	8	methodology	methodology	NOUN
cana-2940	70	9	this	this	DET
cana-2940	70	10	research	research	NOUN
cana-2940	70	11	presents	present	VERB
cana-2940	70	12	a	a	DET
cana-2940	70	13	framework	framework	NOUN
cana-2940	70	14	for	for	ADP
cana-2940	70	15	the	the	DET
cana-2940	70	16	early	early	ADJ
cana-2940	70	17	categorization	categorization	NOUN
cana-2940	70	18	of	of	ADP
cana-2940	70	19	skin	skin	NOUN
cana-2940	70	20	disorders	disorder	NOUN
cana-2940	70	21	,	,	PUNCT
cana-2940	70	22	consisting	consist	VERB
cana-2940	70	23	of	of	ADP
cana-2940	70	24	two	two	NUM
cana-2940	70	25	primary	primary	ADJ
cana-2940	70	26	approaches	approach	NOUN
cana-2940	70	27	.	.	PUNCT
cana-2940	71	1	the	the	DET
cana-2940	71	2	first	first	ADJ
cana-2940	71	3	approach	approach	NOUN
cana-2940	71	4	is	be	AUX
cana-2940	71	5	a	a	DET
cana-2940	71	6	pre	pre	ADJ
cana-2940	71	7	-	-	ADJ
cana-2940	71	8	processed	process	VERB
cana-2940	71	9	enhanced	enhance	VERB
cana-2940	71	10	edge	edge	NOUN
cana-2940	71	11	detection	detection	NOUN
cana-2940	71	12	algorithm	algorithm	NOUN
cana-2940	71	13	along	along	ADP
cana-2940	71	14	with	with	ADP
cana-2940	71	15	deep	deep	ADJ
cana-2940	71	16	learning	learning	NOUN
cana-2940	71	17	and	and	CCONJ
cana-2940	71	18	the	the	DET
cana-2940	71	19	second	second	ADJ
cana-2940	71	20	approach	approach	NOUN
cana-2940	71	21	is	be	AUX
cana-2940	71	22	feature	feature	NOUN
cana-2940	71	23	extraction	extraction	NOUN
cana-2940	71	24	using	use	VERB
cana-2940	71	25	an	an	DET
cana-2940	71	26	efficientnet	efficientnet	NOUN
cana-2940	71	27	-	-	PUNCT
cana-2940	71	28	b0	b0	NOUN
cana-2940	71	29	deep	deep	ADJ
cana-2940	71	30	learning	learning	NOUN
cana-2940	71	31	-	-	PUNCT
cana-2940	71	32	based	base	VERB
cana-2940	71	33	model	model	NOUN
cana-2940	71	34	(	(	PUNCT
cana-2940	71	35	tan	tan	PROPN
cana-2940	71	36	&	&	CCONJ
cana-2940	71	37	le	le	PROPN
cana-2940	71	38	,	,	PUNCT
cana-2940	71	39	2019	2019	NUM
cana-2940	71	40	)	)	PUNCT
cana-2940	71	41	.	.	PUNCT
cana-2940	72	1	figure	figure	NOUN
cana-2940	72	2	1	1	NUM
cana-2940	72	3	illustrates	illustrate	VERB
cana-2940	72	4	the	the	DET
cana-2940	72	5	recommended	recommend	VERB
cana-2940	72	6	structure	structure	NOUN
cana-2940	72	7	for	for	ADP
cana-2940	72	8	the	the	DET
cana-2940	72	9	early	early	ADJ
cana-2940	72	10	categorization	categorization	NOUN
cana-2940	72	11	of	of	ADP
cana-2940	72	12	skin	skin	NOUN
cana-2940	72	13	diseases	disease	NOUN
cana-2940	72	14	.	.	PUNCT
cana-2940	73	1	the	the	DET
cana-2940	73	2	original	original	ADJ
cana-2940	73	3	images	image	NOUN
cana-2940	73	4	are	be	AUX
cana-2940	73	5	sourced	source	VERB
cana-2940	73	6	from	from	ADP
cana-2940	73	7	the	the	DET
cana-2940	73	8	dermnet	dermnet	NOUN
cana-2940	73	9	dataset	dataset	NOUN
cana-2940	73	10	.	.	PUNCT
cana-2940	74	1	the	the	DET
cana-2940	74	2	preprocessing	preprocessing	NOUN
cana-2940	74	3	phase	phase	NOUN
cana-2940	74	4	involves	involve	VERB
cana-2940	74	5	stages	stage	NOUN
cana-2940	74	6	like	like	ADP
cana-2940	74	7	resizing	resize	VERB
cana-2940	74	8	the	the	DET
cana-2940	74	9	image	image	NOUN
cana-2940	74	10	,	,	PUNCT
cana-2940	74	11	removing	remove	VERB
cana-2940	74	12	noise	noise	NOUN
cana-2940	74	13	,	,	PUNCT
cana-2940	74	14	improving	improve	VERB
cana-2940	74	15	contrast	contrast	NOUN
cana-2940	74	16	,	,	PUNCT
cana-2940	74	17	sharpening	sharpen	VERB
cana-2940	74	18	the	the	DET
cana-2940	74	19	image	image	NOUN
cana-2940	74	20	,	,	PUNCT
cana-2940	74	21	and	and	CCONJ
cana-2940	74	22	feature	feature	NOUN
cana-2940	74	23	extraction	extraction	NOUN
cana-2940	74	24	.	.	PUNCT
cana-2940	75	1	the	the	DET
cana-2940	75	2	proposed	propose	VERB
cana-2940	75	3	approach	approach	NOUN
cana-2940	75	4	employs	employ	VERB
cana-2940	75	5	a	a	DET
cana-2940	75	6	cnns	cnn	NOUN
cana-2940	75	7	to	to	PART
cana-2940	75	8	categorize	categorize	VERB
cana-2940	75	9	performance	performance	NOUN
cana-2940	75	10	measures	measure	NOUN
cana-2940	75	11	.	.	PUNCT
cana-2940	76	1	3.2	3.2	NUM
cana-2940	76	2	dataset	dataset	NOUN
cana-2940	76	3	:	:	PUNCT
cana-2940	76	4	the	the	DET
cana-2940	76	5	initial	initial	ADJ
cana-2940	76	6	vital	vital	ADJ
cana-2940	76	7	stage	stage	NOUN
cana-2940	76	8	in	in	ADP
cana-2940	76	9	creating	create	VERB
cana-2940	76	10	an	an	DET
cana-2940	76	11	automated	automate	VERB
cana-2940	76	12	system	system	NOUN
cana-2940	76	13	for	for	ADP
cana-2940	76	14	the	the	DET
cana-2940	76	15	early	early	ADJ
cana-2940	76	16	categorization	categorization	NOUN
cana-2940	76	17	of	of	ADP
cana-2940	76	18	skin	skin	NOUN
cana-2940	76	19	disorders	disorder	NOUN
cana-2940	76	20	is	be	AUX
cana-2940	76	21	data	datum	NOUN
cana-2940	76	22	collection	collection	NOUN
cana-2940	76	23	.	.	PUNCT
cana-2940	77	1	the	the	DET
cana-2940	77	2	dataset	dataset	NOUN
cana-2940	77	3	may	may	AUX
cana-2940	77	4	be	be	AUX
cana-2940	77	5	obtained	obtain	VERB
cana-2940	77	6	from	from	ADP
cana-2940	77	7	several	several	ADJ
cana-2940	77	8	sources	source	NOUN
cana-2940	77	9	,	,	PUNCT
cana-2940	77	10	such	such	ADJ
cana-2940	77	11	as	as	ADP
cana-2940	77	12	skincare	skincare	NOUN
cana-2940	77	13	centers	center	NOUN
cana-2940	77	14	,	,	PUNCT
cana-2940	77	15	hospitals	hospital	NOUN
cana-2940	77	16	,	,	PUNCT
cana-2940	77	17	open	open	ADJ
cana-2940	77	18	-	-	PUNCT
cana-2940	77	19	access	access	NOUN
cana-2940	77	20	platforms	platform	NOUN
cana-2940	77	21	,	,	PUNCT
cana-2940	77	22	official	official	ADJ
cana-2940	77	23	websites	website	NOUN
cana-2940	77	24	,	,	PUNCT
cana-2940	77	25	and	and	CCONJ
cana-2940	77	26	other	other	ADJ
cana-2940	77	27	credible	credible	ADJ
cana-2940	77	28	archives	archive	NOUN
cana-2940	77	29	.	.	PUNCT
cana-2940	78	1	this	this	DET
cana-2940	78	2	study	study	NOUN
cana-2940	78	3	utilizes	utilize	VERB
cana-2940	78	4	the	the	DET
cana-2940	78	5	dermnet	dermnet	NOUN
cana-2940	78	6	dataset	dataset	NOUN
cana-2940	78	7	,	,	PUNCT
cana-2940	78	8	a	a	DET
cana-2940	78	9	publicly	publicly	ADV
cana-2940	78	10	accessible	accessible	ADJ
cana-2940	78	11	online	online	ADJ
cana-2940	78	12	resource	resource	NOUN
cana-2940	78	13	recognized	recognize	VERB
cana-2940	78	14	as	as	ADP
cana-2940	78	15	the	the	DET
cana-2940	78	16	biggest	big	ADJ
cana-2940	78	17	dermatological	dermatological	ADJ
cana-2940	78	18	database	database	NOUN
cana-2940	78	19	for	for	ADP
cana-2940	78	20	medical	medical	ADJ
cana-2940	78	21	teaching	teaching	NOUN
cana-2940	78	22	purposes	purpose	NOUN
cana-2940	78	23	.	.	PUNCT
cana-2940	79	1	the	the	DET
cana-2940	79	2	dataset	dataset	NOUN
cana-2940	79	3	comprises	comprise	VERB
cana-2940	79	4	23	23	NUM
cana-2940	79	5	distinct	distinct	ADJ
cana-2940	79	6	illness	illness	NOUN
cana-2940	79	7	categories	category	NOUN
cana-2940	79	8	.	.	PUNCT
cana-2940	80	1	the	the	DET
cana-2940	80	2	aggregate	aggregate	ADJ
cana-2940	80	3	quantity	quantity	NOUN
cana-2940	80	4	of	of	ADP
cana-2940	80	5	samples	sample	NOUN
cana-2940	80	6	is	be	AUX
cana-2940	80	7	around	around	ADP
cana-2940	80	8	19,500	19,500	NUM
cana-2940	80	9	.	.	PUNCT
cana-2940	81	1	the	the	DET
cana-2940	81	2	images	image	NOUN
cana-2940	81	3	are	be	AUX
cana-2940	81	4	in	in	ADP
cana-2940	81	5	the	the	DET
cana-2940	81	6	form	form	NOUN
cana-2940	81	7	of	of	ADP
cana-2940	81	8	jpeg	jpeg	NOUN
cana-2940	81	9	and	and	CCONJ
cana-2940	81	10	consist	consist	VERB
cana-2940	81	11	of	of	ADP
cana-2940	81	12	three	three	NUM
cana-2940	81	13	channels	channel	NOUN
cana-2940	81	14	red	red	VERB
cana-2940	81	15	,	,	PUNCT
cana-2940	81	16	green	green	ADJ
cana-2940	81	17	and	and	CCONJ
cana-2940	81	18	blue	blue	ADJ
cana-2940	81	19	.	.	PUNCT
cana-2940	82	1	at	at	ADP
cana-2940	82	2	this	this	DET
cana-2940	82	3	step	step	NOUN
cana-2940	82	4	,	,	PUNCT
cana-2940	82	5	input	input	NOUN
cana-2940	82	6	images	image	NOUN
cana-2940	82	7	are	be	AUX
cana-2940	82	8	extracted	extract	VERB
cana-2940	82	9	from	from	ADP
cana-2940	82	10	the	the	DET
cana-2940	82	11	dermnet	dermnet	NOUN
cana-2940	82	12	being	be	AUX
cana-2940	82	13	collected	collect	VERB
cana-2940	82	14	.	.	PUNCT
cana-2940	83	1	the	the	DET
cana-2940	83	2	dataset	dataset	NOUN
cana-2940	83	3	is	be	AUX
cana-2940	83	4	split	split	VERB
cana-2940	83	5	into	into	ADP
cana-2940	83	6	80	80	NUM
cana-2940	83	7	%	%	NOUN
cana-2940	83	8	for	for	ADP
cana-2940	83	9	the	the	DET
cana-2940	83	10	training	training	NOUN
cana-2940	83	11	purpose	purpose	NOUN
cana-2940	83	12	and	and	CCONJ
cana-2940	83	13	20	20	NUM
cana-2940	83	14	%	%	NOUN
cana-2940	83	15	for	for	ADP
cana-2940	83	16	the	the	DET
cana-2940	83	17	test	test	NOUN
cana-2940	83	18	purpose	purpose	NOUN
cana-2940	83	19	.	.	PUNCT
cana-2940	84	1	initially	initially	ADV
cana-2940	84	2	,	,	PUNCT
cana-2940	84	3	the	the	DET
cana-2940	84	4	images	image	NOUN
cana-2940	84	5	are	be	AUX
cana-2940	84	6	loaded	load	VERB
cana-2940	84	7	from	from	ADP
cana-2940	84	8	the	the	DET
cana-2940	84	9	dataset	dataset	NOUN
cana-2940	84	10	to	to	PART
cana-2940	84	11	complete	complete	VERB
cana-2940	84	12	the	the	DET
cana-2940	84	13	pre	pre	ADJ
cana-2940	84	14	-	-	ADJ
cana-2940	84	15	processing	processing	ADJ
cana-2940	84	16	steps	step	NOUN
cana-2940	84	17	.	.	PUNCT
cana-2940	85	1	each	each	DET
cana-2940	85	2	image	image	NOUN
cana-2940	85	3	in	in	ADP
cana-2940	85	4	the	the	DET
cana-2940	85	5	dataset	dataset	NOUN
cana-2940	85	6	represents	represent	VERB
cana-2940	85	7	a	a	DET
cana-2940	85	8	particular	particular	ADJ
cana-2940	85	9	disease	disease	NOUN
cana-2940	85	10	and	and	CCONJ
cana-2940	85	11	provides	provide	VERB
cana-2940	85	12	the	the	DET
cana-2940	85	13	visual	visual	ADJ
cana-2940	85	14	information	information	NOUN
cana-2940	85	15	to	to	PART
cana-2940	85	16	train	train	VERB
cana-2940	85	17	and	and	CCONJ
cana-2940	85	18	test	test	VERB
cana-2940	85	19	the	the	DET
cana-2940	85	20	model	model	NOUN
cana-2940	85	21	.	.	PUNCT
cana-2940	86	1	correctly	correctly	ADV
cana-2940	86	2	loading	load	VERB
cana-2940	86	3	and	and	CCONJ
cana-2940	86	4	managing	manage	VERB
cana-2940	86	5	these	these	DET
cana-2940	86	6	sample	sample	NOUN
cana-2940	86	7	images	image	NOUN
cana-2940	86	8	is	be	AUX
cana-2940	86	9	crucial	crucial	ADJ
cana-2940	86	10	to	to	PART
cana-2940	86	11	guarantee	guarantee	VERB
cana-2940	86	12	the	the	DET
cana-2940	86	13	accuracy	accuracy	NOUN
cana-2940	86	14	and	and	CCONJ
cana-2940	86	15	dependability	dependability	NOUN
cana-2940	86	16	of	of	ADP
cana-2940	86	17	the	the	DET
cana-2940	86	18	processes	process	NOUN
cana-2940	86	19	that	that	PRON
cana-2940	86	20	comprise	comprise	VERB
cana-2940	86	21	the	the	DET
cana-2940	86	22	model	model	NOUN
cana-2940	86	23	.	.	PUNCT
cana-2940	87	1	various	various	ADJ
cana-2940	87	2	categories	category	NOUN
cana-2940	87	3	of	of	ADP
cana-2940	87	4	samples	sample	NOUN
cana-2940	87	5	obtained	obtain	VERB
cana-2940	87	6	from	from	ADP
cana-2940	87	7	the	the	DET
cana-2940	87	8	dermnet	dermnet	NOUN
cana-2940	87	9	dataset	dataset	NOUN
cana-2940	87	10	are	be	AUX
cana-2940	87	11	displayed	display	VERB
cana-2940	87	12	in	in	ADP
cana-2940	87	13	figure	figure	NOUN
cana-2940	87	14	2	2	NUM
cana-2940	87	15	.	.	PUNCT
cana-2940	87	16	communications	communication	NOUN
cana-2940	87	17	on	on	ADP
cana-2940	87	18	applied	apply	VERB
cana-2940	87	19	nonlinear	nonlinear	ADJ
cana-2940	87	20	analysis	analysis	NOUN
cana-2940	87	21	issn	issn	NOUN
cana-2940	87	22	:	:	PUNCT
cana-2940	87	23	1074	1074	NUM
cana-2940	87	24	-	-	PUNCT
cana-2940	87	25	133x	133x	NUM
cana-2940	87	26	vol	vol	NOUN
cana-2940	87	27	32	32	NUM
cana-2940	87	28	no	no	NOUN
cana-2940	87	29	.	.	PUNCT
cana-2940	88	1	5s	5s	NUM
cana-2940	88	2	(	(	PUNCT
cana-2940	88	3	2025	2025	NUM
cana-2940	88	4	)	)	PUNCT
cana-2940	88	5	31	31	NUM
cana-2940	89	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-2940	89	2	figure	figure	NOUN
cana-2940	89	3	1	1	NUM
cana-2940	89	4	:	:	PUNCT
cana-2940	89	5	illustrates	illustrate	VERB
cana-2940	89	6	a	a	DET
cana-2940	89	7	comprehensive	comprehensive	ADJ
cana-2940	89	8	description	description	NOUN
cana-2940	89	9	of	of	ADP
cana-2940	89	10	the	the	DET
cana-2940	89	11	proposed	propose	VERB
cana-2940	89	12	system	system	NOUN
cana-2940	89	13	acne	acne	NOUN
cana-2940	89	14	actinic	actinic	ADJ
cana-2940	89	15	keratosis	keratosis	NOUN
cana-2940	89	16	bullous	bullous	ADJ
cana-2940	89	17	cellulitis	cellulitis	NOUN
cana-2940	89	18	eczema	eczema	NOUN
cana-2940	89	19	figure	figure	VERB
cana-2940	89	20	2	2	NUM
cana-2940	89	21	:	:	PUNCT
cana-2940	89	22	sample	sample	NOUN
cana-2940	89	23	images	image	NOUN
cana-2940	89	24	taken	take	VERB
cana-2940	89	25	from	from	ADP
cana-2940	89	26	the	the	DET
cana-2940	89	27	dermnet	dermnet	NOUN
cana-2940	89	28	dataset	dataset	VERB
cana-2940	89	29	3.3	3.3	NUM
cana-2940	89	30	image	image	NOUN
cana-2940	89	31	pre	pre	ADJ
cana-2940	89	32	-	-	NOUN
cana-2940	89	33	processing	processing	NOUN
cana-2940	89	34	:	:	PUNCT
cana-2940	89	35	here	here	ADV
cana-2940	89	36	,	,	PUNCT
cana-2940	89	37	the	the	DET
cana-2940	89	38	method	method	NOUN
cana-2940	89	39	of	of	ADP
cana-2940	89	40	acquiring	acquire	VERB
cana-2940	89	41	pictures	picture	NOUN
cana-2940	89	42	should	should	AUX
cana-2940	89	43	be	be	AUX
cana-2940	89	44	irregular	irregular	ADJ
cana-2940	89	45	in	in	ADP
cana-2940	89	46	several	several	ADJ
cana-2940	89	47	ways	way	NOUN
cana-2940	89	48	.	.	PUNCT
cana-2940	90	1	in	in	ADP
cana-2940	90	2	the	the	DET
cana-2940	90	3	proposed	propose	VERB
cana-2940	90	4	system	system	NOUN
cana-2940	90	5	,	,	PUNCT
cana-2940	90	6	the	the	DET
cana-2940	90	7	second	second	ADJ
cana-2940	90	8	phase	phase	NOUN
cana-2940	90	9	is	be	AUX
cana-2940	90	10	image	image	NOUN
cana-2940	90	11	pre	pre	ADJ
cana-2940	90	12	-	-	NOUN
cana-2940	90	13	processing	processing	ADJ
cana-2940	90	14	.	.	PUNCT
cana-2940	91	1	thus	thus	ADV
cana-2940	91	2	,	,	PUNCT
cana-2940	91	3	the	the	DET
cana-2940	91	4	primary	primary	ADJ
cana-2940	91	5	objective	objective	NOUN
cana-2940	91	6	of	of	ADP
cana-2940	91	7	the	the	DET
cana-2940	91	8	preprocessing	preprocessing	NOUN
cana-2940	91	9	stage	stage	NOUN
cana-2940	91	10	is	be	AUX
cana-2940	91	11	to	to	PART
cana-2940	91	12	improve	improve	VERB
cana-2940	91	13	the	the	DET
cana-2940	91	14	picture	picture	NOUN
cana-2940	91	15	parameters	parameter	NOUN
cana-2940	91	16	such	such	ADJ
cana-2940	91	17	as	as	ADP
cana-2940	91	18	quality	quality	NOUN
cana-2940	91	19	,	,	PUNCT
cana-2940	91	20	clarity	clarity	NOUN
cana-2940	91	21	,	,	PUNCT
cana-2940	91	22	etc	etc	X
cana-2940	91	23	.	.	X
cana-2940	91	24	,	,	PUNCT
cana-2940	91	25	by	by	ADP
cana-2940	91	26	eliminating	eliminate	VERB
cana-2940	91	27	or	or	CCONJ
cana-2940	91	28	minimizing	minimize	VERB
cana-2940	91	29	the	the	DET
cana-2940	91	30	background	background	NOUN
cana-2940	91	31	or	or	CCONJ
cana-2940	91	32	other	other	ADJ
cana-2940	91	33	undesirable	undesirable	ADJ
cana-2940	91	34	areas	area	NOUN
cana-2940	91	35	of	of	ADP
cana-2940	91	36	the	the	DET
cana-2940	91	37	image	image	NOUN
cana-2940	91	38	.	.	PUNCT
cana-2940	92	1	(	(	PUNCT
cana-2940	92	2	monika	monika	PROPN
cana-2940	92	3	et	et	PROPN
cana-2940	92	4	al	al	PROPN
cana-2940	92	5	.	.	PROPN
cana-2940	92	6	,	,	PUNCT
cana-2940	92	7	2020	2020	NUM
cana-2940	92	8	)	)	PUNCT
cana-2940	92	9	.	.	PUNCT
cana-2940	93	1	in	in	ADP
cana-2940	93	2	medical	medical	ADJ
cana-2940	93	3	imaging	imaging	NOUN
cana-2940	93	4	,	,	PUNCT
cana-2940	93	5	preprocessing	preprocessing	NOUN
cana-2940	93	6	also	also	ADV
cana-2940	93	7	plays	play	VERB
cana-2940	93	8	a	a	DET
cana-2940	93	9	vital	vital	ADJ
cana-2940	93	10	role	role	NOUN
cana-2940	93	11	in	in	ADP
cana-2940	93	12	enhancing	enhance	VERB
cana-2940	93	13	the	the	DET
cana-2940	93	14	visibility	visibility	NOUN
cana-2940	93	15	of	of	ADP
cana-2940	93	16	significant	significant	ADJ
cana-2940	93	17	features	feature	NOUN
cana-2940	93	18	in	in	ADP
cana-2940	93	19	skin	skin	NOUN
cana-2940	93	20	images	image	NOUN
cana-2940	93	21	for	for	ADP
cana-2940	93	22	further	further	ADJ
cana-2940	93	23	analysis	analysis	NOUN
cana-2940	93	24	and	and	CCONJ
cana-2940	93	25	processing	processing	NOUN
cana-2940	93	26	,	,	PUNCT
cana-2940	93	27	making	make	VERB
cana-2940	93	28	it	it	PRON
cana-2940	93	29	easier	easy	ADJ
cana-2940	93	30	to	to	PART
cana-2940	93	31	classify	classify	VERB
cana-2940	93	32	using	use	VERB
cana-2940	93	33	deep	deep	ADJ
cana-2940	93	34	-	-	PUNCT
cana-2940	93	35	learning	learning	NOUN
cana-2940	93	36	-	-	PUNCT
cana-2940	93	37	based	base	VERB
cana-2940	93	38	models	model	NOUN
cana-2940	93	39	.	.	PUNCT
cana-2940	94	1	in	in	ADP
cana-2940	94	2	this	this	DET
cana-2940	94	3	paper	paper	NOUN
cana-2940	94	4	,	,	PUNCT
cana-2940	94	5	this	this	PRON
cana-2940	94	6	is	be	AUX
cana-2940	94	7	done	do	VERB
cana-2940	94	8	by	by	ADP
cana-2940	94	9	using	use	VERB
cana-2940	94	10	three	three	NUM
cana-2940	94	11	main	main	ADJ
cana-2940	94	12	steps	step	NOUN
cana-2940	94	13	:	:	PUNCT
cana-2940	94	14	a	a	X
cana-2940	94	15	)	)	PUNCT
cana-2940	94	16	image	image	NOUN
cana-2940	94	17	resizing	resize	VERB
cana-2940	94	18	b	b	NOUN
cana-2940	94	19	)	)	PUNCT
cana-2940	94	20	noise	noise	NOUN
cana-2940	94	21	reduction	reduction	NOUN
cana-2940	94	22	c	c	NOUN
cana-2940	94	23	)	)	PUNCT
cana-2940	94	24	image	image	NOUN
cana-2940	94	25	enhancement	enhancement	NOUN
cana-2940	94	26	3.3.1	3.3.1	NUM
cana-2940	94	27	image	image	NOUN
cana-2940	94	28	resizing	resizing	NOUN
cana-2940	94	29	:	:	PUNCT
cana-2940	94	30	the	the	DET
cana-2940	94	31	preprocessing	preprocessing	NOUN
cana-2940	94	32	phase	phase	NOUN
cana-2940	94	33	encompasses	encompass	VERB
cana-2940	94	34	many	many	ADJ
cana-2940	94	35	approaches	approach	NOUN
cana-2940	94	36	to	to	PART
cana-2940	94	37	ready	ready	VERB
cana-2940	94	38	the	the	DET
cana-2940	94	39	images	image	NOUN
cana-2940	94	40	for	for	ADP
cana-2940	94	41	evaluation	evaluation	NOUN
cana-2940	94	42	and	and	CCONJ
cana-2940	94	43	training	training	NOUN
cana-2940	94	44	models	model	NOUN
cana-2940	94	45	.	.	PUNCT
cana-2940	95	1	first	first	ADV
cana-2940	95	2	,	,	PUNCT
cana-2940	95	3	image	image	NOUN
cana-2940	95	4	resizing	resizing	NOUN
cana-2940	95	5	is	be	AUX
cana-2940	95	6	performed	perform	VERB
cana-2940	95	7	to	to	PART
cana-2940	95	8	standardize	standardize	VERB
cana-2940	95	9	all	all	DET
cana-2940	95	10	sample	sample	NOUN
cana-2940	95	11	images	image	NOUN
cana-2940	95	12	to	to	ADP
cana-2940	95	13	a	a	DET
cana-2940	95	14	size	size	NOUN
cana-2940	95	15	of	of	ADP
cana-2940	95	16	1024	1024	NUM
cana-2940	95	17	x1024	x1024	NUM
cana-2940	95	18	pixels	pixel	NOUN
cana-2940	95	19	.	.	PUNCT
cana-2940	96	1	this	this	PRON
cana-2940	96	2	ensures	ensure	VERB
cana-2940	96	3	uniformity	uniformity	NOUN
cana-2940	96	4	across	across	ADP
cana-2940	96	5	the	the	DET
cana-2940	96	6	dataset	dataset	NOUN
cana-2940	96	7	,	,	PUNCT
cana-2940	96	8	crucial	crucial	ADJ
cana-2940	96	9	for	for	SCONJ
cana-2940	96	10	communications	communication	NOUN
cana-2940	96	11	on	on	ADP
cana-2940	96	12	applied	apply	VERB
cana-2940	96	13	nonlinear	nonlinear	ADJ
cana-2940	96	14	analysis	analysis	NOUN
cana-2940	96	15	issn	issn	NOUN
cana-2940	96	16	:	:	PUNCT
cana-2940	96	17	1074	1074	NUM
cana-2940	96	18	-	-	PUNCT
cana-2940	96	19	133x	133x	NUM
cana-2940	96	20	vol	vol	NOUN
cana-2940	96	21	32	32	NUM
cana-2940	96	22	no	no	NOUN
cana-2940	96	23	.	.	PUNCT
cana-2940	97	1	5s	5s	NUM
cana-2940	97	2	(	(	PUNCT
cana-2940	97	3	2025	2025	NUM
cana-2940	97	4	)	)	PUNCT
cana-2940	97	5	32	32	NUM
cana-2940	97	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2940	97	7	efficient	efficient	ADJ
cana-2940	97	8	processing	processing	NOUN
cana-2940	97	9	and	and	CCONJ
cana-2940	97	10	accurate	accurate	ADJ
cana-2940	97	11	model	model	NOUN
cana-2940	97	12	training	training	NOUN
cana-2940	97	13	.	.	PUNCT
cana-2940	98	1	bilinear	bilinear	ADJ
cana-2940	98	2	interpolation	interpolation	NOUN
cana-2940	98	3	is	be	AUX
cana-2940	98	4	used	use	VERB
cana-2940	98	5	to	to	PART
cana-2940	98	6	scale	scale	VERB
cana-2940	98	7	up	up	ADP
cana-2940	98	8	or	or	CCONJ
cana-2940	98	9	down	down	ADP
cana-2940	98	10	the	the	DET
cana-2940	98	11	images	image	NOUN
cana-2940	98	12	.	.	PUNCT
cana-2940	99	1	the	the	DET
cana-2940	99	2	equation	equation	NOUN
cana-2940	99	3	for	for	ADP
cana-2940	99	4	bilinear	bilinear	NOUN
cana-2940	99	5	interpolation	interpolation	NOUN
cana-2940	99	6	is	be	AUX
cana-2940	99	7	:	:	PUNCT
cana-2940	99	8	bi′(x′	bi′(x′	X
cana-2940	99	9	,	,	PUNCT
cana-2940	99	10	y′	y′	NUM
cana-2940	99	11	)	)	PUNCT
cana-2940	100	1	=	=	SYM
cana-2940	100	2	(	(	PUNCT
cana-2940	100	3	1	1	NUM
cana-2940	100	4	−	−	NOUN
cana-2940	100	5	𝛿𝑥)(1	𝛿𝑥)(1	NOUN
cana-2940	100	6	−	−	PROPN
cana-2940	100	7	𝛿𝑦)𝐵𝐼(𝑥1	𝛿𝑦)𝐵𝐼(𝑥1	PROPN
cana-2940	100	8	,	,	PUNCT
cana-2940	100	9	𝑦1	𝑦1	PROPN
cana-2940	100	10	)	)	PUNCT
cana-2940	100	11	+	+	PUNCT
cana-2940	100	12	𝛿𝑥(1	𝛿𝑥(1	PROPN
cana-2940	100	13	−	−	PROPN
cana-2940	100	14	𝛿𝑦)𝐵𝐼(𝑥2	𝛿𝑦)𝐵𝐼(𝑥2	PROPN
cana-2940	100	15	,	,	PUNCT
cana-2940	100	16	𝑦1	𝑦1	PROPN
cana-2940	100	17	)	)	PUNCT
cana-2940	100	18	+	+	CCONJ
cana-2940	100	19	(	(	PUNCT
cana-2940	100	20	1	1	NUM
cana-2940	100	21	−	−	PROPN
cana-2940	100	22	𝛿𝑥)𝛿𝑏𝐵𝐼(𝑥1	𝛿𝑥)𝛿𝑏𝐵𝐼(𝑥1	NOUN
cana-2940	100	23	,	,	PUNCT
cana-2940	100	24	𝑦2	𝑦2	NOUN
cana-2940	100	25	)	)	PUNCT
cana-2940	101	1	+	+	CCONJ
cana-2940	101	2	𝛿𝑥𝛿𝑦𝐵𝐼(𝑥2	𝛿𝑥𝛿𝑦𝐵𝐼(𝑥2	PROPN
cana-2940	101	3	,	,	PUNCT
cana-2940	101	4	𝑦2	𝑦2	PROPN
cana-2940	101	5	)	)	PUNCT
cana-2940	101	6	(	(	PUNCT
cana-2940	101	7	1	1	X
cana-2940	101	8	)	)	PUNCT
cana-2940	101	9	in	in	ADP
cana-2940	101	10	equation	equation	NOUN
cana-2940	101	11	(	(	PUNCT
cana-2940	101	12	1	1	NUM
cana-2940	101	13	)	)	PUNCT
cana-2940	101	14	(	(	PUNCT
cana-2940	101	15	x	x	X
cana-2940	101	16	’	'	PUNCT
cana-2940	101	17	,	,	PUNCT
cana-2940	101	18	y	y	PROPN
cana-2940	101	19	’	'	PUNCT
cana-2940	101	20	)	)	PUNCT
cana-2940	101	21	are	be	AUX
cana-2940	101	22	the	the	DET
cana-2940	101	23	pixel	pixel	PROPN
cana-2940	101	24	coordinates	coordinate	NOUN
cana-2940	101	25	in	in	ADP
cana-2940	101	26	the	the	DET
cana-2940	101	27	resized	resize	VERB
cana-2940	101	28	image	image	NOUN
cana-2940	101	29	.	.	PUNCT
cana-2940	102	1	(	(	PUNCT
cana-2940	102	2	x1	x1	PROPN
cana-2940	102	3	,	,	PUNCT
cana-2940	102	4	y1	y1	PROPN
cana-2940	102	5	)	)	PUNCT
cana-2940	102	6	and	and	CCONJ
cana-2940	102	7	(	(	PUNCT
cana-2940	102	8	x2	x2	PROPN
cana-2940	102	9	,	,	PUNCT
cana-2940	102	10	y2	y2	PROPN
cana-2940	102	11	)	)	PUNCT
cana-2940	102	12	are	be	AUX
cana-2940	102	13	the	the	DET
cana-2940	102	14	nearest	near	ADJ
cana-2940	102	15	pixel	pixel	NOUN
cana-2940	102	16	coordinates	coordinate	NOUN
cana-2940	102	17	in	in	ADP
cana-2940	102	18	the	the	DET
cana-2940	102	19	initial	initial	ADJ
cana-2940	102	20	image	image	NOUN
cana-2940	102	21	.	.	PUNCT
cana-2940	103	1	𝛿a	𝛿a	PROPN
cana-2940	103	2	,	,	PUNCT
cana-2940	103	3	and	and	CCONJ
cana-2940	103	4	𝛿b	𝛿b	NOUN
cana-2940	103	5	are	be	AUX
cana-2940	103	6	the	the	DET
cana-2940	103	7	fractional	fractional	ADJ
cana-2940	103	8	parts	part	NOUN
cana-2940	103	9	of	of	ADP
cana-2940	103	10	the	the	DET
cana-2940	103	11	pixel	pixel	PROPN
cana-2940	103	12	coordinates	coordinate	NOUN
cana-2940	103	13	.	.	PUNCT
cana-2940	104	1	the	the	DET
cana-2940	104	2	equation	equation	NOUN
cana-2940	104	3	evaluates	evaluate	VERB
cana-2940	104	4	the	the	DET
cana-2940	104	5	value	value	NOUN
cana-2940	104	6	of	of	ADP
cana-2940	104	7	a	a	DET
cana-2940	104	8	pixel	pixel	NOUN
cana-2940	104	9	in	in	ADP
cana-2940	104	10	the	the	DET
cana-2940	104	11	new	new	ADJ
cana-2940	104	12	image	image	NOUN
cana-2940	104	13	based	base	VERB
cana-2940	104	14	on	on	ADP
cana-2940	104	15	its	its	PRON
cana-2940	104	16	position	position	NOUN
cana-2940	104	17	relative	relative	ADJ
cana-2940	104	18	to	to	ADP
cana-2940	104	19	the	the	DET
cana-2940	104	20	original	original	ADJ
cana-2940	104	21	image	image	NOUN
cana-2940	104	22	,	,	PUNCT
cana-2940	104	23	for	for	ADP
cana-2940	104	24	smooth	smooth	ADJ
cana-2940	104	25	scale	scale	NOUN
cana-2940	104	26	up	up	ADP
cana-2940	104	27	and	and	CCONJ
cana-2940	104	28	down	down	ADV
cana-2940	104	29	.	.	PUNCT
cana-2940	105	1	the	the	DET
cana-2940	105	2	new	new	ADJ
cana-2940	105	3	dimensions	dimension	NOUN
cana-2940	105	4	(	(	PUNCT
cana-2940	105	5	1024	1024	NUM
cana-2940	105	6	x1024	x1024	NUM
cana-2940	105	7	)	)	PUNCT
cana-2940	105	8	represent	represent	VERB
cana-2940	105	9	the	the	DET
cana-2940	105	10	width	width	NOUN
cana-2940	105	11	and	and	CCONJ
cana-2940	105	12	height	height	NOUN
cana-2940	105	13	in	in	ADP
cana-2940	105	14	pixels	pixel	NOUN
cana-2940	105	15	for	for	ADP
cana-2940	105	16	an	an	DET
cana-2940	105	17	image	image	NOUN
cana-2940	105	18	.	.	PUNCT
cana-2940	106	1	the	the	DET
cana-2940	106	2	original	original	ADJ
cana-2940	106	3	image	image	NOUN
cana-2940	106	4	(	(	PUNCT
cana-2940	106	5	size	size	NOUN
cana-2940	106	6	472	472	NUM
cana-2940	106	7	x	x	SYM
cana-2940	106	8	720	720	X
cana-2940	106	9	)	)	PUNCT
cana-2940	106	10	and	and	CCONJ
cana-2940	106	11	resized	resize	VERB
cana-2940	106	12	image	image	NOUN
cana-2940	106	13	data	datum	NOUN
cana-2940	106	14	sample	sample	NOUN
cana-2940	106	15	are	be	AUX
cana-2940	106	16	depicted	depict	VERB
cana-2940	106	17	in	in	ADP
cana-2940	106	18	figures	figure	NOUN
cana-2940	106	19	3	3	NUM
cana-2940	106	20	(	(	PUNCT
cana-2940	106	21	a	a	NOUN
cana-2940	106	22	)	)	PUNCT
cana-2940	106	23	and	and	CCONJ
cana-2940	106	24	(	(	PUNCT
cana-2940	106	25	b	b	NOUN
cana-2940	106	26	)	)	PUNCT
cana-2940	106	27	respectively	respectively	ADV
cana-2940	106	28	.	.	PUNCT
cana-2940	107	1	a	a	DET
cana-2940	107	2	)	)	PUNCT
cana-2940	107	3	b	b	NOUN
cana-2940	107	4	)	)	PUNCT
cana-2940	107	5	figure	figure	NOUN
cana-2940	107	6	3	3	NUM
cana-2940	107	7	:	:	PUNCT
cana-2940	107	8	a	a	X
cana-2940	107	9	)	)	PUNCT
cana-2940	107	10	original	original	ADJ
cana-2940	107	11	image	image	NOUN
cana-2940	107	12	data	datum	NOUN
cana-2940	107	13	sample	sample	NOUN
cana-2940	107	14	b	b	NOUN
cana-2940	107	15	)	)	PUNCT
cana-2940	107	16	resized	resize	VERB
cana-2940	107	17	image	image	NOUN
cana-2940	107	18	data	datum	NOUN
cana-2940	107	19	sample	sample	NOUN
cana-2940	107	20	3.3.2	3.3.2	NUM
cana-2940	107	21	noise	noise	NOUN
cana-2940	107	22	reduction	reduction	NOUN
cana-2940	107	23	:	:	PUNCT
cana-2940	107	24	after	after	ADP
cana-2940	107	25	resizing	resizing	NOUN
cana-2940	107	26	,	,	PUNCT
cana-2940	107	27	image	image	NOUN
cana-2940	107	28	blurring	blurring	NOUN
cana-2940	107	29	,	,	PUNCT
cana-2940	107	30	smoothing	smoothing	NOUN
cana-2940	107	31	,	,	PUNCT
cana-2940	107	32	and	and	CCONJ
cana-2940	107	33	filtering	filter	VERB
cana-2940	107	34	techniques	technique	NOUN
cana-2940	107	35	need	need	VERB
cana-2940	107	36	to	to	PART
cana-2940	107	37	be	be	AUX
cana-2940	107	38	applied	apply	VERB
cana-2940	107	39	to	to	PART
cana-2940	107	40	remove	remove	VERB
cana-2940	107	41	or	or	CCONJ
cana-2940	107	42	reduce	reduce	VERB
cana-2940	107	43	the	the	DET
cana-2940	107	44	unwanted	unwanted	ADJ
cana-2940	107	45	noise	noise	NOUN
cana-2940	107	46	present	present	ADJ
cana-2940	107	47	in	in	ADP
cana-2940	107	48	the	the	DET
cana-2940	107	49	image	image	NOUN
cana-2940	107	50	data	data	NOUN
cana-2940	107	51	samples	sample	NOUN
cana-2940	107	52	.	.	PUNCT
cana-2940	108	1	noise	noise	NOUN
cana-2940	108	2	from	from	ADP
cana-2940	108	3	digital	digital	ADJ
cana-2940	108	4	images	image	NOUN
cana-2940	108	5	can	can	AUX
cana-2940	108	6	be	be	AUX
cana-2940	108	7	reduced	reduce	VERB
cana-2940	108	8	by	by	ADP
cana-2940	108	9	the	the	DET
cana-2940	108	10	commonly	commonly	ADV
cana-2940	108	11	used	use	VERB
cana-2940	108	12	filtration	filtration	NOUN
cana-2940	108	13	technique	technique	NOUN
cana-2940	108	14	which	which	PRON
cana-2940	108	15	is	be	AUX
cana-2940	108	16	gaussian	gaussian	ADJ
cana-2940	108	17	blurring	blurring	NOUN
cana-2940	108	18	.	.	PUNCT
cana-2940	109	1	it	it	PRON
cana-2940	109	2	is	be	AUX
cana-2940	109	3	used	use	VERB
cana-2940	109	4	to	to	PART
cana-2940	109	5	smoothen	smoothen	VERB
cana-2940	109	6	the	the	DET
cana-2940	109	7	images	image	NOUN
cana-2940	109	8	by	by	ADP
cana-2940	109	9	reducing	reduce	VERB
cana-2940	109	10	the	the	DET
cana-2940	109	11	noise	noise	NOUN
cana-2940	109	12	from	from	ADP
cana-2940	109	13	the	the	DET
cana-2940	109	14	images(ahammed	images(ahammed	PROPN
cana-2940	109	15	et	et	PROPN
cana-2940	109	16	al	al	PROPN
cana-2940	109	17	.	.	PROPN
cana-2940	109	18	,	,	PUNCT
cana-2940	109	19	2022b	2022b	NUM
cana-2940	109	20	)	)	PUNCT
cana-2940	109	21	.	.	PUNCT
cana-2940	110	1	in	in	ADP
cana-2940	110	2	the	the	DET
cana-2940	110	3	proposed	propose	VERB
cana-2940	110	4	model	model	NOUN
cana-2940	110	5	,	,	PUNCT
cana-2940	110	6	a	a	DET
cana-2940	110	7	kernel	kernel	NOUN
cana-2940	110	8	of	of	ADP
cana-2940	110	9	size	size	NOUN
cana-2940	110	10	3x3	3x3	NUM
cana-2940	110	11	matrix	matrix	NOUN
cana-2940	110	12	is	be	AUX
cana-2940	110	13	used	use	VERB
cana-2940	110	14	,	,	PUNCT
cana-2940	110	15	to	to	PART
cana-2940	110	16	compute	compute	VERB
cana-2940	110	17	the	the	DET
cana-2940	110	18	blur	blur	NOUN
cana-2940	110	19	for	for	ADP
cana-2940	110	20	each	each	DET
cana-2940	110	21	pixel	pixel	NOUN
cana-2940	110	22	.	.	PUNCT
cana-2940	111	1	the	the	DET
cana-2940	111	2	pixel	pixel	PROPN
cana-2940	111	3	value	value	NOUN
cana-2940	111	4	is	be	AUX
cana-2940	111	5	calculated	calculate	VERB
cana-2940	111	6	as	as	SCONJ
cana-2940	111	7	the	the	DET
cana-2940	111	8	kernel	kernel	NOUN
cana-2940	111	9	moves	move	VERB
cana-2940	111	10	over	over	ADP
cana-2940	111	11	the	the	DET
cana-2940	111	12	image	image	NOUN
cana-2940	111	13	data	datum	NOUN
cana-2940	111	14	.	.	PUNCT
cana-2940	112	1	the	the	DET
cana-2940	112	2	pixel	pixel	PROPN
cana-2940	112	3	value	value	NOUN
cana-2940	112	4	is	be	AUX
cana-2940	112	5	further	far	ADV
cana-2940	112	6	utilized	utilize	VERB
cana-2940	112	7	by	by	ADP
cana-2940	112	8	the	the	DET
cana-2940	112	9	gaussian	gaussian	ADJ
cana-2940	112	10	blur	blur	NOUN
cana-2940	112	11	function	function	NOUN
cana-2940	112	12	.	.	PUNCT
cana-2940	113	1	the	the	DET
cana-2940	113	2	gaussian	gaussian	ADJ
cana-2940	113	3	function	function	NOUN
cana-2940	113	4	is	be	AUX
cana-2940	113	5	given	give	VERB
cana-2940	113	6	by	by	ADP
cana-2940	113	7	equation	equation	NOUN
cana-2940	113	8	:	:	PUNCT
cana-2940	113	9	𝐺(𝑥	𝐺(𝑥	NOUN
cana-2940	113	10	,	,	PUNCT
cana-2940	113	11	𝑦	𝑦	NOUN
cana-2940	113	12	)	)	PUNCT
cana-2940	113	13	=	=	SYM
cana-2940	113	14	1	1	NUM
cana-2940	113	15	√2πσ2	√2πσ2	PROPN
cana-2940	113	16	𝑒	𝑒	X
cana-2940	113	17	(	(	PUNCT
cana-2940	113	18	−	−	PROPN
cana-2940	113	19	(	(	PUNCT
cana-2940	113	20	𝑥2+𝑦2	𝑥2+𝑦2	PROPN
cana-2940	113	21	)	)	PUNCT
cana-2940	113	22	2σ2	2σ2	NUM
cana-2940	113	23	)	)	PUNCT
cana-2940	113	24	(	(	PUNCT
cana-2940	113	25	2	2	X
cana-2940	113	26	)	)	PUNCT
cana-2940	113	27	in	in	ADP
cana-2940	113	28	equation	equation	NOUN
cana-2940	113	29	(	(	PUNCT
cana-2940	113	30	2	2	NUM
cana-2940	113	31	)	)	PUNCT
cana-2940	113	32	,	,	PUNCT
cana-2940	113	33	x	x	PUNCT
cana-2940	113	34	and	and	CCONJ
cana-2940	113	35	y	y	PROPN
cana-2940	113	36	signify	signify	VERB
cana-2940	113	37	pixel	pixel	PROPN
cana-2940	113	38	coordinates	coordinate	NOUN
cana-2940	113	39	in	in	ADP
cana-2940	113	40	the	the	DET
cana-2940	113	41	kernel	kernel	PROPN
cana-2940	113	42	w.r.t	w.r.t	PROPN
cana-2940	113	43	.	.	PUNCT
cana-2940	114	1	to	to	PART
cana-2940	114	2	center	center	VERB
cana-2940	114	3	.	.	PUNCT
cana-2940	115	1	𝜎	𝜎	PROPN
cana-2940	115	2	(	(	PUNCT
cana-2940	115	3	standard	standard	ADJ
cana-2940	115	4	deviation	deviation	NOUN
cana-2940	115	5	)	)	PUNCT
cana-2940	115	6	controls	control	VERB
cana-2940	115	7	the	the	DET
cana-2940	115	8	distribution	distribution	NOUN
cana-2940	115	9	of	of	ADP
cana-2940	115	10	the	the	DET
cana-2940	115	11	gaussian	gaussian	ADJ
cana-2940	115	12	function	function	NOUN
cana-2940	115	13	.	.	PUNCT
cana-2940	116	1	samples	sample	NOUN
cana-2940	116	2	of	of	ADP
cana-2940	116	3	image	image	NOUN
cana-2940	116	4	data	datum	NOUN
cana-2940	116	5	before	before	ADV
cana-2940	116	6	and	and	CCONJ
cana-2940	116	7	after	after	ADP
cana-2940	116	8	are	be	AUX
cana-2940	116	9	illustrated	illustrate	VERB
cana-2940	116	10	in	in	ADP
cana-2940	116	11	figures	figure	NOUN
cana-2940	116	12	4(a	4(a	NUM
cana-2940	116	13	)	)	PUNCT
cana-2940	116	14	&	&	CCONJ
cana-2940	116	15	(	(	PUNCT
cana-2940	116	16	b	b	NOUN
cana-2940	116	17	)	)	PUNCT
cana-2940	116	18	respectively	respectively	ADV
cana-2940	116	19	.	.	PUNCT
cana-2940	117	1	communications	communication	NOUN
cana-2940	117	2	on	on	ADP
cana-2940	117	3	applied	apply	VERB
cana-2940	117	4	nonlinear	nonlinear	ADJ
cana-2940	117	5	analysis	analysis	NOUN
cana-2940	117	6	issn	issn	NOUN
cana-2940	117	7	:	:	PUNCT
cana-2940	117	8	1074	1074	NUM
cana-2940	117	9	-	-	PUNCT
cana-2940	117	10	133x	133x	NUM
cana-2940	117	11	vol	vol	NOUN
cana-2940	117	12	32	32	NUM
cana-2940	117	13	no	no	NOUN
cana-2940	117	14	.	.	PUNCT
cana-2940	118	1	5s	5s	NUM
cana-2940	118	2	(	(	PUNCT
cana-2940	118	3	2025	2025	NUM
cana-2940	118	4	)	)	PUNCT
cana-2940	118	5	33	33	NUM
cana-2940	118	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2940	118	7	a	a	PRON
cana-2940	118	8	)	)	PUNCT
cana-2940	118	9	b	b	NOUN
cana-2940	118	10	)	)	PUNCT
cana-2940	118	11	figure	figure	NOUN
cana-2940	118	12	4	4	NUM
cana-2940	118	13	:	:	PUNCT
cana-2940	118	14	noise	noise	NOUN
cana-2940	118	15	reduction	reduction	NOUN
cana-2940	118	16	a	a	PRON
cana-2940	118	17	)	)	PUNCT
cana-2940	118	18	resized	resize	VERB
cana-2940	118	19	image	image	NOUN
cana-2940	118	20	before	before	ADP
cana-2940	118	21	applying	apply	VERB
cana-2940	118	22	gaussian	gaussian	NOUN
cana-2940	118	23	blurring	blurring	NOUN
cana-2940	118	24	b	b	NOUN
cana-2940	118	25	)	)	PUNCT
cana-2940	118	26	after	after	ADP
cana-2940	118	27	applying	apply	VERB
cana-2940	118	28	gaussian	gaussian	NOUN
cana-2940	118	29	blurring	blur	VERB
cana-2940	118	30	3.3.3	3.3.3	NUM
cana-2940	118	31	image	image	NOUN
cana-2940	118	32	enhancement	enhancement	NOUN
cana-2940	118	33	:	:	PUNCT
cana-2940	118	34	it	it	PRON
cana-2940	118	35	is	be	AUX
cana-2940	118	36	used	use	VERB
cana-2940	118	37	to	to	PART
cana-2940	118	38	improve	improve	VERB
cana-2940	118	39	the	the	DET
cana-2940	118	40	image	image	NOUN
cana-2940	118	41	contrast	contrast	NOUN
cana-2940	118	42	and	and	CCONJ
cana-2940	118	43	brightness	brightness	NOUN
cana-2940	118	44	characteristics	characteristic	NOUN
cana-2940	118	45	.	.	PUNCT
cana-2940	119	1	(	(	PUNCT
cana-2940	119	2	ajith	ajith	PROPN
cana-2940	119	3	et	et	PROPN
cana-2940	119	4	al	al	PROPN
cana-2940	119	5	.	.	PROPN
cana-2940	119	6	,	,	PUNCT
cana-2940	119	7	2017	2017	NUM
cana-2940	119	8	)	)	PUNCT
cana-2940	119	9	.	.	PUNCT
cana-2940	120	1	the	the	DET
cana-2940	120	2	improved	improved	ADJ
cana-2940	120	3	contrast	contrast	NOUN
cana-2940	120	4	of	of	ADP
cana-2940	120	5	the	the	DET
cana-2940	120	6	image	image	NOUN
cana-2940	120	7	ensures	ensure	VERB
cana-2940	120	8	accurate	accurate	ADJ
cana-2940	120	9	performance	performance	NOUN
cana-2940	120	10	metrics	metric	NOUN
cana-2940	120	11	of	of	ADP
cana-2940	120	12	the	the	DET
cana-2940	120	13	classification	classification	NOUN
cana-2940	120	14	model	model	NOUN
cana-2940	120	15	(	(	PUNCT
cana-2940	120	16	saiwaeo	saiwaeo	NOUN
cana-2940	120	17	et	et	PROPN
cana-2940	120	18	al	al	PROPN
cana-2940	120	19	.	.	PROPN
cana-2940	120	20	,	,	PUNCT
cana-2940	120	21	2023	2023	NUM
cana-2940	120	22	)	)	PUNCT
cana-2940	120	23	.	.	PUNCT
cana-2940	121	1	in	in	ADP
cana-2940	121	2	our	our	PRON
cana-2940	121	3	proposed	propose	VERB
cana-2940	121	4	work	work	NOUN
cana-2940	121	5	,	,	PUNCT
cana-2940	121	6	we	we	PRON
cana-2940	121	7	have	have	AUX
cana-2940	121	8	used	use	VERB
cana-2940	121	9	histogram	histogram	NOUN
cana-2940	121	10	equalization	equalization	NOUN
cana-2940	121	11	to	to	PART
cana-2940	121	12	enhance	enhance	VERB
cana-2940	121	13	the	the	DET
cana-2940	121	14	contrast	contrast	NOUN
cana-2940	121	15	of	of	ADP
cana-2940	121	16	the	the	DET
cana-2940	121	17	images	image	NOUN
cana-2940	121	18	by	by	ADP
cana-2940	121	19	stretching	stretch	VERB
cana-2940	121	20	the	the	DET
cana-2940	121	21	histogram	histogram	NOUN
cana-2940	121	22	of	of	ADP
cana-2940	121	23	pixel	pixel	PROPN
cana-2940	121	24	intensities	intensity	NOUN
cana-2940	121	25	,	,	PUNCT
cana-2940	121	26	which	which	PRON
cana-2940	121	27	improves	improve	VERB
cana-2940	121	28	the	the	DET
cana-2940	121	29	visibility	visibility	NOUN
cana-2940	121	30	of	of	ADP
cana-2940	121	31	details	detail	NOUN
cana-2940	121	32	across	across	ADP
cana-2940	121	33	varying	vary	VERB
cana-2940	121	34	lighting	lighting	NOUN
cana-2940	121	35	conditions	condition	NOUN
cana-2940	121	36	as	as	SCONJ
cana-2940	121	37	represented	represent	VERB
cana-2940	121	38	in	in	ADP
cana-2940	121	39	the	the	DET
cana-2940	121	40	equation	equation	NOUN
cana-2940	121	41	(	(	PUNCT
cana-2940	121	42	3	3	NUM
cana-2940	121	43	)	)	PUNCT
cana-2940	121	44	,	,	PUNCT
cana-2940	121	45	where	where	SCONJ
cana-2940	121	46	e	e	X
cana-2940	121	47	’	'	PUNCT
cana-2940	121	48	is	be	AUX
cana-2940	121	49	the	the	DET
cana-2940	121	50	new	new	ADJ
cana-2940	121	51	intensity	intensity	NOUN
cana-2940	121	52	value	value	NOUN
cana-2940	121	53	,	,	PUNCT
cana-2940	121	54	e	e	X
cana-2940	121	55	is	be	AUX
cana-2940	121	56	the	the	DET
cana-2940	121	57	original	original	ADJ
cana-2940	121	58	intensity	intensity	NOUN
cana-2940	121	59	value	value	NOUN
cana-2940	121	60	,	,	PUNCT
cana-2940	121	61	i	i	PRON
cana-2940	121	62	is	be	AUX
cana-2940	121	63	the	the	DET
cana-2940	121	64	number	number	NOUN
cana-2940	121	65	of	of	ADP
cana-2940	121	66	possible	possible	ADJ
cana-2940	121	67	intensity	intensity	NOUN
cana-2940	121	68	levels	level	NOUN
cana-2940	121	69	(	(	PUNCT
cana-2940	121	70	256	256	NUM
cana-2940	121	71	for	for	ADP
cana-2940	121	72	8	8	NUM
cana-2940	121	73	-	-	PUNCT
cana-2940	121	74	bit	bit	NOUN
cana-2940	121	75	image	image	NOUN
cana-2940	121	76	)	)	PUNCT
cana-2940	121	77	,	,	PUNCT
cana-2940	121	78	n	n	PRON
cana-2940	121	79	is	be	AUX
cana-2940	121	80	the	the	DET
cana-2940	121	81	total	total	ADJ
cana-2940	121	82	number	number	NOUN
cana-2940	121	83	of	of	ADP
cana-2940	121	84	pixels	pixel	NOUN
cana-2940	121	85	of	of	ADP
cana-2940	121	86	the	the	DET
cana-2940	121	87	image	image	NOUN
cana-2940	121	88	,	,	PUNCT
cana-2940	121	89	h(j	h(j	PROPN
cana-2940	121	90	)	)	PUNCT
cana-2940	121	91	is	be	AUX
cana-2940	121	92	count	count	NOUN
cana-2940	121	93	of	of	ADP
cana-2940	121	94	pixels	pixel	NOUN
cana-2940	121	95	that	that	PRON
cana-2940	121	96	have	have	VERB
cana-2940	121	97	intensity	intensity	NOUN
cana-2940	121	98	j	j	PROPN
cana-2940	121	99	,	,	PUNCT
cana-2940	121	100	and	and	CCONJ
cana-2940	121	101	∑	∑	PROPN
cana-2940	121	102	ℎ(𝑗)𝐸	ℎ(𝑗)𝐸	PROPN
cana-2940	121	103	𝑗=0	𝑗=0	PROPN
cana-2940	121	104	is	be	AUX
cana-2940	121	105	the	the	DET
cana-2940	121	106	cumulative	cumulative	ADJ
cana-2940	121	107	distribution	distribution	NOUN
cana-2940	121	108	function	function	NOUN
cana-2940	121	109	which	which	PRON
cana-2940	121	110	sums	sum	VERB
cana-2940	121	111	up	up	ADP
cana-2940	121	112	the	the	DET
cana-2940	121	113	histogram	histogram	NOUN
cana-2940	121	114	values	value	NOUN
cana-2940	121	115	up	up	ADP
cana-2940	121	116	to	to	ADP
cana-2940	121	117	intensity	intensity	NOUN
cana-2940	121	118	e	e	NOUN
cana-2940	121	119	𝐸′	𝐸′	PROPN
cana-2940	121	120	=	=	SYM
cana-2940	121	121	(	(	PUNCT
cana-2940	121	122	𝐼−1	𝐼−1	PROPN
cana-2940	121	123	𝑁	𝑁	PROPN
cana-2940	121	124	)	)	PUNCT
cana-2940	121	125	∗	∗	NOUN
cana-2940	121	126	∑	∑	PUNCT
cana-2940	121	127	ℎ(𝑗)𝐸	ℎ(𝑗)𝐸	PROPN
cana-2940	122	1	𝑗=0	𝑗=0	PROPN
cana-2940	122	2	(	(	PUNCT
cana-2940	122	3	3	3	X
cana-2940	122	4	)	)	PUNCT
cana-2940	122	5	additionally	additionally	ADV
cana-2940	122	6	,	,	PUNCT
cana-2940	122	7	contrast	contrast	NOUN
cana-2940	122	8	-	-	PUNCT
cana-2940	122	9	limited	limit	VERB
cana-2940	122	10	adaptive	adaptive	ADJ
cana-2940	122	11	histogram	histogram	NOUN
cana-2940	122	12	equalization	equalization	NOUN
cana-2940	122	13	(	(	PUNCT
cana-2940	122	14	clahe	clahe	PROPN
cana-2940	122	15	)	)	PUNCT
cana-2940	122	16	is	be	AUX
cana-2940	122	17	a	a	DET
cana-2940	122	18	new	new	ADJ
cana-2940	122	19	form	form	NOUN
cana-2940	122	20	of	of	ADP
cana-2940	122	21	adaptive	adaptive	ADJ
cana-2940	122	22	histogram	histogram	NOUN
cana-2940	122	23	equalization	equalization	NOUN
cana-2940	122	24	(	(	PUNCT
cana-2940	122	25	ahe	ahe	NOUN
cana-2940	122	26	)	)	PUNCT
cana-2940	122	27	that	that	PRON
cana-2940	122	28	is	be	AUX
cana-2940	122	29	used	use	VERB
cana-2940	122	30	locally	locally	ADV
cana-2940	122	31	to	to	PART
cana-2940	122	32	enhance	enhance	VERB
cana-2940	122	33	contrast	contrast	NOUN
cana-2940	122	34	in	in	ADP
cana-2940	122	35	small	small	ADJ
cana-2940	122	36	regions	region	NOUN
cana-2940	122	37	by	by	ADP
cana-2940	122	38	preventing	prevent	VERB
cana-2940	122	39	over	over	ADP
cana-2940	122	40	-	-	PUNCT
cana-2940	122	41	amplification	amplification	NOUN
cana-2940	122	42	.	.	PUNCT
cana-2940	123	1	in	in	ADP
cana-2940	123	2	the	the	DET
cana-2940	123	3	proposed	propose	VERB
cana-2940	123	4	methodology	methodology	NOUN
cana-2940	123	5	,	,	PUNCT
cana-2940	123	6	after	after	ADP
cana-2940	123	7	applying	apply	VERB
cana-2940	123	8	the	the	DET
cana-2940	123	9	image	image	NOUN
cana-2940	123	10	resizing	resizing	NOUN
cana-2940	123	11	technique	technique	NOUN
cana-2940	123	12	and	and	CCONJ
cana-2940	123	13	gaussian	gaussian	ADJ
cana-2940	123	14	blurring	blurring	NOUN
cana-2940	123	15	to	to	ADP
cana-2940	123	16	the	the	DET
cana-2940	123	17	input	input	NOUN
cana-2940	123	18	image	image	NOUN
cana-2940	123	19	dataset	dataset	NOUN
cana-2940	123	20	of	of	ADP
cana-2940	123	21	23	23	NUM
cana-2940	123	22	distinct	distinct	ADJ
cana-2940	123	23	classes	class	NOUN
cana-2940	123	24	,	,	PUNCT
cana-2940	123	25	we	we	PRON
cana-2940	123	26	have	have	AUX
cana-2940	123	27	converted	convert	VERB
cana-2940	123	28	our	our	PRON
cana-2940	123	29	images	image	NOUN
cana-2940	123	30	into	into	ADP
cana-2940	123	31	grayscale	grayscale	NOUN
cana-2940	123	32	making	make	VERB
cana-2940	123	33	them	they	PRON
cana-2940	123	34	suitable	suitable	ADJ
cana-2940	123	35	for	for	ADP
cana-2940	123	36	clahe	clahe	NOUN
cana-2940	123	37	.	.	PUNCT
cana-2940	124	1	further	far	ADV
cana-2940	124	2	,	,	PUNCT
cana-2940	124	3	the	the	DET
cana-2940	124	4	image	image	NOUN
cana-2940	124	5	is	be	AUX
cana-2940	124	6	bifurcated	bifurcate	VERB
cana-2940	124	7	into	into	ADP
cana-2940	124	8	nonoverlapping	nonoverlappe	VERB
cana-2940	124	9	rectangular	rectangular	ADJ
cana-2940	124	10	homogeneous	homogeneous	ADJ
cana-2940	124	11	regions	region	NOUN
cana-2940	124	12	of	of	ADP
cana-2940	124	13	8x8	8x8	NUM
cana-2940	124	14	tiles	tile	NOUN
cana-2940	124	15	(	(	PUNCT
cana-2940	124	16	selected	select	VERB
cana-2940	124	17	by	by	ADP
cana-2940	124	18	opencv	opencv	PROPN
cana-2940	124	19	)	)	PUNCT
cana-2940	124	20	,	,	PUNCT
cana-2940	124	21	a	a	DET
cana-2940	124	22	set	set	NOUN
cana-2940	124	23	of	of	ADP
cana-2940	124	24	64	64	NUM
cana-2940	124	25	tiles	tile	NOUN
cana-2940	124	26	.	.	PUNCT
cana-2940	125	1	at	at	ADP
cana-2940	125	2	the	the	DET
cana-2940	125	3	end	end	NOUN
cana-2940	125	4	of	of	ADP
cana-2940	125	5	this	this	DET
cana-2940	125	6	technique	technique	NOUN
cana-2940	125	7	,	,	PUNCT
cana-2940	125	8	bilinear	bilinear	ADJ
cana-2940	125	9	interpolation	interpolation	NOUN
cana-2940	125	10	is	be	AUX
cana-2940	125	11	applied	apply	VERB
cana-2940	125	12	to	to	PART
cana-2940	125	13	merge	merge	VERB
cana-2940	125	14	all	all	DET
cana-2940	125	15	the	the	DET
cana-2940	125	16	tiles	tile	NOUN
cana-2940	125	17	.	.	PUNCT
cana-2940	126	1	figure	figure	VERB
cana-2940	126	2	5	5	NUM
cana-2940	126	3	depicts	depict	VERB
cana-2940	126	4	the	the	DET
cana-2940	126	5	process	process	NOUN
cana-2940	126	6	of	of	ADP
cana-2940	126	7	the	the	DET
cana-2940	126	8	clahe	clahe	NOUN
cana-2940	126	9	method	method	NOUN
cana-2940	126	10	.	.	PUNCT
cana-2940	127	1	the	the	DET
cana-2940	127	2	main	main	ADJ
cana-2940	127	3	objective	objective	NOUN
cana-2940	127	4	of	of	ADP
cana-2940	127	5	this	this	DET
cana-2940	127	6	method	method	NOUN
cana-2940	127	7	is	be	AUX
cana-2940	127	8	to	to	PART
cana-2940	127	9	enhance	enhance	VERB
cana-2940	127	10	the	the	DET
cana-2940	127	11	visibility	visibility	NOUN
cana-2940	127	12	of	of	ADP
cana-2940	127	13	features	feature	NOUN
cana-2940	127	14	and	and	CCONJ
cana-2940	127	15	textures	texture	NOUN
cana-2940	127	16	in	in	ADP
cana-2940	127	17	low	low	ADJ
cana-2940	127	18	contrast	contrast	NOUN
cana-2940	127	19	regions	region	NOUN
cana-2940	127	20	by	by	ADP
cana-2940	127	21	expanding	expand	VERB
cana-2940	127	22	the	the	DET
cana-2940	127	23	range	range	NOUN
cana-2940	127	24	of	of	ADP
cana-2940	127	25	brightness	brightness	NOUN
cana-2940	127	26	for	for	ADP
cana-2940	127	27	every	every	DET
cana-2940	127	28	tile	tile	NOUN
cana-2940	127	29	,	,	PUNCT
cana-2940	127	30	so	so	ADV
cana-2940	127	31	ensuring	ensure	VERB
cana-2940	127	32	deeper	deep	ADJ
cana-2940	127	33	and	and	CCONJ
cana-2940	127	34	lighter	light	ADJ
cana-2940	127	35	parts	part	NOUN
cana-2940	127	36	more	more	ADV
cana-2940	127	37	apparent	apparent	ADJ
cana-2940	127	38	.	.	PUNCT
cana-2940	128	1	figures	figure	NOUN
cana-2940	128	2	6	6	NUM
cana-2940	128	3	(	(	PUNCT
cana-2940	128	4	a	a	NOUN
cana-2940	128	5	)	)	PUNCT
cana-2940	128	6	and	and	CCONJ
cana-2940	128	7	(	(	PUNCT
cana-2940	128	8	b	b	X
cana-2940	128	9	)	)	PUNCT
cana-2940	128	10	represent	represent	VERB
cana-2940	128	11	the	the	DET
cana-2940	128	12	images	image	NOUN
cana-2940	128	13	prior	prior	ADV
cana-2940	128	14	and	and	CCONJ
cana-2940	128	15	after	after	ADP
cana-2940	128	16	clahe	clahe	NOUN
cana-2940	128	17	.	.	PUNCT
cana-2940	129	1	communications	communication	NOUN
cana-2940	129	2	on	on	ADP
cana-2940	129	3	applied	apply	VERB
cana-2940	129	4	nonlinear	nonlinear	ADJ
cana-2940	129	5	analysis	analysis	NOUN
cana-2940	129	6	issn	issn	NOUN
cana-2940	129	7	:	:	PUNCT
cana-2940	129	8	1074	1074	NUM
cana-2940	129	9	-	-	PUNCT
cana-2940	129	10	133x	133x	NUM
cana-2940	129	11	vol	vol	NOUN
cana-2940	129	12	32	32	NUM
cana-2940	129	13	no	no	NOUN
cana-2940	129	14	.	.	PUNCT
cana-2940	130	1	5s	5s	NUM
cana-2940	130	2	(	(	PUNCT
cana-2940	130	3	2025	2025	NUM
cana-2940	130	4	)	)	PUNCT
cana-2940	130	5	34	34	NUM
cana-2940	131	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-2940	131	2	figure	figure	NOUN
cana-2940	131	3	5	5	NUM
cana-2940	131	4	:	:	PUNCT
cana-2940	131	5	systematic	systematic	ADJ
cana-2940	131	6	workflow	workflow	NOUN
cana-2940	131	7	of	of	ADP
cana-2940	131	8	clahe	clahe	PROPN
cana-2940	131	9	a	a	PRON
cana-2940	131	10	)	)	PUNCT
cana-2940	131	11	b	b	NOUN
cana-2940	131	12	)	)	PUNCT
cana-2940	131	13	figure	figure	NOUN
cana-2940	131	14	6	6	NUM
cana-2940	131	15	a	a	DET
cana-2940	131	16	)	)	PUNCT
cana-2940	131	17	image	image	NOUN
cana-2940	131	18	before	before	ADP
cana-2940	131	19	applying	apply	VERB
cana-2940	131	20	clahe	clahe	PROPN
cana-2940	131	21	b	b	NOUN
cana-2940	131	22	)	)	PUNCT
cana-2940	131	23	after	after	ADP
cana-2940	131	24	contrast	contrast	NOUN
cana-2940	131	25	enhancement	enhancement	NOUN
cana-2940	131	26	3.3.4	3.3.4	NUM
cana-2940	131	27	image	image	NOUN
cana-2940	131	28	sharpening	sharpening	NOUN
cana-2940	131	29	:	:	PUNCT
cana-2940	131	30	in	in	ADP
cana-2940	131	31	the	the	DET
cana-2940	131	32	proposed	propose	VERB
cana-2940	131	33	methodology	methodology	NOUN
cana-2940	131	34	,	,	PUNCT
cana-2940	131	35	the	the	DET
cana-2940	131	36	last	last	ADJ
cana-2940	131	37	phase	phase	NOUN
cana-2940	131	38	of	of	ADP
cana-2940	131	39	pre	pre	ADJ
cana-2940	131	40	-	-	ADJ
cana-2940	131	41	processing	processing	NOUN
cana-2940	131	42	is	be	AUX
cana-2940	131	43	image	image	NOUN
cana-2940	131	44	sharpening	sharpening	NOUN
cana-2940	131	45	.	.	PUNCT
cana-2940	132	1	it	it	PRON
cana-2940	132	2	is	be	AUX
cana-2940	132	3	applied	apply	VERB
cana-2940	132	4	to	to	PART
cana-2940	132	5	enhance	enhance	VERB
cana-2940	132	6	the	the	DET
cana-2940	132	7	edges	edge	NOUN
cana-2940	132	8	and	and	CCONJ
cana-2940	132	9	details	detail	NOUN
cana-2940	132	10	in	in	ADP
cana-2940	132	11	the	the	DET
cana-2940	132	12	images	image	NOUN
cana-2940	132	13	,	,	PUNCT
cana-2940	132	14	making	make	VERB
cana-2940	132	15	significant	significant	ADJ
cana-2940	132	16	features	feature	NOUN
cana-2940	132	17	more	more	ADV
cana-2940	132	18	prominent	prominent	ADJ
cana-2940	132	19	.	.	PUNCT
cana-2940	133	1	we	we	PRON
cana-2940	133	2	have	have	AUX
cana-2940	133	3	used	use	VERB
cana-2940	133	4	an	an	DET
cana-2940	133	5	unsharp	unsharp	ADJ
cana-2940	133	6	masking	masking	NOUN
cana-2940	133	7	technique	technique	NOUN
cana-2940	133	8	of	of	ADP
cana-2940	133	9	image	image	NOUN
cana-2940	133	10	sharpening	sharpening	NOUN
cana-2940	133	11	.	.	PUNCT
cana-2940	134	1	the	the	DET
cana-2940	134	2	equation	equation	NOUN
cana-2940	134	3	of	of	ADP
cana-2940	134	4	unsharp	unsharp	ADJ
cana-2940	134	5	masking	masking	NOUN
cana-2940	134	6	is	be	AUX
cana-2940	134	7	:	:	PUNCT
cana-2940	134	8	sharpened	sharpen	VERB
cana-2940	134	9	image	image	NOUN
cana-2940	134	10	=	=	NOUN
cana-2940	134	11	α	α	NOUN
cana-2940	134	12	x	x	PUNCT
cana-2940	134	13	original	original	ADJ
cana-2940	134	14	image	image	NOUN
cana-2940	134	15	−	−	NOUN
cana-2940	134	16	β	β	NOUN
cana-2940	134	17	x	x	NOUN
cana-2940	134	18	blurred	blurred	ADJ
cana-2940	134	19	image	image	NOUN
cana-2940	134	20	+	+	CCONJ
cana-2940	134	21	γ	γ	X
cana-2940	134	22	(	(	PUNCT
cana-2940	134	23	4	4	NUM
cana-2940	134	24	)	)	PUNCT
cana-2940	134	25	where	where	SCONJ
cana-2940	134	26	:	:	PUNCT
cana-2940	134	27	communications	communication	NOUN
cana-2940	134	28	on	on	ADP
cana-2940	134	29	applied	apply	VERB
cana-2940	134	30	nonlinear	nonlinear	ADJ
cana-2940	134	31	analysis	analysis	NOUN
cana-2940	134	32	issn	issn	NOUN
cana-2940	134	33	:	:	PUNCT
cana-2940	134	34	1074	1074	NUM
cana-2940	134	35	-	-	PUNCT
cana-2940	134	36	133x	133x	NUM
cana-2940	134	37	vol	vol	NOUN
cana-2940	134	38	32	32	NUM
cana-2940	134	39	no	no	NOUN
cana-2940	134	40	.	.	PUNCT
cana-2940	135	1	5s	5s	NUM
cana-2940	135	2	(	(	PUNCT
cana-2940	135	3	2025	2025	NUM
cana-2940	135	4	)	)	PUNCT
cana-2940	135	5	35	35	NUM
cana-2940	135	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2940	135	7	i	i	NOUN
cana-2940	135	8	)	)	PUNCT
cana-2940	135	9	𝛼	𝛼	PROPN
cana-2940	135	10	is	be	AUX
cana-2940	135	11	the	the	DET
cana-2940	135	12	positive	positive	ADJ
cana-2940	135	13	constant	constant	NOUN
cana-2940	135	14	that	that	PRON
cana-2940	135	15	controls	control	VERB
cana-2940	135	16	the	the	DET
cana-2940	135	17	originality	originality	NOUN
cana-2940	135	18	of	of	ADP
cana-2940	135	19	the	the	DET
cana-2940	135	20	image	image	PROPN
cana-2940	135	21	ii	ii	PROPN
cana-2940	135	22	)	)	PUNCT
cana-2940	135	23	𝛽	𝛽	NOUN
cana-2940	135	24	is	be	AUX
cana-2940	135	25	the	the	DET
cana-2940	135	26	weight	weight	NOUN
cana-2940	135	27	applied	apply	VERB
cana-2940	135	28	to	to	ADP
cana-2940	135	29	the	the	DET
cana-2940	135	30	blurred	blurred	ADJ
cana-2940	135	31	image	image	NOUN
cana-2940	135	32	iii	iii	NOUN
cana-2940	135	33	)	)	PUNCT
cana-2940	135	34	𝛾	𝛾	ADP
cana-2940	136	1	it	it	PRON
cana-2940	136	2	is	be	AUX
cana-2940	136	3	used	use	VERB
cana-2940	136	4	to	to	PART
cana-2940	136	5	adjust	adjust	VERB
cana-2940	136	6	the	the	DET
cana-2940	136	7	brightness	brightness	NOUN
cana-2940	136	8	for	for	ADP
cana-2940	136	9	our	our	PRON
cana-2940	136	10	proposed	propose	VERB
cana-2940	136	11	work	work	NOUN
cana-2940	136	12	,	,	PUNCT
cana-2940	136	13	equation	equation	NOUN
cana-2940	136	14	(	(	PUNCT
cana-2940	136	15	4	4	X
cana-2940	136	16	)	)	PUNCT
cana-2940	136	17	corresponds	correspond	VERB
cana-2940	136	18	to	to	ADP
cana-2940	136	19	:	:	PUNCT
cana-2940	136	20	sharpened	sharpen	VERB
cana-2940	136	21	image	image	NOUN
cana-2940	136	22	=	=	NOUN
cana-2940	136	23	1.5	1.5	NUM
cana-2940	136	24	x	x	NOUN
cana-2940	136	25	original	original	ADJ
cana-2940	136	26	image	image	NOUN
cana-2940	136	27	–	–	PUNCT
cana-2940	136	28	0.5	0.5	NUM
cana-2940	136	29	x	x	SYM
cana-2940	136	30	blurred	blurred	ADJ
cana-2940	136	31	image	image	NOUN
cana-2940	136	32	+0	+0	ADP
cana-2940	136	33	in	in	ADP
cana-2940	136	34	equation	equation	NOUN
cana-2940	136	35	(	(	PUNCT
cana-2940	136	36	4	4	X
cana-2940	136	37	)	)	PUNCT
cana-2940	136	38	the	the	DET
cana-2940	136	39	original	original	ADJ
cana-2940	136	40	image	image	NOUN
cana-2940	136	41	is	be	AUX
cana-2940	136	42	scaled	scale	VERB
cana-2940	136	43	by	by	ADP
cana-2940	136	44	a	a	DET
cana-2940	136	45	value	value	NOUN
cana-2940	136	46	of	of	ADP
cana-2940	136	47	1.5	1.5	NUM
cana-2940	136	48	,	,	PUNCT
cana-2940	136	49	which	which	PRON
cana-2940	136	50	enhances	enhance	VERB
cana-2940	136	51	the	the	DET
cana-2940	136	52	image	image	NOUN
cana-2940	136	53	’s	’s	PART
cana-2940	136	54	intensity	intensity	NOUN
cana-2940	136	55	.	.	PUNCT
cana-2940	137	1	0.5	0.5	NUM
cana-2940	137	2	is	be	AUX
cana-2940	137	3	the	the	DET
cana-2940	137	4	blurred	blurred	ADJ
cana-2940	137	5	version	version	NOUN
cana-2940	137	6	of	of	ADP
cana-2940	137	7	the	the	DET
cana-2940	137	8	image	image	NOUN
cana-2940	137	9	,	,	PUNCT
cana-2940	137	10	which	which	PRON
cana-2940	137	11	reduces	reduce	VERB
cana-2940	137	12	the	the	DET
cana-2940	137	13	smoothed	smooth	VERB
cana-2940	137	14	areas	area	NOUN
cana-2940	137	15	and	and	CCONJ
cana-2940	137	16	highlights	highlight	NOUN
cana-2940	137	17	the	the	DET
cana-2940	137	18	edges	edge	NOUN
cana-2940	137	19	,	,	PUNCT
cana-2940	137	20	and	and	CCONJ
cana-2940	137	21	no	no	DET
cana-2940	137	22	additional	additional	ADJ
cana-2940	137	23	brightness	brightness	NOUN
cana-2940	137	24	adjustments	adjustment	NOUN
cana-2940	137	25	are	be	AUX
cana-2940	137	26	applied	apply	VERB
cana-2940	137	27	as	as	SCONJ
cana-2940	137	28	shown	show	VERB
cana-2940	137	29	in	in	ADP
cana-2940	137	30	figure	figure	NOUN
cana-2940	137	31	6	6	NUM
cana-2940	137	32	.	.	PUNCT
cana-2940	137	33	a	a	PRON
cana-2940	137	34	)	)	PUNCT
cana-2940	137	35	b	b	NOUN
cana-2940	137	36	)	)	PUNCT
cana-2940	137	37	figure	figure	NOUN
cana-2940	137	38	7	7	NUM
cana-2940	137	39	:	:	PUNCT
cana-2940	137	40	a	a	X
cana-2940	137	41	)	)	PUNCT
cana-2940	137	42	contrast	contrast	NOUN
cana-2940	137	43	-	-	PUNCT
cana-2940	137	44	enhanced	enhance	VERB
cana-2940	137	45	image	image	NOUN
cana-2940	137	46	b	b	NOUN
cana-2940	137	47	)	)	PUNCT
cana-2940	137	48	resultant	resultant	NOUN
cana-2940	137	49	sharpened	sharpen	VERB
cana-2940	137	50	image	image	NOUN
cana-2940	137	51	3.4	3.4	NUM
cana-2940	137	52	image	image	NOUN
cana-2940	137	53	segmentation	segmentation	NOUN
cana-2940	137	54	:	:	PUNCT
cana-2940	137	55	in	in	ADP
cana-2940	137	56	the	the	DET
cana-2940	137	57	proposed	propose	VERB
cana-2940	137	58	methodology	methodology	NOUN
cana-2940	137	59	,	,	PUNCT
cana-2940	137	60	an	an	DET
cana-2940	137	61	enhanced	enhanced	ADJ
cana-2940	137	62	holistically	holistically	ADV
cana-2940	137	63	nested	nest	VERB
cana-2940	137	64	edge	edge	NOUN
cana-2940	137	65	detection	detection	NOUN
cana-2940	137	66	algorithm	algorithm	NOUN
cana-2940	137	67	(	(	PUNCT
cana-2940	137	68	e	e	VERB
cana-2940	137	69	-	-	VERB
cana-2940	137	70	hned	hne	VERB
cana-2940	137	71	)	)	PUNCT
cana-2940	137	72	combining	combine	VERB
cana-2940	137	73	deep	deep	ADJ
cana-2940	137	74	neural	neural	ADJ
cana-2940	137	75	networks	network	NOUN
cana-2940	137	76	is	be	AUX
cana-2940	137	77	used	use	VERB
cana-2940	137	78	to	to	PART
cana-2940	137	79	segment	segment	VERB
cana-2940	137	80	images	image	NOUN
cana-2940	137	81	by	by	ADP
cana-2940	137	82	detecting	detect	VERB
cana-2940	137	83	edges	edge	NOUN
cana-2940	137	84	.	.	PUNCT
cana-2940	138	1	e	e	X
cana-2940	138	2	-	-	VERB
cana-2940	138	3	hned	hne	VERB
cana-2940	138	4	provides	provide	VERB
cana-2940	138	5	more	more	ADV
cana-2940	138	6	detailed	detailed	ADJ
cana-2940	138	7	and	and	CCONJ
cana-2940	138	8	accurate	accurate	ADJ
cana-2940	138	9	edge	edge	NOUN
cana-2940	138	10	maps	map	NOUN
cana-2940	138	11	than	than	ADP
cana-2940	138	12	traditional	traditional	ADJ
cana-2940	138	13	methods	method	NOUN
cana-2940	138	14	.	.	PUNCT
cana-2940	139	1	the	the	DET
cana-2940	139	2	use	use	NOUN
cana-2940	139	3	of	of	ADP
cana-2940	139	4	convolution	convolution	NOUN
cana-2940	139	5	neural	neural	ADJ
cana-2940	139	6	networks	network	NOUN
cana-2940	139	7	(	(	PUNCT
cana-2940	139	8	cnns	cnns	PROPN
cana-2940	139	9	)	)	PUNCT
cana-2940	139	10	in	in	ADP
cana-2940	139	11	hned	hne	VERB
cana-2940	139	12	provides	provide	VERB
cana-2940	139	13	significant	significant	ADJ
cana-2940	139	14	improvements	improvement	NOUN
cana-2940	139	15	by	by	ADP
cana-2940	139	16	predicting	predict	VERB
cana-2940	139	17	edges	edge	NOUN
cana-2940	139	18	at	at	ADP
cana-2940	139	19	various	various	ADJ
cana-2940	139	20	scales	scale	NOUN
cana-2940	139	21	and	and	CCONJ
cana-2940	139	22	combining	combine	VERB
cana-2940	139	23	these	these	DET
cana-2940	139	24	predictions	prediction	NOUN
cana-2940	139	25	for	for	ADP
cana-2940	139	26	a	a	DET
cana-2940	139	27	comprehensive	comprehensive	ADJ
cana-2940	139	28	edge	edge	NOUN
cana-2940	139	29	map	map	NOUN
cana-2940	139	30	.	.	PUNCT
cana-2940	140	1	the	the	DET
cana-2940	140	2	proposed	propose	VERB
cana-2940	140	3	hybrid	hybrid	NOUN
cana-2940	140	4	approach	approach	NOUN
cana-2940	140	5	offers	offer	VERB
cana-2940	140	6	more	more	ADJ
cana-2940	140	7	control	control	NOUN
cana-2940	140	8	and	and	CCONJ
cana-2940	140	9	fine	fine	ADV
cana-2940	140	10	-	-	PUNCT
cana-2940	140	11	tuning	tuning	NOUN
cana-2940	140	12	results	result	NOUN
cana-2940	140	13	.	.	PUNCT
cana-2940	141	1	3.4.1	3.4.1	NUM
cana-2940	141	2	proposed	propose	VERB
cana-2940	141	3	enhanced	enhance	VERB
cana-2940	141	4	cnn	cnn	PROPN
cana-2940	141	5	-	-	PUNCT
cana-2940	141	6	based	base	VERB
cana-2940	141	7	nested	nested	ADJ
cana-2940	141	8	edge	edge	NOUN
cana-2940	141	9	detection	detection	NOUN
cana-2940	141	10	image	image	NOUN
cana-2940	141	11	segmentation	segmentation	NOUN
cana-2940	141	12	technique	technique	NOUN
cana-2940	141	13	a	a	DET
cana-2940	141	14	pre	pre	ADJ
cana-2940	141	15	-	-	ADJ
cana-2940	141	16	processed	processed	ADJ
cana-2940	141	17	convolution	convolution	NOUN
cana-2940	141	18	neural	neural	ADJ
cana-2940	141	19	networks	network	NOUN
cana-2940	141	20	(	(	PUNCT
cana-2940	141	21	cnns	cnns	PROPN
cana-2940	141	22	)	)	PUNCT
cana-2940	141	23	based	base	VERB
cana-2940	141	24	holistically	holistically	ADV
cana-2940	141	25	nested	nest	VERB
cana-2940	141	26	edge	edge	NOUN
cana-2940	141	27	detection	detection	NOUN
cana-2940	141	28	(	(	PUNCT
cana-2940	141	29	hned	hne	VERB
cana-2940	141	30	)	)	PUNCT
cana-2940	141	31	algorithm	algorithm	NOUN
cana-2940	141	32	is	be	AUX
cana-2940	141	33	applied	apply	VERB
cana-2940	141	34	to	to	ADP
cana-2940	141	35	the	the	DET
cana-2940	141	36	input	input	NOUN
cana-2940	141	37	images	image	NOUN
cana-2940	141	38	.	.	PUNCT
cana-2940	142	1	however	however	ADV
cana-2940	142	2	,	,	PUNCT
cana-2940	142	3	the	the	DET
cana-2940	142	4	conventional	conventional	ADJ
cana-2940	142	5	holistically	holistically	ADJ
cana-2940	142	6	edge	edge	NOUN
cana-2940	142	7	detection	detection	NOUN
cana-2940	142	8	segmentation	segmentation	NOUN
cana-2940	142	9	technique	technique	NOUN
cana-2940	142	10	has	have	VERB
cana-2940	142	11	certain	certain	ADJ
cana-2940	142	12	limitations	limitation	NOUN
cana-2940	142	13	in	in	ADP
cana-2940	142	14	handling	handle	VERB
cana-2940	142	15	noise	noise	NOUN
cana-2940	142	16	,	,	PUNCT
cana-2940	142	17	complex	complex	ADJ
cana-2940	142	18	structures	structure	NOUN
cana-2940	142	19	,	,	PUNCT
cana-2940	142	20	semantic	semantic	ADJ
cana-2940	142	21	segmentation	segmentation	NOUN
cana-2940	142	22	,	,	PUNCT
cana-2940	142	23	and	and	CCONJ
cana-2940	142	24	lack	lack	NOUN
cana-2940	142	25	of	of	ADP
cana-2940	142	26	post	post	ADJ
cana-2940	142	27	-	-	ADJ
cana-2940	142	28	processing	processing	ADJ
cana-2940	142	29	optimization(xie	optimization(xie	NOUN
cana-2940	142	30	&	&	CCONJ
cana-2940	142	31	tu	tu	PROPN
cana-2940	142	32	,	,	PUNCT
cana-2940	142	33	2017	2017	NUM
cana-2940	142	34	)	)	PUNCT
cana-2940	142	35	.	.	PUNCT
cana-2940	143	1	in	in	ADP
cana-2940	143	2	order	order	NOUN
cana-2940	143	3	,	,	PUNCT
cana-2940	143	4	to	to	PART
cana-2940	143	5	remove	remove	VERB
cana-2940	143	6	these	these	DET
cana-2940	143	7	problems	problem	NOUN
cana-2940	143	8	,	,	PUNCT
cana-2940	143	9	some	some	DET
cana-2940	143	10	advancements	advancement	NOUN
cana-2940	143	11	have	have	AUX
cana-2940	143	12	been	be	AUX
cana-2940	143	13	accomplished	accomplish	VERB
cana-2940	143	14	by	by	ADP
cana-2940	143	15	the	the	DET
cana-2940	143	16	proposed	propose	VERB
cana-2940	143	17	work	work	NOUN
cana-2940	143	18	,	,	PUNCT
cana-2940	143	19	and	and	CCONJ
cana-2940	143	20	termed	term	VERB
cana-2940	143	21	as	as	ADP
cana-2940	143	22	enhanced	enhanced	ADJ
cana-2940	143	23	holistically	holistically	ADV
cana-2940	143	24	nested	nest	VERB
cana-2940	143	25	edge	edge	NOUN
cana-2940	143	26	detection	detection	NOUN
cana-2940	143	27	(	(	PUNCT
cana-2940	143	28	e	e	NOUN
cana-2940	143	29	-	-	VERB
cana-2940	143	30	hned	hne	VERB
cana-2940	143	31	)	)	PUNCT
cana-2940	143	32	.	.	PUNCT
cana-2940	144	1	the	the	DET
cana-2940	144	2	proposed	propose	VERB
cana-2940	144	3	e	e	NOUN
cana-2940	144	4	-	-	ADJ
cana-2940	144	5	hned	hne	VERB
cana-2940	144	6	works	work	NOUN
cana-2940	144	7	on	on	ADP
cana-2940	144	8	the	the	DET
cana-2940	144	9	following	follow	VERB
cana-2940	144	10	algorithm	algorithm	NOUN
cana-2940	144	11	.	.	PUNCT
cana-2940	145	1	figure	figure	NOUN
cana-2940	145	2	8	8	NUM
cana-2940	145	3	depicts	depict	VERB
cana-2940	145	4	the	the	DET
cana-2940	145	5	workflow	workflow	NOUN
cana-2940	145	6	of	of	ADP
cana-2940	145	7	the	the	DET
cana-2940	145	8	proposed	propose	VERB
cana-2940	145	9	architecture	architecture	NOUN
cana-2940	145	10	for	for	ADP
cana-2940	145	11	image	image	NOUN
cana-2940	145	12	segmentation	segmentation	NOUN
cana-2940	145	13	communications	communication	NOUN
cana-2940	145	14	on	on	ADP
cana-2940	145	15	applied	apply	VERB
cana-2940	145	16	nonlinear	nonlinear	ADJ
cana-2940	145	17	analysis	analysis	NOUN
cana-2940	145	18	issn	issn	NOUN
cana-2940	145	19	:	:	PUNCT
cana-2940	145	20	1074	1074	NUM
cana-2940	145	21	-	-	PUNCT
cana-2940	145	22	133x	133x	NUM
cana-2940	145	23	vol	vol	NOUN
cana-2940	145	24	32	32	NUM
cana-2940	145	25	no	no	NOUN
cana-2940	145	26	.	.	PUNCT
cana-2940	146	1	5s	5s	NUM
cana-2940	146	2	(	(	PUNCT
cana-2940	146	3	2025	2025	NUM
cana-2940	146	4	)	)	PUNCT
cana-2940	146	5	36	36	NUM
cana-2940	146	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2940	146	7	figure	figure	NOUN
cana-2940	146	8	8	8	NUM
cana-2940	146	9	:	:	PUNCT
cana-2940	146	10	proposed	propose	VERB
cana-2940	146	11	enhanced	enhance	VERB
cana-2940	146	12	holistically	holistically	ADV
cana-2940	146	13	nested	nest	VERB
cana-2940	146	14	edge	edge	NOUN
cana-2940	146	15	detection	detection	NOUN
cana-2940	146	16	system	system	NOUN
cana-2940	146	17	design	design	NOUN
cana-2940	146	18	algorithm	algorithm	NOUN
cana-2940	146	19	:	:	PUNCT
cana-2940	147	1	step1	step1	NOUN
cana-2940	147	2	:	:	PUNCT
cana-2940	147	3	input	input	NOUN
cana-2940	147	4	:	:	PUNCT
cana-2940	147	5	load	load	NOUN
cana-2940	147	6	image	image	NOUN
cana-2940	147	7	dataset	dataset	NOUN
cana-2940	147	8	of	of	ADP
cana-2940	147	9	23	23	NUM
cana-2940	147	10	different	different	ADJ
cana-2940	147	11	classes	class	NOUN
cana-2940	147	12	of	of	ADP
cana-2940	147	13	skin	skin	NOUN
cana-2940	147	14	diseases	disease	NOUN
cana-2940	147	15	step	step	VERB
cana-2940	147	16	2	2	NUM
cana-2940	147	17	:	:	PUNCT
cana-2940	147	18	output	output	NOUN
cana-2940	147	19	:	:	PUNCT
cana-2940	147	20	each	each	DET
cana-2940	147	21	convolutional	convolutional	ADJ
cana-2940	147	22	layer	layer	NOUN
cana-2940	147	23	learns	learn	VERB
cana-2940	147	24	to	to	PART
cana-2940	147	25	detect	detect	VERB
cana-2940	147	26	specific	specific	ADJ
cana-2940	147	27	features	feature	NOUN
cana-2940	147	28	,	,	PUNCT
cana-2940	147	29	with	with	ADP
cana-2940	147	30	lower	low	ADJ
cana-2940	147	31	layers	layer	NOUN
cana-2940	147	32	taking	take	VERB
cana-2940	147	33	basic	basic	ADJ
cana-2940	147	34	patterns	pattern	NOUN
cana-2940	147	35	(	(	PUNCT
cana-2940	147	36	such	such	ADJ
cana-2940	147	37	as	as	ADP
cana-2940	147	38	edges	edge	NOUN
cana-2940	147	39	and	and	CCONJ
cana-2940	147	40	textures	texture	NOUN
cana-2940	147	41	)	)	PUNCT
cana-2940	147	42	step	step	NOUN
cana-2940	147	43	3	3	NUM
cana-2940	147	44	:	:	PUNCT
cana-2940	147	45	for	for	SCONJ
cana-2940	147	46	each	each	DET
cana-2940	147	47	image	image	NOUN
cana-2940	147	48	file	file	NOUN
cana-2940	147	49	in	in	ADP
cana-2940	147	50	the	the	DET
cana-2940	147	51	sub	sub	NOUN
cana-2940	147	52	-	-	NOUN
cana-2940	147	53	folder	folder	NOUN
cana-2940	147	54	of	of	ADP
cana-2940	147	55	the	the	DET
cana-2940	147	56	image	image	NOUN
cana-2940	147	57	dataset	dataset	VERB
cana-2940	147	58	a	a	PRON
cana-2940	147	59	)	)	PUNCT
cana-2940	147	60	read	read	VERB
cana-2940	147	61	image	image	NOUN
cana-2940	147	62	b	b	NOUN
cana-2940	147	63	)	)	PUNCT
cana-2940	147	64	preprocess	preprocess	NOUN
cana-2940	147	65	image	image	NOUN
cana-2940	147	66	•	•	NOUN
cana-2940	147	67	convert	convert	NOUN
cana-2940	147	68	image	image	NOUN
cana-2940	147	69	into	into	ADP
cana-2940	147	70	grayscale	grayscale	NOUN
cana-2940	147	71	•	•	NUM
cana-2940	147	72	apply	apply	VERB
cana-2940	147	73	gaussian	gaussian	ADJ
cana-2940	147	74	blur	blur	NOUN
cana-2940	147	75	to	to	ADP
cana-2940	147	76	the	the	DET
cana-2940	147	77	grayscale	grayscale	NOUN
cana-2940	147	78	image	image	NOUN
cana-2940	147	79	•	•	NOUN
cana-2940	147	80	find	find	VERB
cana-2940	147	81	the	the	DET
cana-2940	147	82	minimum	minimum	ADJ
cana-2940	147	83	and	and	CCONJ
cana-2940	147	84	maximum	maximum	ADJ
cana-2940	147	85	pixel	pixel	ADJ
cana-2940	147	86	values	value	NOUN
cana-2940	147	87	in	in	ADP
cana-2940	147	88	the	the	DET
cana-2940	147	89	blurred	blurred	ADJ
cana-2940	147	90	image	image	NOUN
cana-2940	147	91	•	•	ADP
cana-2940	147	92	apply	apply	VERB
cana-2940	147	93	binary	binary	NOUN
cana-2940	147	94	thresholding	thresholding	NOUN
cana-2940	147	95	based	base	VERB
cana-2940	147	96	on	on	ADP
cana-2940	147	97	a	a	DET
cana-2940	147	98	fraction	fraction	NOUN
cana-2940	147	99	of	of	ADP
cana-2940	147	100	the	the	DET
cana-2940	147	101	maximum	maximum	ADJ
cana-2940	147	102	value	value	NOUN
cana-2940	147	103	•	•	NOUN
cana-2940	147	104	apply	apply	VERB
cana-2940	147	105	median	median	NOUN
cana-2940	147	106	blur	blur	NOUN
cana-2940	147	107	to	to	ADP
cana-2940	147	108	the	the	DET
cana-2940	147	109	threshold	threshold	NOUN
cana-2940	147	110	to	to	ADP
cana-2940	147	111	the	the	DET
cana-2940	147	112	threshold	threshold	NOUN
cana-2940	147	113	image	image	NOUN
cana-2940	147	114	•	•	NOUN
cana-2940	147	115	perform	perform	VERB
cana-2940	147	116	morphological	morphological	ADJ
cana-2940	147	117	operations	operation	NOUN
cana-2940	147	118	:	:	PUNCT
cana-2940	147	119	dilation	dilation	NOUN
cana-2940	147	120	and	and	CCONJ
cana-2940	147	121	closing	close	VERB
cana-2940	147	122	c	c	NOUN
cana-2940	147	123	)	)	PUNCT
cana-2940	147	124	contour	contour	NOUN
cana-2940	147	125	detection	detection	NOUN
cana-2940	147	126	and	and	CCONJ
cana-2940	147	127	bounding	bound	VERB
cana-2940	147	128	box	box	NOUN
cana-2940	147	129	•	•	NOUN
cana-2940	147	130	identifies	identify	VERB
cana-2940	147	131	contours	contours	NOUN
cana-2940	147	132	and	and	CCONJ
cana-2940	147	133	finds	find	VERB
cana-2940	147	134	the	the	DET
cana-2940	147	135	largest	large	ADJ
cana-2940	147	136	contour	contour	NOUN
cana-2940	147	137	•	•	NOUN
cana-2940	147	138	calculate	calculate	NOUN
cana-2940	147	139	a	a	DET
cana-2940	147	140	bounding	bounding	NOUN
cana-2940	147	141	rectangle	rectangle	NOUN
cana-2940	147	142	and	and	CCONJ
cana-2940	147	143	draw	draw	VERB
cana-2940	147	144	the	the	DET
cana-2940	147	145	largest	large	ADJ
cana-2940	147	146	contour	contour	NOUN
cana-2940	147	147	on	on	ADP
cana-2940	147	148	the	the	DET
cana-2940	147	149	original	original	ADJ
cana-2940	147	150	image	image	NOUN
cana-2940	147	151	.	.	PUNCT
cana-2940	148	1	communications	communication	NOUN
cana-2940	148	2	on	on	ADP
cana-2940	148	3	applied	apply	VERB
cana-2940	148	4	nonlinear	nonlinear	ADJ
cana-2940	148	5	analysis	analysis	NOUN
cana-2940	148	6	issn	issn	NOUN
cana-2940	148	7	:	:	PUNCT
cana-2940	148	8	1074	1074	NUM
cana-2940	148	9	-	-	PUNCT
cana-2940	148	10	133x	133x	NUM
cana-2940	148	11	vol	vol	NOUN
cana-2940	148	12	32	32	NUM
cana-2940	148	13	no	no	NOUN
cana-2940	148	14	.	.	PUNCT
cana-2940	149	1	5s	5s	NUM
cana-2940	149	2	(	(	PUNCT
cana-2940	149	3	2025	2025	NUM
cana-2940	149	4	)	)	PUNCT
cana-2940	149	5	37	37	NUM
cana-2940	149	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2940	149	7	d	d	NOUN
cana-2940	149	8	)	)	PUNCT
cana-2940	149	9	color	color	NOUN
cana-2940	149	10	adjustment	adjustment	NOUN
cana-2940	149	11	•	•	NOUN
cana-2940	149	12	resize	resize	VERB
cana-2940	149	13	the	the	DET
cana-2940	149	14	image	image	NOUN
cana-2940	149	15	•	•	NOUN
cana-2940	149	16	converts	convert	VERB
cana-2940	149	17	the	the	DET
cana-2940	149	18	image	image	NOUN
cana-2940	149	19	from	from	ADP
cana-2940	149	20	rgb	rgb	PROPN
cana-2940	149	21	to	to	PART
cana-2940	149	22	hsv	hsv	VERB
cana-2940	149	23	color	color	NOUN
cana-2940	149	24	space	space	NOUN
cana-2940	149	25	for	for	ADP
cana-2940	149	26	color	color	NOUN
cana-2940	149	27	manipulation	manipulation	NOUN
cana-2940	149	28	where	where	SCONJ
cana-2940	149	29	hue	hue	NOUN
cana-2940	149	30	is	be	AUX
cana-2940	149	31	reduced	reduce	VERB
cana-2940	149	32	by	by	ADP
cana-2940	149	33	*	*	PROPN
cana-2940	149	34	0.7	0.7	NUM
cana-2940	149	35	,	,	PUNCT
cana-2940	149	36	saturation	saturation	NOUN
cana-2940	149	37	*	*	NOUN
cana-2940	149	38	1.5	1.5	NUM
cana-2940	149	39	,	,	PUNCT
cana-2940	149	40	and	and	CCONJ
cana-2940	149	41	value	value	VERB
cana-2940	149	42	↓	↓	PROPN
cana-2940	149	43	*	*	SYM
cana-2940	149	44	0.5	0.5	NUM
cana-2940	149	45	.	.	NOUN
cana-2940	149	46	•	•	NUM
cana-2940	149	47	converts	convert	VERB
cana-2940	149	48	the	the	DET
cana-2940	149	49	adjusted	adjust	VERB
cana-2940	149	50	image	image	NOUN
cana-2940	149	51	back	back	ADV
cana-2940	149	52	to	to	ADP
cana-2940	149	53	bgr	bgr	PROPN
cana-2940	149	54	color	color	NOUN
cana-2940	149	55	space	space	NOUN
cana-2940	149	56	.	.	PUNCT
cana-2940	150	1	•	•	NOUN
cana-2940	150	2	adjusts	adjust	VERB
cana-2940	150	3	the	the	DET
cana-2940	150	4	brightness	brightness	NOUN
cana-2940	150	5	and	and	CCONJ
cana-2940	150	6	contrast	contrast	NOUN
cana-2940	150	7	e	e	NOUN
cana-2940	150	8	)	)	PUNCT
cana-2940	150	9	convolution	convolution	NOUN
cana-2940	150	10	neural	neural	ADJ
cana-2940	150	11	network	network	NOUN
cana-2940	150	12	-	-	PUNCT
cana-2940	150	13	based	base	VERB
cana-2940	150	14	holistically	holistically	ADV
cana-2940	150	15	nested	nest	VERB
cana-2940	150	16	edge	edge	NOUN
cana-2940	150	17	detection	detection	NOUN
cana-2940	150	18	segmentation	segmentation	NOUN
cana-2940	150	19	:	:	PUNCT
cana-2940	150	20	•	•	ADP
cana-2940	150	21	create	create	VERB
cana-2940	150	22	class	class	NOUN
cana-2940	150	23	•	•	NOUN
cana-2940	150	24	create	create	VERB
cana-2940	150	25	convolution	convolution	NOUN
cana-2940	150	26	layers	layer	NOUN
cana-2940	150	27	:	:	PUNCT
cana-2940	150	28	class	class	NOUN
cana-2940	150	29	contains	contain	VERB
cana-2940	150	30	5	5	NUM
cana-2940	150	31	blocks	block	NOUN
cana-2940	150	32	of	of	ADP
cana-2940	150	33	neural	neural	ADJ
cana-2940	150	34	network	network	NOUN
cana-2940	150	35	and	and	CCONJ
cana-2940	150	36	each	each	DET
cana-2940	150	37	block	block	NOUN
cana-2940	150	38	has	have	VERB
cana-2940	150	39	multiple	multiple	ADJ
cana-2940	150	40	convolution	convolution	NOUN
cana-2940	150	41	layers	layer	NOUN
cana-2940	150	42	(	(	PUNCT
cana-2940	150	43	maximum	maximum	NOUN
cana-2940	150	44	3	3	NUM
cana-2940	150	45	)	)	PUNCT
cana-2940	150	46	followed	follow	VERB
cana-2940	150	47	by	by	ADP
cana-2940	150	48	relu	relu	NOUN
cana-2940	150	49	activation	activation	NOUN
cana-2940	150	50	function	function	NOUN
cana-2940	150	51	.	.	PUNCT
cana-2940	151	1	i	i	PRON
cana-2940	151	2	)	)	PUNCT
cana-2940	151	3	1st	1st	ADJ
cana-2940	151	4	layer	layer	NOUN
cana-2940	151	5	:	:	PUNCT
cana-2940	152	1	conv2d(in_chan=3	conv2d(in_chan=3	ADJ
cana-2940	152	2	,	,	PUNCT
cana-2940	152	3	out_chan=64	out_chan=64	PROPN
cana-2940	152	4	,	,	PUNCT
cana-2940	152	5	ker_size=3	ker_size=3	PROPN
cana-2940	152	6	,	,	PUNCT
cana-2940	152	7	stride=1	stride=1	PROPN
cana-2940	152	8	,	,	PUNCT
cana-2940	152	9	padding=1	padding=1	PROPN
cana-2940	152	10	)	)	PUNCT
cana-2940	152	11	ii	ii	NOUN
cana-2940	152	12	)	)	PUNCT
cana-2940	152	13	2nd	2nd	ADJ
cana-2940	152	14	layer	layer	NOUN
cana-2940	152	15	:	:	PUNCT
cana-2940	152	16	conv2d(in_chan=64	conv2d(in_chan=64	PROPN
cana-2940	152	17	,	,	PUNCT
cana-2940	152	18	out_chan=64	out_chan=64	PROPN
cana-2940	152	19	,	,	PUNCT
cana-2940	152	20	ker_size=3	ker_size=3	PROPN
cana-2940	152	21	,	,	PUNCT
cana-2940	152	22	stride=1	stride=1	PROPN
cana-2940	152	23	,	,	PUNCT
cana-2940	152	24	padding=1	padding=1	PROPN
cana-2940	152	25	)	)	PUNCT
cana-2940	152	26	xiii	xiii	PROPN
cana-2940	152	27	)	)	PUNCT
cana-2940	152	28	13th	13th	NOUN
cana-2940	152	29	layer	layer	NOUN
cana-2940	152	30	:	:	PUNCT
cana-2940	152	31	conv2d(in_chan=512	conv2d(in_chan=512	PROPN
cana-2940	152	32	,	,	PUNCT
cana-2940	152	33	out_chan=512	out_chan=512	NUM
cana-2940	152	34	,	,	PUNCT
cana-2940	152	35	ker_size=3	ker_size=3	PROPN
cana-2940	152	36	,	,	PUNCT
cana-2940	152	37	stride=1	stride=1	PROPN
cana-2940	152	38	,	,	PUNCT
cana-2940	152	39	padding=1	padding=1	PROPN
cana-2940	152	40	)	)	PUNCT
cana-2940	152	41	•	•	X
cana-2940	152	42	create	create	VERB
cana-2940	152	43	score	score	NOUN
cana-2940	152	44	layers	layer	NOUN
cana-2940	152	45	for	for	ADP
cana-2940	152	46	each	each	DET
cana-2940	152	47	block	block	NOUN
cana-2940	152	48	•	•	NOUN
cana-2940	152	49	combine	combine	VERB
cana-2940	152	50	layers	layer	NOUN
cana-2940	152	51	to	to	PART
cana-2940	152	52	merge	merge	VERB
cana-2940	152	53	output	output	NOUN
cana-2940	152	54	•	•	NOUN
cana-2940	152	55	load	load	NOUN
cana-2940	152	56	pre	pre	ADJ
cana-2940	152	57	-	-	ADJ
cana-2940	152	58	trained	train	VERB
cana-2940	152	59	weight	weight	NOUN
cana-2940	152	60	f	f	X
cana-2940	152	61	)	)	PUNCT
cana-2940	152	62	create	create	VERB
cana-2940	152	63	a	a	DET
cana-2940	152	64	forward	forward	ADJ
cana-2940	152	65	method	method	NOUN
cana-2940	152	66	to	to	PART
cana-2940	152	67	process	process	VERB
cana-2940	152	68	input	input	NOUN
cana-2940	152	69	images	image	NOUN
cana-2940	152	70	through	through	ADP
cana-2940	152	71	the	the	DET
cana-2940	152	72	cnns	cnn	NOUN
cana-2940	152	73	.	.	PUNCT
cana-2940	153	1	g	g	NOUN
cana-2940	153	2	)	)	PUNCT
cana-2940	153	3	color	color	NOUN
cana-2940	153	4	adjustment	adjustment	NOUN
cana-2940	153	5	and	and	CCONJ
cana-2940	153	6	brightness	brightness	NOUN
cana-2940	153	7	control	control	PROPN
cana-2940	153	8	h	h	PROPN
cana-2940	153	9	)	)	PUNCT
cana-2940	153	10	store	store	VERB
cana-2940	153	11	the	the	DET
cana-2940	153	12	output	output	NOUN
cana-2940	153	13	in	in	ADP
cana-2940	153	14	the	the	DET
cana-2940	153	15	output	output	NOUN
cana-2940	153	16	directory	directory	NOUN
cana-2940	153	17	named	name	VERB
cana-2940	153	18	segmented	segment	VERB
cana-2940	153	19	output	output	NOUN
cana-2940	153	20	.	.	PUNCT
cana-2940	154	1	i	i	PRON
cana-2940	154	2	)	)	PUNCT
cana-2940	154	3	perform	perform	VERB
cana-2940	154	4	post	post	ADJ
cana-2940	154	5	-	-	ADJ
cana-2940	154	6	processing	processing	NOUN
cana-2940	154	7	:	:	PUNCT
cana-2940	154	8	resizing	resize	VERB
cana-2940	154	9	the	the	DET
cana-2940	154	10	output	output	NOUN
cana-2940	154	11	,	,	PUNCT
cana-2940	154	12	converting	convert	VERB
cana-2940	154	13	it	it	PRON
cana-2940	154	14	back	back	ADV
cana-2940	154	15	to	to	ADP
cana-2940	154	16	bgr	bgr	PROPN
cana-2940	154	17	color	color	NOUN
cana-2940	154	18	space	space	NOUN
cana-2940	154	19	,	,	PUNCT
cana-2940	154	20	and	and	CCONJ
cana-2940	154	21	saving	save	VERB
cana-2940	154	22	it	it	PRON
cana-2940	154	23	.	.	PUNCT
cana-2940	155	1	in	in	ADP
cana-2940	155	2	the	the	DET
cana-2940	155	3	proposed	propose	VERB
cana-2940	155	4	algorithm	algorithm	NOUN
cana-2940	155	5	,	,	PUNCT
cana-2940	155	6	the	the	DET
cana-2940	155	7	handling	handling	NOUN
cana-2940	155	8	of	of	ADP
cana-2940	155	9	image	image	NOUN
cana-2940	155	10	size	size	NOUN
cana-2940	155	11	plays	play	VERB
cana-2940	155	12	a	a	DET
cana-2940	155	13	vital	vital	ADJ
cana-2940	155	14	role	role	NOUN
cana-2940	155	15	in	in	ADP
cana-2940	155	16	ensuring	ensure	VERB
cana-2940	155	17	that	that	SCONJ
cana-2940	155	18	the	the	DET
cana-2940	155	19	image	image	NOUN
cana-2940	155	20	processing	processing	NOUN
cana-2940	155	21	pipeline	pipeline	NOUN
cana-2940	155	22	,	,	PUNCT
cana-2940	155	23	functions	function	VERB
cana-2940	155	24	smoothly	smoothly	ADV
cana-2940	155	25	and	and	CCONJ
cana-2940	155	26	efficiently	efficiently	ADV
cana-2940	155	27	.	.	PUNCT
cana-2940	156	1	by	by	ADP
cana-2940	156	2	standardizing	standardize	VERB
cana-2940	156	3	the	the	DET
cana-2940	156	4	input	input	NOUN
cana-2940	156	5	and	and	CCONJ
cana-2940	156	6	output	output	NOUN
cana-2940	156	7	dimensions	dimension	NOUN
cana-2940	156	8	,	,	PUNCT
cana-2940	156	9	the	the	DET
cana-2940	156	10	algorithm	algorithm	NOUN
cana-2940	156	11	not	not	PART
cana-2940	156	12	only	only	ADV
cana-2940	156	13	meets	meet	VERB
cana-2940	156	14	the	the	DET
cana-2940	156	15	requirements	requirement	NOUN
cana-2940	156	16	of	of	ADP
cana-2940	156	17	the	the	DET
cana-2940	156	18	convolutional	convolutional	ADJ
cana-2940	156	19	neural	neural	ADJ
cana-2940	156	20	network	network	NOUN
cana-2940	156	21	but	but	CCONJ
cana-2940	156	22	also	also	ADV
cana-2940	156	23	optimizes	optimize	VERB
cana-2940	156	24	performance	performance	NOUN
cana-2940	156	25	and	and	CCONJ
cana-2940	156	26	enables	enable	VERB
cana-2940	156	27	consistent	consistent	ADJ
cana-2940	156	28	comparisons	comparison	NOUN
cana-2940	156	29	between	between	ADP
cana-2940	156	30	input	input	NOUN
cana-2940	156	31	and	and	CCONJ
cana-2940	156	32	output	output	NOUN
cana-2940	156	33	images	image	NOUN
cana-2940	156	34	.	.	PUNCT
cana-2940	157	1	this	this	DET
cana-2940	157	2	uniformity	uniformity	NOUN
cana-2940	157	3	is	be	AUX
cana-2940	157	4	critical	critical	ADJ
cana-2940	157	5	in	in	ADP
cana-2940	157	6	image	image	NOUN
cana-2940	157	7	segmentation	segmentation	NOUN
cana-2940	157	8	tasks	task	NOUN
cana-2940	157	9	,	,	PUNCT
cana-2940	157	10	where	where	SCONJ
cana-2940	157	11	precise	precise	ADJ
cana-2940	157	12	delineation	delineation	NOUN
cana-2940	157	13	of	of	ADP
cana-2940	157	14	objects	object	NOUN
cana-2940	157	15	or	or	CCONJ
cana-2940	157	16	regions	region	NOUN
cana-2940	157	17	in	in	ADP
cana-2940	157	18	an	an	DET
cana-2940	157	19	image	image	NOUN
cana-2940	157	20	is	be	AUX
cana-2940	157	21	required	require	VERB
cana-2940	157	22	,	,	PUNCT
cana-2940	157	23	as	as	SCONJ
cana-2940	157	24	it	it	PRON
cana-2940	157	25	facilitates	facilitate	VERB
cana-2940	157	26	the	the	DET
cana-2940	157	27	effective	effective	ADJ
cana-2940	157	28	evaluation	evaluation	NOUN
cana-2940	157	29	of	of	ADP
cana-2940	157	30	the	the	DET
cana-2940	157	31	model	model	NOUN
cana-2940	157	32	's	's	PART
cana-2940	157	33	performance	performance	NOUN
cana-2940	157	34	.	.	PUNCT
cana-2940	158	1	a	a	DET
cana-2940	158	2	)	)	PUNCT
cana-2940	158	3	b	b	NOUN
cana-2940	158	4	)	)	PUNCT
cana-2940	158	5	figure	figure	NOUN
cana-2940	158	6	9	9	NUM
cana-2940	158	7	:	:	PUNCT
cana-2940	158	8	segmentation	segmentation	NOUN
cana-2940	158	9	output	output	NOUN
cana-2940	158	10	image	image	NOUN
cana-2940	158	11	samples	sample	VERB
cana-2940	158	12	a	a	DET
cana-2940	158	13	)	)	PUNCT
cana-2940	158	14	acne	acne	NOUN
cana-2940	158	15	b	b	NOUN
cana-2940	158	16	)	)	PUNCT
cana-2940	158	17	melanoma	melanoma	NOUN
cana-2940	158	18	skin	skin	NOUN
cana-2940	158	19	cancer	cancer	NOUN
cana-2940	158	20	nevi	nevi	ADJ
cana-2940	158	21	communications	communication	NOUN
cana-2940	158	22	on	on	ADP
cana-2940	158	23	applied	apply	VERB
cana-2940	158	24	nonlinear	nonlinear	ADJ
cana-2940	158	25	analysis	analysis	NOUN
cana-2940	158	26	issn	issn	NOUN
cana-2940	158	27	:	:	PUNCT
cana-2940	158	28	1074	1074	NUM
cana-2940	158	29	-	-	PUNCT
cana-2940	158	30	133x	133x	NUM
cana-2940	158	31	vol	vol	NOUN
cana-2940	158	32	32	32	NUM
cana-2940	158	33	no	no	NOUN
cana-2940	158	34	.	.	PUNCT
cana-2940	159	1	5s	5s	NUM
cana-2940	159	2	(	(	PUNCT
cana-2940	159	3	2025	2025	NUM
cana-2940	159	4	)	)	PUNCT
cana-2940	159	5	38	38	NUM
cana-2940	159	6	https://internationalpubls.com	https://internationalpubls.com	SYM
cana-2940	159	7	3.5	3.5	NUM
cana-2940	159	8	feature	feature	NOUN
cana-2940	159	9	extraction	extraction	NOUN
cana-2940	159	10	:	:	PUNCT
cana-2940	159	11	feature	feature	NOUN
cana-2940	159	12	extraction	extraction	NOUN
cana-2940	159	13	is	be	AUX
cana-2940	159	14	a	a	DET
cana-2940	159	15	critical	critical	ADJ
cana-2940	159	16	component	component	NOUN
cana-2940	159	17	in	in	ADP
cana-2940	159	18	the	the	DET
cana-2940	159	19	illness	illness	NOUN
cana-2940	159	20	detection	detection	NOUN
cana-2940	159	21	process	process	NOUN
cana-2940	159	22	.	.	PUNCT
cana-2940	160	1	it	it	PRON
cana-2940	160	2	is	be	AUX
cana-2940	160	3	the	the	DET
cana-2940	160	4	procedure	procedure	NOUN
cana-2940	160	5	by	by	ADP
cana-2940	160	6	which	which	PRON
cana-2940	160	7	unprocessed	unprocesse	VERB
cana-2940	160	8	visual	visual	ADJ
cana-2940	160	9	data	datum	NOUN
cana-2940	160	10	is	be	AUX
cana-2940	160	11	converted	convert	VERB
cana-2940	160	12	into	into	ADP
cana-2940	160	13	numerical	numerical	ADJ
cana-2940	160	14	characteristics	characteristic	NOUN
cana-2940	160	15	.	.	PUNCT
cana-2940	161	1	this	this	PRON
cana-2940	161	2	results	result	VERB
cana-2940	161	3	in	in	ADP
cana-2940	161	4	a	a	DET
cana-2940	161	5	reduction	reduction	NOUN
cana-2940	161	6	in	in	ADP
cana-2940	161	7	the	the	DET
cana-2940	161	8	resources	resource	NOUN
cana-2940	161	9	necessary	necessary	ADJ
cana-2940	161	10	to	to	PART
cana-2940	161	11	display	display	VERB
cana-2940	161	12	the	the	DET
cana-2940	161	13	real	real	ADJ
cana-2940	161	14	data	datum	NOUN
cana-2940	161	15	while	while	SCONJ
cana-2940	161	16	preserving	preserve	VERB
cana-2940	161	17	the	the	DET
cana-2940	161	18	initial	initial	ADJ
cana-2940	161	19	data	datum	NOUN
cana-2940	161	20	for	for	ADP
cana-2940	161	21	subsequent	subsequent	ADJ
cana-2940	161	22	consequences	consequence	NOUN
cana-2940	161	23	.	.	PUNCT
cana-2940	162	1	several	several	ADJ
cana-2940	162	2	features	feature	NOUN
cana-2940	162	3	such	such	ADJ
cana-2940	162	4	as	as	ADP
cana-2940	162	5	edge	edge	NOUN
cana-2940	162	6	,	,	PUNCT
cana-2940	162	7	shape	shape	NOUN
cana-2940	162	8	,	,	PUNCT
cana-2940	162	9	color	color	NOUN
cana-2940	162	10	,	,	PUNCT
cana-2940	162	11	intensity	intensity	NOUN
cana-2940	162	12	,	,	PUNCT
cana-2940	162	13	texture	texture	ADJ
cana-2940	162	14	,	,	PUNCT
cana-2940	162	15	diameter	diameter	NOUN
cana-2940	162	16	,	,	PUNCT
cana-2940	162	17	asymmetric	asymmetric	ADJ
cana-2940	162	18	feature	feature	NOUN
cana-2940	162	19	,	,	PUNCT
cana-2940	162	20	and	and	CCONJ
cana-2940	162	21	more	more	ADJ
cana-2940	162	22	abstract	abstract	ADJ
cana-2940	162	23	patterns	pattern	NOUN
cana-2940	162	24	are	be	AUX
cana-2940	162	25	adjusted	adjust	VERB
cana-2940	162	26	in	in	ADP
cana-2940	162	27	such	such	DET
cana-2940	162	28	a	a	DET
cana-2940	162	29	way	way	NOUN
cana-2940	162	30	that	that	PRON
cana-2940	162	31	the	the	DET
cana-2940	162	32	hidden	hidden	ADJ
cana-2940	162	33	characteristics	characteristic	NOUN
cana-2940	162	34	of	of	ADP
cana-2940	162	35	the	the	DET
cana-2940	162	36	image	image	NOUN
cana-2940	162	37	are	be	AUX
cana-2940	162	38	outshined	outshine	VERB
cana-2940	162	39	.	.	PUNCT
cana-2940	163	1	moreover	moreover	ADV
cana-2940	163	2	,	,	PUNCT
cana-2940	163	3	these	these	DET
cana-2940	163	4	derived	derive	VERB
cana-2940	163	5	traits	trait	NOUN
cana-2940	163	6	will	will	AUX
cana-2940	163	7	significantly	significantly	ADV
cana-2940	163	8	aid	aid	VERB
cana-2940	163	9	in	in	ADP
cana-2940	163	10	the	the	DET
cana-2940	163	11	efficient	efficient	ADJ
cana-2940	163	12	,	,	PUNCT
cana-2940	163	13	rapid	rapid	ADJ
cana-2940	163	14	,	,	PUNCT
cana-2940	163	15	and	and	CCONJ
cana-2940	163	16	accurate	accurate	ADJ
cana-2940	163	17	detection	detection	NOUN
cana-2940	163	18	of	of	ADP
cana-2940	163	19	skin	skin	NOUN
cana-2940	163	20	illnesses	illness	NOUN
cana-2940	163	21	(	(	PUNCT
cana-2940	163	22	krishna	krishna	PROPN
cana-2940	163	23	monika	monika	PROPN
cana-2940	163	24	et	et	PROPN
cana-2940	163	25	al	al	PROPN
cana-2940	163	26	.	.	PROPN
cana-2940	163	27	,	,	PUNCT
cana-2940	163	28	2020)(badiger	2020)(badiger	PROPN
cana-2940	163	29	et	et	PROPN
cana-2940	163	30	al	al	PROPN
cana-2940	163	31	.	.	PROPN
cana-2940	163	32	,	,	PUNCT
cana-2940	163	33	2022	2022	NUM
cana-2940	163	34	)	)	PUNCT
cana-2940	163	35	.	.	PUNCT
cana-2940	164	1	our	our	PRON
cana-2940	164	2	proposed	propose	VERB
cana-2940	164	3	system	system	NOUN
cana-2940	164	4	used	use	VERB
cana-2940	164	5	efficientnet	efficientnet	NOUN
cana-2940	164	6	-	-	PUNCT
cana-2940	164	7	b0	b0	NOUN
cana-2940	164	8	to	to	PART
cana-2940	164	9	extract	extract	VERB
cana-2940	164	10	maximum	maximum	PROPN
cana-2940	164	11	numerical	numerical	ADJ
cana-2940	164	12	features	feature	NOUN
cana-2940	164	13	from	from	ADP
cana-2940	164	14	the	the	DET
cana-2940	164	15	image	image	NOUN
cana-2940	164	16	dataset	dataset	NOUN
cana-2940	164	17	based	base	VERB
cana-2940	164	18	on	on	ADP
cana-2940	164	19	the	the	DET
cana-2940	164	20	number	number	NOUN
cana-2940	164	21	of	of	ADP
cana-2940	164	22	convolutional	convolutional	ADJ
cana-2940	164	23	layers	layer	NOUN
cana-2940	164	24	(	(	PUNCT
cana-2940	164	25	tan	tan	PROPN
cana-2940	164	26	&	&	CCONJ
cana-2940	164	27	le	le	PROPN
cana-2940	164	28	,	,	PUNCT
cana-2940	164	29	2019	2019	NUM
cana-2940	164	30	)	)	PUNCT
cana-2940	164	31	.	.	PUNCT
cana-2940	165	1	efficientnet	efficientnet	NOUN
cana-2940	165	2	-	-	PUNCT
cana-2940	165	3	b0	b0	NOUN
cana-2940	165	4	is	be	AUX
cana-2940	165	5	a	a	DET
cana-2940	165	6	type	type	NOUN
cana-2940	165	7	of	of	ADP
cana-2940	165	8	cnn	cnn	PROPN
cana-2940	165	9	,	,	PUNCT
cana-2940	165	10	curated	curate	VERB
cana-2940	165	11	with	with	ADP
cana-2940	165	12	a	a	DET
cana-2940	165	13	focus	focus	NOUN
cana-2940	165	14	on	on	ADP
cana-2940	165	15	accuracy	accuracy	NOUN
cana-2940	165	16	,	,	PUNCT
cana-2940	165	17	sensitivity	sensitivity	NOUN
cana-2940	165	18	,	,	PUNCT
cana-2940	165	19	and	and	CCONJ
cana-2940	165	20	efficiency	efficiency	NOUN
cana-2940	165	21	.	.	PUNCT
cana-2940	166	1	the	the	DET
cana-2940	166	2	workflow	workflow	NOUN
cana-2940	166	3	of	of	ADP
cana-2940	166	4	efficientne	efficientne	NOUN
cana-2940	166	5	-	-	PUNCT
cana-2940	166	6	b0	b0	NOUN
cana-2940	166	7	is	be	AUX
cana-2940	166	8	:	:	PUNCT
cana-2940	166	9	a	a	X
cana-2940	166	10	)	)	PUNCT
cana-2940	166	11	input	input	NOUN
cana-2940	166	12	image	image	NOUN
cana-2940	166	13	:	:	PUNCT
cana-2940	166	14	the	the	DET
cana-2940	166	15	input	input	NOUN
cana-2940	166	16	layer	layer	NOUN
cana-2940	166	17	of	of	ADP
cana-2940	166	18	the	the	DET
cana-2940	166	19	model	model	NOUN
cana-2940	166	20	considers	consider	VERB
cana-2940	166	21	an	an	DET
cana-2940	166	22	image	image	NOUN
cana-2940	166	23	of	of	ADP
cana-2940	166	24	dimensions	dimension	NOUN
cana-2940	166	25	224x224x3	224x224x3	NUM
cana-2940	166	26	.	.	PUNCT
cana-2940	167	1	b	b	X
cana-2940	167	2	)	)	PUNCT
cana-2940	167	3	convolution	convolution	NOUN
cana-2940	167	4	layer	layer	NOUN
cana-2940	167	5	(	(	PUNCT
cana-2940	167	6	cl	cl	NOUN
cana-2940	167	7	):	):	PUNCT
cana-2940	167	8	the	the	DET
cana-2940	167	9	first	first	ADJ
cana-2940	167	10	layer	layer	NOUN
cana-2940	167	11	implements	implement	VERB
cana-2940	167	12	a	a	DET
cana-2940	167	13	3x3	3x3	NUM
cana-2940	167	14	convolution	convolution	NOUN
cana-2940	167	15	using	use	VERB
cana-2940	167	16	32	32	NUM
cana-2940	167	17	filters	filter	NOUN
cana-2940	167	18	and	and	CCONJ
cana-2940	167	19	a	a	DET
cana-2940	167	20	stride	stride	NOUN
cana-2940	167	21	of	of	ADP
cana-2940	167	22	2	2	NUM
cana-2940	167	23	and	and	CCONJ
cana-2940	167	24	reduces	reduce	VERB
cana-2940	167	25	the	the	DET
cana-2940	167	26	image	image	NOUN
cana-2940	167	27	size	size	NOUN
cana-2940	167	28	to	to	ADP
cana-2940	167	29	112x112	112x112	PROPN
cana-2940	167	30	.	.	PUNCT
cana-2940	168	1	each	each	DET
cana-2940	168	2	convolution	convolution	NOUN
cana-2940	168	3	layer	layer	NOUN
cana-2940	168	4	applied	apply	VERB
cana-2940	168	5	a	a	DET
cana-2940	168	6	filter	filter	NOUN
cana-2940	168	7	to	to	ADP
cana-2940	168	8	the	the	DET
cana-2940	168	9	image	image	NOUN
cana-2940	168	10	data	datum	NOUN
cana-2940	168	11	for	for	ADP
cana-2940	168	12	generating	generate	VERB
cana-2940	168	13	feature	feature	NOUN
cana-2940	168	14	maps	map	NOUN
cana-2940	168	15	.	.	PUNCT
cana-2940	169	1	the	the	DET
cana-2940	169	2	mathematical	mathematical	ADJ
cana-2940	169	3	expression	expression	NOUN
cana-2940	169	4	for	for	ADP
cana-2940	169	5	the	the	DET
cana-2940	169	6	convolution	convolution	NOUN
cana-2940	169	7	layer	layer	NOUN
cana-2940	169	8	is	be	AUX
cana-2940	169	9	given	give	VERB
cana-2940	169	10	below	below	ADP
cana-2940	169	11	:	:	PUNCT
cana-2940	169	12	y	y	PROPN
cana-2940	169	13	a	a	PRON
cana-2940	169	14	,	,	PUNCT
cana-2940	169	15	b	b	NOUN
cana-2940	169	16	,	,	PUNCT
cana-2940	169	17	c	c	NOUN
cana-2940	169	18	=	=	PUNCT
cana-2940	169	19	∑	∑	PUNCT
cana-2940	169	20	∑	∑	PROPN
cana-2940	169	21	∑	∑	PROPN
cana-2940	169	22	xa+l	xa+l	PROPN
cana-2940	169	23	,	,	PUNCT
cana-2940	169	24	b+m	b+m	NUM
cana-2940	169	25	,	,	PUNCT
cana-2940	169	26	n	n	PROPN
cana-2940	169	27	∗	∗	X
cana-2940	169	28	wl	wl	PROPN
cana-2940	169	29	,	,	PUNCT
cana-2940	169	30	m	m	PROPN
cana-2940	169	31	,	,	PUNCT
cana-2940	169	32	n	n	CCONJ
cana-2940	169	33	,	,	PUNCT
cana-2940	169	34	c	c	PROPN
cana-2940	169	35	n	n	PROPN
cana-2940	169	36	n=1	n=1	PROPN
cana-2940	169	37	m	m	PROPN
cana-2940	169	38	m=1	m=1	X
cana-2940	169	39	l	l	NOUN
cana-2940	170	1	l=1	l=1	X
cana-2940	170	2	(	(	PUNCT
cana-2940	170	3	5	5	NUM
cana-2940	170	4	)	)	PUNCT
cana-2940	170	5	in	in	ADP
cana-2940	170	6	the	the	DET
cana-2940	170	7	above	above	ADJ
cana-2940	170	8	equation	equation	NOUN
cana-2940	170	9	(	(	PUNCT
cana-2940	170	10	5	5	NUM
cana-2940	170	11	)	)	PUNCT
cana-2940	170	12	x	x	PRON
cana-2940	170	13	signifies	signify	VERB
cana-2940	170	14	input	input	NOUN
cana-2940	170	15	image	image	NOUN
cana-2940	170	16	,	,	PUNCT
cana-2940	170	17	w	w	PROPN
cana-2940	170	18	signifies	signifie	NOUN
cana-2940	170	19	kernel	kernel	PROPN
cana-2940	170	20	,	,	PUNCT
cana-2940	170	21	y	y	PROPN
cana-2940	170	22	represents	represent	VERB
cana-2940	170	23	output	output	NOUN
cana-2940	170	24	feature	feature	NOUN
cana-2940	170	25	map	map	NOUN
cana-2940	170	26	after	after	SCONJ
cana-2940	170	27	the	the	DET
cana-2940	170	28	convolution	convolution	NOUN
cana-2940	170	29	,	,	PUNCT
cana-2940	170	30	a	a	PRON
cana-2940	170	31	,	,	PUNCT
cana-2940	170	32	and	and	CCONJ
cana-2940	170	33	b	b	X
cana-2940	170	34	are	be	AUX
cana-2940	170	35	the	the	DET
cana-2940	170	36	height	height	NOUN
cana-2940	170	37	and	and	CCONJ
cana-2940	170	38	weight	weight	NOUN
cana-2940	170	39	of	of	ADP
cana-2940	170	40	the	the	DET
cana-2940	170	41	image	image	NOUN
cana-2940	170	42	,	,	PUNCT
cana-2940	170	43	c	c	PROPN
cana-2940	170	44	is	be	AUX
cana-2940	170	45	the	the	DET
cana-2940	170	46	index	index	NOUN
cana-2940	170	47	of	of	ADP
cana-2940	170	48	the	the	DET
cana-2940	170	49	output	output	NOUN
cana-2940	170	50	channel	channel	NOUN
cana-2940	170	51	,	,	PUNCT
cana-2940	170	52	l	l	PROPN
cana-2940	170	53	and	and	CCONJ
cana-2940	170	54	m	m	PROPN
cana-2940	170	55	are	be	AUX
cana-2940	170	56	the	the	DET
cana-2940	170	57	filter	filter	NOUN
cana-2940	170	58	dimensions	dimension	NOUN
cana-2940	170	59	,	,	PUNCT
cana-2940	170	60	and	and	CCONJ
cana-2940	170	61	n	n	PRON
cana-2940	170	62	is	be	AUX
cana-2940	170	63	the	the	DET
cana-2940	170	64	count	count	NOUN
cana-2940	170	65	of	of	ADP
cana-2940	170	66	input	input	NOUN
cana-2940	170	67	channel	channel	NOUN
cana-2940	170	68	.	.	PUNCT
cana-2940	171	1	c	c	X
cana-2940	171	2	)	)	PUNCT
cana-2940	171	3	batch	batch	NOUN
cana-2940	171	4	normalization	normalization	NOUN
cana-2940	171	5	(	(	PUNCT
cana-2940	171	6	bn	bn	X
cana-2940	171	7	):	):	PUNCT
cana-2940	171	8	it	it	PRON
cana-2940	171	9	is	be	AUX
cana-2940	171	10	employed	employ	VERB
cana-2940	171	11	to	to	PART
cana-2940	171	12	regulate	regulate	VERB
cana-2940	171	13	the	the	DET
cana-2940	171	14	activation	activation	NOUN
cana-2940	171	15	mechanisms	mechanism	NOUN
cana-2940	171	16	of	of	ADP
cana-2940	171	17	a	a	DET
cana-2940	171	18	layer	layer	NOUN
cana-2940	171	19	for	for	ADP
cana-2940	171	20	improved	improved	ADJ
cana-2940	171	21	stability	stability	NOUN
cana-2940	171	22	and	and	CCONJ
cana-2940	171	23	accelerated	accelerate	VERB
cana-2940	171	24	convergence	convergence	NOUN
cana-2940	171	25	to	to	PART
cana-2940	171	26	ensure	ensure	VERB
cana-2940	171	27	that	that	SCONJ
cana-2940	171	28	the	the	DET
cana-2940	171	29	mean	mean	ADJ
cana-2940	171	30	activation	activation	NOUN
cana-2940	171	31	remains	remain	VERB
cana-2940	171	32	0	0	NUM
cana-2940	171	33	and	and	CCONJ
cana-2940	171	34	variance	variance	NOUN
cana-2940	171	35	remains	remain	VERB
cana-2940	171	36	around	around	ADP
cana-2940	171	37	1	1	NUM
cana-2940	171	38	.	.	PUNCT
cana-2940	172	1	the	the	DET
cana-2940	172	2	following	follow	VERB
cana-2940	172	3	equation	equation	NOUN
cana-2940	172	4	(	(	PUNCT
cana-2940	172	5	6	6	NUM
cana-2940	172	6	)	)	PUNCT
cana-2940	172	7	represents	represent	VERB
cana-2940	172	8	the	the	DET
cana-2940	172	9	working	working	NOUN
cana-2940	172	10	of	of	ADP
cana-2940	172	11	batch	batch	NOUN
cana-2940	172	12	normalization	normalization	NOUN
cana-2940	172	13	where	where	SCONJ
cana-2940	172	14	xa	xa	PROPN
cana-2940	172	15	is	be	AUX
cana-2940	172	16	the	the	DET
cana-2940	172	17	activation	activation	NOUN
cana-2940	172	18	from	from	ADP
cana-2940	172	19	the	the	DET
cana-2940	172	20	convolution	convolution	NOUN
cana-2940	172	21	layer	layer	NOUN
cana-2940	172	22	,	,	PUNCT
cana-2940	172	23	µbatch	µbatch	VERB
cana-2940	172	24	is	be	AUX
cana-2940	172	25	the	the	DET
cana-2940	172	26	mean	mean	ADJ
cana-2940	172	27	value	value	NOUN
cana-2940	172	28	,	,	PUNCT
cana-2940	172	29	𝜎batch	𝜎batch	VERB
cana-2940	172	30	2	2	NUM
cana-2940	172	31	is	be	AUX
cana-2940	172	32	the	the	DET
cana-2940	172	33	variance	variance	NOUN
cana-2940	172	34	of	of	ADP
cana-2940	172	35	the	the	DET
cana-2940	172	36	batch	batch	NOUN
cana-2940	172	37	,	,	PUNCT
cana-2940	172	38	and	and	CCONJ
cana-2940	172	39	is	be	AUX
cana-2940	172	40	the	the	DET
cana-2940	172	41	constant	constant	ADJ
cana-2940	172	42	.	.	PUNCT
cana-2940	173	1	xa	xa	PROPN
cana-2940	173	2	=	=	SYM
cana-2940	173	3	xa−	xa−	PROPN
cana-2940	173	4	µbatch	µbatch	NOUN
cana-2940	173	5	√𝜎batch	√𝜎batch	NOUN
cana-2940	173	6	2	2	NUM
cana-2940	173	7	+	+	NOUN
cana-2940	173	8	∈	∈	PROPN
cana-2940	173	9	(	(	PUNCT
cana-2940	173	10	6	6	NUM
cana-2940	173	11	)	)	PUNCT
cana-2940	173	12	d	d	NOUN
cana-2940	173	13	)	)	PUNCT
cana-2940	173	14	activation	activation	NOUN
cana-2940	173	15	function	function	NOUN
cana-2940	173	16	:	:	PUNCT
cana-2940	173	17	in	in	ADP
cana-2940	173	18	this	this	DET
cana-2940	173	19	model	model	NOUN
cana-2940	173	20	,	,	PUNCT
cana-2940	173	21	the	the	DET
cana-2940	173	22	swish	swish	ADJ
cana-2940	173	23	activation	activation	NOUN
cana-2940	173	24	function	function	NOUN
cana-2940	173	25	is	be	AUX
cana-2940	173	26	used	use	VERB
cana-2940	173	27	which	which	PRON
cana-2940	173	28	provides	provide	VERB
cana-2940	173	29	nonlinearity	nonlinearity	NOUN
cana-2940	173	30	and	and	CCONJ
cana-2940	173	31	improves	improve	VERB
cana-2940	173	32	the	the	DET
cana-2940	173	33	model	model	NOUN
cana-2940	173	34	performance	performance	NOUN
cana-2940	173	35	.	.	PUNCT
cana-2940	174	1	the	the	DET
cana-2940	174	2	equation	equation	NOUN
cana-2940	174	3	for	for	ADP
cana-2940	174	4	the	the	DET
cana-2940	174	5	swish	swish	ADJ
cana-2940	174	6	function	function	NOUN
cana-2940	174	7	is	be	AUX
cana-2940	174	8	:	:	PUNCT
cana-2940	174	9	swish(x)=	swish(x)=	PROPN
cana-2940	174	10	x.𝜎(𝑥	x.𝜎(𝑥	PROPN
cana-2940	174	11	)	)	PUNCT
cana-2940	174	12	(	(	PUNCT
cana-2940	174	13	7	7	X
cana-2940	174	14	)	)	PUNCT
cana-2940	174	15	where	where	SCONJ
cana-2940	174	16	,	,	PUNCT
cana-2940	174	17	𝜎(𝑥	𝜎(𝑥	PROPN
cana-2940	174	18	)	)	PUNCT
cana-2940	174	19	is	be	AUX
cana-2940	174	20	the	the	DET
cana-2940	174	21	sigmoid	sigmoid	NOUN
cana-2940	174	22	function	function	NOUN
cana-2940	174	23	.	.	PUNCT
cana-2940	175	1	e	e	X
cana-2940	175	2	)	)	PUNCT
cana-2940	175	3	mbconv	mbconv	NOUN
cana-2940	175	4	blocks	block	NOUN
cana-2940	175	5	(	(	PUNCT
cana-2940	175	6	depthwise	depthwise	NOUN
cana-2940	175	7	separable	separable	ADJ
cana-2940	175	8	convolutions	convolution	NOUN
cana-2940	175	9	):	):	PUNCT
cana-2940	175	10	the	the	DET
cana-2940	175	11	efficientnet	efficientnet	NOUN
cana-2940	175	12	-	-	PUNCT
cana-2940	175	13	b0	b0	NOUN
cana-2940	175	14	’s	’s	PART
cana-2940	175	15	layers	layer	NOUN
cana-2940	175	16	consist	consist	VERB
cana-2940	175	17	of	of	ADP
cana-2940	175	18	several	several	ADJ
cana-2940	175	19	mobile	mobile	ADJ
cana-2940	175	20	inverted	inverted	ADJ
cana-2940	175	21	bottleneck	bottleneck	NOUN
cana-2940	175	22	convolution	convolution	NOUN
cana-2940	175	23	(	(	PUNCT
cana-2940	175	24	mbconv	mbconv	NOUN
cana-2940	175	25	)	)	PUNCT
cana-2940	175	26	blocks	block	NOUN
cana-2940	175	27	.	.	PUNCT
cana-2940	176	1	for	for	ADP
cana-2940	176	2	each	each	DET
cana-2940	176	3	mbconv	mbconv	NOUN
cana-2940	176	4	block	block	NOUN
cana-2940	176	5	,	,	PUNCT
cana-2940	176	6	depthwise	depthwise	NOUN
cana-2940	176	7	separable	separable	ADJ
cana-2940	176	8	convolution	convolution	NOUN
cana-2940	176	9	followed	follow	VERB
cana-2940	176	10	by	by	ADP
cana-2940	176	11	pointwise	pointwise	NOUN
cana-2940	176	12	convolution	convolution	NOUN
cana-2940	176	13	is	be	AUX
cana-2940	176	14	calculated	calculate	VERB
cana-2940	176	15	which	which	PRON
cana-2940	176	16	diminishes	diminish	VERB
cana-2940	176	17	the	the	DET
cana-2940	176	18	further	further	ADJ
cana-2940	176	19	computational	computational	ADJ
cana-2940	176	20	complexity	complexity	NOUN
cana-2940	176	21	.	.	PUNCT
cana-2940	177	1	depthwise	depthwise	NOUN
cana-2940	177	2	convolution	convolution	NOUN
cana-2940	177	3	,	,	PUNCT
cana-2940	177	4	performed	perform	VERB
cana-2940	177	5	separately	separately	ADV
cana-2940	177	6	to	to	ADP
cana-2940	177	7	each	each	DET
cana-2940	177	8	channel	channel	NOUN
cana-2940	177	9	,	,	PUNCT
cana-2940	177	10	communications	communication	NOUN
cana-2940	177	11	on	on	ADP
cana-2940	177	12	applied	apply	VERB
cana-2940	177	13	nonlinear	nonlinear	ADJ
cana-2940	177	14	analysis	analysis	NOUN
cana-2940	177	15	issn	issn	NOUN
cana-2940	177	16	:	:	PUNCT
cana-2940	177	17	1074	1074	NUM
cana-2940	177	18	-	-	PUNCT
cana-2940	177	19	133x	133x	NUM
cana-2940	177	20	vol	vol	NOUN
cana-2940	177	21	32	32	NUM
cana-2940	177	22	no	no	NOUN
cana-2940	177	23	.	.	PUNCT
cana-2940	178	1	5s	5s	NUM
cana-2940	178	2	(	(	PUNCT
cana-2940	178	3	2025	2025	NUM
cana-2940	178	4	)	)	PUNCT
cana-2940	178	5	39	39	NUM
cana-2940	178	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2940	178	7	generally	generally	ADV
cana-2940	178	8	changes	change	VERB
cana-2940	178	9	the	the	DET
cana-2940	178	10	feature	feature	NOUN
cana-2940	178	11	by	by	ADP
cana-2940	178	12	continuously	continuously	ADV
cana-2940	178	13	multiplying	multiply	VERB
cana-2940	178	14	the	the	DET
cana-2940	178	15	result	result	NOUN
cana-2940	178	16	by	by	ADP
cana-2940	178	17	an	an	DET
cana-2940	178	18	identical	identical	ADJ
cana-2940	178	19	factor	factor	NOUN
cana-2940	178	20	,	,	PUNCT
cana-2940	178	21	as	as	SCONJ
cana-2940	178	22	shown	show	VERB
cana-2940	178	23	in	in	ADP
cana-2940	178	24	the	the	DET
cana-2940	178	25	equation	equation	NOUN
cana-2940	178	26	(	(	PUNCT
cana-2940	178	27	8)	8)	NUM
cana-2940	178	28	:	:	PUNCT
cana-2940	178	29	y	y	PROPN
cana-2940	178	30	a	a	PRON
cana-2940	178	31	,	,	PUNCT
cana-2940	178	32	b	b	NOUN
cana-2940	178	33	,	,	PUNCT
cana-2940	178	34	c	c	NOUN
cana-2940	178	35	=	=	PUNCT
cana-2940	178	36	∑	∑	PROPN
cana-2940	178	37	∑	∑	PROPN
cana-2940	178	38	xa+m	xa+m	PROPN
cana-2940	178	39	,	,	PUNCT
cana-2940	178	40	b+n	b+n	X
cana-2940	178	41	,	,	PUNCT
cana-2940	178	42	c	c	PROPN
cana-2940	178	43	∗	∗	X
cana-2940	178	44	wm	wm	PROPN
cana-2940	178	45	,	,	PUNCT
cana-2940	178	46	n	n	CCONJ
cana-2940	178	47	,	,	PUNCT
cana-2940	178	48	c	c	PROPN
cana-2940	178	49	n	n	PROPN
cana-2940	178	50	n=1	n=1	PROPN
cana-2940	178	51	m	m	PROPN
cana-2940	178	52	m=1	m=1	X
cana-2940	178	53	(	(	PUNCT
cana-2940	178	54	8)	8)	NUM
cana-2940	178	55	here	here	ADV
cana-2940	178	56	,	,	PUNCT
cana-2940	178	57	x	x	PRON
cana-2940	178	58	signifies	signifie	NOUN
cana-2940	178	59	input	input	NOUN
cana-2940	178	60	,	,	PUNCT
cana-2940	178	61	w	w	NOUN
cana-2940	178	62	is	be	AUX
cana-2940	178	63	the	the	DET
cana-2940	178	64	depthwise	depthwise	NOUN
cana-2940	178	65	filter	filter	NOUN
cana-2940	178	66	,	,	PUNCT
cana-2940	178	67	and	and	CCONJ
cana-2940	178	68	y	y	PROPN
cana-2940	178	69	signifies	signify	VERB
cana-2940	178	70	output	output	VERB
cana-2940	178	71	.	.	PUNCT
cana-2940	179	1	further	far	ADV
cana-2940	179	2	,	,	PUNCT
cana-2940	179	3	equation	equation	NOUN
cana-2940	179	4	(	(	PUNCT
cana-2940	179	5	9	9	NUM
cana-2940	179	6	)	)	PUNCT
cana-2940	179	7	for	for	ADP
cana-2940	179	8	pointwise	pointwise	ADJ
cana-2940	179	9	convolution	convolution	NOUN
cana-2940	179	10	in	in	ADP
cana-2940	179	11	which	which	PRON
cana-2940	179	12	a	a	DET
cana-2940	179	13	1x1	1x1	NUM
cana-2940	179	14	filter	filter	NOUN
cana-2940	179	15	is	be	AUX
cana-2940	179	16	applied	apply	VERB
cana-2940	179	17	to	to	ADP
cana-2940	179	18	the	the	DET
cana-2940	179	19	output	output	NOUN
cana-2940	179	20	(	(	PUNCT
cana-2940	179	21	saiwaeo	saiwaeo	NOUN
cana-2940	179	22	et	et	PROPN
cana-2940	179	23	al	al	PROPN
cana-2940	179	24	.	.	PROPN
cana-2940	179	25	,	,	PUNCT
cana-2940	179	26	2023	2023	NUM
cana-2940	179	27	)	)	PUNCT
cana-2940	179	28	is	be	AUX
cana-2940	179	29	represented	represent	VERB
cana-2940	179	30	as	as	ADP
cana-2940	179	31	:	:	PUNCT
cana-2940	179	32	y	y	PROPN
cana-2940	179	33	a	a	DET
cana-2940	179	34	,	,	PUNCT
cana-2940	179	35	b	b	NOUN
cana-2940	179	36	,	,	PUNCT
cana-2940	179	37	k	k	PROPN
cana-2940	179	38	=	=	SYM
cana-2940	179	39	∑	∑	PROPN
cana-2940	179	40	xa	xa	PROPN
cana-2940	179	41	,	,	PUNCT
cana-2940	179	42	b	b	PROPN
cana-2940	179	43	,	,	PUNCT
cana-2940	179	44	l	l	NOUN
cana-2940	179	45	,	,	PUNCT
cana-2940	179	46	∗l	∗l	ADJ
cana-2940	179	47	l=1	l=1	NOUN
cana-2940	179	48	w1,1,l	w1,1,l	NOUN
cana-2940	179	49	,	,	PUNCT
cana-2940	179	50	k	k	PROPN
cana-2940	179	51	(	(	PUNCT
cana-2940	179	52	9	9	NUM
cana-2940	179	53	)	)	PUNCT
cana-2940	179	54	f	f	X
cana-2940	179	55	)	)	PUNCT
cana-2940	179	56	global	global	ADJ
cana-2940	179	57	average	average	ADJ
cana-2940	179	58	pooling	pooling	NOUN
cana-2940	179	59	(	(	PUNCT
cana-2940	179	60	gap	gap	NOUN
cana-2940	179	61	):	):	PUNCT
cana-2940	179	62	it	it	PRON
cana-2940	179	63	decreases	decrease	VERB
cana-2940	179	64	the	the	DET
cana-2940	179	65	spatial	spatial	ADJ
cana-2940	179	66	dimensionality	dimensionality	NOUN
cana-2940	179	67	of	of	ADP
cana-2940	179	68	the	the	DET
cana-2940	179	69	output	output	NOUN
cana-2940	179	70	feature	feature	NOUN
cana-2940	179	71	map	map	NOUN
cana-2940	179	72	to	to	PART
cana-2940	179	73	create	create	VERB
cana-2940	179	74	a	a	DET
cana-2940	179	75	1x1	1x1	NUM
cana-2940	179	76	feature	feature	NOUN
cana-2940	179	77	map	map	NOUN
cana-2940	179	78	.	.	PUNCT
cana-2940	180	1	global	global	ADJ
cana-2940	180	2	average	average	ADJ
cana-2940	180	3	pooling	pooling	NOUN
cana-2940	180	4	(	(	PUNCT
cana-2940	180	5	gap	gap	NOUN
cana-2940	180	6	)	)	PUNCT
cana-2940	180	7	takes	take	VERB
cana-2940	180	8	the	the	DET
cana-2940	180	9	average	average	ADJ
cana-2940	180	10	value	value	NOUN
cana-2940	180	11	of	of	ADP
cana-2940	180	12	each	each	DET
cana-2940	180	13	feature	feature	NOUN
cana-2940	180	14	map	map	NOUN
cana-2940	180	15	,	,	PUNCT
cana-2940	180	16	a	a	DET
cana-2940	180	17	7x7x1280	7x7x1280	NUM
cana-2940	180	18	feature	feature	NOUN
cana-2940	180	19	map	map	NOUN
cana-2940	180	20	in	in	ADP
cana-2940	180	21	this	this	DET
cana-2940	180	22	experimental	experimental	ADJ
cana-2940	180	23	work	work	NOUN
cana-2940	180	24	,	,	PUNCT
cana-2940	180	25	which	which	PRON
cana-2940	180	26	is	be	AUX
cana-2940	180	27	reduced	reduce	VERB
cana-2940	180	28	to	to	ADP
cana-2940	180	29	a	a	DET
cana-2940	180	30	1d	1d	NUM
cana-2940	180	31	vector	vector	NOUN
cana-2940	180	32	of	of	ADP
cana-2940	180	33	length	length	NOUN
cana-2940	180	34	1280	1280	NUM
cana-2940	180	35	,	,	PUNCT
cana-2940	180	36	by	by	ADP
cana-2940	180	37	preserving	preserve	VERB
cana-2940	180	38	the	the	DET
cana-2940	180	39	important	important	ADJ
cana-2940	180	40	characteristics	characteristic	NOUN
cana-2940	180	41	of	of	ADP
cana-2940	180	42	the	the	DET
cana-2940	180	43	image	image	NOUN
cana-2940	180	44	.	.	PUNCT
cana-2940	181	1	the	the	DET
cana-2940	181	2	feature	feature	NOUN
cana-2940	181	3	map	map	NOUN
cana-2940	181	4	of	of	ADP
cana-2940	181	5	the	the	DET
cana-2940	181	6	1d	1d	NUM
cana-2940	181	7	vector	vector	NOUN
cana-2940	181	8	is	be	AUX
cana-2940	181	9	calculated	calculate	VERB
cana-2940	181	10	using	use	VERB
cana-2940	181	11	the	the	DET
cana-2940	181	12	equation	equation	NOUN
cana-2940	181	13	(	(	PUNCT
cana-2940	181	14	10	10	NUM
cana-2940	181	15	)	)	PUNCT
cana-2940	181	16	,	,	PUNCT
cana-2940	181	17	where	where	SCONJ
cana-2940	181	18	𝑥𝑖,𝑗,𝑚	𝑥𝑖,𝑗,𝑚	NOUN
cana-2940	181	19	is	be	AUX
cana-2940	181	20	the	the	DET
cana-2940	181	21	value	value	NOUN
cana-2940	181	22	at	at	ADP
cana-2940	181	23	position	position	NOUN
cana-2940	181	24	(	(	PUNCT
cana-2940	181	25	i	i	PROPN
cana-2940	181	26	,	,	PUNCT
cana-2940	181	27	j	j	PROPN
cana-2940	181	28	)	)	PUNCT
cana-2940	181	29	in	in	ADP
cana-2940	181	30	channel	channel	PROPN
cana-2940	181	31	m	m	PROPN
cana-2940	181	32	,	,	PUNCT
cana-2940	181	33	h	h	NOUN
cana-2940	182	1	x	x	X
cana-2940	182	2	w	w	NOUN
cana-2940	182	3	is	be	AUX
cana-2940	182	4	the	the	DET
cana-2940	182	5	spatial	spatial	ADJ
cana-2940	182	6	dimensionality	dimensionality	NOUN
cana-2940	182	7	of	of	ADP
cana-2940	182	8	the	the	DET
cana-2940	182	9	feature	feature	NOUN
cana-2940	182	10	map	map	NOUN
cana-2940	182	11	,	,	PUNCT
cana-2940	182	12	and	and	CCONJ
cana-2940	182	13	fm	fm	PROPN
cana-2940	182	14	is	be	AUX
cana-2940	182	15	the	the	DET
cana-2940	182	16	average	average	ADJ
cana-2940	182	17	value	value	NOUN
cana-2940	182	18	of	of	ADP
cana-2940	182	19	channel	channel	NOUN
cana-2940	183	1	c.	c.	PROPN
cana-2940	183	2	fm	fm	PROPN
cana-2940	184	1	=	=	SYM
cana-2940	184	2	1	1	NUM
cana-2940	184	3	h	h	NOUN
cana-2940	184	4	x	x	VERB
cana-2940	184	5	w	w	ADP
cana-2940	184	6	∑	∑	PROPN
cana-2940	184	7	∑	∑	PROPN
cana-2940	184	8	xi	xi	PROPN
cana-2940	184	9	,	,	PUNCT
cana-2940	184	10	j	j	PROPN
cana-2940	184	11	,	,	PUNCT
cana-2940	184	12	m	m	PROPN
cana-2940	184	13	w	w	NOUN
cana-2940	184	14	j=1	j=1	PROPN
cana-2940	184	15	h	h	NOUN
cana-2940	184	16	i=1	i=1	PROPN
cana-2940	184	17	(	(	PUNCT
cana-2940	184	18	10	10	NUM
cana-2940	184	19	)	)	PUNCT
cana-2940	184	20	in	in	ADP
cana-2940	184	21	this	this	DET
cana-2940	184	22	feature	feature	NOUN
cana-2940	184	23	extraction	extraction	NOUN
cana-2940	184	24	technique	technique	NOUN
cana-2940	184	25	,	,	PUNCT
cana-2940	184	26	each	each	DET
cana-2940	184	27	feature	feature	NOUN
cana-2940	184	28	map	map	NOUN
cana-2940	184	29	summarizes	summarize	VERB
cana-2940	184	30	the	the	DET
cana-2940	184	31	image	image	NOUN
cana-2940	184	32	’s	’s	PART
cana-2940	184	33	patterns	pattern	NOUN
cana-2940	184	34	,	,	PUNCT
cana-2940	184	35	edges	edge	NOUN
cana-2940	184	36	,	,	PUNCT
cana-2940	184	37	textures	texture	NOUN
cana-2940	184	38	,	,	PUNCT
cana-2940	184	39	etc	etc	X
cana-2940	184	40	.	.	X
cana-2940	185	1	the	the	DET
cana-2940	185	2	extracted	extract	VERB
cana-2940	185	3	features	feature	NOUN
cana-2940	185	4	can	can	AUX
cana-2940	185	5	be	be	AUX
cana-2940	185	6	used	use	VERB
cana-2940	185	7	in	in	ADP
cana-2940	185	8	many	many	ADJ
cana-2940	185	9	other	other	ADJ
cana-2940	185	10	tasks	task	NOUN
cana-2940	185	11	like	like	ADP
cana-2940	185	12	classification	classification	NOUN
cana-2940	185	13	,	,	PUNCT
cana-2940	185	14	clustering	clustering	NOUN
cana-2940	185	15	,	,	PUNCT
cana-2940	185	16	etc	etc	X
cana-2940	185	17	.	.	X
cana-2940	185	18	table	table	NOUN
cana-2940	185	19	2	2	NUM
cana-2940	185	20	depicts	depict	VERB
cana-2940	185	21	the	the	DET
cana-2940	185	22	structure	structure	NOUN
cana-2940	185	23	of	of	ADP
cana-2940	185	24	each	each	DET
cana-2940	185	25	mbconv	mbconv	NOUN
cana-2940	185	26	block	block	NOUN
cana-2940	185	27	along	along	ADP
cana-2940	185	28	with	with	ADP
cana-2940	185	29	input	input	NOUN
cana-2940	185	30	and	and	CCONJ
cana-2940	185	31	output	output	NOUN
cana-2940	185	32	layers	layer	NOUN
cana-2940	185	33	.	.	PUNCT
cana-2940	186	1	table	table	NOUN
cana-2940	186	2	2	2	NUM
cana-2940	186	3	:	:	PUNCT
cana-2940	186	4	represents	represent	VERB
cana-2940	186	5	the	the	DET
cana-2940	186	6	structure	structure	NOUN
cana-2940	186	7	of	of	ADP
cana-2940	186	8	each	each	DET
cana-2940	186	9	block	block	NOUN
cana-2940	186	10	of	of	ADP
cana-2940	186	11	the	the	DET
cana-2940	186	12	feature	feature	NOUN
cana-2940	186	13	extraction	extraction	NOUN
cana-2940	186	14	technique	technique	NOUN
cana-2940	186	15	layer	layer	NOUN
cana-2940	186	16	name	name	NOUN
cana-2940	186	17	output	output	NOUN
cana-2940	186	18	shape	shape	NOUN
cana-2940	186	19	layer	layer	NOUN
cana-2940	186	20	description	description	NOUN
cana-2940	186	21	input	input	NOUN
cana-2940	186	22	layer	layer	NOUN
cana-2940	186	23	(	(	PUNCT
cana-2940	186	24	224,224,3	224,224,3	NUM
cana-2940	186	25	)	)	PUNCT
cana-2940	186	26	input	input	NOUN
cana-2940	186	27	image	image	NOUN
cana-2940	186	28	size	size	NOUN
cana-2940	186	29	with	with	ADP
cana-2940	186	30	3	3	NUM
cana-2940	186	31	color	color	NOUN
cana-2940	186	32	channels	channel	NOUN
cana-2940	186	33	conv2d	conv2d	VERB
cana-2940	186	34	(	(	PUNCT
cana-2940	186	35	112.112.32	112.112.32	NUM
cana-2940	186	36	)	)	PUNCT
cana-2940	186	37	starting	start	VERB
cana-2940	186	38	initial	initial	ADJ
cana-2940	186	39	convolution	convolution	NOUN
cana-2940	186	40	layer	layer	NOUN
cana-2940	186	41	with	with	ADP
cana-2940	186	42	32	32	NUM
cana-2940	186	43	filters	filter	NOUN
cana-2940	186	44	and	and	CCONJ
cana-2940	186	45	stride	stride	ADJ
cana-2940	186	46	2	2	NUM
cana-2940	186	47	mbconv1	mbconv1	NOUN
cana-2940	186	48	(	(	PUNCT
cana-2940	186	49	112	112	NUM
cana-2940	186	50	,	,	PUNCT
cana-2940	186	51	112	112	NUM
cana-2940	186	52	,	,	PUNCT
cana-2940	186	53	16	16	NUM
cana-2940	186	54	)	)	SYM
cana-2940	186	55	1	1	NUM
cana-2940	186	56	block	block	NOUN
cana-2940	186	57	with	with	ADP
cana-2940	186	58	16	16	NUM
cana-2940	186	59	filters	filter	NOUN
cana-2940	186	60	mbconv6	mbconv6	PROPN
cana-2940	186	61	(	(	PUNCT
cana-2940	186	62	56	56	NUM
cana-2940	186	63	,	,	PUNCT
cana-2940	186	64	56	56	NUM
cana-2940	186	65	,	,	PUNCT
cana-2940	186	66	24	24	NUM
cana-2940	186	67	)	)	SYM
cana-2940	186	68	2	2	NUM
cana-2940	186	69	blocks	block	NOUN
cana-2940	186	70	with	with	ADP
cana-2940	186	71	24	24	NUM
cana-2940	186	72	filters	filter	NOUN
cana-2940	186	73	mbconv6	mbconv6	PROPN
cana-2940	186	74	(	(	PUNCT
cana-2940	186	75	28	28	NUM
cana-2940	186	76	,	,	PUNCT
cana-2940	186	77	28,40	28,40	NUM
cana-2940	186	78	)	)	PUNCT
cana-2940	186	79	2	2	NUM
cana-2940	186	80	blocks	block	NOUN
cana-2940	186	81	with	with	ADP
cana-2940	186	82	40	40	NUM
cana-2940	186	83	filters	filter	NOUN
cana-2940	186	84	mbconv6	mbconv6	PROPN
cana-2940	186	85	(	(	PUNCT
cana-2940	186	86	14	14	NUM
cana-2940	186	87	,	,	PUNCT
cana-2940	186	88	14	14	NUM
cana-2940	186	89	,	,	PUNCT
cana-2940	186	90	80	80	NUM
cana-2940	186	91	)	)	PUNCT
cana-2940	186	92	3	3	NUM
cana-2940	186	93	blocks	block	NOUN
cana-2940	186	94	with	with	ADP
cana-2940	186	95	80	80	NUM
cana-2940	186	96	filters	filter	NOUN
cana-2940	186	97	mbconv6	mbconv6	PROPN
cana-2940	186	98	(	(	PUNCT
cana-2940	186	99	14	14	NUM
cana-2940	186	100	,	,	PUNCT
cana-2940	186	101	14	14	NUM
cana-2940	186	102	,	,	PUNCT
cana-2940	186	103	112	112	NUM
cana-2940	186	104	)	)	PUNCT
cana-2940	186	105	3	3	NUM
cana-2940	186	106	blocks	block	NOUN
cana-2940	186	107	with	with	ADP
cana-2940	186	108	112	112	NUM
cana-2940	186	109	filters	filter	NOUN
cana-2940	186	110	mbconv6	mbconv6	PROPN
cana-2940	186	111	(	(	PUNCT
cana-2940	186	112	7	7	NUM
cana-2940	186	113	,	,	PUNCT
cana-2940	186	114	7	7	NUM
cana-2940	186	115	,	,	PUNCT
cana-2940	186	116	192	192	NUM
cana-2940	186	117	)	)	PUNCT
cana-2940	186	118	4	4	NUM
cana-2940	186	119	blocks	block	NOUN
cana-2940	186	120	with	with	ADP
cana-2940	186	121	192	192	NUM
cana-2940	186	122	filters	filter	NOUN
cana-2940	186	123	mbconv6	mbconv6	PROPN
cana-2940	186	124	(	(	PUNCT
cana-2940	186	125	7	7	NUM
cana-2940	186	126	,	,	PUNCT
cana-2940	186	127	7	7	NUM
cana-2940	186	128	,	,	PUNCT
cana-2940	186	129	320	320	NUM
cana-2940	186	130	)	)	PUNCT
cana-2940	186	131	1	1	NUM
cana-2940	186	132	block	block	NOUN
cana-2940	186	133	with	with	ADP
cana-2940	186	134	320	320	NUM
cana-2940	186	135	filters	filter	NOUN
cana-2940	186	136	con2d(1x1	con2d(1x1	ADV
cana-2940	186	137	)	)	PUNCT
cana-2940	186	138	(	(	PUNCT
cana-2940	186	139	7,7,1280	7,7,1280	NOUN
cana-2940	186	140	)	)	PUNCT
cana-2940	186	141	final	final	ADJ
cana-2940	186	142	1x1	1x1	NUM
cana-2940	186	143	convolution	convolution	NOUN
cana-2940	186	144	is	be	AUX
cana-2940	186	145	performed	perform	VERB
cana-2940	186	146	before	before	ADP
cana-2940	186	147	gap	gap	NOUN
cana-2940	186	148	gap	gap	NOUN
cana-2940	186	149	(	(	PUNCT
cana-2940	186	150	1280	1280	NUM
cana-2940	186	151	,	,	PUNCT
cana-2940	186	152	1	1	NUM
cana-2940	186	153	)	)	PUNCT
cana-2940	186	154	it	it	PRON
cana-2940	186	155	is	be	AUX
cana-2940	186	156	applied	apply	VERB
cana-2940	186	157	to	to	PART
cana-2940	186	158	reduce	reduce	VERB
cana-2940	186	159	the	the	DET
cana-2940	186	160	feature	feature	NOUN
cana-2940	186	161	map	map	NOUN
cana-2940	186	162	to	to	ADP
cana-2940	186	163	a	a	DET
cana-2940	186	164	1d	1d	NUM
cana-2940	186	165	vector	vector	NOUN
cana-2940	186	166	g	g	NOUN
cana-2940	186	167	)	)	PUNCT
cana-2940	186	168	label	label	NOUN
cana-2940	186	169	mapping	mapping	NOUN
cana-2940	186	170	:	:	PUNCT
cana-2940	186	171	in	in	ADP
cana-2940	186	172	the	the	DET
cana-2940	186	173	proposed	propose	VERB
cana-2940	186	174	work	work	NOUN
cana-2940	186	175	,	,	PUNCT
cana-2940	186	176	the	the	DET
cana-2940	186	177	label	label	NOUN
cana-2940	186	178	represents	represent	VERB
cana-2940	186	179	the	the	DET
cana-2940	186	180	numeric	numeric	ADJ
cana-2940	186	181	class	class	NOUN
cana-2940	186	182	identifier	identifier	NOUN
cana-2940	186	183	corresponding	correspond	VERB
cana-2940	186	184	to	to	ADP
cana-2940	186	185	the	the	DET
cana-2940	186	186	skin	skin	NOUN
cana-2940	186	187	condition	condition	NOUN
cana-2940	186	188	category	category	NOUN
cana-2940	186	189	of	of	ADP
cana-2940	186	190	an	an	DET
cana-2940	186	191	image	image	NOUN
cana-2940	186	192	.	.	PUNCT
cana-2940	187	1	image	image	NOUN
cana-2940	187	2	belongs	belong	VERB
cana-2940	187	3	to	to	ADP
cana-2940	187	4	a	a	DET
cana-2940	187	5	specific	specific	ADJ
cana-2940	187	6	skin	skin	NOUN
cana-2940	187	7	disease	disease	NOUN
cana-2940	187	8	category	category	NOUN
cana-2940	187	9	,	,	PUNCT
cana-2940	187	10	and	and	CCONJ
cana-2940	187	11	these	these	DET
cana-2940	187	12	categories	category	NOUN
cana-2940	187	13	are	be	AUX
cana-2940	187	14	mapped	map	VERB
cana-2940	187	15	to	to	ADP
cana-2940	187	16	numerical	numerical	ADJ
cana-2940	187	17	values	value	NOUN
cana-2940	187	18	using	use	VERB
cana-2940	187	19	a	a	DET
cana-2940	187	20	dictionary	dictionary	NOUN
cana-2940	187	21	called	call	VERB
cana-2940	187	22	label	label	NOUN
cana-2940	187	23	mapping	mapping	NOUN
cana-2940	187	24	.	.	PUNCT
cana-2940	188	1	each	each	DET
cana-2940	188	2	entry	entry	NOUN
cana-2940	188	3	in	in	ADP
cana-2940	188	4	this	this	DET
cana-2940	188	5	list	list	NOUN
cana-2940	188	6	corresponds	correspond	VERB
cana-2940	188	7	to	to	ADP
cana-2940	188	8	a	a	DET
cana-2940	188	9	numerical	numerical	ADJ
cana-2940	188	10	label	label	NOUN
cana-2940	188	11	that	that	PRON
cana-2940	188	12	matches	match	VERB
cana-2940	188	13	the	the	DET
cana-2940	188	14	extracted	extract	VERB
cana-2940	188	15	features	feature	NOUN
cana-2940	188	16	for	for	ADP
cana-2940	188	17	an	an	DET
cana-2940	188	18	image	image	NOUN
cana-2940	188	19	stored	store	VERB
cana-2940	188	20	in	in	ADP
cana-2940	188	21	the	the	DET
cana-2940	188	22	features	feature	NOUN
cana-2940	188	23	list	list	NOUN
cana-2940	188	24	.	.	PUNCT
cana-2940	189	1	the	the	DET
cana-2940	189	2	label	label	NOUN
cana-2940	189	3	is	be	AUX
cana-2940	189	4	then	then	ADV
cana-2940	189	5	appended	append	VERB
cana-2940	189	6	to	to	ADP
cana-2940	189	7	the	the	DET
cana-2940	189	8	labels	label	NOUN
cana-2940	189	9	list	list	NOUN
cana-2940	189	10	,	,	PUNCT
cana-2940	189	11	which	which	PRON
cana-2940	189	12	stores	store	VERB
cana-2940	189	13	the	the	DET
cana-2940	189	14	numerical	numerical	ADJ
cana-2940	189	15	labels	label	NOUN
cana-2940	189	16	for	for	ADP
cana-2940	189	17	each	each	DET
cana-2940	189	18	image	image	NOUN
cana-2940	189	19	.	.	PUNCT
cana-2940	190	1	in	in	ADP
cana-2940	190	2	this	this	DET
cana-2940	190	3	work	work	NOUN
cana-2940	190	4	,	,	PUNCT
cana-2940	190	5	if	if	SCONJ
cana-2940	190	6	an	an	DET
cana-2940	190	7	image	image	NOUN
cana-2940	190	8	is	be	AUX
cana-2940	190	9	found	find	VERB
cana-2940	190	10	in	in	ADP
cana-2940	190	11	the	the	DET
cana-2940	190	12	subfolder	subfolder	NOUN
cana-2940	190	13	'	'	PART
cana-2940	190	14	acne	acne	NOUN
cana-2940	190	15	and	and	CCONJ
cana-2940	190	16	rosacea	rosacea	NOUN
cana-2940	190	17	photos	photo	NOUN
cana-2940	190	18	'	'	PART
cana-2940	190	19	,	,	PUNCT
cana-2940	190	20	the	the	DET
cana-2940	190	21	corresponding	corresponding	ADJ
cana-2940	190	22	label	label	NOUN
cana-2940	190	23	is	be	AUX
cana-2940	190	24	0	0	NUM
cana-2940	190	25	.	.	PUNCT
cana-2940	191	1	if	if	SCONJ
cana-2940	191	2	another	another	DET
cana-2940	191	3	image	image	NOUN
cana-2940	191	4	is	be	AUX
cana-2940	191	5	from	from	ADP
cana-2940	191	6	the	the	DET
cana-2940	191	7	subfolder	subfolder	NOUN
cana-2940	191	8	'	'	PUNCT
cana-2940	191	9	melanoma	melanoma	NOUN
cana-2940	191	10	skin	skin	NOUN
cana-2940	191	11	communications	communication	NOUN
cana-2940	191	12	on	on	ADP
cana-2940	191	13	applied	apply	VERB
cana-2940	191	14	nonlinear	nonlinear	ADJ
cana-2940	191	15	analysis	analysis	NOUN
cana-2940	191	16	issn	issn	NOUN
cana-2940	191	17	:	:	PUNCT
cana-2940	191	18	1074	1074	NUM
cana-2940	191	19	-	-	PUNCT
cana-2940	191	20	133x	133x	NUM
cana-2940	191	21	vol	vol	NOUN
cana-2940	191	22	32	32	NUM
cana-2940	191	23	no	no	NOUN
cana-2940	191	24	.	.	PUNCT
cana-2940	192	1	5s	5s	NUM
cana-2940	192	2	(	(	PUNCT
cana-2940	192	3	2025	2025	NUM
cana-2940	192	4	)	)	PUNCT
cana-2940	192	5	40	40	NUM
cana-2940	192	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2940	192	7	cancer	cancer	NOUN
cana-2940	192	8	nevi	nevi	NOUN
cana-2940	192	9	and	and	CCONJ
cana-2940	192	10	moles	mole	NOUN
cana-2940	192	11	'	'	PART
cana-2940	192	12	,	,	PUNCT
cana-2940	192	13	its	its	PRON
cana-2940	192	14	label	label	NOUN
cana-2940	192	15	will	will	AUX
cana-2940	192	16	be	be	AUX
cana-2940	192	17	11	11	NUM
cana-2940	192	18	.	.	PUNCT
cana-2940	193	1	these	these	DET
cana-2940	193	2	labels	label	NOUN
cana-2940	193	3	help	help	VERB
cana-2940	193	4	the	the	DET
cana-2940	193	5	model	model	NOUN
cana-2940	193	6	learn	learn	VERB
cana-2940	193	7	to	to	PART
cana-2940	193	8	associate	associate	VERB
cana-2940	193	9	certain	certain	ADJ
cana-2940	193	10	patterns	pattern	NOUN
cana-2940	193	11	in	in	ADP
cana-2940	193	12	images	image	NOUN
cana-2940	193	13	with	with	ADP
cana-2940	193	14	specific	specific	ADJ
cana-2940	193	15	categories	category	NOUN
cana-2940	193	16	of	of	ADP
cana-2940	193	17	skin	skin	NOUN
cana-2940	193	18	diseases	disease	NOUN
cana-2940	193	19	.	.	PUNCT
cana-2940	194	1	4	4	X
cana-2940	194	2	.	.	X
cana-2940	194	3	classification	classification	NOUN
cana-2940	194	4	results	result	NOUN
cana-2940	194	5	:	:	PUNCT
cana-2940	194	6	the	the	DET
cana-2940	194	7	proposed	propose	VERB
cana-2940	194	8	framework	framework	NOUN
cana-2940	194	9	of	of	ADP
cana-2940	194	10	enhanced	enhanced	ADJ
cana-2940	194	11	holistically	holistically	ADV
cana-2940	194	12	nested	nest	VERB
cana-2940	194	13	edge	edge	NOUN
cana-2940	194	14	detection	detection	NOUN
cana-2940	194	15	and	and	CCONJ
cana-2940	194	16	efficientnet	efficientnet	NOUN
cana-2940	194	17	-	-	PUNCT
cana-2940	194	18	b0	b0	NOUN
cana-2940	194	19	used	use	VERB
cana-2940	194	20	for	for	ADP
cana-2940	194	21	feature	feature	NOUN
cana-2940	194	22	extraction	extraction	NOUN
cana-2940	194	23	is	be	AUX
cana-2940	194	24	utilized	utilize	VERB
cana-2940	194	25	further	far	ADV
cana-2940	194	26	using	use	VERB
cana-2940	194	27	convolution	convolution	NOUN
cana-2940	194	28	neural	neural	ADJ
cana-2940	194	29	networks	network	NOUN
cana-2940	194	30	(	(	PUNCT
cana-2940	194	31	cnns	cnns	PROPN
cana-2940	194	32	)	)	PUNCT
cana-2940	194	33	(	(	PUNCT
cana-2940	194	34	maqsood	maqsood	PROPN
cana-2940	194	35	&	&	CCONJ
cana-2940	194	36	damaševičius	damaševičius	PROPN
cana-2940	194	37	,	,	PUNCT
cana-2940	194	38	2023	2023	NUM
cana-2940	194	39	)	)	PUNCT
cana-2940	194	40	for	for	ADP
cana-2940	194	41	the	the	DET
cana-2940	194	42	classification	classification	NOUN
cana-2940	194	43	of	of	ADP
cana-2940	194	44	skin	skin	NOUN
cana-2940	194	45	diseases	disease	NOUN
cana-2940	194	46	.	.	PUNCT
cana-2940	195	1	from	from	ADP
cana-2940	195	2	the	the	DET
cana-2940	195	3	given	give	VERB
cana-2940	195	4	dataset	dataset	ADJ
cana-2940	195	5	dermnet	dermnet	NOUN
cana-2940	195	6	15,500	15,500	NUM
cana-2940	195	7	images	image	NOUN
cana-2940	195	8	(	(	PUNCT
cana-2940	195	9	80	80	NUM
cana-2940	195	10	%	%	NOUN
cana-2940	195	11	data	datum	NOUN
cana-2940	195	12	)	)	PUNCT
cana-2940	195	13	are	be	AUX
cana-2940	195	14	used	use	VERB
cana-2940	195	15	for	for	ADP
cana-2940	195	16	training	train	VERB
cana-2940	195	17	the	the	DET
cana-2940	195	18	cnn	cnn	PROPN
cana-2940	195	19	model	model	NOUN
cana-2940	195	20	and	and	CCONJ
cana-2940	195	21	3500	3500	NUM
cana-2940	195	22	images	image	NOUN
cana-2940	195	23	(	(	PUNCT
cana-2940	195	24	20	20	NUM
cana-2940	195	25	%	%	NOUN
cana-2940	195	26	data	datum	NOUN
cana-2940	195	27	)	)	PUNCT
cana-2940	195	28	are	be	AUX
cana-2940	195	29	used	use	VERB
cana-2940	195	30	for	for	ADP
cana-2940	195	31	testing	test	VERB
cana-2940	195	32	the	the	DET
cana-2940	195	33	model	model	NOUN
cana-2940	195	34	.	.	PUNCT
cana-2940	196	1	in	in	ADP
cana-2940	196	2	this	this	DET
cana-2940	196	3	model	model	NOUN
cana-2940	196	4	,	,	PUNCT
cana-2940	196	5	the	the	DET
cana-2940	196	6	conv1d	conv1d	ADJ
cana-2940	196	7	layer	layer	NOUN
cana-2940	196	8	has	have	VERB
cana-2940	196	9	64	64	NUM
cana-2940	196	10	filters	filter	NOUN
cana-2940	196	11	,	,	PUNCT
cana-2940	196	12	each	each	PRON
cana-2940	196	13	having	have	VERB
cana-2940	196	14	a	a	DET
cana-2940	196	15	3	3	NUM
cana-2940	196	16	x	x	SYM
cana-2940	196	17	3	3	NUM
cana-2940	196	18	kernel	kernel	NOUN
cana-2940	196	19	size	size	NOUN
cana-2940	196	20	,	,	PUNCT
cana-2940	196	21	and	and	CCONJ
cana-2940	196	22	uses	use	VERB
cana-2940	196	23	the	the	DET
cana-2940	196	24	rectified	rectified	ADJ
cana-2940	196	25	linear	linear	NOUN
cana-2940	196	26	unit	unit	NOUN
cana-2940	196	27	(	(	PUNCT
cana-2940	196	28	relu	relu	NOUN
cana-2940	196	29	)	)	PUNCT
cana-2940	196	30	activation	activation	NOUN
cana-2940	196	31	function	function	NOUN
cana-2940	196	32	.	.	PUNCT
cana-2940	197	1	relu	relu	NOUN
cana-2940	197	2	introduces	introduce	VERB
cana-2940	197	3	non	non	ADJ
cana-2940	197	4	-	-	NOUN
cana-2940	197	5	linearity	linearity	NOUN
cana-2940	197	6	into	into	ADP
cana-2940	197	7	the	the	DET
cana-2940	197	8	model	model	NOUN
cana-2940	197	9	,	,	PUNCT
cana-2940	197	10	allowing	allow	VERB
cana-2940	197	11	it	it	PRON
cana-2940	197	12	to	to	PART
cana-2940	197	13	learn	learn	VERB
cana-2940	197	14	complex	complex	ADJ
cana-2940	197	15	patterns	pattern	NOUN
cana-2940	197	16	.	.	PUNCT
cana-2940	198	1	the	the	DET
cana-2940	198	2	model	model	NOUN
cana-2940	198	3	is	be	AUX
cana-2940	198	4	trained	train	VERB
cana-2940	198	5	for	for	ADP
cana-2940	198	6	50	50	NUM
cana-2940	198	7	epochs	epoch	NOUN
cana-2940	198	8	.	.	PUNCT
cana-2940	199	1	the	the	DET
cana-2940	199	2	achieved	achieve	VERB
cana-2940	199	3	performance	performance	NOUN
cana-2940	199	4	metrics	metric	NOUN
cana-2940	199	5	of	of	ADP
cana-2940	199	6	this	this	DET
cana-2940	199	7	framework	framework	NOUN
cana-2940	199	8	are	be	AUX
cana-2940	199	9	accuracy	accuracy	NOUN
cana-2940	199	10	77	77	NUM
cana-2940	199	11	%	%	NOUN
cana-2940	199	12	,	,	PUNCT
cana-2940	199	13	precision	precision	NOUN
cana-2940	199	14	75	75	NUM
cana-2940	199	15	%	%	NOUN
cana-2940	199	16	,	,	PUNCT
cana-2940	199	17	recall	recall	VERB
cana-2940	199	18	91	91	NUM
cana-2940	199	19	%	%	NOUN
cana-2940	199	20	,	,	PUNCT
cana-2940	199	21	f1	f1	NOUN
cana-2940	199	22	-	-	PUNCT
cana-2940	199	23	score	score	NOUN
cana-2940	199	24	76	76	NUM
cana-2940	199	25	%	%	NOUN
cana-2940	199	26	,	,	PUNCT
cana-2940	199	27	and	and	CCONJ
cana-2940	199	28	specificity	specificity	NOUN
cana-2940	199	29	96	96	NUM
cana-2940	199	30	%	%	NOUN
cana-2940	199	31	.	.	PUNCT
cana-2940	200	1	5	5	X
cana-2940	200	2	.	.	X
cana-2940	200	3	experimental	experimental	ADJ
cana-2940	200	4	setup	setup	NOUN
cana-2940	200	5	and	and	CCONJ
cana-2940	200	6	outcomes	outcome	NOUN
cana-2940	200	7	the	the	DET
cana-2940	200	8	experiment	experiment	NOUN
cana-2940	200	9	is	be	AUX
cana-2940	200	10	implemented	implement	VERB
cana-2940	200	11	with	with	ADP
cana-2940	200	12	the	the	DET
cana-2940	200	13	python	python	NOUN
cana-2940	200	14	tool	tool	NOUN
cana-2940	200	15	using	use	VERB
cana-2940	200	16	the	the	DET
cana-2940	200	17	pytorch	pytorch	NOUN
cana-2940	200	18	framework	framework	NOUN
cana-2940	200	19	.	.	PUNCT
cana-2940	201	1	while	while	SCONJ
cana-2940	201	2	implementing	implement	VERB
cana-2940	201	3	the	the	DET
cana-2940	201	4	proposed	propose	VERB
cana-2940	201	5	framework	framework	NOUN
cana-2940	201	6	of	of	ADP
cana-2940	201	7	ehned	ehned	PROPN
cana-2940	201	8	&	&	CCONJ
cana-2940	201	9	efficientnetb0	efficientnetb0	VERB
cana-2940	201	10	the	the	DET
cana-2940	201	11	initial	initial	ADJ
cana-2940	201	12	resolution	resolution	NOUN
cana-2940	201	13	of	of	ADP
cana-2940	201	14	each	each	DET
cana-2940	201	15	image	image	NOUN
cana-2940	201	16	is	be	AUX
cana-2940	201	17	resized	resize	VERB
cana-2940	201	18	.	.	PUNCT
cana-2940	202	1	the	the	DET
cana-2940	202	2	dermnet	dermnet	NOUN
cana-2940	202	3	dataset	dataset	NOUN
cana-2940	202	4	is	be	AUX
cana-2940	202	5	taken	take	VERB
cana-2940	202	6	from	from	ADP
cana-2940	202	7	the	the	DET
cana-2940	202	8	online	online	ADJ
cana-2940	202	9	open	open	ADJ
cana-2940	202	10	source	source	NOUN
cana-2940	202	11	library	library	NOUN
cana-2940	202	12	.	.	PUNCT
cana-2940	203	1	cnn	cnn	PROPN
cana-2940	203	2	-	-	PUNCT
cana-2940	203	3	based	base	VERB
cana-2940	203	4	image	image	NOUN
cana-2940	203	5	segmentation	segmentation	NOUN
cana-2940	203	6	is	be	AUX
cana-2940	203	7	implemented	implement	VERB
cana-2940	203	8	to	to	PART
cana-2940	203	9	determine	determine	VERB
cana-2940	203	10	edges	edge	NOUN
cana-2940	203	11	and	and	CCONJ
cana-2940	203	12	the	the	DET
cana-2940	203	13	pre	pre	ADJ
cana-2940	203	14	-	-	ADJ
cana-2940	203	15	trained	train	VERB
cana-2940	203	16	efficientnet	efficientnet	NOUN
cana-2940	203	17	-	-	PUNCT
cana-2940	203	18	b0	b0	NOUN
cana-2940	203	19	model	model	NOUN
cana-2940	203	20	is	be	AUX
cana-2940	203	21	used	use	VERB
cana-2940	203	22	to	to	PART
cana-2940	203	23	extract	extract	VERB
cana-2940	203	24	1280	1280	NUM
cana-2940	203	25	features	feature	NOUN
cana-2940	203	26	for	for	ADP
cana-2940	203	27	each	each	DET
cana-2940	203	28	image	image	NOUN
cana-2940	203	29	and	and	CCONJ
cana-2940	203	30	represent	represent	VERB
cana-2940	203	31	them	they	PRON
cana-2940	203	32	in	in	ADP
cana-2940	203	33	1d	1d	NUM
cana-2940	203	34	vector	vector	NOUN
cana-2940	203	35	.	.	PUNCT
cana-2940	204	1	the	the	DET
cana-2940	204	2	cnns	cnns	PROPN
cana-2940	204	3	model	model	NOUN
cana-2940	204	4	with	with	ADP
cana-2940	204	5	the	the	DET
cana-2940	204	6	relu	relu	NOUN
cana-2940	204	7	activation	activation	NOUN
cana-2940	204	8	is	be	AUX
cana-2940	204	9	used	use	VERB
cana-2940	204	10	to	to	PART
cana-2940	204	11	determine	determine	VERB
cana-2940	204	12	the	the	DET
cana-2940	204	13	performance	performance	NOUN
cana-2940	204	14	metrics	metric	NOUN
cana-2940	204	15	.	.	PUNCT
cana-2940	205	1	a.	a.	NOUN
cana-2940	205	2	evaluation	evaluation	NOUN
cana-2940	205	3	metrics	metric	NOUN
cana-2940	205	4	to	to	PART
cana-2940	205	5	accurately	accurately	ADV
cana-2940	205	6	measure	measure	VERB
cana-2940	205	7	the	the	DET
cana-2940	205	8	effectiveness	effectiveness	NOUN
cana-2940	205	9	of	of	ADP
cana-2940	205	10	the	the	DET
cana-2940	205	11	cnns	cnns	ADJ
cana-2940	205	12	model	model	NOUN
cana-2940	205	13	for	for	ADP
cana-2940	205	14	this	this	DET
cana-2940	205	15	framework	framework	NOUN
cana-2940	205	16	,	,	PUNCT
cana-2940	205	17	five	five	NUM
cana-2940	205	18	widely	widely	ADV
cana-2940	205	19	used	use	VERB
cana-2940	205	20	metrics	metric	NOUN
cana-2940	205	21	as	as	SCONJ
cana-2940	205	22	mentioned	mention	VERB
cana-2940	205	23	below	below	ADV
cana-2940	205	24	are	be	AUX
cana-2940	205	25	evaluated	evaluate	VERB
cana-2940	205	26	.	.	PUNCT
cana-2940	206	1	the	the	DET
cana-2940	206	2	calculation	calculation	NOUN
cana-2940	206	3	equations	equation	NOUN
cana-2940	206	4	for	for	ADP
cana-2940	206	5	these	these	DET
cana-2940	206	6	five	five	NUM
cana-2940	206	7	metrics	metric	NOUN
cana-2940	206	8	are	be	AUX
cana-2940	206	9	as	as	SCONJ
cana-2940	206	10	follows	follow	VERB
cana-2940	206	11	:	:	PUNCT
cana-2940	206	12	accuracy	accuracy	NOUN
cana-2940	206	13	=	=	PRON
cana-2940	206	14	tp+fn	tp+fn	VERB
cana-2940	206	15	tp+	tp+	ADJ
cana-2940	206	16	tn+fp+fn	tn+fp+fn	PROPN
cana-2940	206	17	(	(	PUNCT
cana-2940	206	18	11	11	NUM
cana-2940	206	19	)	)	PUNCT
cana-2940	206	20	precision	precision	NOUN
cana-2940	206	21	=	=	PUNCT
cana-2940	206	22	tp	tp	X
cana-2940	206	23	tp+fp	tp+fp	NUM
cana-2940	206	24	(	(	PUNCT
cana-2940	206	25	12	12	NUM
cana-2940	206	26	)	)	PUNCT
cana-2940	206	27	recall	recall	NOUN
cana-2940	206	28	=	=	PUNCT
cana-2940	206	29	tp	tp	NOUN
cana-2940	206	30	tp+fn	tp+fn	X
cana-2940	206	31	(	(	PUNCT
cana-2940	206	32	13	13	NUM
cana-2940	206	33	)	)	PUNCT
cana-2940	206	34	f1	f1	NOUN
cana-2940	206	35	-	-	PUNCT
cana-2940	206	36	score	score	NOUN
cana-2940	206	37	=	=	SYM
cana-2940	206	38	2	2	X
cana-2940	206	39	.	.	X
cana-2940	206	40	percision	percision	NOUN
cana-2940	206	41	x	x	SYM
cana-2940	206	42	recall	recall	PROPN
cana-2940	206	43	percision	percision	NOUN
cana-2940	206	44	+	+	NOUN
cana-2940	206	45	recall	recall	NOUN
cana-2940	206	46	(	(	PUNCT
cana-2940	206	47	14	14	NUM
cana-2940	206	48	)	)	PUNCT
cana-2940	206	49	specificity	specificity	NOUN
cana-2940	206	50	=	=	SYM
cana-2940	206	51	tn	tn	PROPN
cana-2940	206	52	tp+fp	tp+fp	NUM
cana-2940	206	53	(	(	PUNCT
cana-2940	206	54	15	15	NUM
cana-2940	206	55	)	)	PUNCT
cana-2940	206	56	b.	b.	NOUN
cana-2940	206	57	outcomes	outcome	NOUN
cana-2940	206	58	:	:	PUNCT
cana-2940	206	59	from	from	ADP
cana-2940	206	60	the	the	DET
cana-2940	206	61	above	above	ADV
cana-2940	206	62	-	-	PUNCT
cana-2940	206	63	discussed	discuss	VERB
cana-2940	206	64	metrics	metric	NOUN
cana-2940	206	65	,	,	PUNCT
cana-2940	206	66	it	it	PRON
cana-2940	206	67	is	be	AUX
cana-2940	206	68	observed	observe	VERB
cana-2940	206	69	that	that	SCONJ
cana-2940	206	70	the	the	DET
cana-2940	206	71	convolution	convolution	NOUN
cana-2940	206	72	neural	neural	ADJ
cana-2940	206	73	networks	network	NOUN
cana-2940	206	74	with	with	ADP
cana-2940	206	75	the	the	DET
cana-2940	206	76	dermnet	dermnet	NOUN
cana-2940	206	77	dataset	dataset	NOUN
cana-2940	206	78	of	of	ADP
cana-2940	206	79	23	23	NUM
cana-2940	206	80	different	different	ADJ
cana-2940	206	81	classes	class	NOUN
cana-2940	206	82	provide	provide	VERB
cana-2940	206	83	promising	promising	ADJ
cana-2940	206	84	results	result	NOUN
cana-2940	206	85	of	of	ADP
cana-2940	206	86	recall	recall	NOUN
cana-2940	206	87	of	of	ADP
cana-2940	206	88	91	91	NUM
cana-2940	206	89	%	%	NOUN
cana-2940	206	90	and	and	CCONJ
cana-2940	206	91	specificity	specificity	NOUN
cana-2940	206	92	of	of	ADP
cana-2940	206	93	96	96	NUM
cana-2940	206	94	%	%	NOUN
cana-2940	206	95	for	for	ADP
cana-2940	206	96	the	the	DET
cana-2940	206	97	designed	design	VERB
cana-2940	206	98	framework	framework	NOUN
cana-2940	206	99	of	of	ADP
cana-2940	206	100	enhanced	enhanced	ADJ
cana-2940	206	101	holistically	holistically	ADV
cana-2940	206	102	nested	nest	VERB
cana-2940	206	103	edge	edge	NOUN
cana-2940	206	104	detection	detection	NOUN
cana-2940	206	105	and	and	CCONJ
cana-2940	206	106	deep	deep	ADJ
cana-2940	206	107	learning	learning	NOUN
cana-2940	206	108	-	-	PUNCT
cana-2940	206	109	based	base	VERB
cana-2940	206	110	feature	feature	NOUN
cana-2940	206	111	extraction	extraction	NOUN
cana-2940	206	112	using	use	VERB
cana-2940	206	113	efficientnet	efficientnet	NOUN
cana-2940	206	114	-	-	PUNCT
cana-2940	206	115	b0	b0	NOUN
cana-2940	206	116	pre	pre	ADJ
cana-2940	206	117	-	-	ADJ
cana-2940	206	118	trained	train	VERB
cana-2940	206	119	model	model	NOUN
cana-2940	206	120	.	.	PUNCT
cana-2940	207	1	6	6	NUM
cana-2940	207	2	.	.	X
cana-2940	207	3	conclusion	conclusion	NOUN
cana-2940	207	4	in	in	ADP
cana-2940	207	5	conclusion	conclusion	NOUN
cana-2940	207	6	,	,	PUNCT
cana-2940	207	7	the	the	DET
cana-2940	207	8	study	study	NOUN
cana-2940	207	9	proposed	propose	VERB
cana-2940	207	10	an	an	DET
cana-2940	207	11	automated	automate	VERB
cana-2940	207	12	detection	detection	NOUN
cana-2940	207	13	of	of	ADP
cana-2940	207	14	skin	skin	NOUN
cana-2940	207	15	disorders	disorder	NOUN
cana-2940	207	16	by	by	ADP
cana-2940	207	17	introducing	introduce	VERB
cana-2940	207	18	a	a	DET
cana-2940	207	19	novel	novel	ADJ
cana-2940	207	20	deep	deep	ADJ
cana-2940	207	21	learning	learning	NOUN
cana-2940	207	22	-	-	PUNCT
cana-2940	207	23	based	base	VERB
cana-2940	207	24	framework	framework	NOUN
cana-2940	207	25	that	that	PRON
cana-2940	207	26	improves	improve	VERB
cana-2940	207	27	image	image	NOUN
cana-2940	207	28	segmentation	segmentation	NOUN
cana-2940	207	29	and	and	CCONJ
cana-2940	207	30	feature	feature	NOUN
cana-2940	207	31	extraction	extraction	NOUN
cana-2940	207	32	methods	method	NOUN
cana-2940	207	33	.	.	PUNCT
cana-2940	208	1	in	in	ADP
cana-2940	208	2	this	this	DET
cana-2940	208	3	study	study	NOUN
cana-2940	208	4	,	,	PUNCT
cana-2940	208	5	enhanced	enhance	VERB
cana-2940	208	6	holistically	holistically	ADV
cana-2940	208	7	nested	nest	VERB
cana-2940	208	8	edge	edge	NOUN
cana-2940	208	9	detection	detection	NOUN
cana-2940	208	10	(	(	PUNCT
cana-2940	208	11	hned	hne	VERB
cana-2940	208	12	)	)	PUNCT
cana-2940	208	13	is	be	AUX
cana-2940	208	14	used	use	VERB
cana-2940	208	15	for	for	ADP
cana-2940	208	16	improved	improved	ADJ
cana-2940	208	17	edge	edge	NOUN
cana-2940	208	18	detail	detail	NOUN
cana-2940	208	19	in	in	ADP
cana-2940	208	20	segmentation	segmentation	NOUN
cana-2940	208	21	and	and	CCONJ
cana-2940	208	22	a	a	DET
cana-2940	208	23	deep	deep	ADJ
cana-2940	208	24	learning	learning	NOUN
cana-2940	208	25	-	-	PUNCT
cana-2940	208	26	based	base	VERB
cana-2940	208	27	efficientnet	efficientnet	NOUN
cana-2940	208	28	-	-	PUNCT
cana-2940	208	29	b0	b0	NOUN
cana-2940	208	30	model	model	NOUN
cana-2940	208	31	is	be	AUX
cana-2940	208	32	used	use	VERB
cana-2940	208	33	to	to	PART
cana-2940	208	34	extract	extract	VERB
cana-2940	208	35	a	a	DET
cana-2940	208	36	complete	complete	ADJ
cana-2940	208	37	set	set	NOUN
cana-2940	208	38	of	of	ADP
cana-2940	208	39	1280	1280	NUM
cana-2940	208	40	features	feature	NOUN
cana-2940	208	41	per	per	ADP
cana-2940	208	42	image	image	NOUN
cana-2940	208	43	,	,	PUNCT
cana-2940	208	44	the	the	DET
cana-2940	208	45	study	study	NOUN
cana-2940	208	46	effectively	effectively	ADV
cana-2940	208	47	captures	capture	VERB
cana-2940	208	48	important	important	ADJ
cana-2940	208	49	information	information	NOUN
cana-2940	208	50	required	require	VERB
cana-2940	208	51	for	for	ADP
cana-2940	208	52	correct	correct	ADJ
cana-2940	208	53	classification	classification	NOUN
cana-2940	208	54	.	.	PUNCT
cana-2940	209	1	the	the	DET
cana-2940	209	2	assessment	assessment	NOUN
cana-2940	209	3	of	of	ADP
cana-2940	209	4	cnns	cnn	NOUN
cana-2940	209	5	which	which	PRON
cana-2940	209	6	is	be	AUX
cana-2940	209	7	trained	train	VERB
cana-2940	209	8	on	on	ADP
cana-2940	209	9	the	the	DET
cana-2940	209	10	communications	communication	NOUN
cana-2940	209	11	on	on	ADP
cana-2940	209	12	applied	apply	VERB
cana-2940	209	13	nonlinear	nonlinear	ADJ
cana-2940	209	14	analysis	analysis	NOUN
cana-2940	209	15	issn	issn	NOUN
cana-2940	209	16	:	:	PUNCT
cana-2940	209	17	1074	1074	NUM
cana-2940	209	18	-	-	PUNCT
cana-2940	209	19	133x	133x	NUM
cana-2940	209	20	vol	vol	NOUN
cana-2940	209	21	32	32	NUM
cana-2940	209	22	no	no	NOUN
cana-2940	209	23	.	.	PUNCT
cana-2940	210	1	5s	5s	NUM
cana-2940	210	2	(	(	PUNCT
cana-2940	210	3	2025	2025	NUM
cana-2940	210	4	)	)	PUNCT
cana-2940	210	5	41	41	NUM
cana-2940	211	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-2940	211	2	dermnet	dermnet	NOUN
cana-2940	211	3	dataset	dataset	NOUN
cana-2940	211	4	,	,	PUNCT
cana-2940	211	5	comprises	comprise	VERB
cana-2940	211	6	23	23	NUM
cana-2940	211	7	skin	skin	NOUN
cana-2940	211	8	disease	disease	NOUN
cana-2940	211	9	classifications	classification	NOUN
cana-2940	211	10	and	and	CCONJ
cana-2940	211	11	shows	show	VERB
cana-2940	211	12	that	that	SCONJ
cana-2940	211	13	the	the	DET
cana-2940	211	14	framework	framework	NOUN
cana-2940	211	15	is	be	AUX
cana-2940	211	16	capable	capable	ADJ
cana-2940	211	17	of	of	ADP
cana-2940	211	18	achieving	achieve	VERB
cana-2940	211	19	promising	promise	VERB
cana-2940	211	20	performance	performance	NOUN
cana-2940	211	21	metrics	metric	NOUN
cana-2940	211	22	like	like	ADP
cana-2940	211	23	as	as	ADP
cana-2940	211	24	91	91	NUM
cana-2940	211	25	%	%	NOUN
cana-2940	211	26	recall	recall	NOUN
cana-2940	211	27	and	and	CCONJ
cana-2940	211	28	96	96	NUM
cana-2940	211	29	%	%	NOUN
cana-2940	211	30	specificity	specificity	NOUN
cana-2940	211	31	.	.	PUNCT
cana-2940	212	1	these	these	DET
cana-2940	212	2	findings	finding	NOUN
cana-2940	212	3	demonstrate	demonstrate	VERB
cana-2940	212	4	the	the	DET
cana-2940	212	5	proposed	propose	VERB
cana-2940	212	6	approach	approach	NOUN
cana-2940	212	7	's	's	PART
cana-2940	212	8	resilience	resilience	NOUN
cana-2940	212	9	in	in	ADP
cana-2940	212	10	addressing	address	VERB
cana-2940	212	11	the	the	DET
cana-2940	212	12	challenges	challenge	NOUN
cana-2940	212	13	of	of	ADP
cana-2940	212	14	skin	skin	NOUN
cana-2940	212	15	disease	disease	NOUN
cana-2940	212	16	diagnosis	diagnosis	NOUN
cana-2940	212	17	,	,	PUNCT
cana-2940	212	18	such	such	ADJ
cana-2940	212	19	as	as	ADP
cana-2940	212	20	various	various	ADJ
cana-2940	212	21	lesion	lesion	NOUN
cana-2940	212	22	features	feature	NOUN
cana-2940	212	23	and	and	CCONJ
cana-2940	212	24	difficult	difficult	ADJ
cana-2940	212	25	edge	edge	NOUN
cana-2940	212	26	delineations	delineation	NOUN
cana-2940	212	27	.	.	PUNCT
cana-2940	213	1	this	this	DET
cana-2940	213	2	research	research	NOUN
cana-2940	213	3	highlights	highlight	VERB
cana-2940	213	4	the	the	DET
cana-2940	213	5	promise	promise	NOUN
cana-2940	213	6	of	of	ADP
cana-2940	213	7	sophisticated	sophisticated	ADJ
cana-2940	213	8	dl	dl	NOUN
cana-2940	213	9	techniques	technique	NOUN
cana-2940	213	10	for	for	ADP
cana-2940	213	11	improving	improve	VERB
cana-2940	213	12	diagnosis	diagnosis	NOUN
cana-2940	213	13	metrics	metric	NOUN
cana-2940	213	14	and	and	CCONJ
cana-2940	213	15	also	also	ADV
cana-2940	213	16	provides	provide	VERB
cana-2940	213	17	useful	useful	ADJ
cana-2940	213	18	insights	insight	NOUN
cana-2940	213	19	into	into	ADP
cana-2940	213	20	the	the	DET
cana-2940	213	21	area	area	NOUN
cana-2940	213	22	of	of	ADP
cana-2940	213	23	dermatology	dermatology	NOUN
cana-2940	213	24	.	.	PUNCT
cana-2940	214	1	furthermore	furthermore	ADV
cana-2940	214	2	,	,	PUNCT
cana-2940	214	3	in	in	ADP
cana-2940	214	4	the	the	DET
cana-2940	214	5	future	future	NOUN
cana-2940	214	6	,	,	PUNCT
cana-2940	214	7	this	this	DET
cana-2940	214	8	work	work	NOUN
cana-2940	214	9	intends	intend	VERB
cana-2940	214	10	to	to	PART
cana-2940	214	11	increase	increase	VERB
cana-2940	214	12	accuracy	accuracy	NOUN
cana-2940	214	13	by	by	ADP
cana-2940	214	14	focusing	focus	VERB
cana-2940	214	15	on	on	ADP
cana-2940	214	16	feature	feature	NOUN
cana-2940	214	17	selection	selection	NOUN
cana-2940	214	18	approaches	approach	NOUN
cana-2940	214	19	and	and	CCONJ
cana-2940	214	20	dimensionality	dimensionality	NOUN
cana-2940	214	21	reduction	reduction	NOUN
cana-2940	214	22	techniques	technique	NOUN
cana-2940	214	23	.	.	PUNCT
cana-2940	215	1	references	reference	NOUN
cana-2940	215	2	[	[	X
cana-2940	215	3	1	1	NUM
cana-2940	215	4	]	]	PUNCT
cana-2940	215	5	ahammed	ahamme	VERB
cana-2940	215	6	,	,	PUNCT
cana-2940	215	7	m.	m.	NOUN
cana-2940	215	8	,	,	PUNCT
cana-2940	215	9	mamun	mamun	PROPN
cana-2940	215	10	,	,	PUNCT
cana-2940	215	11	m.	m.	PROPN
cana-2940	215	12	al	al	PROPN
cana-2940	215	13	,	,	PUNCT
cana-2940	215	14	&	&	CCONJ
cana-2940	215	15	uddin	uddin	PROPN
cana-2940	215	16	,	,	PUNCT
cana-2940	215	17	m.	m.	NOUN
cana-2940	215	18	s.	s.	PROPN
cana-2940	215	19	(	(	PUNCT
cana-2940	215	20	2022a	2022a	NUM
cana-2940	215	21	)	)	PUNCT
cana-2940	215	22	.	.	PUNCT
cana-2940	216	1	a	a	DET
cana-2940	216	2	machine	machine	NOUN
cana-2940	216	3	learning	learn	VERB
cana-2940	216	4	approach	approach	NOUN
cana-2940	216	5	for	for	ADP
cana-2940	216	6	skin	skin	NOUN
cana-2940	216	7	disease	disease	NOUN
cana-2940	216	8	detection	detection	NOUN
cana-2940	216	9	and	and	CCONJ
cana-2940	216	10	classification	classification	NOUN
cana-2940	216	11	using	use	VERB
cana-2940	216	12	image	image	NOUN
cana-2940	216	13	segmentation	segmentation	NOUN
cana-2940	216	14	.	.	PUNCT
cana-2940	217	1	healthcare	healthcare	PROPN
cana-2940	217	2	analytics	analytic	NOUN
cana-2940	217	3	,	,	PUNCT
cana-2940	217	4	2(april	2(april	NUM
cana-2940	217	5	)	)	PUNCT
cana-2940	217	6	,	,	PUNCT
cana-2940	217	7	100122	100122	NUM
cana-2940	217	8	.	.	PUNCT
cana-2940	218	1	https://doi.org/10.1016/j.health.2022.100122	https://doi.org/10.1016/j.health.2022.100122	PROPN
cana-2940	219	1	[	[	X
cana-2940	219	2	2	2	NUM
cana-2940	219	3	]	]	PUNCT
cana-2940	219	4	ahammed	ahamme	VERB
cana-2940	219	5	,	,	PUNCT
cana-2940	219	6	m.	m.	NOUN
cana-2940	219	7	,	,	PUNCT
cana-2940	219	8	mamun	mamun	PROPN
cana-2940	219	9	,	,	PUNCT
cana-2940	219	10	m.	m.	PROPN
cana-2940	219	11	al	al	PROPN
cana-2940	219	12	,	,	PUNCT
cana-2940	219	13	&	&	CCONJ
cana-2940	219	14	uddin	uddin	PROPN
cana-2940	219	15	,	,	PUNCT
cana-2940	219	16	m.	m.	NOUN
cana-2940	219	17	s.	s.	PROPN
cana-2940	219	18	(	(	PUNCT
cana-2940	219	19	2022b	2022b	NUM
cana-2940	219	20	)	)	PUNCT
cana-2940	219	21	.	.	PUNCT
cana-2940	220	1	a	a	DET
cana-2940	220	2	machine	machine	NOUN
cana-2940	220	3	learning	learn	VERB
cana-2940	220	4	approach	approach	NOUN
cana-2940	220	5	for	for	ADP
cana-2940	220	6	skin	skin	NOUN
cana-2940	220	7	disease	disease	NOUN
cana-2940	220	8	detection	detection	NOUN
cana-2940	220	9	and	and	CCONJ
cana-2940	220	10	classification	classification	NOUN
cana-2940	220	11	using	use	VERB
cana-2940	220	12	image	image	NOUN
cana-2940	220	13	segmentation	segmentation	NOUN
cana-2940	220	14	.	.	PUNCT
cana-2940	221	1	healthcare	healthcare	NOUN
cana-2940	221	2	analytics	analytic	NOUN
cana-2940	221	3	,	,	PUNCT
cana-2940	221	4	2	2	NUM
cana-2940	221	5	,	,	PUNCT
cana-2940	221	6	100122	100122	NUM
cana-2940	221	7	.	.	PUNCT
cana-2940	222	1	https://doi.org/10.1016/j.health.2022.100122	https://doi.org/10.1016/j.health.2022.100122	PROPN
cana-2940	222	2	[	[	X
cana-2940	222	3	3	3	X
cana-2940	222	4	]	]	X
cana-2940	222	5	ajith	ajith	PROPN
cana-2940	222	6	,	,	PUNCT
cana-2940	222	7	a.	a.	PROPN
cana-2940	222	8	,	,	PUNCT
cana-2940	222	9	goel	goel	PROPN
cana-2940	222	10	,	,	PUNCT
cana-2940	222	11	v.	v.	ADV
cana-2940	222	12	,	,	PUNCT
cana-2940	222	13	vazirani	vazirani	NOUN
cana-2940	222	14	,	,	PUNCT
cana-2940	222	15	p.	p.	NOUN
cana-2940	222	16	,	,	PUNCT
cana-2940	222	17	&	&	CCONJ
cana-2940	222	18	roja	roja	PROPN
cana-2940	222	19	,	,	PUNCT
cana-2940	222	20	m.	m.	NOUN
cana-2940	222	21	m.	m.	NOUN
cana-2940	222	22	(	(	PUNCT
cana-2940	222	23	2017	2017	NUM
cana-2940	222	24	)	)	PUNCT
cana-2940	222	25	.	.	PUNCT
cana-2940	223	1	digital	digital	ADJ
cana-2940	223	2	dermatology	dermatology	NOUN
cana-2940	223	3	:	:	PUNCT
cana-2940	223	4	skin	skin	NOUN
cana-2940	223	5	disease	disease	NOUN
cana-2940	223	6	detection	detection	NOUN
cana-2940	223	7	model	model	NOUN
cana-2940	223	8	using	use	VERB
cana-2940	223	9	image	image	NOUN
cana-2940	223	10	processing	processing	NOUN
cana-2940	223	11	.	.	PUNCT
cana-2940	224	1	2017	2017	NUM
cana-2940	224	2	international	international	ADJ
cana-2940	224	3	conference	conference	NOUN
cana-2940	224	4	on	on	ADP
cana-2940	224	5	intelligent	intelligent	ADJ
cana-2940	224	6	computing	computing	NOUN
cana-2940	224	7	and	and	CCONJ
cana-2940	224	8	control	control	NOUN
cana-2940	224	9	systems	system	NOUN
cana-2940	224	10	(	(	PUNCT
cana-2940	224	11	iciccs	iciccs	PROPN
cana-2940	224	12	)	)	PUNCT
cana-2940	224	13	,	,	PUNCT
cana-2940	224	14	168	168	NUM
cana-2940	224	15	–	–	PUNCT
cana-2940	224	16	173	173	NUM
cana-2940	224	17	.	.	PUNCT
cana-2940	225	1	https://doi.org/10.1109/iccons.2017.8250703	https://doi.org/10.1109/iccons.2017.8250703	PROPN
cana-2940	225	2	[	[	X
cana-2940	225	3	4	4	NUM
cana-2940	225	4	]	]	X
cana-2940	225	5	aldera	aldera	NOUN
cana-2940	225	6	,	,	PUNCT
cana-2940	225	7	s.	s.	PROPN
cana-2940	225	8	a.	a.	PROPN
cana-2940	225	9	,	,	PUNCT
cana-2940	225	10	&	&	CCONJ
cana-2940	225	11	othman	othman	PROPN
cana-2940	225	12	,	,	PUNCT
cana-2940	225	13	m.	m.	NOUN
cana-2940	225	14	t.	t.	PROPN
cana-2940	225	15	ben	ben	PROPN
cana-2940	225	16	.	.	PROPN
cana-2940	225	17	(	(	PUNCT
cana-2940	225	18	2022	2022	NUM
cana-2940	225	19	)	)	PUNCT
cana-2940	225	20	.	.	PUNCT
cana-2940	226	1	a	a	DET
cana-2940	226	2	model	model	NOUN
cana-2940	226	3	for	for	ADP
cana-2940	226	4	classification	classification	NOUN
cana-2940	226	5	and	and	CCONJ
cana-2940	226	6	diagnosis	diagnosis	NOUN
cana-2940	226	7	of	of	ADP
cana-2940	226	8	skin	skin	NOUN
cana-2940	226	9	disease	disease	NOUN
cana-2940	226	10	using	use	VERB
cana-2940	226	11	machine	machine	NOUN
cana-2940	226	12	learning	learning	NOUN
cana-2940	226	13	and	and	CCONJ
cana-2940	226	14	image	image	NOUN
cana-2940	226	15	processing	processing	NOUN
cana-2940	226	16	techniques	technique	NOUN
cana-2940	226	17	.	.	PUNCT
cana-2940	227	1	international	international	ADJ
cana-2940	227	2	journal	journal	NOUN
cana-2940	227	3	of	of	ADP
cana-2940	227	4	advanced	advanced	ADJ
cana-2940	227	5	computer	computer	NOUN
cana-2940	227	6	science	science	NOUN
cana-2940	227	7	and	and	CCONJ
cana-2940	227	8	applications	application	NOUN
cana-2940	227	9	,	,	PUNCT
cana-2940	227	10	13(5	13(5	NUM
cana-2940	227	11	)	)	PUNCT
cana-2940	227	12	,	,	PUNCT
cana-2940	227	13	252–259	252–259	NUM
cana-2940	227	14	.	.	PUNCT
cana-2940	228	1	https://doi.org/10.14569/ijacsa.2022.0130531	https://doi.org/10.14569/ijacsa.2022.0130531	PROPN
cana-2940	228	2	[	[	X
cana-2940	228	3	5	5	NUM
cana-2940	228	4	]	]	PUNCT
cana-2940	228	5	badiger	badiger	NOUN
cana-2940	228	6	,	,	PUNCT
cana-2940	228	7	m.	m.	NOUN
cana-2940	228	8	,	,	PUNCT
cana-2940	228	9	kumara	kumara	PROPN
cana-2940	228	10	,	,	PUNCT
cana-2940	228	11	v.	v.	PROPN
cana-2940	228	12	,	,	PUNCT
cana-2940	228	13	shetty	shetty	PROPN
cana-2940	228	14	,	,	PUNCT
cana-2940	228	15	s.	s.	PROPN
cana-2940	228	16	c.	c.	PROPN
cana-2940	228	17	n.	n.	PROPN
cana-2940	228	18	,	,	PUNCT
cana-2940	228	19	&	&	CCONJ
cana-2940	228	20	poojary	poojary	PROPN
cana-2940	228	21	,	,	PUNCT
cana-2940	228	22	s.	s.	PROPN
cana-2940	228	23	(	(	PUNCT
cana-2940	228	24	2022	2022	NUM
cana-2940	228	25	)	)	PUNCT
cana-2940	228	26	.	.	PUNCT
cana-2940	229	1	leaf	leaf	NOUN
cana-2940	229	2	and	and	CCONJ
cana-2940	229	3	skin	skin	NOUN
cana-2940	229	4	disease	disease	NOUN
cana-2940	229	5	detection	detection	NOUN
cana-2940	229	6	using	use	VERB
cana-2940	229	7	image	image	NOUN
cana-2940	229	8	processing	processing	NOUN
cana-2940	229	9	.	.	PUNCT
cana-2940	230	1	global	global	ADJ
cana-2940	230	2	transitions	transition	NOUN
cana-2940	230	3	proceedings	proceeding	NOUN
cana-2940	230	4	,	,	PUNCT
cana-2940	230	5	3(1	3(1	NUM
cana-2940	230	6	)	)	PUNCT
cana-2940	230	7	,	,	PUNCT
cana-2940	230	8	272–278	272–278	NUM
cana-2940	230	9	.	.	PUNCT
cana-2940	231	1	https://doi.org/10.1016/j.gltp.2022.03.010	https://doi.org/10.1016/j.gltp.2022.03.010	NOUN
cana-2940	231	2	[	[	X
cana-2940	231	3	6	6	NUM
cana-2940	231	4	]	]	X
cana-2940	231	5	balaji	balaji	PROPN
cana-2940	231	6	,	,	PUNCT
cana-2940	231	7	v.	v.	PROPN
cana-2940	231	8	r.	r.	PROPN
cana-2940	231	9	,	,	PUNCT
cana-2940	231	10	suganthi	suganthi	PROPN
cana-2940	231	11	,	,	PUNCT
cana-2940	231	12	s.	s.	PROPN
cana-2940	231	13	t.	t.	PROPN
cana-2940	231	14	,	,	PUNCT
cana-2940	231	15	rajadevi	rajadevi	NOUN
cana-2940	231	16	,	,	PUNCT
cana-2940	231	17	r.	r.	PROPN
cana-2940	231	18	,	,	PUNCT
cana-2940	231	19	krishna	krishna	PROPN
cana-2940	231	20	kumar	kumar	PROPN
cana-2940	231	21	,	,	PUNCT
cana-2940	231	22	v.	v.	PROPN
cana-2940	231	23	,	,	PUNCT
cana-2940	231	24	saravana	saravana	PROPN
cana-2940	231	25	balaji	balaji	PROPN
cana-2940	231	26	,	,	PUNCT
cana-2940	231	27	b.	b.	PROPN
cana-2940	231	28	,	,	PUNCT
cana-2940	231	29	&	&	CCONJ
cana-2940	231	30	pandiyan	pandiyan	PROPN
cana-2940	231	31	,	,	PUNCT
cana-2940	231	32	s.	s.	PROPN
cana-2940	231	33	(	(	PUNCT
cana-2940	231	34	2020	2020	NUM
cana-2940	231	35	)	)	PUNCT
cana-2940	231	36	.	.	PUNCT
cana-2940	232	1	skin	skin	NOUN
cana-2940	232	2	disease	disease	NOUN
cana-2940	232	3	detection	detection	NOUN
cana-2940	232	4	and	and	CCONJ
cana-2940	232	5	segmentation	segmentation	NOUN
cana-2940	232	6	using	use	VERB
cana-2940	232	7	dynamic	dynamic	ADJ
cana-2940	232	8	graph	graph	NOUN
cana-2940	232	9	cut	cut	VERB
cana-2940	232	10	algorithm	algorithm	NOUN
cana-2940	232	11	and	and	CCONJ
cana-2940	232	12	classification	classification	NOUN
cana-2940	232	13	through	through	ADP
cana-2940	232	14	naive	naive	ADJ
cana-2940	232	15	bayes	bayes	NOUN
cana-2940	232	16	classifier	classifier	PROPN
cana-2940	232	17	.	.	PUNCT
cana-2940	233	1	measurement	measurement	NOUN
cana-2940	233	2	:	:	PUNCT
cana-2940	233	3	journal	journal	NOUN
cana-2940	233	4	of	of	ADP
cana-2940	233	5	the	the	DET
cana-2940	233	6	international	international	PROPN
cana-2940	233	7	measurement	measurement	PROPN
cana-2940	233	8	confederation	confederation	PROPN
cana-2940	233	9	,	,	PUNCT
cana-2940	233	10	163	163	NUM
cana-2940	233	11	.	.	PUNCT
cana-2940	234	1	https://doi.org/10.1016/j.measurement.2020.107922	https://doi.org/10.1016/j.measurement.2020.107922	PROPN
cana-2940	234	2	[	[	X
cana-2940	234	3	7	7	NUM
cana-2940	234	4	]	]	X
cana-2940	234	5	ballerini	ballerini	PROPN
cana-2940	234	6	,	,	PUNCT
cana-2940	234	7	l.	l.	PROPN
cana-2940	234	8	,	,	PUNCT
cana-2940	234	9	fisher	fisher	PROPN
cana-2940	234	10	,	,	PUNCT
cana-2940	234	11	r.	r.	PROPN
cana-2940	234	12	b.	b.	PROPN
cana-2940	234	13	,	,	PUNCT
cana-2940	234	14	aldridge	aldridge	PROPN
cana-2940	234	15	,	,	PUNCT
cana-2940	234	16	b.	b.	PROPN
cana-2940	234	17	,	,	PUNCT
cana-2940	234	18	&	&	CCONJ
cana-2940	234	19	rees	rees	PROPN
cana-2940	234	20	,	,	PUNCT
cana-2940	234	21	j.	j.	PROPN
cana-2940	234	22	(	(	PUNCT
cana-2940	234	23	2013	2013	NUM
cana-2940	234	24	)	)	PUNCT
cana-2940	234	25	.	.	PUNCT
cana-2940	235	1	a	a	DET
cana-2940	235	2	color	color	NOUN
cana-2940	235	3	and	and	CCONJ
cana-2940	235	4	texture	texture	NOUN
cana-2940	235	5	based	base	VERB
cana-2940	235	6	hierarchical	hierarchical	ADJ
cana-2940	235	7	k	k	PROPN
cana-2940	235	8	-	-	PUNCT
cana-2940	235	9	nn	nn	ADJ
cana-2940	235	10	approach	approach	NOUN
cana-2940	235	11	to	to	ADP
cana-2940	235	12	the	the	DET
cana-2940	235	13	classification	classification	NOUN
cana-2940	235	14	of	of	ADP
cana-2940	235	15	non	non	ADJ
cana-2940	235	16	-	-	ADJ
cana-2940	235	17	melanoma	melanoma	ADJ
cana-2940	235	18	skin	skin	NOUN
cana-2940	235	19	lesions	lesion	NOUN
cana-2940	235	20	.	.	PUNCT
cana-2940	236	1	lecture	lecture	NOUN
cana-2940	236	2	notes	note	NOUN
cana-2940	236	3	in	in	ADP
cana-2940	236	4	computational	computational	ADJ
cana-2940	236	5	vision	vision	NOUN
cana-2940	236	6	and	and	CCONJ
cana-2940	236	7	biomechanics	biomechanic	NOUN
cana-2940	236	8	,	,	PUNCT
cana-2940	236	9	6	6	NUM
cana-2940	236	10	,	,	PUNCT
cana-2940	236	11	63–86	63–86	NUM
cana-2940	236	12	.	.	PUNCT
cana-2940	237	1	https://doi.org/10.1007/978-94-007-5389-1_4	https://doi.org/10.1007/978-94-007-5389-1_4	NOUN
cana-2940	237	2	[	[	NOUN
cana-2940	237	3	8	8	NUM
cana-2940	237	4	]	]	PUNCT
cana-2940	237	5	bandyopadhyay	bandyopadhyay	NOUN
cana-2940	237	6	,	,	PUNCT
cana-2940	237	7	s.	s.	PROPN
cana-2940	237	8	k.	k.	PROPN
cana-2940	237	9	,	,	PUNCT
cana-2940	237	10	bose	bose	PROPN
cana-2940	237	11	,	,	PUNCT
cana-2940	237	12	p.	p.	PROPN
cana-2940	237	13	,	,	PUNCT
cana-2940	237	14	bhaumik	bhaumik	PROPN
cana-2940	237	15	,	,	PUNCT
cana-2940	237	16	a.	a.	NOUN
cana-2940	237	17	,	,	PUNCT
cana-2940	237	18	&	&	CCONJ
cana-2940	237	19	poddar	poddar	PROPN
cana-2940	237	20	,	,	PUNCT
cana-2940	237	21	s.	s.	PROPN
cana-2940	237	22	(	(	PUNCT
cana-2940	237	23	2022	2022	NUM
cana-2940	237	24	)	)	PUNCT
cana-2940	237	25	.	.	PUNCT
cana-2940	238	1	machine	machine	NOUN
cana-2940	238	2	learning	learning	NOUN
cana-2940	238	3	and	and	CCONJ
cana-2940	238	4	deep	deep	ADJ
cana-2940	238	5	learning	learning	NOUN
cana-2940	238	6	integration	integration	NOUN
cana-2940	238	7	for	for	ADP
cana-2940	238	8	skin	skin	NOUN
cana-2940	238	9	diseases	disease	NOUN
cana-2940	238	10	prediction	prediction	NOUN
cana-2940	238	11	.	.	PUNCT
cana-2940	239	1	international	international	ADJ
cana-2940	239	2	journal	journal	NOUN
cana-2940	239	3	of	of	ADP
cana-2940	239	4	engineering	engineering	NOUN
cana-2940	239	5	trends	trend	NOUN
cana-2940	239	6	and	and	CCONJ
cana-2940	239	7	technology	technology	NOUN
cana-2940	239	8	,	,	PUNCT
cana-2940	239	9	70(2	70(2	PROPN
cana-2940	239	10	)	)	PUNCT
cana-2940	239	11	,	,	PUNCT
cana-2940	239	12	11	11	NUM
cana-2940	239	13	–	–	SYM
cana-2940	239	14	18	18	NUM
cana-2940	239	15	.	.	PUNCT
cana-2940	239	16	https://doi.org/10.14445/22315381/ijett-v70i2p202	https://doi.org/10.14445/22315381/ijett-v70i2p202	NOUN
cana-2940	240	1	[	[	X
cana-2940	240	2	9	9	NUM
cana-2940	240	3	]	]	PUNCT
cana-2940	240	4	benyahia	benyahia	NOUN
cana-2940	240	5	,	,	PUNCT
cana-2940	240	6	s.	s.	PROPN
cana-2940	240	7	,	,	PUNCT
cana-2940	240	8	meftah	meftah	PROPN
cana-2940	240	9	,	,	PUNCT
cana-2940	240	10	b.	b.	PROPN
cana-2940	240	11	,	,	PUNCT
cana-2940	240	12	&	&	CCONJ
cana-2940	240	13	lézoray	lézoray	PROPN
cana-2940	240	14	,	,	PUNCT
cana-2940	240	15	o.	o.	PROPN
cana-2940	240	16	(	(	PUNCT
cana-2940	240	17	2022	2022	NUM
cana-2940	240	18	)	)	PUNCT
cana-2940	240	19	.	.	PUNCT
cana-2940	241	1	multi	multi	ADJ
cana-2940	241	2	-	-	ADJ
cana-2940	241	3	features	features	ADJ
cana-2940	241	4	extraction	extraction	NOUN
cana-2940	241	5	based	base	VERB
cana-2940	241	6	on	on	ADP
cana-2940	241	7	deep	deep	ADJ
cana-2940	241	8	learning	learning	NOUN
cana-2940	241	9	for	for	ADP
cana-2940	241	10	skin	skin	NOUN
cana-2940	241	11	lesion	lesion	NOUN
cana-2940	241	12	classification	classification	NOUN
cana-2940	241	13	.	.	PUNCT
cana-2940	242	1	tissue	tissue	NOUN
cana-2940	242	2	and	and	CCONJ
cana-2940	242	3	cell	cell	NOUN
cana-2940	242	4	,	,	PUNCT
cana-2940	242	5	74(april	74(april	NUM
cana-2940	242	6	2021	2021	NUM
cana-2940	242	7	)	)	PUNCT
cana-2940	242	8	.	.	PUNCT
cana-2940	243	1	https://doi.org/10.1016/j.tice.2021.101701	https://doi.org/10.1016/j.tice.2021.101701	NOUN
cana-2940	243	2	[	[	X
cana-2940	243	3	10	10	NUM
cana-2940	243	4	]	]	X
cana-2940	243	5	gede	gede	PROPN
cana-2940	243	6	,	,	PUNCT
cana-2940	243	7	i.	i.	PROPN
cana-2940	243	8	k.	k.	PROPN
cana-2940	243	9	,	,	PUNCT
cana-2940	243	10	putra	putra	PROPN
cana-2940	243	11	,	,	PUNCT
cana-2940	243	12	d.	d.	PROPN
cana-2940	243	13	,	,	PUNCT
cana-2940	243	14	putu	putu	PROPN
cana-2940	243	15	,	,	PUNCT
cana-2940	243	16	n.	n.	NOUN
cana-2940	243	17	,	,	PUNCT
cana-2940	243	18	oka	oka	PROPN
cana-2940	243	19	,	,	PUNCT
cana-2940	243	20	a.	a.	NOUN
cana-2940	243	21	,	,	PUNCT
cana-2940	243	22	wibawa	wibawa	PROPN
cana-2940	243	23	,	,	PUNCT
cana-2940	243	24	k.	k.	PROPN
cana-2940	243	25	s.	s.	PROPN
cana-2940	243	26	,	,	PUNCT
cana-2940	243	27	&	&	CCONJ
cana-2940	243	28	suwija	suwija	NOUN
cana-2940	243	29	,	,	PUNCT
cana-2940	243	30	i.	i.	NOUN
cana-2940	243	31	m.	m.	PROPN
cana-2940	243	32	(	(	PUNCT
cana-2940	243	33	2020	2020	NUM
cana-2940	243	34	)	)	PUNCT
cana-2940	243	35	.	.	PUNCT
cana-2940	244	1	identification	identification	NOUN
cana-2940	244	2	of	of	ADP
cana-2940	244	3	skin	skin	NOUN
cana-2940	244	4	disease	disease	NOUN
cana-2940	244	5	using	use	VERB
cana-2940	244	6	k	k	PROPN
cana-2940	244	7	-	-	PUNCT
cana-2940	244	8	means	means	NOUN
cana-2940	244	9	clustering	clustering	NOUN
cana-2940	244	10	,	,	PUNCT
cana-2940	244	11	discrete	discrete	ADJ
cana-2940	244	12	wavelet	wavelet	NOUN
cana-2940	244	13	transform	transform	NOUN
cana-2940	244	14	,	,	PUNCT
cana-2940	244	15	color	color	NOUN
cana-2940	244	16	moments	moment	NOUN
cana-2940	244	17	and	and	CCONJ
cana-2940	244	18	support	support	VERB
cana-2940	244	19	vector	vector	NOUN
cana-2940	244	20	machine	machine	NOUN
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cana-2940	245	1	10(4	10(4	NUM
cana-2940	245	2	)	)	PUNCT
cana-2940	245	3	,	,	PUNCT
cana-2940	246	1	542–548	542–548	NUM
cana-2940	246	2	.	.	PUNCT
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cana-2940	247	2	[	[	X
cana-2940	247	3	11	11	NUM
cana-2940	247	4	]	]	X
cana-2940	247	5	hameed	hameed	NOUN
cana-2940	247	6	,	,	PUNCT
cana-2940	247	7	n.	n.	NOUN
cana-2940	247	8	,	,	PUNCT
cana-2940	247	9	shabut	shabut	PROPN
cana-2940	247	10	,	,	PUNCT
cana-2940	247	11	a.	a.	NOUN
cana-2940	247	12	m.	m.	NOUN
cana-2940	247	13	,	,	PUNCT
cana-2940	247	14	ghosh	ghosh	PROPN
cana-2940	247	15	,	,	PUNCT
cana-2940	247	16	m.	m.	PROPN
cana-2940	247	17	k.	k.	PROPN
cana-2940	247	18	,	,	PUNCT
cana-2940	247	19	&	&	CCONJ
cana-2940	247	20	hossain	hossain	PROPN
cana-2940	247	21	,	,	PUNCT
cana-2940	247	22	m.	m.	NOUN
cana-2940	247	23	a.	a.	NOUN
cana-2940	247	24	(	(	PUNCT
cana-2940	247	25	2020	2020	NUM
cana-2940	247	26	)	)	PUNCT
cana-2940	247	27	.	.	PUNCT
cana-2940	248	1	multi	multi	ADJ
cana-2940	248	2	-	-	ADJ
cana-2940	248	3	class	class	ADJ
cana-2940	248	4	multi	multi	ADJ
cana-2940	248	5	-	-	ADJ
cana-2940	248	6	level	level	ADJ
cana-2940	248	7	classification	classification	NOUN
cana-2940	248	8	algorithm	algorithm	NOUN
cana-2940	248	9	for	for	ADP
cana-2940	248	10	skin	skin	NOUN
cana-2940	248	11	lesions	lesion	NOUN
cana-2940	248	12	classification	classification	NOUN
cana-2940	248	13	using	use	VERB
cana-2940	248	14	machine	machine	NOUN
cana-2940	248	15	learning	learn	VERB
cana-2940	248	16	techniques	technique	NOUN
cana-2940	248	17	.	.	PUNCT
cana-2940	249	1	expert	expert	NOUN
cana-2940	249	2	systems	system	NOUN
cana-2940	249	3	with	with	ADP
cana-2940	249	4	applications	application	NOUN
cana-2940	249	5	,	,	PUNCT
cana-2940	249	6	141	141	NUM
cana-2940	249	7	,	,	PUNCT
cana-2940	249	8	112961	112961	NUM
cana-2940	249	9	.	.	PUNCT
cana-2940	250	1	https://doi.org/10.1016/j.eswa.2019.112961	https://doi.org/10.1016/j.eswa.2019.112961	PROPN
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cana-2940	250	3	12	12	NUM
cana-2940	250	4	]	]	PUNCT
cana-2940	250	5	hegde	hegde	NOUN
cana-2940	250	6	,	,	PUNCT
cana-2940	250	7	p.	p.	PROPN
cana-2940	250	8	r.	r.	PROPN
cana-2940	250	9	,	,	PUNCT
cana-2940	250	10	shenoy	shenoy	NOUN
cana-2940	250	11	,	,	PUNCT
cana-2940	250	12	m.	m.	NOUN
cana-2940	250	13	m.	m.	NOUN
cana-2940	250	14	,	,	PUNCT
cana-2940	250	15	&	&	CCONJ
cana-2940	250	16	shekar	shekar	PROPN
cana-2940	250	17	,	,	PUNCT
cana-2940	250	18	b.	b.	PROPN
cana-2940	250	19	h.	h.	PROPN
cana-2940	250	20	(	(	PUNCT
cana-2940	250	21	2018	2018	NUM
cana-2940	250	22	)	)	PUNCT
cana-2940	250	23	.	.	PUNCT
cana-2940	251	1	comparison	comparison	NOUN
cana-2940	251	2	of	of	ADP
cana-2940	251	3	machine	machine	NOUN
cana-2940	251	4	learning	learn	VERB
cana-2940	251	5	algorithms	algorithm	NOUN
cana-2940	251	6	for	for	ADP
cana-2940	251	7	skin	skin	NOUN
cana-2940	251	8	disease	disease	NOUN
cana-2940	251	9	classification	classification	NOUN
cana-2940	251	10	using	use	VERB
cana-2940	251	11	color	color	NOUN
cana-2940	251	12	and	and	CCONJ
cana-2940	251	13	texture	texture	NOUN
cana-2940	251	14	features	feature	NOUN
cana-2940	251	15	.	.	PUNCT
cana-2940	252	1	2018	2018	NUM
cana-2940	252	2	international	international	ADJ
cana-2940	252	3	conference	conference	NOUN
cana-2940	252	4	on	on	ADP
cana-2940	252	5	advances	advance	NOUN
cana-2940	252	6	in	in	ADP
cana-2940	252	7	computing	computing	NOUN
cana-2940	252	8	,	,	PUNCT
cana-2940	252	9	communications	communication	NOUN
cana-2940	252	10	and	and	CCONJ
cana-2940	252	11	informatics	informatic	NOUN
cana-2940	252	12	,	,	PUNCT
cana-2940	252	13	icacci	icacci	PROPN
cana-2940	252	14	2018	2018	NUM
cana-2940	252	15	,	,	PUNCT
cana-2940	252	16	1825–1828	1825–1828	NUM
cana-2940	252	17	.	.	PUNCT
cana-2940	253	1	https://doi.org/10.1109/icacci.2018.8554512	https://doi.org/10.1109/icacci.2018.8554512	PROPN
cana-2940	254	1	[	[	X
cana-2940	254	2	13	13	NUM
cana-2940	254	3	]	]	X
cana-2940	254	4	jagdish	jagdish	PROPN
cana-2940	254	5	,	,	PUNCT
cana-2940	254	6	m.	m.	NOUN
cana-2940	254	7	,	,	PUNCT
cana-2940	254	8	paola	paola	PROPN
cana-2940	254	9	,	,	PUNCT
cana-2940	254	10	s.	s.	PROPN
cana-2940	254	11	,	,	PUNCT
cana-2940	254	12	guamangate	guamangate	NOUN
cana-2940	254	13	,	,	PUNCT
cana-2940	254	14	g.	g.	PROPN
cana-2940	254	15	,	,	PUNCT
cana-2940	254	16	garcía	garcía	ADJ
cana-2940	254	17	lópez	lópez	NOUN
cana-2940	254	18	,	,	PUNCT
cana-2940	254	19	m.	m.	NOUN
cana-2940	254	20	a.	a.	PROPN
cana-2940	254	21	,	,	PUNCT
cana-2940	254	22	de	de	PROPN
cana-2940	254	23	,	,	PUNCT
cana-2940	254	24	j.	j.	PROPN
cana-2940	254	25	a.	a.	PROPN
cana-2940	254	26	,	,	PUNCT
cana-2940	254	27	cruz	cruz	NOUN
cana-2940	254	28	-	-	PUNCT
cana-2940	254	29	vargas	vargas	PROPN
cana-2940	254	30	,	,	PUNCT
cana-2940	254	31	l.	l.	PROPN
cana-2940	254	32	,	,	PUNCT
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cana-2940	254	35	m.	m.	NOUN
cana-2940	254	36	,	,	PUNCT
cana-2940	254	37	&	&	CCONJ
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cana-2940	254	39	,	,	PUNCT
cana-2940	254	40	r.	r.	PROPN
cana-2940	254	41	(	(	PUNCT
cana-2940	254	42	2022	2022	NUM
cana-2940	254	43	)	)	PUNCT
cana-2940	254	44	.	.	PUNCT
cana-2940	255	1	advance	advance	NOUN
cana-2940	255	2	study	study	NOUN
cana-2940	255	3	of	of	ADP
cana-2940	255	4	skin	skin	NOUN
cana-2940	255	5	diseases	disease	NOUN
cana-2940	255	6	detection	detection	NOUN
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cana-2940	255	8	image	image	NOUN
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cana-2940	255	10	methods	method	NOUN
cana-2940	255	11	.	.	PUNCT
cana-2940	256	1	volatiles	volatile	NOUN
cana-2940	256	2	&	&	CCONJ
cana-2940	256	3	essent	essent	NOUN
cana-2940	256	4	.	.	PUNCT
cana-2940	257	1	oils	oil	NOUN
cana-2940	257	2	,	,	PUNCT
cana-2940	257	3	9(1	9(1	NUM
cana-2940	257	4	)	)	PUNCT
cana-2940	257	5	,	,	PUNCT
cana-2940	257	6	997–1007	997–1007	NUM
cana-2940	257	7	.	.	PUNCT
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cana-2940	259	1	[	[	X
cana-2940	259	2	14	14	NUM
cana-2940	259	3	]	]	X
cana-2940	259	4	jeong	jeong	PROPN
cana-2940	259	5	,	,	PUNCT
cana-2940	259	6	h.	h.	PROPN
cana-2940	259	7	k.	k.	PROPN
cana-2940	259	8	,	,	PUNCT
cana-2940	259	9	park	park	PROPN
cana-2940	259	10	,	,	PUNCT
cana-2940	259	11	c.	c.	PROPN
cana-2940	259	12	,	,	PUNCT
cana-2940	259	13	henao	henao	PROPN
cana-2940	259	14	,	,	PUNCT
cana-2940	259	15	r.	r.	PROPN
cana-2940	259	16	,	,	PUNCT
cana-2940	259	17	&	&	CCONJ
cana-2940	259	18	kheterpal	kheterpal	PROPN
cana-2940	259	19	,	,	PUNCT
cana-2940	259	20	m.	m.	NOUN
cana-2940	259	21	(	(	PUNCT
cana-2940	259	22	2023	2023	NUM
cana-2940	259	23	)	)	PUNCT
cana-2940	259	24	.	.	PUNCT
cana-2940	260	1	deep	deep	ADJ
cana-2940	260	2	learning	learning	NOUN
cana-2940	260	3	in	in	ADP
cana-2940	260	4	dermatology	dermatology	NOUN
cana-2940	260	5	:	:	PUNCT
cana-2940	260	6	a	a	DET
cana-2940	260	7	systematic	systematic	ADJ
cana-2940	260	8	review	review	NOUN
cana-2940	260	9	of	of	ADP
cana-2940	260	10	current	current	ADJ
cana-2940	260	11	approaches	approach	NOUN
cana-2940	260	12	,	,	PUNCT
cana-2940	260	13	outcomes	outcome	NOUN
cana-2940	260	14	,	,	PUNCT
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cana-2940	260	16	limitations	limitation	NOUN
cana-2940	260	17	.	.	PUNCT
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cana-2940	261	2	innovations	innovation	NOUN
cana-2940	261	3	,	,	PUNCT
cana-2940	261	4	3(1	3(1	NUM
cana-2940	261	5	)	)	PUNCT
cana-2940	261	6	,	,	PUNCT
cana-2940	261	7	100150	100150	NUM
cana-2940	261	8	.	.	PUNCT
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cana-2940	262	3	15	15	NUM
cana-2940	262	4	]	]	X
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cana-2940	262	6	,	,	PUNCT
cana-2940	262	7	n.	n.	PROPN
cana-2940	262	8	,	,	PUNCT
cana-2940	262	9	&	&	CCONJ
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cana-2940	262	11	,	,	PUNCT
cana-2940	262	12	a.	a.	PROPN
cana-2940	262	13	k.	k.	PROPN
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cana-2940	262	16	)	)	PUNCT
cana-2940	262	17	.	.	PUNCT
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cana-2940	263	2	skin	skin	NOUN
cana-2940	263	3	disease	disease	NOUN
cana-2940	263	4	detection	detection	NOUN
cana-2940	263	5	and	and	CCONJ
cana-2940	263	6	classification	classification	NOUN
cana-2940	263	7	system	system	NOUN
cana-2940	263	8	using	use	VERB
cana-2940	263	9	deep	deep	ADJ
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cana-2940	263	11	technique	technique	NOUN
cana-2940	263	12	.	.	PUNCT
cana-2940	264	1	international	international	ADJ
cana-2940	264	2	journal	journal	NOUN
cana-2940	264	3	of	of	ADP
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cana-2940	264	5	technology	technology	NOUN
cana-2940	264	6	and	and	CCONJ
cana-2940	264	7	social	social	ADJ
cana-2940	264	8	sciences	science	NOUN
cana-2940	264	9	,	,	PUNCT
cana-2940	264	10	2(1	2(1	NUM
cana-2940	264	11	)	)	PUNCT
cana-2940	264	12	,	,	PUNCT
cana-2940	264	13	93–104	93–104	PROPN
cana-2940	264	14	.	.	PUNCT
cana-2940	265	1	https://doi.org/10.59890/ijatss.v2i1.1292	https://doi.org/10.59890/ijatss.v2i1.1292	PROPN
cana-2940	266	1	[	[	X
cana-2940	266	2	16	16	NUM
cana-2940	266	3	]	]	X
cana-2940	266	4	krishna	krishna	PROPN
cana-2940	266	5	monika	monika	PROPN
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cana-2940	266	7	m.	m.	NOUN
cana-2940	266	8	,	,	PUNCT
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cana-2940	266	11	,	,	PUNCT
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cana-2940	266	13	,	,	PUNCT
cana-2940	266	14	usha	usha	PROPN
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cana-2940	266	16	,	,	PUNCT
cana-2940	266	17	c.	c.	PROPN
cana-2940	266	18	,	,	PUNCT
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cana-2940	266	20	,	,	PUNCT
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cana-2940	266	22	n.	n.	PROPN
cana-2940	266	23	v.	v.	PROPN
cana-2940	266	24	s.	s.	PROPN
cana-2940	266	25	s.	s.	PROPN
cana-2940	266	26	,	,	PUNCT
cana-2940	266	27	&	&	CCONJ
cana-2940	266	28	laxmi	laxmi	PROPN
cana-2940	266	29	lydia	lydia	PROPN
cana-2940	266	30	,	,	PUNCT
cana-2940	266	31	e.	e.	PROPN
cana-2940	266	32	(	(	PUNCT
cana-2940	266	33	2020	2020	NUM
cana-2940	266	34	)	)	PUNCT
cana-2940	266	35	.	.	PUNCT
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cana-2940	267	7	issn	issn	NOUN
cana-2940	267	8	:	:	PUNCT
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cana-2940	267	10	-	-	PUNCT
cana-2940	267	11	133x	133x	NUM
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cana-2940	267	13	32	32	NUM
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cana-2940	267	15	.	.	PUNCT
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cana-2940	268	2	(	(	PUNCT
cana-2940	268	3	2025	2025	NUM
cana-2940	268	4	)	)	PUNCT
cana-2940	268	5	42	42	NUM
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cana-2940	268	7	cancer	cancer	NOUN
cana-2940	268	8	detection	detection	NOUN
cana-2940	268	9	and	and	CCONJ
cana-2940	268	10	classification	classification	NOUN
cana-2940	268	11	using	use	VERB
cana-2940	268	12	machine	machine	NOUN
cana-2940	268	13	learning	learning	NOUN
cana-2940	268	14	.	.	PUNCT
cana-2940	269	1	materials	material	NOUN
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cana-2940	269	3	:	:	PUNCT
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cana-2940	269	5	,	,	PUNCT
cana-2940	269	6	33	33	NUM
cana-2940	269	7	,	,	PUNCT
cana-2940	269	8	4266–4270	4266–4270	NUM
cana-2940	269	9	.	.	PUNCT
cana-2940	270	1	https://doi.org/10.1016/j.matpr.2020.07.366	https://doi.org/10.1016/j.matpr.2020.07.366	NUM
cana-2940	271	1	[	[	X
cana-2940	271	2	17	17	NUM
cana-2940	271	3	]	]	X
cana-2940	271	4	manerkar	manerkar	NOUN
cana-2940	271	5	,	,	PUNCT
cana-2940	271	6	m.	m.	NOUN
cana-2940	271	7	s.	s.	PROPN
cana-2940	271	8	,	,	PUNCT
cana-2940	271	9	snekhalatha	snekhalatha	ADJ
cana-2940	271	10	,	,	PUNCT
cana-2940	271	11	u.	u.	PROPN
cana-2940	271	12	,	,	PUNCT
cana-2940	271	13	harsh	harsh	ADJ
cana-2940	271	14	,	,	PUNCT
cana-2940	271	15	s.	s.	PROPN
cana-2940	271	16	,	,	PUNCT
cana-2940	271	17	saxena	saxena	PROPN
cana-2940	271	18	,	,	PUNCT
cana-2940	271	19	j.	j.	PROPN
cana-2940	271	20	,	,	PUNCT
cana-2940	271	21	sarma	sarma	PROPN
cana-2940	271	22	,	,	PUNCT
cana-2940	271	23	s.	s.	PROPN
cana-2940	271	24	p.	p.	PROPN
cana-2940	271	25	,	,	PUNCT
cana-2940	271	26	&	&	CCONJ
cana-2940	271	27	anburajan	anburajan	PROPN
cana-2940	271	28	,	,	PUNCT
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cana-2940	271	30	(	(	PUNCT
cana-2940	271	31	2016	2016	NUM
cana-2940	271	32	)	)	PUNCT
cana-2940	271	33	.	.	PUNCT
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cana-2940	272	2	skin	skin	NOUN
cana-2940	272	3	disease	disease	NOUN
cana-2940	272	4	segmentation	segmentation	NOUN
cana-2940	272	5	and	and	CCONJ
cana-2940	272	6	classification	classification	NOUN
cana-2940	272	7	using	use	VERB
cana-2940	272	8	multi	multi	ADJ
cana-2940	272	9	-	-	ADJ
cana-2940	272	10	class	class	ADJ
cana-2940	272	11	svm	svm	ADJ
cana-2940	272	12	classifier	classifier	NOUN
cana-2940	272	13	.	.	PUNCT
cana-2940	273	1	iet	iet	PROPN
cana-2940	273	2	conference	conference	NOUN
cana-2940	273	3	publications	publication	NOUN
cana-2940	273	4	,	,	PUNCT
cana-2940	273	5	2016(cp739	2016(cp739	NUM
cana-2940	273	6	)	)	PUNCT
cana-2940	273	7	.	.	PUNCT
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cana-2940	275	1	[	[	X
cana-2940	275	2	18	18	NUM
cana-2940	275	3	]	]	X
cana-2940	275	4	maqsood	maqsood	PROPN
cana-2940	275	5	,	,	PUNCT
cana-2940	275	6	s.	s.	PROPN
cana-2940	275	7	,	,	PUNCT
cana-2940	275	8	&	&	CCONJ
cana-2940	275	9	damaševičius	damaševičius	PROPN
cana-2940	275	10	,	,	PUNCT
cana-2940	275	11	r.	r.	PROPN
cana-2940	275	12	(	(	PUNCT
cana-2940	275	13	2023	2023	NUM
cana-2940	275	14	)	)	PUNCT
cana-2940	275	15	.	.	PUNCT
cana-2940	276	1	multiclass	multiclass	ADJ
cana-2940	276	2	skin	skin	NOUN
cana-2940	276	3	lesion	lesion	NOUN
cana-2940	276	4	localization	localization	NOUN
cana-2940	276	5	and	and	CCONJ
cana-2940	276	6	classification	classification	NOUN
cana-2940	276	7	using	use	VERB
cana-2940	276	8	deep	deep	ADJ
cana-2940	276	9	learning	learning	NOUN
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cana-2940	276	11	features	feature	NOUN
cana-2940	276	12	fusion	fusion	NOUN
cana-2940	276	13	and	and	CCONJ
cana-2940	276	14	selection	selection	NOUN
cana-2940	276	15	framework	framework	NOUN
cana-2940	276	16	for	for	ADP
cana-2940	276	17	smart	smart	ADJ
cana-2940	276	18	healthcare	healthcare	NOUN
cana-2940	276	19	.	.	PUNCT
cana-2940	277	1	neural	neural	ADJ
cana-2940	277	2	networks	network	NOUN
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cana-2940	277	4	160	160	NUM
cana-2940	277	5	,	,	PUNCT
cana-2940	277	6	238–258	238–258	NUM
cana-2940	277	7	.	.	PUNCT
cana-2940	278	1	https://doi.org/10.1016/j.neunet.2023.01.022	https://doi.org/10.1016/j.neunet.2023.01.022	NOUN
cana-2940	278	2	[	[	X
cana-2940	278	3	19	19	NUM
cana-2940	278	4	]	]	X
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cana-2940	278	7	m.	m.	PROPN
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cana-2940	278	9	,	,	PUNCT
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cana-2940	278	12	,	,	PUNCT
cana-2940	278	13	n.	n.	PROPN
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cana-2940	278	15	usha	usha	PROPN
cana-2940	278	16	kumari	kumari	PROPN
cana-2940	278	17	,	,	PUNCT
cana-2940	278	18	c.	c.	PROPN
cana-2940	278	19	,	,	PUNCT
cana-2940	278	20	kumar	kumar	PROPN
cana-2940	278	21	,	,	PUNCT
cana-2940	278	22	m.	m.	NOUN
cana-2940	278	23	n.	n.	PROPN
cana-2940	278	24	v.	v.	PROPN
cana-2940	278	25	s.	s.	PROPN
cana-2940	278	26	s.	s.	PROPN
cana-2940	278	27	,	,	PUNCT
cana-2940	278	28	&	&	CCONJ
cana-2940	278	29	lydia	lydia	PROPN
cana-2940	278	30	,	,	PUNCT
cana-2940	278	31	e.	e.	PROPN
cana-2940	278	32	l.	l.	PROPN
cana-2940	278	33	(	(	PUNCT
cana-2940	278	34	2020	2020	NUM
cana-2940	278	35	)	)	PUNCT
cana-2940	278	36	.	.	PUNCT
cana-2940	279	1	skin	skin	NOUN
cana-2940	279	2	cancer	cancer	NOUN
cana-2940	279	3	detection	detection	NOUN
cana-2940	279	4	and	and	CCONJ
cana-2940	279	5	classification	classification	NOUN
cana-2940	279	6	using	use	VERB
cana-2940	279	7	machine	machine	NOUN
cana-2940	279	8	learning	learning	NOUN
cana-2940	279	9	.	.	PUNCT
cana-2940	280	1	materials	material	NOUN
cana-2940	280	2	today	today	NOUN
cana-2940	280	3	:	:	PUNCT
cana-2940	280	4	proceedings	proceeding	NOUN
cana-2940	280	5	,	,	PUNCT
cana-2940	280	6	33	33	NUM
cana-2940	280	7	,	,	PUNCT
cana-2940	280	8	4266–4270	4266–4270	NUM
cana-2940	280	9	.	.	PUNCT
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cana-2940	282	1	[	[	X
cana-2940	282	2	20	20	NUM
cana-2940	282	3	]	]	PUNCT
cana-2940	282	4	putatunda	putatunda	NOUN
cana-2940	282	5	,	,	PUNCT
cana-2940	282	6	s.	s.	PROPN
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cana-2940	282	8	2019	2019	NUM
cana-2940	282	9	)	)	PUNCT
cana-2940	282	10	.	.	PUNCT
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cana-2940	283	4	learning	learning	NOUN
cana-2940	283	5	approach	approach	NOUN
cana-2940	283	6	for	for	ADP
cana-2940	283	7	diagnosis	diagnosis	NOUN
cana-2940	283	8	of	of	ADP
cana-2940	283	9	the	the	DET
cana-2940	283	10	erythemato	erythemato	ADJ
cana-2940	283	11	-	-	PUNCT
cana-2940	283	12	squamous	squamous	ADJ
cana-2940	283	13	disease	disease	NOUN
cana-2940	283	14	.	.	PUNCT
cana-2940	284	1	proceedings	proceeding	NOUN
cana-2940	284	2	of	of	ADP
cana-2940	284	3	conecct	conecct	NOUN
cana-2940	284	4	2020	2020	NUM
cana-2940	284	5	6th	6th	ADJ
cana-2940	284	6	ieee	ieee	PROPN
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cana-2940	284	8	conference	conference	NOUN
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cana-2940	284	10	electronics	electronic	NOUN
cana-2940	284	11	,	,	PUNCT
cana-2940	284	12	computing	computing	NOUN
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cana-2940	284	16	.	.	PUNCT
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cana-2940	285	2	[	[	X
cana-2940	285	3	21	21	NUM
cana-2940	285	4	]	]	X
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cana-2940	285	13	t.	t.	PROPN
cana-2940	285	14	,	,	PUNCT
cana-2940	285	15	&	&	CCONJ
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cana-2940	285	18	s.	s.	PROPN
cana-2940	285	19	a.	a.	PROPN
cana-2940	285	20	(	(	PUNCT
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cana-2940	285	22	.	.	PROPN
cana-2940	285	23	)	)	PUNCT
cana-2940	285	24	.	.	PUNCT
cana-2940	286	1	skin	skin	NOUN
cana-2940	286	2	lesions	lesion	NOUN
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cana-2940	286	4	based	base	VERB
cana-2940	286	5	on	on	ADP
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cana-2940	286	7	.	.	PUNCT
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cana-2940	287	2	.	.	PUNCT
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cana-2940	288	2	22	22	NUM
cana-2940	288	3	]	]	SYM
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cana-2940	288	6	k.	k.	PROPN
cana-2940	288	7	s.	s.	PROPN
cana-2940	288	8	(	(	PUNCT
cana-2940	288	9	2021	2021	NUM
cana-2940	288	10	)	)	PUNCT
cana-2940	288	11	.	.	PUNCT
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cana-2940	289	3	detection	detection	NOUN
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cana-2940	289	8	.	.	PUNCT
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cana-2940	290	6	15(4	15(4	NUM
cana-2940	290	7	)	)	PUNCT
cana-2940	290	8	,	,	PUNCT
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cana-2940	290	10	.	.	PUNCT
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cana-2940	292	2	23	23	NUM
cana-2940	292	3	]	]	X
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cana-2940	292	11	s.	s.	PROPN
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cana-2940	292	13	kumar	kumar	PROPN
cana-2940	292	14	,	,	PUNCT
cana-2940	292	15	s.	s.	PROPN
cana-2940	292	16	,	,	PUNCT
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cana-2940	292	19	r.	r.	PROPN
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cana-2940	292	21	&	&	CCONJ
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cana-2940	292	25	(	(	PUNCT
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cana-2940	292	27	)	)	PUNCT
cana-2940	292	28	.	.	PUNCT
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cana-2940	293	3	based	base	VERB
cana-2940	293	4	segmentation	segmentation	NOUN
cana-2940	293	5	with	with	ADP
cana-2940	293	6	hybrid	hybrid	ADJ
cana-2940	293	7	classification	classification	NOUN
cana-2940	293	8	for	for	ADP
cana-2940	293	9	skin	skin	NOUN
cana-2940	293	10	disease	disease	NOUN
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cana-2940	293	12	.	.	PUNCT
cana-2940	294	1	procedia	procedia	PROPN
cana-2940	294	2	computer	computer	NOUN
cana-2940	294	3	science	science	NOUN
cana-2940	294	4	,	,	PUNCT
cana-2940	294	5	235	235	NUM
cana-2940	294	6	,	,	PUNCT
cana-2940	294	7	2237–2250	2237–2250	NUM
cana-2940	294	8	.	.	PUNCT
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cana-2940	296	3	]	]	X
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cana-2940	296	10	s.	s.	PROPN
cana-2940	296	11	s.	s.	PROPN
cana-2940	296	12	,	,	PUNCT
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cana-2940	296	14	,	,	PUNCT
cana-2940	296	15	s.	s.	PROPN
cana-2940	296	16	,	,	PUNCT
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cana-2940	296	18	,	,	PUNCT
cana-2940	296	19	s.	s.	PROPN
cana-2940	296	20	k.	k.	PROPN
cana-2940	296	21	,	,	PUNCT
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cana-2940	296	24	p.	p.	PROPN
cana-2940	296	25	,	,	PUNCT
cana-2940	296	26	&	&	CCONJ
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cana-2940	296	30	(	(	PUNCT
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cana-2940	296	32	)	)	PUNCT
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cana-2940	297	2	disease	disease	NOUN
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cana-2940	297	8	techniques	technique	NOUN
cana-2940	297	9	.	.	PUNCT
cana-2940	298	1	2019	2019	NUM
cana-2940	298	2	international	international	ADJ
cana-2940	298	3	conference	conference	NOUN
cana-2940	298	4	on	on	ADP
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cana-2940	298	6	-	-	PUNCT
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cana-2940	298	12	optronix	optronix	NOUN
cana-2940	298	13	)	)	PUNCT
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cana-2940	299	3	25	25	NUM
cana-2940	299	4	]	]	PUNCT
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cana-2940	299	15	l.	l.	PROPN
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cana-2940	299	19	w.	w.	PROPN
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cana-2940	299	25	(	(	PUNCT
cana-2940	299	26	2023	2023	NUM
cana-2940	299	27	)	)	PUNCT
cana-2940	299	28	.	.	PUNCT
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cana-2940	300	2	skin	skin	NOUN
cana-2940	300	3	type	type	NOUN
cana-2940	300	4	classification	classification	NOUN
cana-2940	300	5	using	use	VERB
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cana-2940	301	3	9(11	9(11	NUM
cana-2940	301	4	)	)	PUNCT
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cana-2940	302	2	26	26	NUM
cana-2940	302	3	]	]	PUNCT
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cana-2940	302	13	s.	s.	PROPN
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cana-2940	302	32	&	&	CCONJ
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cana-2940	302	39	)	)	PUNCT
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cana-2940	303	4	based	base	VERB
cana-2940	303	5	skin	skin	NOUN
cana-2940	303	6	disease	disease	NOUN
cana-2940	303	7	detection	detection	NOUN
cana-2940	303	8	using	use	VERB
cana-2940	303	9	convolutional	convolutional	ADJ
cana-2940	303	10	neural	neural	ADJ
cana-2940	303	11	networks	network	NOUN
cana-2940	303	12	(	(	PUNCT
cana-2940	303	13	cnn	cnn	PROPN
cana-2940	303	14	)	)	PUNCT
cana-2940	303	15	.	.	PUNCT
cana-2940	304	1	lecture	lecture	NOUN
cana-2940	304	2	notes	note	NOUN
cana-2940	304	3	in	in	ADP
cana-2940	304	4	electrical	electrical	ADJ
cana-2940	304	5	engineering	engineering	NOUN
cana-2940	304	6	,	,	PUNCT
cana-2940	304	7	980	980	NUM
cana-2940	304	8	lnee	lnee	NOUN
cana-2940	304	9	,	,	PUNCT
cana-2940	304	10	551–564	551–564	NUM
cana-2940	304	11	.	.	PUNCT
cana-2940	305	1	https://doi.org/10.1007/978-98119-8032-9_39/cover	https://doi.org/10.1007/978-98119-8032-9_39/cover	NOUN
cana-2940	305	2	[	[	X
cana-2940	305	3	27	27	NUM
cana-2940	305	4	]	]	X
cana-2940	305	5	sharma	sharma	PROPN
cana-2940	305	6	,	,	PUNCT
cana-2940	305	7	r.	r.	PROPN
cana-2940	305	8	,	,	PUNCT
cana-2940	305	9	&	&	CCONJ
cana-2940	305	10	mehan	mehan	PROPN
cana-2940	305	11	,	,	PUNCT
cana-2940	305	12	v.	v.	PROPN
cana-2940	305	13	(	(	PUNCT
cana-2940	305	14	2022	2022	NUM
cana-2940	305	15	)	)	PUNCT
cana-2940	305	16	.	.	PUNCT
cana-2940	306	1	skin	skin	NOUN
cana-2940	306	2	disease	disease	NOUN
cana-2940	306	3	detection	detection	NOUN
cana-2940	306	4	using	use	VERB
cana-2940	306	5	image	image	NOUN
cana-2940	306	6	processing	processing	NOUN
cana-2940	306	7	and	and	CCONJ
cana-2940	306	8	soft	soft	ADJ
cana-2940	306	9	computing	computing	NOUN
cana-2940	306	10	.	.	PUNCT
cana-2940	307	1	ecs	ec	NOUN
cana-2940	307	2	transactions	transaction	NOUN
cana-2940	307	3	,	,	PUNCT
cana-2940	307	4	107(1	107(1	NUM
cana-2940	307	5	)	)	PUNCT
cana-2940	307	6	.	.	PUNCT
cana-2940	308	1	https://doi.org/10.1149/10701.17051ecst	https://doi.org/10.1149/10701.17051ecst	PROPN
cana-2940	309	1	[	[	X
cana-2940	309	2	28	28	NUM
cana-2940	309	3	]	]	X
cana-2940	309	4	sharma	sharma	NOUN
cana-2940	309	5	,	,	PUNCT
cana-2940	309	6	y.	y.	PROPN
cana-2940	309	7	,	,	PUNCT
cana-2940	309	8	tiwari	tiwari	PROPN
cana-2940	309	9	,	,	PUNCT
cana-2940	309	10	n.	n.	PROPN
cana-2940	309	11	k.	k.	PROPN
cana-2940	309	12	,	,	PUNCT
cana-2940	309	13	&	&	CCONJ
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cana-2940	309	16	v.	v.	PROPN
cana-2940	309	17	k.	k.	PROPN
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cana-2940	309	19	2024	2024	NUM
cana-2940	309	20	)	)	PUNCT
cana-2940	309	21	.	.	PUNCT
cana-2940	310	1	effsvmnet	effsvmnet	NOUN
cana-2940	310	2	:	:	PUNCT
cana-2940	310	3	an	an	DET
cana-2940	310	4	efficient	efficient	ADJ
cana-2940	310	5	hybrid	hybrid	ADJ
cana-2940	310	6	neural	neural	ADJ
cana-2940	310	7	network	network	NOUN
cana-2940	310	8	for	for	ADP
cana-2940	310	9	improved	improved	ADJ
cana-2940	310	10	skin	skin	NOUN
cana-2940	310	11	disease	disease	NOUN
cana-2940	310	12	classification	classification	NOUN
cana-2940	310	13	.	.	PUNCT
cana-2940	311	1	smart	smart	ADJ
cana-2940	311	2	health	health	NOUN
cana-2940	311	3	,	,	PUNCT
cana-2940	311	4	34(october	34(october	NUM
cana-2940	311	5	)	)	PUNCT
cana-2940	311	6	,	,	PUNCT
cana-2940	311	7	100520	100520	NUM
cana-2940	311	8	.	.	PUNCT
cana-2940	312	1	https://doi.org/10.1016/j.smhl.2024.100520	https://doi.org/10.1016/j.smhl.2024.100520	PROPN
cana-2940	313	1	[	[	X
cana-2940	313	2	29	29	NUM
cana-2940	313	3	]	]	X
cana-2940	313	4	shetty	shetty	PROPN
cana-2940	313	5	,	,	PUNCT
cana-2940	313	6	b.	b.	PROPN
cana-2940	313	7	,	,	PUNCT
cana-2940	313	8	fernandes	fernandes	PROPN
cana-2940	313	9	,	,	PUNCT
cana-2940	313	10	r.	r.	PROPN
cana-2940	313	11	,	,	PUNCT
cana-2940	313	12	rodrigues	rodrigues	PROPN
cana-2940	313	13	,	,	PUNCT
cana-2940	313	14	a.	a.	NOUN
cana-2940	313	15	p.	p.	PROPN
cana-2940	313	16	,	,	PUNCT
cana-2940	313	17	chengoden	chengoden	PROPN
cana-2940	313	18	,	,	PUNCT
cana-2940	313	19	r.	r.	PROPN
cana-2940	313	20	,	,	PUNCT
cana-2940	313	21	bhattacharya	bhattacharya	PROPN
cana-2940	313	22	,	,	PUNCT
cana-2940	313	23	s.	s.	PROPN
cana-2940	313	24	,	,	PUNCT
cana-2940	313	25	&	&	CCONJ
cana-2940	313	26	lakshmanna	lakshmanna	PROPN
cana-2940	313	27	,	,	PUNCT
cana-2940	313	28	k.	k.	PROPN
cana-2940	313	29	(	(	PUNCT
cana-2940	313	30	2022	2022	NUM
cana-2940	313	31	)	)	PUNCT
cana-2940	313	32	.	.	PUNCT
cana-2940	314	1	skin	skin	NOUN
cana-2940	314	2	lesion	lesion	NOUN
cana-2940	314	3	classification	classification	NOUN
cana-2940	314	4	of	of	ADP
cana-2940	314	5	dermoscopic	dermoscopic	ADJ
cana-2940	314	6	images	image	NOUN
cana-2940	314	7	using	use	VERB
cana-2940	314	8	machine	machine	NOUN
cana-2940	314	9	learning	learning	NOUN
cana-2940	314	10	and	and	CCONJ
cana-2940	314	11	convolutional	convolutional	ADJ
cana-2940	314	12	neural	neural	ADJ
cana-2940	314	13	network	network	NOUN
cana-2940	314	14	.	.	PUNCT
cana-2940	315	1	scientific	scientific	ADJ
cana-2940	315	2	reports	report	NOUN
cana-2940	315	3	2022	2022	NUM
cana-2940	315	4	12:1	12:1	NUM
cana-2940	315	5	,	,	PUNCT
cana-2940	315	6	12(1	12(1	NUM
cana-2940	315	7	)	)	PUNCT
cana-2940	315	8	,	,	PUNCT
cana-2940	315	9	1–11	1–11	PROPN
cana-2940	315	10	.	.	PUNCT
cana-2940	315	11	https://doi.org/10.1038/s41598-022-22644-9	https://doi.org/10.1038/s41598-022-22644-9	PUNCT
cana-2940	316	1	[	[	X
cana-2940	316	2	30	30	NUM
cana-2940	316	3	]	]	X
cana-2940	316	4	sinthura	sinthura	PROPN
cana-2940	316	5	,	,	PUNCT
cana-2940	316	6	s.	s.	PROPN
cana-2940	316	7	s.	s.	PROPN
cana-2940	316	8	,	,	PUNCT
cana-2940	316	9	sharon	sharon	PROPN
cana-2940	316	10	,	,	PUNCT
cana-2940	316	11	k.	k.	PROPN
cana-2940	316	12	r.	r.	PROPN
cana-2940	316	13	,	,	PUNCT
cana-2940	316	14	bhavani	bhavani	PROPN
cana-2940	316	15	,	,	PUNCT
cana-2940	316	16	g.	g.	PROPN
cana-2940	316	17	,	,	PUNCT
cana-2940	316	18	mounika	mounika	X
cana-2940	316	19	,	,	PUNCT
cana-2940	316	20	l.	l.	PROPN
cana-2940	316	21	,	,	PUNCT
cana-2940	316	22	&	&	CCONJ
cana-2940	316	23	joshika	joshika	PROPN
cana-2940	316	24	,	,	PUNCT
cana-2940	316	25	b.	b.	PROPN
cana-2940	316	26	(	(	PUNCT
cana-2940	316	27	2020	2020	NUM
cana-2940	316	28	)	)	PUNCT
cana-2940	316	29	.	.	PUNCT
cana-2940	317	1	advanced	advanced	ADJ
cana-2940	317	2	skin	skin	NOUN
cana-2940	317	3	diseases	disease	NOUN
cana-2940	317	4	diagnosis	diagnosis	VERB
cana-2940	317	5	leveraging	leverage	VERB
cana-2940	317	6	image	image	NOUN
cana-2940	317	7	processing	processing	NOUN
cana-2940	317	8	.	.	PUNCT
cana-2940	318	1	proceedings	proceeding	NOUN
cana-2940	318	2	of	of	ADP
cana-2940	318	3	the	the	DET
cana-2940	318	4	international	international	ADJ
cana-2940	318	5	conference	conference	NOUN
cana-2940	318	6	on	on	ADP
cana-2940	318	7	electronics	electronic	NOUN
cana-2940	318	8	and	and	CCONJ
cana-2940	318	9	sustainable	sustainable	ADJ
cana-2940	318	10	communication	communication	NOUN
cana-2940	318	11	systems	system	NOUN
cana-2940	318	12	,	,	PUNCT
cana-2940	318	13	icesc	icesc	NOUN
cana-2940	318	14	2020	2020	NUM
cana-2940	318	15	,	,	PUNCT
cana-2940	318	16	icesc	icesc	NOUN
cana-2940	318	17	,	,	PUNCT
cana-2940	318	18	440–444	440–444	NUM
cana-2940	318	19	.	.	PUNCT
cana-2940	319	1	https://doi.org/10.1109/icesc48915.2020.9155914	https://doi.org/10.1109/icesc48915.2020.9155914	PROPN
cana-2940	319	2	[	[	X
cana-2940	319	3	31	31	NUM
cana-2940	319	4	]	]	X
cana-2940	319	5	tan	tan	PROPN
cana-2940	319	6	,	,	PUNCT
cana-2940	319	7	m.	m.	NOUN
cana-2940	319	8	,	,	PUNCT
cana-2940	319	9	&	&	CCONJ
cana-2940	319	10	le	le	PROPN
cana-2940	319	11	,	,	PUNCT
cana-2940	319	12	q.	q.	PROPN
cana-2940	319	13	v.	v.	PROPN
cana-2940	319	14	(	(	PUNCT
cana-2940	319	15	2019	2019	NUM
cana-2940	319	16	)	)	PUNCT
cana-2940	319	17	.	.	PUNCT
cana-2940	320	1	efficientnet	efficientnet	PROPN
cana-2940	320	2	:	:	PUNCT
cana-2940	320	3	rethinking	rethink	VERB
cana-2940	320	4	model	model	NOUN
cana-2940	320	5	scaling	scale	VERB
cana-2940	320	6	for	for	ADP
cana-2940	320	7	convolutional	convolutional	ADJ
cana-2940	320	8	neural	neural	ADJ
cana-2940	320	9	networks	network	NOUN
cana-2940	320	10	.	.	PUNCT
cana-2940	321	1	36th	36th	ADJ
cana-2940	321	2	international	international	ADJ
cana-2940	321	3	conference	conference	NOUN
cana-2940	321	4	on	on	ADP
cana-2940	321	5	machine	machine	NOUN
cana-2940	321	6	learning	learning	NOUN
cana-2940	321	7	,	,	PUNCT
cana-2940	321	8	icml	icml	VERB
cana-2940	321	9	2019	2019	NUM
cana-2940	321	10	,	,	PUNCT
cana-2940	321	11	2019	2019	NUM
cana-2940	321	12	-	-	SYM
cana-2940	321	13	june	june	PROPN
cana-2940	321	14	,	,	PUNCT
cana-2940	321	15	10691–10700	10691–10700	NUM
cana-2940	321	16	.	.	PUNCT
cana-2940	322	1	[	[	X
cana-2940	322	2	32	32	NUM
cana-2940	322	3	]	]	X
cana-2940	322	4	wei	wei	PROPN
cana-2940	322	5	,	,	PUNCT
cana-2940	322	6	l.	l.	PROPN
cana-2940	322	7	s.	s.	PROPN
cana-2940	322	8	,	,	PUNCT
cana-2940	322	9	gan	gan	PROPN
cana-2940	322	10	,	,	PUNCT
cana-2940	322	11	q.	q.	PROPN
cana-2940	322	12	,	,	PUNCT
cana-2940	322	13	&	&	CCONJ
cana-2940	322	14	ji	ji	PROPN
cana-2940	322	15	,	,	PUNCT
cana-2940	322	16	t.	t.	PROPN
cana-2940	322	17	(	(	PUNCT
cana-2940	322	18	2018	2018	NUM
cana-2940	322	19	)	)	PUNCT
cana-2940	322	20	.	.	PUNCT
cana-2940	323	1	skin	skin	NOUN
cana-2940	323	2	disease	disease	NOUN
cana-2940	323	3	recognition	recognition	NOUN
cana-2940	323	4	method	method	NOUN
cana-2940	323	5	based	base	VERB
cana-2940	323	6	on	on	ADP
cana-2940	323	7	image	image	NOUN
cana-2940	323	8	color	color	NOUN
cana-2940	323	9	and	and	CCONJ
cana-2940	323	10	texture	texture	NOUN
cana-2940	323	11	features	feature	NOUN
cana-2940	323	12	.	.	PUNCT
cana-2940	324	1	computational	computational	ADJ
cana-2940	324	2	and	and	CCONJ
cana-2940	324	3	mathematical	mathematical	ADJ
cana-2940	324	4	methods	method	NOUN
cana-2940	324	5	in	in	ADP
cana-2940	324	6	medicine	medicine	NOUN
cana-2940	324	7	,	,	PUNCT
cana-2940	324	8	2018	2018	NUM
cana-2940	324	9	.	.	PUNCT
cana-2940	325	1	https://doi.org/10.1155/2018/8145713	https://doi.org/10.1155/2018/8145713	PROPN
cana-2940	326	1	[	[	X
cana-2940	326	2	33	33	NUM
cana-2940	326	3	]	]	X
cana-2940	326	4	xie	xie	PROPN
cana-2940	326	5	,	,	PUNCT
cana-2940	326	6	s.	s.	PROPN
cana-2940	326	7	,	,	PUNCT
cana-2940	326	8	&	&	CCONJ
cana-2940	326	9	tu	tu	PROPN
cana-2940	326	10	,	,	PUNCT
cana-2940	326	11	z.	z.	PROPN
cana-2940	326	12	(	(	PUNCT
cana-2940	326	13	2017	2017	NUM
cana-2940	326	14	)	)	PUNCT
cana-2940	326	15	.	.	PUNCT
cana-2940	327	1	holistically	holistically	ADV
cana-2940	327	2	-	-	PUNCT
cana-2940	327	3	nested	nest	VERB
cana-2940	327	4	edge	edge	NOUN
cana-2940	327	5	detection	detection	NOUN
cana-2940	327	6	.	.	PUNCT
cana-2940	328	1	international	international	ADJ
cana-2940	328	2	journal	journal	NOUN
cana-2940	328	3	of	of	ADP
cana-2940	328	4	computer	computer	NOUN
cana-2940	328	5	vision	vision	NOUN
cana-2940	328	6	,	,	PUNCT
cana-2940	328	7	125(1–3	125(1–3	NUM
cana-2940	328	8	)	)	PUNCT
cana-2940	328	9	,	,	PUNCT
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