id	sid	tid	token	lemma	pos
aiti-9488	1	1	microsoft	microsoft	PROPN
aiti-9488	1	2	word	word	PROPN
aiti-9488	1	3	5	5	NUM
aiti-9488	1	4	-	-	PUNCT
aiti-9488	1	5	v8n1(2023)-aiti#9488(59	v8n1(2023)-aiti#9488(59	NOUN
aiti-9488	1	6	-	-	PUNCT
aiti-9488	1	7	72).docx	72).docx	NUM
aiti-9488	1	8	advances	advance	NOUN
aiti-9488	1	9	in	in	ADP
aiti-9488	1	10	technology	technology	NOUN
aiti-9488	1	11	innovation	innovation	NOUN
aiti-9488	1	12	,	,	PUNCT
aiti-9488	1	13	vol	vol	NOUN
aiti-9488	1	14	.	.	PROPN
aiti-9488	1	15	8	8	NUM
aiti-9488	1	16	,	,	PUNCT
aiti-9488	1	17	no	no	INTJ
aiti-9488	1	18	.	.	NOUN
aiti-9488	1	19	1	1	NUM
aiti-9488	1	20	,	,	PUNCT
aiti-9488	1	21	2023	2023	NUM
aiti-9488	1	22	,	,	PUNCT
aiti-9488	1	23	pp	pp	ADJ
aiti-9488	1	24	.	.	PUNCT
aiti-9488	2	1	59	59	NUM
aiti-9488	2	2	-	-	SYM
aiti-9488	2	3	72	72	NUM
aiti-9488	2	4	skin	skin	NOUN
aiti-9488	2	5	lesion	lesion	NOUN
aiti-9488	2	6	classification	classification	NOUN
aiti-9488	2	7	towards	towards	ADP
aiti-9488	2	8	melanoma	melanoma	NOUN
aiti-9488	2	9	detection	detection	NOUN
aiti-9488	2	10	using	use	VERB
aiti-9488	2	11	efficientnetb3	efficientnetb3	PROPN
aiti-9488	2	12	saumya	saumya	PROPN
aiti-9488	2	13	salian	salian	PROPN
aiti-9488	2	14	*	*	PROPN
aiti-9488	2	15	,	,	PUNCT
aiti-9488	2	16	sudhir	sudhir	PROPN
aiti-9488	2	17	sawarkar	sawarkar	PROPN
aiti-9488	2	18	department	department	PROPN
aiti-9488	2	19	of	of	ADP
aiti-9488	2	20	computer	computer	NOUN
aiti-9488	2	21	engineering	engineering	NOUN
aiti-9488	2	22	,	,	PUNCT
aiti-9488	2	23	datta	datta	PROPN
aiti-9488	2	24	meghe	meghe	PROPN
aiti-9488	2	25	college	college	PROPN
aiti-9488	2	26	of	of	ADP
aiti-9488	2	27	engineering	engineering	PROPN
aiti-9488	2	28	,	,	PUNCT
aiti-9488	2	29	mumbai	mumbai	PROPN
aiti-9488	2	30	university	university	PROPN
aiti-9488	2	31	,	,	PUNCT
aiti-9488	2	32	mumbai	mumbai	PROPN
aiti-9488	2	33	,	,	PUNCT
aiti-9488	2	34	india	india	PROPN
aiti-9488	2	35	received	receive	VERB
aiti-9488	2	36	16	16	NUM
aiti-9488	2	37	february	february	NOUN
aiti-9488	2	38	2022	2022	NUM
aiti-9488	2	39	;	;	PUNCT
aiti-9488	2	40	received	receive	VERB
aiti-9488	2	41	in	in	ADP
aiti-9488	2	42	revised	revise	VERB
aiti-9488	2	43	form	form	NOUN
aiti-9488	2	44	27	27	NUM
aiti-9488	2	45	april	april	PROPN
aiti-9488	2	46	2022	2022	NUM
aiti-9488	2	47	;	;	PUNCT
aiti-9488	2	48	accepted	accept	VERB
aiti-9488	2	49	01	01	NUM
aiti-9488	2	50	may	may	AUX
aiti-9488	2	51	2022	2022	NUM
aiti-9488	2	52	doi	doi	NOUN
aiti-9488	2	53	:	:	PUNCT
aiti-9488	2	54	https://doi.org/10.46604/aiti.2023.9488	https://doi.org/10.46604/aiti.2023.9488	NOUN
aiti-9488	2	55	abstract	abstract	ADJ
aiti-9488	2	56	the	the	DET
aiti-9488	2	57	rise	rise	NOUN
aiti-9488	2	58	of	of	ADP
aiti-9488	2	59	incidences	incidence	NOUN
aiti-9488	2	60	of	of	ADP
aiti-9488	2	61	melanoma	melanoma	NOUN
aiti-9488	2	62	skin	skin	NOUN
aiti-9488	2	63	cancer	cancer	NOUN
aiti-9488	2	64	is	be	AUX
aiti-9488	2	65	a	a	DET
aiti-9488	2	66	global	global	ADJ
aiti-9488	2	67	health	health	NOUN
aiti-9488	2	68	problem	problem	NOUN
aiti-9488	2	69	.	.	PUNCT
aiti-9488	3	1	skin	skin	NOUN
aiti-9488	3	2	cancer	cancer	NOUN
aiti-9488	3	3	,	,	PUNCT
aiti-9488	3	4	if	if	SCONJ
aiti-9488	3	5	diagnosed	diagnose	VERB
aiti-9488	3	6	at	at	ADP
aiti-9488	3	7	an	an	DET
aiti-9488	3	8	early	early	ADJ
aiti-9488	3	9	stage	stage	NOUN
aiti-9488	3	10	,	,	PUNCT
aiti-9488	3	11	enhances	enhance	VERB
aiti-9488	3	12	the	the	DET
aiti-9488	3	13	chances	chance	NOUN
aiti-9488	3	14	of	of	ADP
aiti-9488	3	15	a	a	DET
aiti-9488	3	16	patient	patient	NOUN
aiti-9488	3	17	’s	’s	PART
aiti-9488	3	18	survival	survival	NOUN
aiti-9488	3	19	.	.	PUNCT
aiti-9488	4	1	building	build	VERB
aiti-9488	4	2	an	an	DET
aiti-9488	4	3	automated	automate	VERB
aiti-9488	4	4	and	and	CCONJ
aiti-9488	4	5	effective	effective	ADJ
aiti-9488	4	6	melanoma	melanoma	NOUN
aiti-9488	4	7	classification	classification	NOUN
aiti-9488	4	8	system	system	NOUN
aiti-9488	4	9	is	be	AUX
aiti-9488	4	10	the	the	DET
aiti-9488	4	11	need	need	NOUN
aiti-9488	4	12	of	of	ADP
aiti-9488	4	13	the	the	DET
aiti-9488	4	14	hour	hour	NOUN
aiti-9488	4	15	.	.	PUNCT
aiti-9488	5	1	in	in	ADP
aiti-9488	5	2	this	this	DET
aiti-9488	5	3	paper	paper	NOUN
aiti-9488	5	4	,	,	PUNCT
aiti-9488	5	5	an	an	DET
aiti-9488	5	6	automated	automate	VERB
aiti-9488	5	7	computer	computer	NOUN
aiti-9488	5	8	-	-	PUNCT
aiti-9488	5	9	based	base	VERB
aiti-9488	5	10	diagnostic	diagnostic	ADJ
aiti-9488	5	11	system	system	NOUN
aiti-9488	5	12	for	for	ADP
aiti-9488	5	13	melanoma	melanoma	NOUN
aiti-9488	5	14	skin	skin	NOUN
aiti-9488	5	15	lesion	lesion	NOUN
aiti-9488	5	16	classification	classification	NOUN
aiti-9488	5	17	is	be	AUX
aiti-9488	5	18	presented	present	VERB
aiti-9488	5	19	using	use	VERB
aiti-9488	5	20	fine	fine	ADV
aiti-9488	5	21	-	-	PUNCT
aiti-9488	5	22	tuned	tune	VERB
aiti-9488	5	23	efficientnetb3	efficientnetb3	PROPN
aiti-9488	5	24	model	model	NOUN
aiti-9488	5	25	over	over	ADP
aiti-9488	5	26	isic	isic	PROPN
aiti-9488	5	27	2017	2017	NUM
aiti-9488	5	28	dataset	dataset	NOUN
aiti-9488	5	29	.	.	PUNCT
aiti-9488	6	1	to	to	PART
aiti-9488	6	2	improve	improve	VERB
aiti-9488	6	3	classification	classification	NOUN
aiti-9488	6	4	results	result	NOUN
aiti-9488	6	5	,	,	PUNCT
aiti-9488	6	6	an	an	DET
aiti-9488	6	7	automated	automate	VERB
aiti-9488	6	8	image	image	NOUN
aiti-9488	6	9	pre	pre	ADJ
aiti-9488	6	10	-	-	ADJ
aiti-9488	6	11	processing	processing	ADJ
aiti-9488	6	12	phase	phase	NOUN
aiti-9488	6	13	is	be	AUX
aiti-9488	6	14	incorporated	incorporate	VERB
aiti-9488	6	15	in	in	ADP
aiti-9488	6	16	this	this	DET
aiti-9488	6	17	study	study	NOUN
aiti-9488	6	18	,	,	PUNCT
aiti-9488	6	19	it	it	PRON
aiti-9488	6	20	can	can	AUX
aiti-9488	6	21	effectively	effectively	ADV
aiti-9488	6	22	remove	remove	VERB
aiti-9488	6	23	noise	noise	NOUN
aiti-9488	6	24	artifacts	artifact	NOUN
aiti-9488	6	25	such	such	ADJ
aiti-9488	6	26	as	as	ADP
aiti-9488	6	27	hair	hair	NOUN
aiti-9488	6	28	structures	structure	NOUN
aiti-9488	6	29	and	and	CCONJ
aiti-9488	6	30	ink	ink	NOUN
aiti-9488	6	31	markers	marker	NOUN
aiti-9488	6	32	from	from	ADP
aiti-9488	6	33	dermoscopic	dermoscopic	ADJ
aiti-9488	6	34	images	image	NOUN
aiti-9488	6	35	.	.	PUNCT
aiti-9488	7	1	comparative	comparative	ADJ
aiti-9488	7	2	analyses	analysis	NOUN
aiti-9488	7	3	of	of	ADP
aiti-9488	7	4	various	various	ADJ
aiti-9488	7	5	advanced	advanced	ADJ
aiti-9488	7	6	models	model	NOUN
aiti-9488	7	7	like	like	ADP
aiti-9488	7	8	resnet50	resnet50	NOUN
aiti-9488	7	9	,	,	PUNCT
aiti-9488	7	10	inceptionv3	inceptionv3	NOUN
aiti-9488	7	11	,	,	PUNCT
aiti-9488	7	12	inceptionresnetv2	inceptionresnetv2	NOUN
aiti-9488	7	13	,	,	PUNCT
aiti-9488	7	14	and	and	CCONJ
aiti-9488	7	15	efficientnetb0	efficientnetb0	NOUN
aiti-9488	7	16	-	-	PUNCT
aiti-9488	7	17	b2	b2	NOUN
aiti-9488	7	18	are	be	AUX
aiti-9488	7	19	conducted	conduct	VERB
aiti-9488	7	20	to	to	PART
aiti-9488	7	21	corroborate	corroborate	VERB
aiti-9488	7	22	the	the	DET
aiti-9488	7	23	performance	performance	NOUN
aiti-9488	7	24	of	of	ADP
aiti-9488	7	25	the	the	DET
aiti-9488	7	26	proposed	propose	VERB
aiti-9488	7	27	model	model	NOUN
aiti-9488	7	28	.	.	PUNCT
aiti-9488	8	1	the	the	DET
aiti-9488	8	2	proposed	propose	VERB
aiti-9488	8	3	system	system	NOUN
aiti-9488	8	4	also	also	ADV
aiti-9488	8	5	addressed	address	VERB
aiti-9488	8	6	the	the	DET
aiti-9488	8	7	issue	issue	NOUN
aiti-9488	8	8	of	of	ADP
aiti-9488	8	9	model	model	NOUN
aiti-9488	8	10	overfitting	overfitting	NOUN
aiti-9488	8	11	and	and	CCONJ
aiti-9488	8	12	achieved	achieve	VERB
aiti-9488	8	13	a	a	DET
aiti-9488	8	14	precision	precision	NOUN
aiti-9488	8	15	of	of	ADP
aiti-9488	8	16	88.00	88.00	NUM
aiti-9488	8	17	%	%	NOUN
aiti-9488	8	18	,	,	PUNCT
aiti-9488	8	19	an	an	DET
aiti-9488	8	20	accuracy	accuracy	NOUN
aiti-9488	8	21	of	of	ADP
aiti-9488	8	22	88.13	88.13	NUM
aiti-9488	8	23	%	%	NOUN
aiti-9488	8	24	,	,	PUNCT
aiti-9488	8	25	recall	recall	NOUN
aiti-9488	8	26	of	of	ADP
aiti-9488	8	27	88	88	NUM
aiti-9488	8	28	%	%	NOUN
aiti-9488	8	29	,	,	PUNCT
aiti-9488	8	30	and	and	CCONJ
aiti-9488	8	31	f1	f1	NOUN
aiti-9488	8	32	-	-	PUNCT
aiti-9488	8	33	score	score	NOUN
aiti-9488	8	34	of	of	ADP
aiti-9488	8	35	88	88	NUM
aiti-9488	8	36	%	%	NOUN
aiti-9488	8	37	.	.	PUNCT
aiti-9488	9	1	keywords	keyword	NOUN
aiti-9488	9	2	:	:	PUNCT
aiti-9488	9	3	malignant	malignant	ADJ
aiti-9488	9	4	,	,	PUNCT
aiti-9488	9	5	skin	skin	NOUN
aiti-9488	9	6	lesion	lesion	NOUN
aiti-9488	9	7	,	,	PUNCT
aiti-9488	9	8	deep	deep	ADJ
aiti-9488	9	9	learning	learning	NOUN
aiti-9488	9	10	,	,	PUNCT
aiti-9488	9	11	classification	classification	NOUN
aiti-9488	9	12	1	1	NUM
aiti-9488	9	13	.	.	PUNCT
aiti-9488	10	1	introduction	introduction	NOUN
aiti-9488	10	2	malignant	malignant	ADJ
aiti-9488	10	3	skin	skin	NOUN
aiti-9488	10	4	cancer	cancer	NOUN
aiti-9488	10	5	is	be	AUX
aiti-9488	10	6	wreaked	wreak	VERB
aiti-9488	10	7	due	due	ADP
aiti-9488	10	8	to	to	ADP
aiti-9488	10	9	anomalous	anomalous	ADJ
aiti-9488	10	10	expansion	expansion	NOUN
aiti-9488	10	11	of	of	ADP
aiti-9488	10	12	melanocyte	melanocyte	ADJ
aiti-9488	10	13	skin	skin	NOUN
aiti-9488	10	14	cells	cell	NOUN
aiti-9488	10	15	and	and	CCONJ
aiti-9488	10	16	causing	cause	VERB
aiti-9488	10	17	tumors	tumor	NOUN
aiti-9488	10	18	to	to	PART
aiti-9488	10	19	form	form	VERB
aiti-9488	10	20	.	.	PUNCT
aiti-9488	11	1	tumors	tumor	NOUN
aiti-9488	11	2	can	can	AUX
aiti-9488	11	3	be	be	AUX
aiti-9488	11	4	malignant	malignant	ADJ
aiti-9488	11	5	or	or	CCONJ
aiti-9488	11	6	benign	benign	ADJ
aiti-9488	11	7	in	in	ADP
aiti-9488	11	8	nature	nature	NOUN
aiti-9488	11	9	.	.	PUNCT
aiti-9488	12	1	malignant	malignant	ADJ
aiti-9488	12	2	tumors	tumor	NOUN
aiti-9488	12	3	are	be	AUX
aiti-9488	12	4	a	a	DET
aiti-9488	12	5	threat	threat	NOUN
aiti-9488	12	6	to	to	ADP
aiti-9488	12	7	human	human	ADJ
aiti-9488	12	8	life	life	NOUN
aiti-9488	12	9	.	.	PUNCT
aiti-9488	13	1	skin	skin	NOUN
aiti-9488	13	2	cancer	cancer	NOUN
aiti-9488	13	3	generally	generally	ADV
aiti-9488	13	4	occurs	occur	VERB
aiti-9488	13	5	in	in	ADP
aiti-9488	13	6	skin	skin	NOUN
aiti-9488	13	7	that	that	PRON
aiti-9488	13	8	is	be	AUX
aiti-9488	13	9	exposed	expose	VERB
aiti-9488	13	10	to	to	ADP
aiti-9488	13	11	sunlight	sunlight	NOUN
aiti-9488	13	12	.	.	PUNCT
aiti-9488	14	1	the	the	DET
aiti-9488	14	2	high	high	ADJ
aiti-9488	14	3	threat	threat	NOUN
aiti-9488	14	4	factor	factor	NOUN
aiti-9488	14	5	causing	cause	VERB
aiti-9488	14	6	any	any	DET
aiti-9488	14	7	type	type	NOUN
aiti-9488	14	8	of	of	ADP
aiti-9488	14	9	skin	skin	NOUN
aiti-9488	14	10	cancer	cancer	NOUN
aiti-9488	14	11	is	be	AUX
aiti-9488	14	12	exposure	exposure	NOUN
aiti-9488	14	13	to	to	ADP
aiti-9488	14	14	natural	natural	ADJ
aiti-9488	14	15	or	or	CCONJ
aiti-9488	14	16	artificial	artificial	ADJ
aiti-9488	14	17	ultraviolet	ultraviolet	ADJ
aiti-9488	14	18	light	light	NOUN
aiti-9488	14	19	.	.	PUNCT
aiti-9488	15	1	out	out	ADP
aiti-9488	15	2	of	of	ADP
aiti-9488	15	3	100	100	NUM
aiti-9488	15	4	different	different	ADJ
aiti-9488	15	5	types	type	NOUN
aiti-9488	15	6	of	of	ADP
aiti-9488	15	7	cancer	cancer	NOUN
aiti-9488	15	8	,	,	PUNCT
aiti-9488	15	9	skin	skin	NOUN
aiti-9488	15	10	cancer	cancer	NOUN
aiti-9488	15	11	is	be	AUX
aiti-9488	15	12	considered	consider	VERB
aiti-9488	15	13	the	the	DET
aiti-9488	15	14	most	most	ADV
aiti-9488	15	15	prevalent	prevalent	ADJ
aiti-9488	15	16	and	and	CCONJ
aiti-9488	15	17	lethal	lethal	ADJ
aiti-9488	15	18	category	category	NOUN
aiti-9488	15	19	of	of	ADP
aiti-9488	15	20	cancer	cancer	NOUN
aiti-9488	15	21	worldwide	worldwide	ADV
aiti-9488	15	22	.	.	PUNCT
aiti-9488	16	1	in	in	ADP
aiti-9488	16	2	america	america	PROPN
aiti-9488	16	3	,	,	PUNCT
aiti-9488	16	4	more	more	ADJ
aiti-9488	16	5	than	than	ADP
aiti-9488	16	6	9500	9500	NUM
aiti-9488	16	7	people	people	NOUN
aiti-9488	16	8	are	be	AUX
aiti-9488	16	9	detected	detect	VERB
aiti-9488	16	10	with	with	ADP
aiti-9488	16	11	skin	skin	NOUN
aiti-9488	16	12	cancer	cancer	NOUN
aiti-9488	16	13	every	every	DET
aiti-9488	16	14	day	day	NOUN
aiti-9488	17	1	[	[	X
aiti-9488	17	2	1	1	NUM
aiti-9488	17	3	]	]	PUNCT
aiti-9488	17	4	.	.	PUNCT
aiti-9488	18	1	the	the	DET
aiti-9488	18	2	number	number	NOUN
aiti-9488	18	3	of	of	ADP
aiti-9488	18	4	detected	detect	VERB
aiti-9488	18	5	skin	skin	NOUN
aiti-9488	18	6	cancer	cancer	NOUN
aiti-9488	18	7	cases	case	NOUN
aiti-9488	18	8	gradually	gradually	ADV
aiti-9488	18	9	increased	increase	VERB
aiti-9488	18	10	to	to	ADP
aiti-9488	18	11	44	44	NUM
aiti-9488	18	12	percent	percent	NOUN
aiti-9488	18	13	from	from	ADP
aiti-9488	18	14	2011	2011	NUM
aiti-9488	18	15	to	to	ADP
aiti-9488	18	16	2021	2021	NUM
aiti-9488	18	17	.	.	PUNCT
aiti-9488	19	1	according	accord	VERB
aiti-9488	19	2	to	to	ADP
aiti-9488	19	3	national	national	PROPN
aiti-9488	19	4	cancer	cancer	PROPN
aiti-9488	19	5	institute	institute	PROPN
aiti-9488	19	6	(	(	PUNCT
aiti-9488	19	7	nih	nih	PROPN
aiti-9488	19	8	)	)	PUNCT
aiti-9488	19	9	,	,	PUNCT
aiti-9488	19	10	around	around	ADP
aiti-9488	19	11	106110	106110	NUM
aiti-9488	19	12	skin	skin	NOUN
aiti-9488	19	13	melanoma	melanoma	NOUN
aiti-9488	19	14	cases	case	NOUN
aiti-9488	19	15	are	be	AUX
aiti-9488	19	16	estimated	estimate	VERB
aiti-9488	19	17	in	in	ADP
aiti-9488	19	18	2022	2022	NUM
aiti-9488	19	19	[	[	X
aiti-9488	19	20	2	2	NUM
aiti-9488	19	21	]	]	PUNCT
aiti-9488	19	22	.	.	PUNCT
aiti-9488	20	1	medical	medical	ADJ
aiti-9488	20	2	experts	expert	NOUN
aiti-9488	20	3	like	like	ADP
aiti-9488	20	4	dermatologists	dermatologist	NOUN
aiti-9488	20	5	examine	examine	VERB
aiti-9488	20	6	skin	skin	NOUN
aiti-9488	20	7	lesions	lesion	NOUN
aiti-9488	20	8	using	use	VERB
aiti-9488	20	9	a	a	DET
aiti-9488	20	10	special	special	ADJ
aiti-9488	20	11	magnifying	magnify	VERB
aiti-9488	20	12	lens	lens	NOUN
aiti-9488	20	13	known	know	VERB
aiti-9488	20	14	as	as	ADP
aiti-9488	20	15	dermatoscopy	dermatoscopy	NOUN
aiti-9488	20	16	[	[	X
aiti-9488	20	17	2	2	NUM
aiti-9488	20	18	]	]	PUNCT
aiti-9488	20	19	.	.	PUNCT
aiti-9488	21	1	other	other	ADJ
aiti-9488	21	2	imaging	imaging	NOUN
aiti-9488	21	3	tests	test	NOUN
aiti-9488	21	4	like	like	ADP
aiti-9488	21	5	ct	ct	NUM
aiti-9488	21	6	scans	scan	NOUN
aiti-9488	21	7	,	,	PUNCT
aiti-9488	21	8	x	x	NOUN
aiti-9488	21	9	-	-	NOUN
aiti-9488	21	10	ray	ray	NOUN
aiti-9488	21	11	,	,	PUNCT
aiti-9488	21	12	and	and	CCONJ
aiti-9488	21	13	mri	mri	NOUN
aiti-9488	21	14	are	be	AUX
aiti-9488	21	15	also	also	ADV
aiti-9488	21	16	used	use	VERB
aiti-9488	21	17	to	to	PART
aiti-9488	21	18	understand	understand	VERB
aiti-9488	21	19	the	the	DET
aiti-9488	21	20	metastases	metastasis	NOUN
aiti-9488	21	21	of	of	ADP
aiti-9488	21	22	pigmented	pigmented	ADJ
aiti-9488	21	23	skin	skin	NOUN
aiti-9488	21	24	cells	cell	NOUN
aiti-9488	21	25	.	.	PUNCT
aiti-9488	22	1	visual	visual	ADJ
aiti-9488	22	2	examination	examination	NOUN
aiti-9488	22	3	of	of	ADP
aiti-9488	22	4	skin	skin	NOUN
aiti-9488	22	5	lesions	lesion	NOUN
aiti-9488	22	6	using	use	VERB
aiti-9488	22	7	dermatoscopy	dermatoscopy	NOUN
aiti-9488	22	8	is	be	AUX
aiti-9488	22	9	a	a	DET
aiti-9488	22	10	method	method	NOUN
aiti-9488	22	11	followed	follow	VERB
aiti-9488	22	12	by	by	ADP
aiti-9488	22	13	medical	medical	ADJ
aiti-9488	22	14	experts	expert	NOUN
aiti-9488	22	15	,	,	PUNCT
aiti-9488	22	16	and	and	CCONJ
aiti-9488	22	17	its	its	PRON
aiti-9488	22	18	prognosis	prognosis	NOUN
aiti-9488	22	19	usually	usually	ADV
aiti-9488	22	20	relies	rely	VERB
aiti-9488	22	21	on	on	ADP
aiti-9488	22	22	their	their	PRON
aiti-9488	22	23	experience	experience	NOUN
aiti-9488	22	24	.	.	PUNCT
aiti-9488	23	1	skin	skin	NOUN
aiti-9488	23	2	cancer	cancer	NOUN
aiti-9488	23	3	,	,	PUNCT
aiti-9488	23	4	if	if	SCONJ
aiti-9488	23	5	discovered	discover	VERB
aiti-9488	23	6	at	at	ADP
aiti-9488	23	7	a	a	DET
aiti-9488	23	8	preliminary	preliminary	ADJ
aiti-9488	23	9	stage	stage	NOUN
aiti-9488	23	10	,	,	PUNCT
aiti-9488	23	11	will	will	AUX
aiti-9488	23	12	increase	increase	VERB
aiti-9488	23	13	the	the	DET
aiti-9488	23	14	survival	survival	NOUN
aiti-9488	23	15	rate	rate	NOUN
aiti-9488	23	16	among	among	ADP
aiti-9488	23	17	the	the	DET
aiti-9488	23	18	patients	patient	NOUN
aiti-9488	23	19	.	.	PUNCT
aiti-9488	24	1	hence	hence	ADV
aiti-9488	24	2	,	,	PUNCT
aiti-9488	24	3	it	it	PRON
aiti-9488	24	4	is	be	AUX
aiti-9488	24	5	vital	vital	ADJ
aiti-9488	24	6	to	to	PART
aiti-9488	24	7	build	build	VERB
aiti-9488	24	8	a	a	DET
aiti-9488	24	9	diagnostic	diagnostic	ADJ
aiti-9488	24	10	system	system	NOUN
aiti-9488	24	11	based	base	VERB
aiti-9488	24	12	on	on	ADP
aiti-9488	24	13	a	a	DET
aiti-9488	24	14	deep	deep	ADJ
aiti-9488	24	15	learning	learning	NOUN
aiti-9488	24	16	network	network	NOUN
aiti-9488	24	17	to	to	PART
aiti-9488	24	18	detect	detect	VERB
aiti-9488	24	19	malignant	malignant	ADJ
aiti-9488	24	20	categories	category	NOUN
aiti-9488	24	21	of	of	ADP
aiti-9488	24	22	skin	skin	NOUN
aiti-9488	24	23	cancer	cancer	NOUN
aiti-9488	24	24	.	.	PUNCT
aiti-9488	25	1	building	build	VERB
aiti-9488	25	2	a	a	DET
aiti-9488	25	3	computer	computer	NOUN
aiti-9488	25	4	-	-	PUNCT
aiti-9488	25	5	based	base	VERB
aiti-9488	25	6	diagnostic	diagnostic	ADJ
aiti-9488	25	7	system	system	NOUN
aiti-9488	25	8	will	will	AUX
aiti-9488	25	9	support	support	VERB
aiti-9488	25	10	medical	medical	ADJ
aiti-9488	25	11	practitioners	practitioner	NOUN
aiti-9488	25	12	to	to	PART
aiti-9488	25	13	take	take	VERB
aiti-9488	25	14	advantage	advantage	NOUN
aiti-9488	25	15	of	of	ADP
aiti-9488	25	16	technological	technological	ADJ
aiti-9488	25	17	overtures	overture	NOUN
aiti-9488	25	18	and	and	CCONJ
aiti-9488	25	19	help	help	VERB
aiti-9488	25	20	them	they	PRON
aiti-9488	25	21	to	to	PART
aiti-9488	25	22	have	have	VERB
aiti-9488	25	23	a	a	DET
aiti-9488	25	24	second	second	ADJ
aiti-9488	25	25	opinion	opinion	NOUN
aiti-9488	25	26	.	.	PUNCT
aiti-9488	26	1	since	since	SCONJ
aiti-9488	26	2	2015	2015	NUM
aiti-9488	26	3	,	,	PUNCT
aiti-9488	26	4	several	several	ADJ
aiti-9488	26	5	deep	deep	ADJ
aiti-9488	26	6	learning	learning	NOUN
aiti-9488	26	7	architectures	architecture	NOUN
aiti-9488	26	8	have	have	AUX
aiti-9488	26	9	been	be	AUX
aiti-9488	26	10	explored	explore	VERB
aiti-9488	26	11	to	to	PART
aiti-9488	26	12	build	build	VERB
aiti-9488	26	13	an	an	DET
aiti-9488	26	14	automated	automate	VERB
aiti-9488	26	15	diagnostic	diagnostic	ADJ
aiti-9488	26	16	system	system	NOUN
aiti-9488	26	17	that	that	PRON
aiti-9488	26	18	is	be	AUX
aiti-9488	26	19	forced	force	VERB
aiti-9488	26	20	to	to	PART
aiti-9488	26	21	play	play	VERB
aiti-9488	26	22	a	a	DET
aiti-9488	26	23	fundamental	fundamental	ADJ
aiti-9488	26	24	contribution	contribution	NOUN
aiti-9488	26	25	in	in	ADP
aiti-9488	26	26	the	the	DET
aiti-9488	26	27	timely	timely	ADJ
aiti-9488	26	28	discovery	discovery	NOUN
aiti-9488	26	29	of	of	ADP
aiti-9488	26	30	malignant	malignant	ADJ
aiti-9488	26	31	cancer	cancer	NOUN
aiti-9488	27	1	[	[	X
aiti-9488	27	2	3	3	NUM
aiti-9488	27	3	]	]	PUNCT
aiti-9488	27	4	.	.	PUNCT
aiti-9488	28	1	convolutional	convolutional	ADJ
aiti-9488	28	2	neural	neural	ADJ
aiti-9488	28	3	network	network	NOUN
aiti-9488	28	4	(	(	PUNCT
aiti-9488	28	5	cnn	cnn	PROPN
aiti-9488	28	6	)	)	PUNCT
aiti-9488	28	7	model	model	PROPN
aiti-9488	28	8	serves	serve	VERB
aiti-9488	28	9	a	a	DET
aiti-9488	28	10	significant	significant	ADJ
aiti-9488	28	11	part	part	NOUN
aiti-9488	28	12	in	in	ADP
aiti-9488	28	13	medical	medical	ADJ
aiti-9488	28	14	image	image	NOUN
aiti-9488	28	15	analysis	analysis	NOUN
aiti-9488	28	16	.	.	PUNCT
aiti-9488	29	1	with	with	ADP
aiti-9488	29	2	diversified	diversify	VERB
aiti-9488	29	3	cnn	cnn	PROPN
aiti-9488	29	4	architectures	architecture	NOUN
aiti-9488	29	5	,	,	PUNCT
aiti-9488	29	6	it	it	PRON
aiti-9488	29	7	becomes	become	VERB
aiti-9488	29	8	arduous	arduous	ADJ
aiti-9488	29	9	to	to	PART
aiti-9488	29	10	select	select	VERB
aiti-9488	29	11	the	the	DET
aiti-9488	29	12	apposite	apposite	ADJ
aiti-9488	29	13	model	model	NOUN
aiti-9488	29	14	for	for	ADP
aiti-9488	29	15	melanoma	melanoma	NOUN
aiti-9488	29	16	classification	classification	NOUN
aiti-9488	29	17	.	.	PUNCT
aiti-9488	30	1	choosing	choose	VERB
aiti-9488	30	2	the	the	DET
aiti-9488	30	3	right	right	ADJ
aiti-9488	30	4	model	model	NOUN
aiti-9488	30	5	will	will	AUX
aiti-9488	30	6	aid	aid	VERB
aiti-9488	30	7	in	in	ADP
aiti-9488	30	8	developing	develop	VERB
aiti-9488	30	9	an	an	DET
aiti-9488	30	10	accurate	accurate	ADJ
aiti-9488	30	11	melanoma	melanoma	NOUN
aiti-9488	30	12	skin	skin	NOUN
aiti-9488	30	13	lesion	lesion	NOUN
aiti-9488	30	14	classification	classification	NOUN
aiti-9488	30	15	model	model	NOUN
aiti-9488	30	16	.	.	PUNCT
aiti-9488	31	1	*	*	PUNCT
aiti-9488	31	2	corresponding	correspond	VERB
aiti-9488	31	3	author	author	NOUN
aiti-9488	31	4	.	.	PUNCT
aiti-9488	32	1	e	e	X
aiti-9488	32	2	-	-	NOUN
aiti-9488	32	3	mail	mail	NOUN
aiti-9488	32	4	address	address	NOUN
aiti-9488	32	5	:	:	PUNCT
aiti-9488	32	6	srs.cm.dmce@gmail.com	srs.cm.dmce@gmail.com	X
aiti-9488	32	7	advances	advance	NOUN
aiti-9488	32	8	in	in	ADP
aiti-9488	32	9	technology	technology	NOUN
aiti-9488	32	10	innovation	innovation	NOUN
aiti-9488	32	11	,	,	PUNCT
aiti-9488	32	12	vol	vol	NOUN
aiti-9488	32	13	.	.	PROPN
aiti-9488	32	14	8	8	NUM
aiti-9488	32	15	,	,	PUNCT
aiti-9488	32	16	no	no	INTJ
aiti-9488	32	17	.	.	NOUN
aiti-9488	32	18	1	1	NUM
aiti-9488	32	19	,	,	PUNCT
aiti-9488	32	20	2023	2023	NUM
aiti-9488	32	21	,	,	PUNCT
aiti-9488	32	22	pp	pp	ADJ
aiti-9488	32	23	.	.	PUNCT
aiti-9488	33	1	59	59	NUM
aiti-9488	33	2	-	-	SYM
aiti-9488	33	3	72	72	NUM
aiti-9488	33	4	60	60	NUM
aiti-9488	33	5	transfer	transfer	NOUN
aiti-9488	33	6	learning	learning	NOUN
aiti-9488	33	7	is	be	AUX
aiti-9488	33	8	an	an	DET
aiti-9488	33	9	approach	approach	NOUN
aiti-9488	33	10	to	to	ADP
aiti-9488	33	11	utilizing	utilize	VERB
aiti-9488	33	12	the	the	DET
aiti-9488	33	13	learning	learning	NOUN
aiti-9488	33	14	acquired	acquire	VERB
aiti-9488	33	15	by	by	ADP
aiti-9488	33	16	a	a	DET
aiti-9488	33	17	model	model	NOUN
aiti-9488	33	18	that	that	PRON
aiti-9488	33	19	is	be	AUX
aiti-9488	33	20	trained	train	VERB
aiti-9488	33	21	and	and	CCONJ
aiti-9488	33	22	built	build	VERB
aiti-9488	33	23	on	on	ADP
aiti-9488	33	24	a	a	DET
aiti-9488	33	25	peculiar	peculiar	ADJ
aiti-9488	33	26	target	target	NOUN
aiti-9488	33	27	and	and	CCONJ
aiti-9488	33	28	constructs	construct	VERB
aiti-9488	33	29	a	a	DET
aiti-9488	33	30	solution	solution	NOUN
aiti-9488	33	31	for	for	ADP
aiti-9488	33	32	a	a	DET
aiti-9488	33	33	similar	similar	ADJ
aiti-9488	33	34	target	target	NOUN
aiti-9488	33	35	.	.	PUNCT
aiti-9488	34	1	most	most	ADJ
aiti-9488	34	2	of	of	ADP
aiti-9488	34	3	the	the	DET
aiti-9488	34	4	pre	pre	ADJ
aiti-9488	34	5	-	-	ADJ
aiti-9488	34	6	trained	train	VERB
aiti-9488	34	7	models	model	NOUN
aiti-9488	34	8	are	be	AUX
aiti-9488	34	9	trained	train	VERB
aiti-9488	34	10	over	over	ADP
aiti-9488	34	11	the	the	DET
aiti-9488	34	12	imagenet	imagenet	NOUN
aiti-9488	34	13	dataset	dataset	NOUN
aiti-9488	34	14	.	.	PUNCT
aiti-9488	35	1	imagenet	imagenet	PROPN
aiti-9488	35	2	consists	consist	VERB
aiti-9488	35	3	of	of	ADP
aiti-9488	35	4	over	over	ADP
aiti-9488	35	5	15	15	NUM
aiti-9488	35	6	million	million	NUM
aiti-9488	35	7	diverse	diverse	ADJ
aiti-9488	35	8	labeled	label	VERB
aiti-9488	35	9	images	image	NOUN
aiti-9488	35	10	with	with	ADP
aiti-9488	35	11	1000	1000	NUM
aiti-9488	35	12	classes	class	NOUN
aiti-9488	35	13	.	.	PUNCT
aiti-9488	36	1	fine	fine	ADJ
aiti-9488	36	2	-	-	PUNCT
aiti-9488	36	3	tuning	tune	VERB
aiti-9488	36	4	pre	pre	ADJ
aiti-9488	36	5	-	-	ADJ
aiti-9488	36	6	trained	train	VERB
aiti-9488	36	7	models	model	NOUN
aiti-9488	36	8	is	be	AUX
aiti-9488	36	9	a	a	DET
aiti-9488	36	10	prerequisite	prerequisite	NOUN
aiti-9488	36	11	to	to	ADP
aiti-9488	36	12	adjusting	adjust	VERB
aiti-9488	36	13	these	these	DET
aiti-9488	36	14	models	model	NOUN
aiti-9488	36	15	to	to	ADP
aiti-9488	36	16	the	the	DET
aiti-9488	36	17	target	target	NOUN
aiti-9488	36	18	domain	domain	NOUN
aiti-9488	36	19	of	of	ADP
aiti-9488	36	20	malignant	malignant	ADJ
aiti-9488	36	21	and	and	CCONJ
aiti-9488	36	22	benign	benign	ADJ
aiti-9488	36	23	lesion	lesion	NOUN
aiti-9488	36	24	classification	classification	NOUN
aiti-9488	36	25	.	.	PUNCT
aiti-9488	37	1	the	the	DET
aiti-9488	37	2	fundamental	fundamental	ADJ
aiti-9488	37	3	weights	weight	NOUN
aiti-9488	37	4	of	of	ADP
aiti-9488	37	5	the	the	DET
aiti-9488	37	6	pretrained	pretraine	VERB
aiti-9488	37	7	models	model	NOUN
aiti-9488	37	8	are	be	AUX
aiti-9488	37	9	fine	fine	ADV
aiti-9488	37	10	-	-	PUNCT
aiti-9488	37	11	tuned	tune	VERB
aiti-9488	37	12	to	to	PART
aiti-9488	37	13	adapt	adapt	VERB
aiti-9488	37	14	to	to	ADP
aiti-9488	37	15	the	the	DET
aiti-9488	37	16	two	two	NUM
aiti-9488	37	17	-	-	PUNCT
aiti-9488	37	18	class	class	NOUN
aiti-9488	37	19	classification	classification	NOUN
aiti-9488	37	20	task	task	NOUN
aiti-9488	37	21	.	.	PUNCT
aiti-9488	38	1	many	many	ADJ
aiti-9488	38	2	of	of	ADP
aiti-9488	38	3	the	the	DET
aiti-9488	38	4	research	research	NOUN
aiti-9488	38	5	papers	paper	NOUN
aiti-9488	38	6	addressed	address	VERB
aiti-9488	38	7	the	the	DET
aiti-9488	38	8	problem	problem	NOUN
aiti-9488	38	9	of	of	ADP
aiti-9488	38	10	noise	noise	NOUN
aiti-9488	38	11	artifacts	artifact	NOUN
aiti-9488	38	12	like	like	ADP
aiti-9488	38	13	the	the	DET
aiti-9488	38	14	presence	presence	NOUN
aiti-9488	38	15	of	of	ADP
aiti-9488	38	16	hair	hair	NOUN
aiti-9488	38	17	,	,	PUNCT
aiti-9488	38	18	low	low	ADJ
aiti-9488	38	19	contrast	contrast	NOUN
aiti-9488	38	20	images	image	NOUN
aiti-9488	38	21	,	,	PUNCT
aiti-9488	38	22	etc	etc	X
aiti-9488	38	23	.	.	X
aiti-9488	38	24	very	very	ADV
aiti-9488	38	25	few	few	ADJ
aiti-9488	38	26	articles	article	NOUN
aiti-9488	38	27	addressed	address	VERB
aiti-9488	38	28	the	the	DET
aiti-9488	38	29	issue	issue	NOUN
aiti-9488	38	30	of	of	ADP
aiti-9488	38	31	ink	ink	NOUN
aiti-9488	38	32	markers	marker	NOUN
aiti-9488	38	33	in	in	ADP
aiti-9488	38	34	lesion	lesion	NOUN
aiti-9488	38	35	images	image	NOUN
aiti-9488	38	36	.	.	PUNCT
aiti-9488	39	1	when	when	SCONJ
aiti-9488	39	2	building	build	VERB
aiti-9488	39	3	deep	deep	ADJ
aiti-9488	39	4	learning	learning	NOUN
aiti-9488	39	5	models	model	NOUN
aiti-9488	39	6	,	,	PUNCT
aiti-9488	39	7	these	these	DET
aiti-9488	39	8	ink	ink	NOUN
aiti-9488	39	9	markings	marking	NOUN
aiti-9488	39	10	may	may	AUX
aiti-9488	39	11	be	be	AUX
aiti-9488	39	12	mistaken	mistaken	ADJ
aiti-9488	39	13	for	for	ADP
aiti-9488	39	14	skin	skin	NOUN
aiti-9488	39	15	lesions	lesion	NOUN
aiti-9488	39	16	and	and	CCONJ
aiti-9488	39	17	result	result	VERB
aiti-9488	39	18	in	in	ADP
aiti-9488	39	19	incorrect	incorrect	ADJ
aiti-9488	39	20	interpretations	interpretation	NOUN
aiti-9488	39	21	.	.	PUNCT
aiti-9488	40	1	in	in	ADP
aiti-9488	40	2	the	the	DET
aiti-9488	40	3	proposed	propose	VERB
aiti-9488	40	4	model	model	NOUN
aiti-9488	40	5	,	,	PUNCT
aiti-9488	40	6	an	an	DET
aiti-9488	40	7	automated	automate	VERB
aiti-9488	40	8	image	image	NOUN
aiti-9488	40	9	preprocessing	preprocessing	NOUN
aiti-9488	40	10	method	method	NOUN
aiti-9488	40	11	is	be	AUX
aiti-9488	40	12	employed	employ	VERB
aiti-9488	40	13	that	that	SCONJ
aiti-9488	40	14	effectively	effectively	ADV
aiti-9488	40	15	eliminates	eliminate	VERB
aiti-9488	40	16	both	both	CCONJ
aiti-9488	40	17	surgical	surgical	ADJ
aiti-9488	40	18	ink	ink	NOUN
aiti-9488	40	19	markers	marker	NOUN
aiti-9488	40	20	and	and	CCONJ
aiti-9488	40	21	hair	hair	NOUN
aiti-9488	40	22	artifacts	artifact	NOUN
aiti-9488	40	23	from	from	ADP
aiti-9488	40	24	the	the	DET
aiti-9488	40	25	isic	isic	PROPN
aiti-9488	40	26	2017	2017	NUM
aiti-9488	40	27	dataset	dataset	NOUN
aiti-9488	40	28	.	.	PUNCT
aiti-9488	41	1	in	in	ADP
aiti-9488	41	2	this	this	DET
aiti-9488	41	3	work	work	NOUN
aiti-9488	41	4	,	,	PUNCT
aiti-9488	41	5	a	a	DET
aiti-9488	41	6	deep	deep	ADJ
aiti-9488	41	7	learning	learning	NOUN
aiti-9488	41	8	-	-	PUNCT
aiti-9488	41	9	based	base	VERB
aiti-9488	41	10	,	,	PUNCT
aiti-9488	41	11	fine	fine	ADV
aiti-9488	41	12	-	-	PUNCT
aiti-9488	41	13	tuned	tune	VERB
aiti-9488	41	14	efficientnetb3	efficientnetb3	NOUN
aiti-9488	41	15	skin	skin	NOUN
aiti-9488	41	16	lesion	lesion	NOUN
aiti-9488	41	17	classification	classification	NOUN
aiti-9488	41	18	model	model	NOUN
aiti-9488	41	19	is	be	AUX
aiti-9488	41	20	proposed	propose	VERB
aiti-9488	41	21	that	that	PRON
aiti-9488	41	22	classifies	classify	VERB
aiti-9488	41	23	lesion	lesion	NOUN
aiti-9488	41	24	images	image	NOUN
aiti-9488	41	25	into	into	ADP
aiti-9488	41	26	malignant	malignant	ADJ
aiti-9488	41	27	and	and	CCONJ
aiti-9488	41	28	benign	benign	ADJ
aiti-9488	41	29	classes	class	NOUN
aiti-9488	41	30	.	.	PUNCT
aiti-9488	42	1	to	to	PART
aiti-9488	42	2	improve	improve	VERB
aiti-9488	42	3	model	model	NOUN
aiti-9488	42	4	performance	performance	NOUN
aiti-9488	42	5	and	and	CCONJ
aiti-9488	42	6	reduce	reduce	VERB
aiti-9488	42	7	model	model	NOUN
aiti-9488	42	8	overfitting	overfitting	NOUN
aiti-9488	42	9	,	,	PUNCT
aiti-9488	42	10	various	various	ADJ
aiti-9488	42	11	data	datum	NOUN
aiti-9488	42	12	augmentation	augmentation	NOUN
aiti-9488	42	13	methods	method	NOUN
aiti-9488	42	14	along	along	ADP
aiti-9488	42	15	with	with	ADP
aiti-9488	42	16	global	global	ADJ
aiti-9488	42	17	average	average	ADJ
aiti-9488	42	18	pooling	pool	VERB
aiti-9488	42	19	gap	gap	NOUN
aiti-9488	42	20	)	)	PUNCT
aiti-9488	42	21	and	and	CCONJ
aiti-9488	42	22	a	a	DET
aiti-9488	42	23	fully	fully	ADV
aiti-9488	42	24	connected	connected	ADJ
aiti-9488	42	25	classification	classification	NOUN
aiti-9488	42	26	layer	layer	NOUN
aiti-9488	42	27	using	use	VERB
aiti-9488	42	28	softmax	softmax	NOUN
aiti-9488	42	29	are	be	AUX
aiti-9488	42	30	incorporated	incorporate	VERB
aiti-9488	42	31	into	into	ADP
aiti-9488	42	32	the	the	DET
aiti-9488	42	33	proposed	propose	VERB
aiti-9488	42	34	model	model	NOUN
aiti-9488	42	35	.	.	PUNCT
aiti-9488	43	1	in	in	ADP
aiti-9488	43	2	this	this	DET
aiti-9488	43	3	paper	paper	NOUN
aiti-9488	43	4	,	,	PUNCT
aiti-9488	43	5	an	an	DET
aiti-9488	43	6	experimental	experimental	ADJ
aiti-9488	43	7	evaluation	evaluation	NOUN
aiti-9488	43	8	of	of	ADP
aiti-9488	43	9	the	the	DET
aiti-9488	43	10	proposed	propose	VERB
aiti-9488	43	11	approach	approach	NOUN
aiti-9488	43	12	with	with	ADP
aiti-9488	43	13	other	other	ADJ
aiti-9488	43	14	advanced	advanced	ADJ
aiti-9488	43	15	models	model	NOUN
aiti-9488	43	16	is	be	AUX
aiti-9488	43	17	carried	carry	VERB
aiti-9488	43	18	out	out	ADP
aiti-9488	43	19	to	to	PART
aiti-9488	43	20	review	review	VERB
aiti-9488	43	21	the	the	DET
aiti-9488	43	22	potency	potency	NOUN
aiti-9488	43	23	of	of	ADP
aiti-9488	43	24	the	the	DET
aiti-9488	43	25	proposed	propose	VERB
aiti-9488	43	26	model	model	NOUN
aiti-9488	43	27	.	.	PUNCT
aiti-9488	44	1	all	all	DET
aiti-9488	44	2	the	the	DET
aiti-9488	44	3	experimental	experimental	ADJ
aiti-9488	44	4	analyses	analysis	NOUN
aiti-9488	44	5	are	be	AUX
aiti-9488	44	6	carried	carry	VERB
aiti-9488	44	7	out	out	ADP
aiti-9488	44	8	on	on	ADP
aiti-9488	44	9	isic	isic	PROPN
aiti-9488	44	10	2017	2017	NUM
aiti-9488	44	11	dataset	dataset	NOUN
aiti-9488	45	1	[	[	X
aiti-9488	45	2	4	4	NUM
aiti-9488	45	3	]	]	PUNCT
aiti-9488	45	4	.	.	PUNCT
aiti-9488	46	1	evaluation	evaluation	NOUN
aiti-9488	46	2	metrics	metric	NOUN
aiti-9488	46	3	like	like	ADP
aiti-9488	46	4	f1	f1	NOUN
aiti-9488	46	5	-	-	PUNCT
aiti-9488	46	6	score	score	NOUN
aiti-9488	46	7	,	,	PUNCT
aiti-9488	46	8	accuracy	accuracy	NOUN
aiti-9488	46	9	,	,	PUNCT
aiti-9488	46	10	recall	recall	NOUN
aiti-9488	46	11	,	,	PUNCT
aiti-9488	46	12	and	and	CCONJ
aiti-9488	46	13	precision	precision	NOUN
aiti-9488	46	14	are	be	AUX
aiti-9488	46	15	computed	compute	VERB
aiti-9488	46	16	.	.	PUNCT
aiti-9488	47	1	empirical	empirical	ADJ
aiti-9488	47	2	findings	finding	NOUN
aiti-9488	47	3	testify	testify	VERB
aiti-9488	47	4	to	to	ADP
aiti-9488	47	5	the	the	DET
aiti-9488	47	6	efficiency	efficiency	NOUN
aiti-9488	47	7	of	of	ADP
aiti-9488	47	8	the	the	DET
aiti-9488	47	9	proposed	propose	VERB
aiti-9488	47	10	model	model	NOUN
aiti-9488	47	11	in	in	ADP
aiti-9488	47	12	comparison	comparison	NOUN
aiti-9488	47	13	to	to	ADP
aiti-9488	47	14	other	other	ADJ
aiti-9488	47	15	pre	pre	ADJ
aiti-9488	47	16	-	-	ADJ
aiti-9488	47	17	trained	train	VERB
aiti-9488	47	18	models	model	NOUN
aiti-9488	47	19	and	and	CCONJ
aiti-9488	47	20	also	also	ADV
aiti-9488	47	21	deliver	deliver	VERB
aiti-9488	47	22	favorable	favorable	ADJ
aiti-9488	47	23	outcomes	outcome	NOUN
aiti-9488	47	24	which	which	PRON
aiti-9488	47	25	address	address	VERB
aiti-9488	47	26	the	the	DET
aiti-9488	47	27	problem	problem	NOUN
aiti-9488	47	28	of	of	ADP
aiti-9488	47	29	model	model	NOUN
aiti-9488	47	30	overfitting	overfitting	NOUN
aiti-9488	47	31	.	.	PUNCT
aiti-9488	48	1	the	the	DET
aiti-9488	48	2	contribution	contribution	NOUN
aiti-9488	48	3	of	of	ADP
aiti-9488	48	4	the	the	DET
aiti-9488	48	5	work	work	NOUN
aiti-9488	48	6	is	be	AUX
aiti-9488	48	7	listed	list	VERB
aiti-9488	48	8	below	below	ADP
aiti-9488	48	9	:	:	PUNCT
aiti-9488	48	10	(	(	PUNCT
aiti-9488	48	11	1	1	X
aiti-9488	48	12	)	)	PUNCT
aiti-9488	48	13	an	an	DET
aiti-9488	48	14	automated	automate	VERB
aiti-9488	48	15	image	image	NOUN
aiti-9488	48	16	preprocessing	preprocessing	NOUN
aiti-9488	48	17	model	model	NOUN
aiti-9488	48	18	that	that	PRON
aiti-9488	48	19	removes	remove	VERB
aiti-9488	48	20	noise	noise	NOUN
aiti-9488	48	21	artifacts	artifact	NOUN
aiti-9488	48	22	like	like	ADP
aiti-9488	48	23	thin	thin	ADJ
aiti-9488	48	24	and	and	CCONJ
aiti-9488	48	25	thick	thick	ADJ
aiti-9488	48	26	hair	hair	NOUN
aiti-9488	48	27	structures	structure	NOUN
aiti-9488	48	28	and	and	CCONJ
aiti-9488	48	29	surgical	surgical	ADJ
aiti-9488	48	30	ink	ink	NOUN
aiti-9488	48	31	markers	marker	NOUN
aiti-9488	48	32	from	from	ADP
aiti-9488	48	33	lesion	lesion	NOUN
aiti-9488	48	34	images	image	NOUN
aiti-9488	48	35	is	be	AUX
aiti-9488	48	36	presented	present	VERB
aiti-9488	48	37	in	in	ADP
aiti-9488	48	38	this	this	DET
aiti-9488	48	39	study	study	NOUN
aiti-9488	48	40	.	.	PUNCT
aiti-9488	49	1	(	(	PUNCT
aiti-9488	49	2	2	2	X
aiti-9488	49	3	)	)	PUNCT
aiti-9488	49	4	fine	fine	ADV
aiti-9488	49	5	-	-	PUNCT
aiti-9488	49	6	tuned	tune	VERB
aiti-9488	49	7	efficientnetb3	efficientnetb3	NOUN
aiti-9488	49	8	deep	deep	ADJ
aiti-9488	49	9	learning	learning	NOUN
aiti-9488	49	10	model	model	NOUN
aiti-9488	49	11	is	be	AUX
aiti-9488	49	12	proposed	propose	VERB
aiti-9488	49	13	to	to	PART
aiti-9488	49	14	build	build	VERB
aiti-9488	49	15	an	an	DET
aiti-9488	49	16	efficient	efficient	ADJ
aiti-9488	49	17	computer	computer	NOUN
aiti-9488	49	18	-	-	PUNCT
aiti-9488	49	19	based	base	VERB
aiti-9488	49	20	diagnostic	diagnostic	ADJ
aiti-9488	49	21	system	system	NOUN
aiti-9488	49	22	for	for	ADP
aiti-9488	49	23	improved	improved	ADJ
aiti-9488	49	24	melanoma	melanoma	NOUN
aiti-9488	49	25	classification	classification	NOUN
aiti-9488	49	26	.	.	PUNCT
aiti-9488	50	1	(	(	PUNCT
aiti-9488	50	2	3	3	X
aiti-9488	50	3	)	)	PUNCT
aiti-9488	50	4	to	to	PART
aiti-9488	50	5	achieve	achieve	VERB
aiti-9488	50	6	better	well	ADJ
aiti-9488	50	7	accuracy	accuracy	NOUN
aiti-9488	50	8	and	and	CCONJ
aiti-9488	50	9	overcome	overcome	VERB
aiti-9488	50	10	the	the	DET
aiti-9488	50	11	drawback	drawback	NOUN
aiti-9488	50	12	of	of	ADP
aiti-9488	50	13	model	model	NOUN
aiti-9488	50	14	overfitting	overfitting	NOUN
aiti-9488	50	15	,	,	PUNCT
aiti-9488	50	16	data	datum	NOUN
aiti-9488	50	17	augmentation	augmentation	NOUN
aiti-9488	50	18	techniques	technique	NOUN
aiti-9488	50	19	are	be	AUX
aiti-9488	50	20	employed	employ	VERB
aiti-9488	50	21	,	,	PUNCT
aiti-9488	50	22	and	and	CCONJ
aiti-9488	50	23	a	a	DET
aiti-9488	50	24	custom	custom	NOUN
aiti-9488	50	25	layer	layer	NOUN
aiti-9488	50	26	of	of	ADP
aiti-9488	50	27	gap	gap	NOUN
aiti-9488	50	28	is	be	AUX
aiti-9488	50	29	exerted	exert	VERB
aiti-9488	50	30	over	over	ADP
aiti-9488	50	31	the	the	DET
aiti-9488	50	32	training	training	NOUN
aiti-9488	50	33	and	and	CCONJ
aiti-9488	50	34	testing	testing	NOUN
aiti-9488	50	35	phase	phase	NOUN
aiti-9488	50	36	.	.	PUNCT
aiti-9488	51	1	(	(	PUNCT
aiti-9488	51	2	4	4	NUM
aiti-9488	51	3	)	)	PUNCT
aiti-9488	51	4	to	to	PART
aiti-9488	51	5	review	review	VERB
aiti-9488	51	6	the	the	DET
aiti-9488	51	7	efficacy	efficacy	NOUN
aiti-9488	51	8	of	of	ADP
aiti-9488	51	9	the	the	DET
aiti-9488	51	10	proposed	propose	VERB
aiti-9488	51	11	design	design	NOUN
aiti-9488	51	12	,	,	PUNCT
aiti-9488	51	13	comparative	comparative	ADJ
aiti-9488	51	14	experimental	experimental	ADJ
aiti-9488	51	15	analyses	analysis	NOUN
aiti-9488	51	16	with	with	ADP
aiti-9488	51	17	other	other	ADJ
aiti-9488	51	18	pre	pre	ADJ
aiti-9488	51	19	-	-	ADJ
aiti-9488	51	20	trained	train	VERB
aiti-9488	51	21	deep	deep	ADJ
aiti-9488	51	22	learning	learning	NOUN
aiti-9488	51	23	models	model	NOUN
aiti-9488	51	24	are	be	AUX
aiti-9488	51	25	carried	carry	VERB
aiti-9488	51	26	out	out	ADP
aiti-9488	51	27	.	.	PUNCT
aiti-9488	52	1	the	the	DET
aiti-9488	52	2	paper	paper	NOUN
aiti-9488	52	3	is	be	AUX
aiti-9488	52	4	illustrated	illustrate	VERB
aiti-9488	52	5	in	in	ADP
aiti-9488	52	6	the	the	DET
aiti-9488	52	7	following	following	ADJ
aiti-9488	52	8	way	way	NOUN
aiti-9488	52	9	,	,	PUNCT
aiti-9488	52	10	related	relate	VERB
aiti-9488	52	11	recent	recent	ADJ
aiti-9488	52	12	works	work	NOUN
aiti-9488	52	13	are	be	AUX
aiti-9488	52	14	stated	state	VERB
aiti-9488	52	15	in	in	ADP
aiti-9488	52	16	section	section	NOUN
aiti-9488	52	17	2	2	NUM
aiti-9488	52	18	,	,	PUNCT
aiti-9488	52	19	and	and	CCONJ
aiti-9488	52	20	a	a	DET
aiti-9488	52	21	detailed	detailed	ADJ
aiti-9488	52	22	explanation	explanation	NOUN
aiti-9488	52	23	of	of	ADP
aiti-9488	52	24	the	the	DET
aiti-9488	52	25	proposed	propose	VERB
aiti-9488	52	26	methodology	methodology	NOUN
aiti-9488	52	27	is	be	AUX
aiti-9488	52	28	described	describe	VERB
aiti-9488	52	29	in	in	ADP
aiti-9488	52	30	section	section	NOUN
aiti-9488	52	31	3	3	NUM
aiti-9488	52	32	.	.	PUNCT
aiti-9488	53	1	experimental	experimental	ADJ
aiti-9488	53	2	results	result	NOUN
aiti-9488	53	3	and	and	CCONJ
aiti-9488	53	4	analysis	analysis	NOUN
aiti-9488	53	5	are	be	AUX
aiti-9488	53	6	outlined	outline	VERB
aiti-9488	53	7	in	in	ADP
aiti-9488	53	8	section	section	NOUN
aiti-9488	53	9	4	4	NUM
aiti-9488	53	10	,	,	PUNCT
aiti-9488	53	11	and	and	CCONJ
aiti-9488	53	12	the	the	DET
aiti-9488	53	13	paper	paper	NOUN
aiti-9488	53	14	is	be	AUX
aiti-9488	53	15	inferred	infer	VERB
aiti-9488	53	16	in	in	ADP
aiti-9488	53	17	section	section	NOUN
aiti-9488	53	18	5	5	NUM
aiti-9488	53	19	.	.	SYM
aiti-9488	53	20	2	2	NUM
aiti-9488	53	21	.	.	NUM
aiti-9488	53	22	related	relate	VERB
aiti-9488	53	23	work	work	NOUN
aiti-9488	53	24	naronglerdrit	naronglerdrit	NOUN
aiti-9488	53	25	et	et	PROPN
aiti-9488	53	26	al	al	PROPN
aiti-9488	53	27	.	.	PUNCT
aiti-9488	54	1	[	[	X
aiti-9488	54	2	5	5	NUM
aiti-9488	54	3	]	]	PUNCT
aiti-9488	54	4	,	,	PUNCT
aiti-9488	54	5	offered	offer	VERB
aiti-9488	54	6	an	an	DET
aiti-9488	54	7	experimental	experimental	ADJ
aiti-9488	54	8	study	study	NOUN
aiti-9488	54	9	of	of	ADP
aiti-9488	54	10	diverse	diverse	ADJ
aiti-9488	54	11	pre	pre	ADJ
aiti-9488	54	12	-	-	ADJ
aiti-9488	54	13	trained	train	VERB
aiti-9488	54	14	transfer	transfer	NOUN
aiti-9488	54	15	learning	learning	NOUN
aiti-9488	54	16	models	model	NOUN
aiti-9488	54	17	for	for	ADP
aiti-9488	54	18	the	the	DET
aiti-9488	54	19	classification	classification	NOUN
aiti-9488	54	20	of	of	ADP
aiti-9488	54	21	malignant	malignant	ADJ
aiti-9488	54	22	skin	skin	NOUN
aiti-9488	54	23	lesions	lesion	NOUN
aiti-9488	54	24	.	.	PUNCT
aiti-9488	55	1	using	use	VERB
aiti-9488	55	2	various	various	ADJ
aiti-9488	55	3	pre	pre	ADJ
aiti-9488	55	4	-	-	ADJ
aiti-9488	55	5	trained	train	VERB
aiti-9488	55	6	models	model	NOUN
aiti-9488	55	7	,	,	PUNCT
aiti-9488	55	8	the	the	DET
aiti-9488	55	9	authors	author	NOUN
aiti-9488	55	10	carried	carry	VERB
aiti-9488	55	11	out	out	ADP
aiti-9488	55	12	tasks	task	NOUN
aiti-9488	55	13	like	like	ADP
aiti-9488	55	14	pre	pre	ADJ
aiti-9488	55	15	-	-	ADJ
aiti-9488	55	16	processing	processing	ADJ
aiti-9488	55	17	(	(	PUNCT
aiti-9488	55	18	hair	hair	NOUN
aiti-9488	55	19	removal	removal	NOUN
aiti-9488	55	20	)	)	PUNCT
aiti-9488	55	21	,	,	PUNCT
aiti-9488	55	22	lesion	lesion	NOUN
aiti-9488	55	23	segmentation	segmentation	NOUN
aiti-9488	55	24	,	,	PUNCT
aiti-9488	55	25	batch	batch	VERB
aiti-9488	55	26	normalization	normalization	NOUN
aiti-9488	55	27	,	,	PUNCT
aiti-9488	55	28	and	and	CCONJ
aiti-9488	55	29	melanoma	melanoma	NOUN
aiti-9488	55	30	classification	classification	NOUN
aiti-9488	55	31	.	.	PUNCT
aiti-9488	56	1	the	the	DET
aiti-9488	56	2	experimental	experimental	ADJ
aiti-9488	56	3	analyses	analysis	NOUN
aiti-9488	56	4	noted	note	VERB
aiti-9488	56	5	that	that	SCONJ
aiti-9488	56	6	resnet-101	resnet-101	NOUN
aiti-9488	56	7	achieved	achieve	VERB
aiti-9488	56	8	better	well	ADJ
aiti-9488	56	9	sensitivity	sensitivity	NOUN
aiti-9488	56	10	(	(	PUNCT
aiti-9488	56	11	recall	recall	NOUN
aiti-9488	56	12	)	)	PUNCT
aiti-9488	56	13	of	of	ADP
aiti-9488	56	14	85.18	85.18	NUM
aiti-9488	56	15	%	%	NOUN
aiti-9488	56	16	and	and	CCONJ
aiti-9488	56	17	accuracy	accuracy	NOUN
aiti-9488	56	18	of	of	ADP
aiti-9488	56	19	97.12	97.12	NUM
aiti-9488	56	20	%	%	NOUN
aiti-9488	56	21	.	.	PUNCT
aiti-9488	57	1	siddique	siddique	PROPN
aiti-9488	57	2	et	et	PROPN
aiti-9488	57	3	al	al	PROPN
aiti-9488	57	4	.	.	PUNCT
aiti-9488	58	1	[	[	X
aiti-9488	58	2	6	6	NUM
aiti-9488	58	3	]	]	PUNCT
aiti-9488	58	4	,	,	PUNCT
aiti-9488	58	5	furnished	furnish	VERB
aiti-9488	58	6	an	an	DET
aiti-9488	58	7	image	image	NOUN
aiti-9488	58	8	segmentation	segmentation	NOUN
aiti-9488	58	9	model	model	NOUN
aiti-9488	58	10	attributed	attribute	VERB
aiti-9488	58	11	to	to	ADP
aiti-9488	58	12	the	the	DET
aiti-9488	58	13	deep	deep	ADJ
aiti-9488	58	14	learning	learn	VERB
aiti-9488	58	15	u	u	ADJ
aiti-9488	58	16	-	-	ADJ
aiti-9488	58	17	net	net	ADJ
aiti-9488	58	18	framework	framework	NOUN
aiti-9488	58	19	along	along	ADP
aiti-9488	58	20	with	with	ADP
aiti-9488	58	21	a	a	DET
aiti-9488	58	22	pre	pre	ADJ
aiti-9488	58	23	-	-	ADJ
aiti-9488	58	24	trained	train	VERB
aiti-9488	58	25	efficientnet	efficientnet	NOUN
aiti-9488	58	26	model	model	NOUN
aiti-9488	58	27	.	.	PUNCT
aiti-9488	59	1	to	to	PART
aiti-9488	59	2	enhance	enhance	VERB
aiti-9488	59	3	gradient	gradient	NOUN
aiti-9488	59	4	learning	learning	NOUN
aiti-9488	59	5	and	and	CCONJ
aiti-9488	59	6	build	build	VERB
aiti-9488	59	7	a	a	DET
aiti-9488	59	8	deeper	deep	ADJ
aiti-9488	59	9	u	u	ADJ
aiti-9488	59	10	-	-	ADJ
aiti-9488	59	11	net	net	ADJ
aiti-9488	59	12	model	model	NOUN
aiti-9488	59	13	,	,	PUNCT
aiti-9488	59	14	residual	residual	ADJ
aiti-9488	59	15	connection	connection	NOUN
aiti-9488	59	16	and	and	CCONJ
aiti-9488	59	17	recurrent	recurrent	ADJ
aiti-9488	59	18	feedback	feedback	NOUN
aiti-9488	59	19	with	with	ADP
aiti-9488	59	20	efficientnet	efficientnet	NOUN
aiti-9488	59	21	as	as	ADP
aiti-9488	59	22	an	an	DET
aiti-9488	59	23	encoder	encoder	NOUN
aiti-9488	59	24	was	be	AUX
aiti-9488	59	25	proposed	propose	VERB
aiti-9488	59	26	.	.	PUNCT
aiti-9488	60	1	the	the	DET
aiti-9488	60	2	proposed	propose	VERB
aiti-9488	60	3	model	model	NOUN
aiti-9488	60	4	achieved	achieve	VERB
aiti-9488	60	5	higher	high	ADJ
aiti-9488	60	6	segmentation	segmentation	NOUN
aiti-9488	60	7	performance	performance	NOUN
aiti-9488	60	8	with	with	ADP
aiti-9488	60	9	a	a	DET
aiti-9488	60	10	jaccard	jaccard	ADJ
aiti-9488	60	11	index	index	NOUN
aiti-9488	60	12	of	of	ADP
aiti-9488	60	13	95.34	95.34	NUM
aiti-9488	60	14	%	%	NOUN
aiti-9488	60	15	and	and	CCONJ
aiti-9488	60	16	a	a	DET
aiti-9488	60	17	dice	dice	NOUN
aiti-9488	60	18	coefficient	coefficient	NOUN
aiti-9488	60	19	of	of	ADP
aiti-9488	60	20	88.62	88.62	NUM
aiti-9488	60	21	%	%	NOUN
aiti-9488	60	22	.	.	PUNCT
aiti-9488	61	1	advances	advance	NOUN
aiti-9488	61	2	in	in	ADP
aiti-9488	61	3	technology	technology	NOUN
aiti-9488	61	4	innovation	innovation	NOUN
aiti-9488	61	5	,	,	PUNCT
aiti-9488	61	6	vol	vol	NOUN
aiti-9488	61	7	.	.	PROPN
aiti-9488	61	8	8	8	NUM
aiti-9488	61	9	,	,	PUNCT
aiti-9488	61	10	no	no	INTJ
aiti-9488	61	11	.	.	NOUN
aiti-9488	61	12	1	1	NUM
aiti-9488	61	13	,	,	PUNCT
aiti-9488	61	14	2023	2023	NUM
aiti-9488	61	15	,	,	PUNCT
aiti-9488	61	16	pp	pp	ADJ
aiti-9488	61	17	.	.	PUNCT
aiti-9488	62	1	59	59	NUM
aiti-9488	62	2	-	-	SYM
aiti-9488	62	3	72	72	NUM
aiti-9488	62	4	61	61	NUM
aiti-9488	62	5	chaturvedi	chaturvedi	PROPN
aiti-9488	62	6	et	et	PROPN
aiti-9488	62	7	al	al	PROPN
aiti-9488	62	8	.	.	PUNCT
aiti-9488	63	1	[	[	X
aiti-9488	63	2	7	7	NUM
aiti-9488	63	3	]	]	PUNCT
aiti-9488	63	4	,	,	PUNCT
aiti-9488	63	5	furnished	furnish	VERB
aiti-9488	63	6	a	a	DET
aiti-9488	63	7	skin	skin	NOUN
aiti-9488	63	8	cancer	cancer	NOUN
aiti-9488	63	9	classification	classification	NOUN
aiti-9488	63	10	method	method	NOUN
aiti-9488	63	11	using	use	VERB
aiti-9488	63	12	mobilenet	mobilenet	NOUN
aiti-9488	63	13	.	.	PUNCT
aiti-9488	64	1	experiments	experiment	NOUN
aiti-9488	64	2	were	be	AUX
aiti-9488	64	3	carried	carry	VERB
aiti-9488	64	4	out	out	ADP
aiti-9488	64	5	on	on	ADP
aiti-9488	64	6	the	the	DET
aiti-9488	64	7	ham10000	ham10000	PROPN
aiti-9488	64	8	dataset	dataset	NOUN
aiti-9488	64	9	,	,	PUNCT
aiti-9488	64	10	and	and	CCONJ
aiti-9488	64	11	pre	pre	ADJ
aiti-9488	64	12	-	-	ADJ
aiti-9488	64	13	processing	processing	ADJ
aiti-9488	64	14	approaches	approach	NOUN
aiti-9488	64	15	like	like	ADP
aiti-9488	64	16	image	image	NOUN
aiti-9488	64	17	rescaling	rescaling	NOUN
aiti-9488	64	18	and	and	CCONJ
aiti-9488	64	19	data	datum	NOUN
aiti-9488	64	20	augmentation	augmentation	NOUN
aiti-9488	64	21	were	be	AUX
aiti-9488	64	22	applied	apply	VERB
aiti-9488	64	23	to	to	ADP
aiti-9488	64	24	the	the	DET
aiti-9488	64	25	dataset	dataset	NOUN
aiti-9488	64	26	.	.	PUNCT
aiti-9488	65	1	the	the	DET
aiti-9488	65	2	mobilenet	mobilenet	PROPN
aiti-9488	65	3	model	model	NOUN
aiti-9488	65	4	achieved	achieve	VERB
aiti-9488	65	5	an	an	DET
aiti-9488	65	6	overall	overall	ADJ
aiti-9488	65	7	accuracy	accuracy	NOUN
aiti-9488	65	8	of	of	ADP
aiti-9488	65	9	83.15	83.15	NUM
aiti-9488	65	10	%	%	NOUN
aiti-9488	65	11	,	,	PUNCT
aiti-9488	65	12	top2	top2	NOUN
aiti-9488	65	13	accuracy	accuracy	NOUN
aiti-9488	65	14	of	of	ADP
aiti-9488	65	15	91.36	91.36	NUM
aiti-9488	65	16	%	%	NOUN
aiti-9488	65	17	,	,	PUNCT
aiti-9488	65	18	and	and	CCONJ
aiti-9488	65	19	top3	top3	NOUN
aiti-9488	65	20	accuracy	accuracy	NOUN
aiti-9488	65	21	of	of	ADP
aiti-9488	65	22	95.84	95.84	NUM
aiti-9488	65	23	%	%	NOUN
aiti-9488	65	24	.	.	PUNCT
aiti-9488	66	1	the	the	DET
aiti-9488	66	2	precision	precision	NOUN
aiti-9488	66	3	,	,	PUNCT
aiti-9488	66	4	recall	recall	NOUN
aiti-9488	66	5	,	,	PUNCT
aiti-9488	66	6	and	and	CCONJ
aiti-9488	66	7	f1	f1	NOUN
aiti-9488	66	8	-	-	PUNCT
aiti-9488	66	9	score	score	NOUN
aiti-9488	66	10	of	of	ADP
aiti-9488	66	11	the	the	DET
aiti-9488	66	12	model	model	NOUN
aiti-9488	66	13	were	be	AUX
aiti-9488	66	14	89	89	NUM
aiti-9488	66	15	%	%	NOUN
aiti-9488	66	16	,	,	PUNCT
aiti-9488	66	17	83	83	NUM
aiti-9488	66	18	%	%	NOUN
aiti-9488	66	19	,	,	PUNCT
aiti-9488	66	20	and	and	CCONJ
aiti-9488	66	21	83	83	NUM
aiti-9488	66	22	%	%	NOUN
aiti-9488	66	23	,	,	PUNCT
aiti-9488	66	24	respectively	respectively	ADV
aiti-9488	66	25	.	.	PUNCT
aiti-9488	67	1	zhang	zhang	PROPN
aiti-9488	68	1	[	[	X
aiti-9488	68	2	8	8	NUM
aiti-9488	68	3	]	]	PUNCT
aiti-9488	68	4	,	,	PUNCT
aiti-9488	68	5	presented	present	VERB
aiti-9488	68	6	the	the	DET
aiti-9488	68	7	efficientnet	efficientnet	NOUN
aiti-9488	68	8	-	-	PUNCT
aiti-9488	68	9	b6	b6	NOUN
aiti-9488	68	10	model	model	NOUN
aiti-9488	68	11	for	for	ADP
aiti-9488	68	12	melanoma	melanoma	NOUN
aiti-9488	68	13	detection	detection	NOUN
aiti-9488	68	14	on	on	ADP
aiti-9488	68	15	the	the	DET
aiti-9488	68	16	isic	isic	PROPN
aiti-9488	68	17	dataset	dataset	NOUN
aiti-9488	68	18	.	.	PUNCT
aiti-9488	69	1	to	to	PART
aiti-9488	69	2	assess	assess	VERB
aiti-9488	69	3	model	model	NOUN
aiti-9488	69	4	performance	performance	NOUN
aiti-9488	69	5	,	,	PUNCT
aiti-9488	69	6	the	the	DET
aiti-9488	69	7	proposed	propose	VERB
aiti-9488	69	8	model	model	NOUN
aiti-9488	69	9	was	be	AUX
aiti-9488	69	10	compared	compare	VERB
aiti-9488	69	11	with	with	ADP
aiti-9488	69	12	other	other	ADJ
aiti-9488	69	13	standard	standard	ADJ
aiti-9488	69	14	models	model	NOUN
aiti-9488	69	15	like	like	ADP
aiti-9488	69	16	vgg16	vgg16	PROPN
aiti-9488	69	17	and	and	CCONJ
aiti-9488	69	18	vgg19	vgg19	PROPN
aiti-9488	69	19	.	.	PUNCT
aiti-9488	70	1	training	training	NOUN
aiti-9488	70	2	and	and	CCONJ
aiti-9488	70	3	testing	testing	NOUN
aiti-9488	70	4	of	of	ADP
aiti-9488	70	5	the	the	DET
aiti-9488	70	6	model	model	NOUN
aiti-9488	70	7	were	be	AUX
aiti-9488	70	8	done	do	VERB
aiti-9488	70	9	for	for	ADP
aiti-9488	70	10	22	22	NUM
aiti-9488	70	11	epochs	epoch	NOUN
aiti-9488	70	12	with	with	ADP
aiti-9488	70	13	a	a	DET
aiti-9488	70	14	batch	batch	NOUN
aiti-9488	70	15	size	size	NOUN
aiti-9488	70	16	of	of	ADP
aiti-9488	70	17	32	32	NUM
aiti-9488	70	18	.	.	PUNCT
aiti-9488	71	1	efficientnet	efficientnet	NOUN
aiti-9488	71	2	-	-	PUNCT
aiti-9488	71	3	b6	b6	NOUN
aiti-9488	71	4	obtained	obtain	VERB
aiti-9488	71	5	an	an	DET
aiti-9488	71	6	auc	auc	NOUN
aiti-9488	71	7	-	-	PUNCT
aiti-9488	71	8	roc	roc	NOUN
aiti-9488	71	9	score	score	NOUN
aiti-9488	71	10	of	of	ADP
aiti-9488	71	11	91.7	91.7	NUM
aiti-9488	71	12	,	,	PUNCT
aiti-9488	71	13	whereas	whereas	SCONJ
aiti-9488	71	14	vgg16	vgg16	NOUN
aiti-9488	71	15	and	and	CCONJ
aiti-9488	71	16	vgg19	vgg19	PROPN
aiti-9488	71	17	achieved	achieve	VERB
aiti-9488	71	18	a	a	DET
aiti-9488	71	19	score	score	NOUN
aiti-9488	71	20	of	of	ADP
aiti-9488	71	21	89.1	89.1	NUM
aiti-9488	71	22	%	%	NOUN
aiti-9488	71	23	and	and	CCONJ
aiti-9488	71	24	90.2	90.2	NUM
aiti-9488	71	25	%	%	NOUN
aiti-9488	71	26	,	,	PUNCT
aiti-9488	71	27	respectively	respectively	ADV
aiti-9488	71	28	.	.	PUNCT
aiti-9488	72	1	zhang	zhang	PROPN
aiti-9488	72	2	and	and	CCONJ
aiti-9488	72	3	wang	wang	PROPN
aiti-9488	73	1	[	[	X
aiti-9488	73	2	9	9	NUM
aiti-9488	73	3	]	]	PUNCT
aiti-9488	73	4	,	,	PUNCT
aiti-9488	73	5	proposed	propose	VERB
aiti-9488	73	6	a	a	DET
aiti-9488	73	7	densenet201	densenet201	PROPN
aiti-9488	73	8	-	-	PUNCT
aiti-9488	73	9	based	base	VERB
aiti-9488	73	10	melanoma	melanoma	NOUN
aiti-9488	73	11	recognition	recognition	NOUN
aiti-9488	73	12	model	model	NOUN
aiti-9488	73	13	for	for	ADP
aiti-9488	73	14	lesion	lesion	NOUN
aiti-9488	73	15	images	image	NOUN
aiti-9488	73	16	.	.	PUNCT
aiti-9488	74	1	all	all	DET
aiti-9488	74	2	the	the	DET
aiti-9488	74	3	investigations	investigation	NOUN
aiti-9488	74	4	were	be	AUX
aiti-9488	74	5	carried	carry	VERB
aiti-9488	74	6	out	out	ADP
aiti-9488	74	7	on	on	ADP
aiti-9488	74	8	the	the	DET
aiti-9488	74	9	isic	isic	PROPN
aiti-9488	74	10	dataset	dataset	NOUN
aiti-9488	74	11	from	from	ADP
aiti-9488	74	12	the	the	DET
aiti-9488	74	13	kaggle	kaggle	ADJ
aiti-9488	74	14	challenge	challenge	NOUN
aiti-9488	74	15	.	.	PUNCT
aiti-9488	75	1	training	training	NOUN
aiti-9488	75	2	of	of	ADP
aiti-9488	75	3	the	the	DET
aiti-9488	75	4	proposed	propose	VERB
aiti-9488	75	5	model	model	NOUN
aiti-9488	75	6	was	be	AUX
aiti-9488	75	7	carried	carry	VERB
aiti-9488	75	8	out	out	ADP
aiti-9488	75	9	for	for	ADP
aiti-9488	75	10	20	20	NUM
aiti-9488	75	11	epochs	epoch	NOUN
aiti-9488	75	12	with	with	ADP
aiti-9488	75	13	a	a	DET
aiti-9488	75	14	batch	batch	NOUN
aiti-9488	75	15	size	size	NOUN
aiti-9488	75	16	of	of	ADP
aiti-9488	75	17	8	8	NUM
aiti-9488	75	18	using	use	VERB
aiti-9488	75	19	adam	adam	PROPN
aiti-9488	75	20	optimizer	optimizer	NOUN
aiti-9488	75	21	and	and	CCONJ
aiti-9488	75	22	a	a	DET
aiti-9488	75	23	learning	learning	NOUN
aiti-9488	75	24	rate	rate	NOUN
aiti-9488	75	25	of	of	ADP
aiti-9488	75	26	le-4	le-4	NOUN
aiti-9488	75	27	.	.	PUNCT
aiti-9488	76	1	densenet201	densenet201	PROPN
aiti-9488	76	2	model	model	NOUN
aiti-9488	76	3	performance	performance	NOUN
aiti-9488	76	4	was	be	AUX
aiti-9488	76	5	compared	compare	VERB
aiti-9488	76	6	with	with	ADP
aiti-9488	76	7	vgg16	vgg16	NOUN
aiti-9488	76	8	and	and	CCONJ
aiti-9488	76	9	resnet50	resnet50	NOUN
aiti-9488	76	10	over	over	ADP
aiti-9488	76	11	the	the	DET
aiti-9488	76	12	auc	auc	NOUN
aiti-9488	76	13	-	-	PUNCT
aiti-9488	76	14	roc	roc	NOUN
aiti-9488	76	15	score	score	NOUN
aiti-9488	76	16	.	.	PUNCT
aiti-9488	77	1	densenet201	densenet201	PROPN
aiti-9488	77	2	achieved	achieve	VERB
aiti-9488	77	3	a	a	DET
aiti-9488	77	4	better	well	ADJ
aiti-9488	77	5	auc	auc	NOUN
aiti-9488	77	6	-	-	PUNCT
aiti-9488	77	7	roc	roc	NOUN
aiti-9488	77	8	score	score	NOUN
aiti-9488	77	9	of	of	ADP
aiti-9488	77	10	92.5	92.5	NUM
aiti-9488	77	11	as	as	SCONJ
aiti-9488	77	12	compared	compare	VERB
aiti-9488	77	13	to	to	ADP
aiti-9488	77	14	vgg16	vgg16	NOUN
aiti-9488	77	15	and	and	CCONJ
aiti-9488	77	16	resnet50	resnet50	NOUN
aiti-9488	77	17	.	.	PUNCT
aiti-9488	78	1	ashim	ashim	PRON
aiti-9488	78	2	et	et	PROPN
aiti-9488	78	3	al	al	PROPN
aiti-9488	78	4	.	.	PUNCT
aiti-9488	79	1	[	[	X
aiti-9488	79	2	10	10	NUM
aiti-9488	79	3	]	]	PUNCT
aiti-9488	79	4	,	,	PUNCT
aiti-9488	79	5	reviewed	review	VERB
aiti-9488	79	6	diverse	diverse	ADJ
aiti-9488	79	7	pre	pre	ADJ
aiti-9488	79	8	-	-	ADJ
aiti-9488	79	9	trained	train	VERB
aiti-9488	79	10	models	model	NOUN
aiti-9488	79	11	such	such	ADJ
aiti-9488	79	12	as	as	ADP
aiti-9488	79	13	vgg16	vgg16	PROPN
aiti-9488	79	14	,	,	PUNCT
aiti-9488	79	15	resnet50	resnet50	NOUN
aiti-9488	79	16	,	,	PUNCT
aiti-9488	79	17	efficientnet	efficientnet	NOUN
aiti-9488	79	18	,	,	PUNCT
aiti-9488	79	19	densenet	densenet	NOUN
aiti-9488	79	20	,	,	PUNCT
aiti-9488	79	21	and	and	CCONJ
aiti-9488	79	22	xception	xception	NOUN
aiti-9488	79	23	for	for	ADP
aiti-9488	79	24	lesion	lesion	NOUN
aiti-9488	79	25	classification	classification	NOUN
aiti-9488	79	26	over	over	ADP
aiti-9488	79	27	the	the	DET
aiti-9488	79	28	kaggle	kaggle	ADJ
aiti-9488	79	29	dataset	dataset	NOUN
aiti-9488	79	30	.	.	PUNCT
aiti-9488	80	1	the	the	DET
aiti-9488	80	2	analyses	analysis	NOUN
aiti-9488	80	3	were	be	AUX
aiti-9488	80	4	carried	carry	VERB
aiti-9488	80	5	out	out	ADP
aiti-9488	80	6	on	on	ADP
aiti-9488	80	7	only	only	ADV
aiti-9488	80	8	660	660	NUM
aiti-9488	80	9	images	image	NOUN
aiti-9488	80	10	of	of	ADP
aiti-9488	80	11	skin	skin	NOUN
aiti-9488	80	12	lesions	lesion	NOUN
aiti-9488	80	13	,	,	PUNCT
aiti-9488	80	14	including	include	VERB
aiti-9488	80	15	360	360	NUM
aiti-9488	80	16	of	of	ADP
aiti-9488	80	17	class	class	NOUN
aiti-9488	80	18	benign	benign	NOUN
aiti-9488	80	19	and	and	CCONJ
aiti-9488	80	20	300	300	NUM
aiti-9488	80	21	of	of	ADP
aiti-9488	80	22	type	type	NOUN
aiti-9488	80	23	malignant	malignant	ADJ
aiti-9488	80	24	.	.	PUNCT
aiti-9488	81	1	to	to	PART
aiti-9488	81	2	handle	handle	VERB
aiti-9488	81	3	the	the	DET
aiti-9488	81	4	low	low	ADJ
aiti-9488	81	5	precision	precision	NOUN
aiti-9488	81	6	problem	problem	NOUN
aiti-9488	81	7	,	,	PUNCT
aiti-9488	81	8	data	datum	NOUN
aiti-9488	81	9	augmentation	augmentation	NOUN
aiti-9488	81	10	techniques	technique	NOUN
aiti-9488	81	11	like	like	ADP
aiti-9488	81	12	rotation	rotation	NOUN
aiti-9488	81	13	,	,	PUNCT
aiti-9488	81	14	crop	crop	NOUN
aiti-9488	81	15	,	,	PUNCT
aiti-9488	81	16	compression	compression	NOUN
aiti-9488	81	17	,	,	PUNCT
aiti-9488	81	18	brightness	brightness	NOUN
aiti-9488	81	19	,	,	PUNCT
aiti-9488	81	20	and	and	CCONJ
aiti-9488	81	21	contrast	contrast	NOUN
aiti-9488	81	22	were	be	AUX
aiti-9488	81	23	applied	apply	VERB
aiti-9488	81	24	during	during	ADP
aiti-9488	81	25	the	the	DET
aiti-9488	81	26	training	training	NOUN
aiti-9488	81	27	of	of	ADP
aiti-9488	81	28	the	the	DET
aiti-9488	81	29	model	model	NOUN
aiti-9488	81	30	.	.	PUNCT
aiti-9488	82	1	from	from	ADP
aiti-9488	82	2	the	the	DET
aiti-9488	82	3	analyses	analysis	NOUN
aiti-9488	82	4	,	,	PUNCT
aiti-9488	82	5	resnet50	resnet50	NOUN
aiti-9488	82	6	furnished	furnish	VERB
aiti-9488	82	7	better	well	ADJ
aiti-9488	82	8	results	result	NOUN
aiti-9488	82	9	with	with	ADP
aiti-9488	82	10	a	a	DET
aiti-9488	82	11	training	training	NOUN
aiti-9488	82	12	accuracy	accuracy	NOUN
aiti-9488	82	13	of	of	ADP
aiti-9488	82	14	88.61	88.61	NUM
aiti-9488	82	15	%	%	NOUN
aiti-9488	82	16	,	,	PUNCT
aiti-9488	82	17	whereas	whereas	SCONJ
aiti-9488	82	18	efficientnetb0	efficientnetb0	PROPN
aiti-9488	82	19	furnished	furnish	VERB
aiti-9488	82	20	a	a	DET
aiti-9488	82	21	training	training	NOUN
aiti-9488	82	22	accuracy	accuracy	NOUN
aiti-9488	82	23	of	of	ADP
aiti-9488	82	24	78.41	78.41	NUM
aiti-9488	82	25	%	%	NOUN
aiti-9488	82	26	.	.	PUNCT
aiti-9488	83	1	chen	chen	PROPN
aiti-9488	83	2	et	et	PROPN
aiti-9488	83	3	al	al	PROPN
aiti-9488	83	4	.	.	PUNCT
aiti-9488	84	1	[	[	X
aiti-9488	84	2	11	11	NUM
aiti-9488	84	3	]	]	PUNCT
aiti-9488	84	4	,	,	PUNCT
aiti-9488	84	5	proposed	propose	VERB
aiti-9488	84	6	an	an	DET
aiti-9488	84	7	efficientnetb1	efficientnetb1	NOUN
aiti-9488	84	8	-	-	PUNCT
aiti-9488	84	9	based	base	VERB
aiti-9488	84	10	deep	deep	ADJ
aiti-9488	84	11	learning	learning	NOUN
aiti-9488	84	12	-	-	PUNCT
aiti-9488	84	13	based	base	VERB
aiti-9488	84	14	model	model	NOUN
aiti-9488	84	15	using	use	VERB
aiti-9488	84	16	the	the	DET
aiti-9488	84	17	cyclegan	cyclegan	ADJ
aiti-9488	84	18	data	data	NOUN
aiti-9488	84	19	augmentation	augmentation	NOUN
aiti-9488	84	20	technique	technique	NOUN
aiti-9488	84	21	to	to	PART
aiti-9488	84	22	boost	boost	VERB
aiti-9488	84	23	skin	skin	NOUN
aiti-9488	84	24	lesion	lesion	NOUN
aiti-9488	84	25	classification	classification	NOUN
aiti-9488	84	26	accuracy	accuracy	NOUN
aiti-9488	84	27	.	.	PUNCT
aiti-9488	85	1	cyclegan	cyclegan	PROPN
aiti-9488	85	2	approach	approach	NOUN
aiti-9488	85	3	aided	aid	VERB
aiti-9488	85	4	in	in	ADP
aiti-9488	85	5	creating	create	VERB
aiti-9488	85	6	additional	additional	ADJ
aiti-9488	85	7	training	training	NOUN
aiti-9488	85	8	images	image	NOUN
aiti-9488	85	9	with	with	ADP
aiti-9488	85	10	labeled	label	VERB
aiti-9488	85	11	information	information	NOUN
aiti-9488	85	12	and	and	CCONJ
aiti-9488	85	13	helped	help	VERB
aiti-9488	85	14	in	in	ADP
aiti-9488	85	15	saving	save	VERB
aiti-9488	85	16	costs	cost	NOUN
aiti-9488	85	17	in	in	ADP
aiti-9488	85	18	manual	manual	ADJ
aiti-9488	85	19	labeling	labeling	NOUN
aiti-9488	85	20	.	.	PUNCT
aiti-9488	86	1	efficientnet	efficientnet	NOUN
aiti-9488	86	2	-	-	PUNCT
aiti-9488	86	3	b1	b1	NOUN
aiti-9488	86	4	with	with	ADP
aiti-9488	86	5	cyclegan	cyclegan	ADJ
aiti-9488	86	6	data	datum	NOUN
aiti-9488	86	7	augmentation	augmentation	NOUN
aiti-9488	86	8	accomplished	accomplish	VERB
aiti-9488	86	9	an	an	DET
aiti-9488	86	10	accuracy	accuracy	NOUN
aiti-9488	86	11	of	of	ADP
aiti-9488	86	12	94.5	94.5	NUM
aiti-9488	86	13	%	%	NOUN
aiti-9488	86	14	.	.	PUNCT
aiti-9488	87	1	le	le	PROPN
aiti-9488	87	2	et	et	PROPN
aiti-9488	87	3	al	al	PROPN
aiti-9488	87	4	.	.	PUNCT
aiti-9488	88	1	[	[	X
aiti-9488	88	2	12	12	NUM
aiti-9488	88	3	]	]	PUNCT
aiti-9488	88	4	,	,	PUNCT
aiti-9488	88	5	exhibited	exhibit	VERB
aiti-9488	88	6	a	a	DET
aiti-9488	88	7	deep	deep	ADJ
aiti-9488	88	8	learning	learning	NOUN
aiti-9488	88	9	framework	framework	NOUN
aiti-9488	88	10	that	that	PRON
aiti-9488	88	11	classifies	classify	VERB
aiti-9488	88	12	skin	skin	NOUN
aiti-9488	88	13	lesions	lesion	NOUN
aiti-9488	88	14	into	into	ADP
aiti-9488	88	15	seven	seven	NUM
aiti-9488	88	16	different	different	ADJ
aiti-9488	88	17	classes	class	NOUN
aiti-9488	88	18	.	.	PUNCT
aiti-9488	89	1	the	the	DET
aiti-9488	89	2	authors	author	NOUN
aiti-9488	89	3	carried	carry	VERB
aiti-9488	89	4	out	out	ADP
aiti-9488	89	5	the	the	DET
aiti-9488	89	6	training	training	NOUN
aiti-9488	89	7	and	and	CCONJ
aiti-9488	89	8	testing	testing	NOUN
aiti-9488	89	9	of	of	ADP
aiti-9488	89	10	the	the	DET
aiti-9488	89	11	network	network	NOUN
aiti-9488	89	12	on	on	ADP
aiti-9488	89	13	the	the	DET
aiti-9488	89	14	ham10000	ham10000	NOUN
aiti-9488	89	15	dataset	dataset	VERB
aiti-9488	89	16	by	by	ADP
aiti-9488	89	17	removing	remove	VERB
aiti-9488	89	18	duplicate	duplicate	ADJ
aiti-9488	89	19	images	image	NOUN
aiti-9488	89	20	from	from	ADP
aiti-9488	89	21	the	the	DET
aiti-9488	89	22	dataset	dataset	NOUN
aiti-9488	89	23	.	.	PUNCT
aiti-9488	90	1	to	to	PART
aiti-9488	90	2	yield	yield	VERB
aiti-9488	90	3	better	well	ADJ
aiti-9488	90	4	performance	performance	NOUN
aiti-9488	90	5	,	,	PUNCT
aiti-9488	90	6	the	the	DET
aiti-9488	90	7	resnet50	resnet50	NOUN
aiti-9488	90	8	classifier	classifier	NOUN
aiti-9488	90	9	model	model	NOUN
aiti-9488	90	10	architecture	architecture	NOUN
aiti-9488	90	11	was	be	AUX
aiti-9488	90	12	modified	modify	VERB
aiti-9488	90	13	by	by	ADP
aiti-9488	90	14	adding	add	VERB
aiti-9488	90	15	an	an	DET
aiti-9488	90	16	average	average	ADJ
aiti-9488	90	17	pooling	pooling	NOUN
aiti-9488	90	18	and	and	CCONJ
aiti-9488	90	19	a	a	DET
aiti-9488	90	20	dropout	dropout	NOUN
aiti-9488	90	21	layer	layer	NOUN
aiti-9488	90	22	of	of	ADP
aiti-9488	90	23	0.5	0.5	NUM
aiti-9488	90	24	,	,	PUNCT
aiti-9488	90	25	along	along	ADP
aiti-9488	90	26	with	with	ADP
aiti-9488	90	27	fine	fine	ADV
aiti-9488	90	28	-	-	PUNCT
aiti-9488	90	29	tuning	tune	VERB
aiti-9488	90	30	their	their	PRON
aiti-9488	90	31	weights	weight	NOUN
aiti-9488	90	32	.	.	PUNCT
aiti-9488	91	1	the	the	DET
aiti-9488	91	2	proposed	propose	VERB
aiti-9488	91	3	resnet	resnet	NOUN
aiti-9488	91	4	model	model	NOUN
aiti-9488	91	5	achieved	achieve	VERB
aiti-9488	91	6	an	an	DET
aiti-9488	91	7	average	average	ADJ
aiti-9488	91	8	accuracy	accuracy	NOUN
aiti-9488	91	9	of	of	ADP
aiti-9488	91	10	93	93	NUM
aiti-9488	91	11	%	%	NOUN
aiti-9488	91	12	,	,	PUNCT
aiti-9488	91	13	precision	precision	NOUN
aiti-9488	91	14	of	of	ADP
aiti-9488	91	15	81	81	NUM
aiti-9488	91	16	%	%	NOUN
aiti-9488	91	17	,	,	PUNCT
aiti-9488	91	18	recall	recall	NOUN
aiti-9488	91	19	,	,	PUNCT
aiti-9488	91	20	and	and	CCONJ
aiti-9488	91	21	f1	f1	NOUN
aiti-9488	91	22	-	-	PUNCT
aiti-9488	91	23	score	score	NOUN
aiti-9488	91	24	of	of	ADP
aiti-9488	91	25	80	80	NUM
aiti-9488	91	26	%	%	NOUN
aiti-9488	91	27	,	,	PUNCT
aiti-9488	91	28	which	which	PRON
aiti-9488	91	29	outperformed	outperform	VERB
aiti-9488	91	30	other	other	ADJ
aiti-9488	91	31	base	base	NOUN
aiti-9488	91	32	models	model	NOUN
aiti-9488	91	33	like	like	ADP
aiti-9488	91	34	vgg16	vgg16	PROPN
aiti-9488	91	35	,	,	PUNCT
aiti-9488	91	36	efficientnetb1	efficientnetb1	PROPN
aiti-9488	91	37	,	,	PUNCT
aiti-9488	91	38	and	and	CCONJ
aiti-9488	91	39	mobilenet	mobilenet	PROPN
aiti-9488	91	40	.	.	PUNCT
aiti-9488	92	1	manzo	manzo	PROPN
aiti-9488	92	2	and	and	CCONJ
aiti-9488	92	3	pellino	pellino	PROPN
aiti-9488	93	1	[	[	X
aiti-9488	93	2	13	13	NUM
aiti-9488	93	3	]	]	PUNCT
aiti-9488	93	4	,	,	PUNCT
aiti-9488	93	5	presented	present	VERB
aiti-9488	93	6	an	an	DET
aiti-9488	93	7	ensemble	ensemble	ADJ
aiti-9488	93	8	deep	deep	ADJ
aiti-9488	93	9	learning	learning	NOUN
aiti-9488	93	10	architecture	architecture	NOUN
aiti-9488	93	11	with	with	ADP
aiti-9488	93	12	a	a	DET
aiti-9488	93	13	transfer	transfer	NOUN
aiti-9488	93	14	learning	learn	VERB
aiti-9488	93	15	approach	approach	NOUN
aiti-9488	93	16	to	to	PART
aiti-9488	93	17	extract	extract	VERB
aiti-9488	93	18	features	feature	NOUN
aiti-9488	93	19	from	from	ADP
aiti-9488	93	20	images	image	NOUN
aiti-9488	93	21	.	.	PUNCT
aiti-9488	94	1	imbalance	imbalance	NOUN
aiti-9488	94	2	class	class	NOUN
aiti-9488	94	3	datasets	dataset	NOUN
aiti-9488	94	4	were	be	AUX
aiti-9488	94	5	addressed	address	VERB
aiti-9488	94	6	for	for	ADP
aiti-9488	94	7	the	the	DET
aiti-9488	94	8	task	task	NOUN
aiti-9488	94	9	of	of	ADP
aiti-9488	94	10	classification	classification	NOUN
aiti-9488	94	11	of	of	ADP
aiti-9488	94	12	melanoma	melanoma	NOUN
aiti-9488	94	13	.	.	PUNCT
aiti-9488	95	1	pre	pre	VERB
aiti-9488	95	2	-	-	ADJ
aiti-9488	95	3	trained	train	VERB
aiti-9488	95	4	models	model	NOUN
aiti-9488	95	5	,	,	PUNCT
aiti-9488	95	6	namely	namely	ADV
aiti-9488	95	7	resnet-50	resnet-50	NUM
aiti-9488	95	8	,	,	PUNCT
aiti-9488	95	9	alexnet	alexnet	NOUN
aiti-9488	95	10	,	,	PUNCT
aiti-9488	95	11	and	and	CCONJ
aiti-9488	95	12	googlenet	googlenet	NOUN
aiti-9488	95	13	were	be	AUX
aiti-9488	95	14	adopted	adopt	VERB
aiti-9488	95	15	to	to	PART
aiti-9488	95	16	extract	extract	VERB
aiti-9488	95	17	features	feature	NOUN
aiti-9488	95	18	from	from	ADP
aiti-9488	95	19	the	the	DET
aiti-9488	95	20	med	me	VERB
aiti-9488	95	21	-	-	PUNCT
aiti-9488	95	22	node	node	NOUN
aiti-9488	95	23	dataset	dataset	NOUN
aiti-9488	95	24	,	,	PUNCT
aiti-9488	95	25	which	which	PRON
aiti-9488	95	26	achieved	achieve	VERB
aiti-9488	95	27	an	an	DET
aiti-9488	95	28	accuracy	accuracy	NOUN
aiti-9488	95	29	of	of	ADP
aiti-9488	95	30	0.90	0.90	NUM
aiti-9488	95	31	.	.	PUNCT
aiti-9488	96	1	multiple	multiple	ADJ
aiti-9488	96	2	image	image	NOUN
aiti-9488	96	3	representations	representation	NOUN
aiti-9488	96	4	were	be	AUX
aiti-9488	96	5	designed	design	VERB
aiti-9488	96	6	to	to	PART
aiti-9488	96	7	extract	extract	VERB
aiti-9488	96	8	features	feature	NOUN
aiti-9488	96	9	built	build	VERB
aiti-9488	96	10	on	on	ADP
aiti-9488	96	11	a	a	DET
aiti-9488	96	12	deep	deep	ADJ
aiti-9488	96	13	neural	neural	ADJ
aiti-9488	96	14	network	network	NOUN
aiti-9488	96	15	for	for	ADP
aiti-9488	96	16	the	the	DET
aiti-9488	96	17	correct	correct	ADJ
aiti-9488	96	18	classification	classification	NOUN
aiti-9488	96	19	of	of	ADP
aiti-9488	96	20	a	a	DET
aiti-9488	96	21	melanoma	melanoma	NOUN
aiti-9488	96	22	lesion	lesion	NOUN
aiti-9488	96	23	.	.	PUNCT
aiti-9488	97	1	kadampur	kadampur	NOUN
aiti-9488	97	2	and	and	CCONJ
aiti-9488	97	3	riyaee	riyaee	ADJ
aiti-9488	98	1	[	[	X
aiti-9488	98	2	14	14	NUM
aiti-9488	98	3	]	]	PUNCT
aiti-9488	98	4	,	,	PUNCT
aiti-9488	98	5	gave	give	VERB
aiti-9488	98	6	a	a	DET
aiti-9488	98	7	cloud	cloud	ADJ
aiti-9488	98	8	deep	deep	ADJ
aiti-9488	98	9	neural	neural	ADJ
aiti-9488	98	10	learning	learn	VERB
aiti-9488	98	11	framework	framework	NOUN
aiti-9488	98	12	to	to	PART
aiti-9488	98	13	predict	predict	VERB
aiti-9488	98	14	skin	skin	NOUN
aiti-9488	98	15	cancer	cancer	NOUN
aiti-9488	98	16	with	with	ADP
aiti-9488	98	17	improved	improved	ADJ
aiti-9488	98	18	accuracy	accuracy	NOUN
aiti-9488	98	19	.	.	PUNCT
aiti-9488	99	1	deep	deep	ADJ
aiti-9488	99	2	learning	learning	PROPN
aiti-9488	99	3	studio	studio	NOUN
aiti-9488	99	4	(	(	PUNCT
aiti-9488	99	5	dls	dls	PROPN
aiti-9488	99	6	)	)	PUNCT
aiti-9488	99	7	provided	provide	VERB
aiti-9488	99	8	a	a	DET
aiti-9488	99	9	menu	menu	NOUN
aiti-9488	99	10	-	-	PUNCT
aiti-9488	99	11	driven	drive	VERB
aiti-9488	99	12	option	option	NOUN
aiti-9488	99	13	to	to	PART
aiti-9488	99	14	construct	construct	VERB
aiti-9488	99	15	suitable	suitable	ADJ
aiti-9488	99	16	higher	high	ADJ
aiti-9488	99	17	convolutional	convolutional	ADJ
aiti-9488	99	18	neural	neural	ADJ
aiti-9488	99	19	networks	network	NOUN
aiti-9488	99	20	with	with	ADP
aiti-9488	99	21	deep	deep	ADJ
aiti-9488	99	22	layers	layer	NOUN
aiti-9488	99	23	such	such	ADJ
aiti-9488	99	24	as	as	ADP
aiti-9488	99	25	normalization	normalization	NOUN
aiti-9488	99	26	,	,	PUNCT
aiti-9488	99	27	pooling	pooling	NOUN
aiti-9488	99	28	,	,	PUNCT
aiti-9488	99	29	dropout	dropout	NOUN
aiti-9488	99	30	,	,	PUNCT
aiti-9488	99	31	and	and	CCONJ
aiti-9488	99	32	flattening	flattening	NOUN
aiti-9488	99	33	.	.	PUNCT
aiti-9488	100	1	a	a	DET
aiti-9488	100	2	comparative	comparative	ADJ
aiti-9488	100	3	assessment	assessment	NOUN
aiti-9488	100	4	of	of	ADP
aiti-9488	100	5	the	the	DET
aiti-9488	100	6	proposed	propose	VERB
aiti-9488	100	7	model	model	NOUN
aiti-9488	100	8	with	with	ADP
aiti-9488	100	9	diverse	diverse	ADJ
aiti-9488	100	10	pre	pre	ADJ
aiti-9488	100	11	-	-	ADJ
aiti-9488	100	12	trained	train	VERB
aiti-9488	100	13	models	model	NOUN
aiti-9488	100	14	like	like	ADP
aiti-9488	100	15	resnet	resnet	NOUN
aiti-9488	100	16	,	,	PUNCT
aiti-9488	100	17	densenet	densenet	NOUN
aiti-9488	100	18	,	,	PUNCT
aiti-9488	100	19	squeezenet	squeezenet	NOUN
aiti-9488	100	20	,	,	PUNCT
aiti-9488	100	21	and	and	CCONJ
aiti-9488	100	22	inceptionnet	inceptionnet	NOUN
aiti-9488	100	23	was	be	AUX
aiti-9488	100	24	performed	perform	VERB
aiti-9488	100	25	on	on	ADP
aiti-9488	100	26	the	the	DET
aiti-9488	100	27	ham10000	ham10000	PROPN
aiti-9488	100	28	dataset	dataset	PROPN
aiti-9488	100	29	.	.	PUNCT
aiti-9488	101	1	the	the	DET
aiti-9488	101	2	proposed	propose	VERB
aiti-9488	101	3	model	model	NOUN
aiti-9488	101	4	performed	perform	VERB
aiti-9488	101	5	better	well	ADV
aiti-9488	101	6	with	with	ADP
aiti-9488	101	7	an	an	DET
aiti-9488	101	8	area	area	NOUN
aiti-9488	101	9	under	under	ADP
aiti-9488	101	10	the	the	DET
aiti-9488	101	11	curve	curve	NOUN
aiti-9488	101	12	value	value	NOUN
aiti-9488	101	13	of	of	ADP
aiti-9488	101	14	0.99	0.99	NUM
aiti-9488	101	15	.	.	PUNCT
aiti-9488	102	1	acosta	acosta	PROPN
aiti-9488	102	2	et	et	PROPN
aiti-9488	102	3	al	al	PROPN
aiti-9488	102	4	.	.	PUNCT
aiti-9488	103	1	[	[	X
aiti-9488	103	2	15	15	NUM
aiti-9488	103	3	]	]	PUNCT
aiti-9488	103	4	,	,	PUNCT
aiti-9488	103	5	reviewed	review	VERB
aiti-9488	103	6	a	a	DET
aiti-9488	103	7	diverse	diverse	ADJ
aiti-9488	103	8	list	list	NOUN
aiti-9488	103	9	of	of	ADP
aiti-9488	103	10	state	state	NOUN
aiti-9488	103	11	-	-	PUNCT
aiti-9488	103	12	of	of	ADP
aiti-9488	103	13	-	-	PUNCT
aiti-9488	103	14	art	art	NOUN
aiti-9488	103	15	methods	method	NOUN
aiti-9488	103	16	for	for	ADP
aiti-9488	103	17	melanoma	melanoma	NOUN
aiti-9488	103	18	classification	classification	NOUN
aiti-9488	103	19	over	over	ADP
aiti-9488	103	20	the	the	DET
aiti-9488	103	21	isic	isic	PROPN
aiti-9488	103	22	challenge	challenge	PROPN
aiti-9488	103	23	2017	2017	NUM
aiti-9488	103	24	dataset	dataset	NOUN
aiti-9488	103	25	.	.	PUNCT
aiti-9488	104	1	the	the	DET
aiti-9488	104	2	proposed	propose	VERB
aiti-9488	104	3	model	model	NOUN
aiti-9488	104	4	incorporated	incorporate	VERB
aiti-9488	104	5	the	the	DET
aiti-9488	104	6	resnet152	resnet152	PROPN
aiti-9488	104	7	model	model	NOUN
aiti-9488	104	8	with	with	ADP
aiti-9488	104	9	various	various	ADJ
aiti-9488	104	10	data	datum	NOUN
aiti-9488	104	11	augmentation	augmentation	NOUN
aiti-9488	104	12	techniques	technique	NOUN
aiti-9488	104	13	like	like	ADP
aiti-9488	104	14	rotation	rotation	NOUN
aiti-9488	104	15	,	,	PUNCT
aiti-9488	104	16	advances	advance	NOUN
aiti-9488	104	17	in	in	ADP
aiti-9488	104	18	technology	technology	NOUN
aiti-9488	104	19	innovation	innovation	NOUN
aiti-9488	104	20	,	,	PUNCT
aiti-9488	104	21	vol	vol	NOUN
aiti-9488	104	22	.	.	PROPN
aiti-9488	104	23	8	8	NUM
aiti-9488	104	24	,	,	PUNCT
aiti-9488	104	25	no	no	INTJ
aiti-9488	104	26	.	.	NOUN
aiti-9488	104	27	1	1	NUM
aiti-9488	104	28	,	,	PUNCT
aiti-9488	104	29	2023	2023	NUM
aiti-9488	104	30	,	,	PUNCT
aiti-9488	104	31	pp	pp	ADJ
aiti-9488	104	32	.	.	PUNCT
aiti-9488	105	1	59	59	NUM
aiti-9488	105	2	-	-	SYM
aiti-9488	105	3	72	72	NUM
aiti-9488	105	4	62	62	NUM
aiti-9488	105	5	random	random	ADJ
aiti-9488	105	6	flip	flip	NOUN
aiti-9488	105	7	,	,	PUNCT
aiti-9488	105	8	random	random	ADJ
aiti-9488	105	9	zoom	zoom	NOUN
aiti-9488	105	10	,	,	PUNCT
aiti-9488	105	11	and	and	CCONJ
aiti-9488	105	12	contrast	contrast	NOUN
aiti-9488	105	13	enhancement	enhancement	NOUN
aiti-9488	105	14	.	.	PUNCT
aiti-9488	106	1	the	the	DET
aiti-9488	106	2	resnet152	resnet152	PROPN
aiti-9488	106	3	model	model	NOUN
aiti-9488	106	4	was	be	AUX
aiti-9488	106	5	compared	compare	VERB
aiti-9488	106	6	with	with	ADP
aiti-9488	106	7	20	20	NUM
aiti-9488	106	8	other	other	ADJ
aiti-9488	106	9	methods	method	NOUN
aiti-9488	106	10	proposed	propose	VERB
aiti-9488	106	11	by	by	ADP
aiti-9488	106	12	other	other	ADJ
aiti-9488	106	13	researchers	researcher	NOUN
aiti-9488	106	14	over	over	ADP
aiti-9488	106	15	isic	isic	PROPN
aiti-9488	106	16	2017	2017	NUM
aiti-9488	106	17	dataset	dataset	VERB
aiti-9488	106	18	and	and	CCONJ
aiti-9488	106	19	achieved	achieve	VERB
aiti-9488	106	20	the	the	DET
aiti-9488	106	21	highest	high	ADJ
aiti-9488	106	22	accuracy	accuracy	NOUN
aiti-9488	106	23	of	of	ADP
aiti-9488	106	24	87.2	87.2	NUM
aiti-9488	106	25	%	%	NOUN
aiti-9488	106	26	,	,	PUNCT
aiti-9488	106	27	sensitivity	sensitivity	NOUN
aiti-9488	106	28	of	of	ADP
aiti-9488	106	29	82	82	NUM
aiti-9488	106	30	%	%	NOUN
aiti-9488	106	31	,	,	PUNCT
aiti-9488	106	32	and	and	CCONJ
aiti-9488	106	33	f1	f1	NOUN
aiti-9488	106	34	-	-	PUNCT
aiti-9488	106	35	score	score	NOUN
aiti-9488	106	36	of	of	ADP
aiti-9488	106	37	84.8	84.8	NUM
aiti-9488	106	38	%	%	NOUN
aiti-9488	106	39	.	.	PUNCT
aiti-9488	107	1	rezaoana	rezaoana	PROPN
aiti-9488	107	2	et	et	PROPN
aiti-9488	107	3	al	al	PROPN
aiti-9488	107	4	.	.	PUNCT
aiti-9488	108	1	[	[	X
aiti-9488	108	2	16	16	NUM
aiti-9488	108	3	]	]	PUNCT
aiti-9488	108	4	,	,	PUNCT
aiti-9488	108	5	proposed	propose	VERB
aiti-9488	108	6	a	a	DET
aiti-9488	108	7	convolution	convolution	NOUN
aiti-9488	108	8	neural	neural	ADJ
aiti-9488	108	9	network	network	NOUN
aiti-9488	108	10	(	(	PUNCT
aiti-9488	108	11	cnn	cnn	PROPN
aiti-9488	108	12	)	)	PUNCT
aiti-9488	108	13	model	model	NOUN
aiti-9488	108	14	using	use	VERB
aiti-9488	108	15	the	the	DET
aiti-9488	108	16	transfer	transfer	NOUN
aiti-9488	108	17	learning	learning	NOUN
aiti-9488	108	18	technique	technique	NOUN
aiti-9488	108	19	to	to	PART
aiti-9488	108	20	classify	classify	VERB
aiti-9488	108	21	the	the	DET
aiti-9488	108	22	lesion	lesion	NOUN
aiti-9488	108	23	images	image	NOUN
aiti-9488	108	24	into	into	ADP
aiti-9488	108	25	benign	benign	ADJ
aiti-9488	108	26	and	and	CCONJ
aiti-9488	108	27	malignant	malignant	ADJ
aiti-9488	108	28	classes	class	NOUN
aiti-9488	108	29	.	.	PUNCT
aiti-9488	109	1	the	the	DET
aiti-9488	109	2	proposed	propose	VERB
aiti-9488	109	3	model	model	NOUN
aiti-9488	109	4	was	be	AUX
aiti-9488	109	5	trained	train	VERB
aiti-9488	109	6	on	on	ADP
aiti-9488	109	7	the	the	DET
aiti-9488	109	8	kaggle	kaggle	PROPN
aiti-9488	109	9	isic	isic	PROPN
aiti-9488	109	10	dataset	dataset	NOUN
aiti-9488	109	11	and	and	CCONJ
aiti-9488	109	12	various	various	ADJ
aiti-9488	109	13	augmentation	augmentation	NOUN
aiti-9488	109	14	techniques	technique	NOUN
aiti-9488	109	15	such	such	ADJ
aiti-9488	109	16	as	as	ADP
aiti-9488	109	17	shear	shear	NOUN
aiti-9488	109	18	range	range	NOUN
aiti-9488	109	19	,	,	PUNCT
aiti-9488	109	20	horizontal	horizontal	ADJ
aiti-9488	109	21	flip	flip	NOUN
aiti-9488	109	22	,	,	PUNCT
aiti-9488	109	23	rotation	rotation	NOUN
aiti-9488	109	24	,	,	PUNCT
aiti-9488	109	25	and	and	CCONJ
aiti-9488	109	26	image	image	NOUN
aiti-9488	109	27	zooming	zooming	NOUN
aiti-9488	109	28	.	.	PUNCT
aiti-9488	110	1	the	the	DET
aiti-9488	110	2	proposed	propose	VERB
aiti-9488	110	3	model	model	NOUN
aiti-9488	110	4	based	base	VERB
aiti-9488	110	5	on	on	ADP
aiti-9488	110	6	parallel	parallel	ADJ
aiti-9488	110	7	convolution	convolution	NOUN
aiti-9488	110	8	feature	feature	NOUN
aiti-9488	110	9	blocks	block	NOUN
aiti-9488	110	10	achieved	achieve	VERB
aiti-9488	110	11	a	a	DET
aiti-9488	110	12	weighted	weighted	ADJ
aiti-9488	110	13	average	average	ADJ
aiti-9488	110	14	accuracy	accuracy	NOUN
aiti-9488	110	15	of	of	ADP
aiti-9488	110	16	79.45	79.45	NUM
aiti-9488	110	17	%	%	NOUN
aiti-9488	110	18	.	.	PUNCT
aiti-9488	111	1	3	3	X
aiti-9488	111	2	.	.	NUM
aiti-9488	111	3	proposed	propose	VERB
aiti-9488	111	4	methodology	methodology	NOUN
aiti-9488	111	5	in	in	ADP
aiti-9488	111	6	this	this	DET
aiti-9488	111	7	section	section	NOUN
aiti-9488	111	8	,	,	PUNCT
aiti-9488	111	9	a	a	DET
aiti-9488	111	10	detailed	detailed	ADJ
aiti-9488	111	11	explanation	explanation	NOUN
aiti-9488	111	12	of	of	ADP
aiti-9488	111	13	the	the	DET
aiti-9488	111	14	proposed	propose	VERB
aiti-9488	111	15	methodology	methodology	NOUN
aiti-9488	111	16	for	for	ADP
aiti-9488	111	17	malignant	malignant	ADJ
aiti-9488	111	18	skin	skin	NOUN
aiti-9488	111	19	lesion	lesion	NOUN
aiti-9488	111	20	classification	classification	NOUN
aiti-9488	111	21	is	be	AUX
aiti-9488	111	22	provided	provide	VERB
aiti-9488	111	23	.	.	PUNCT
aiti-9488	112	1	the	the	DET
aiti-9488	112	2	design	design	NOUN
aiti-9488	112	3	methodology	methodology	NOUN
aiti-9488	112	4	consists	consist	VERB
aiti-9488	112	5	of	of	ADP
aiti-9488	112	6	the	the	DET
aiti-9488	112	7	following	follow	VERB
aiti-9488	112	8	subsections	subsection	NOUN
aiti-9488	112	9	:	:	PUNCT
aiti-9488	112	10	(	(	PUNCT
aiti-9488	112	11	1	1	X
aiti-9488	112	12	)	)	PUNCT
aiti-9488	112	13	data	datum	NOUN
aiti-9488	112	14	preprocessing	preprocessing	NOUN
aiti-9488	112	15	,	,	PUNCT
aiti-9488	112	16	(	(	PUNCT
aiti-9488	112	17	2	2	X
aiti-9488	112	18	)	)	PUNCT
aiti-9488	112	19	data	datum	NOUN
aiti-9488	112	20	augmentation	augmentation	NOUN
aiti-9488	112	21	,	,	PUNCT
aiti-9488	112	22	and	and	CCONJ
aiti-9488	112	23	(	(	PUNCT
aiti-9488	112	24	3	3	X
aiti-9488	112	25	)	)	PUNCT
aiti-9488	112	26	finetuned	finetune	VERB
aiti-9488	112	27	efficientnetb3	efficientnetb3	NOUN
aiti-9488	112	28	model	model	NOUN
aiti-9488	112	29	architecture	architecture	NOUN
aiti-9488	112	30	.	.	PUNCT
aiti-9488	113	1	3.1	3.1	NUM
aiti-9488	113	2	.	.	PUNCT
aiti-9488	113	3	dataset	dataset	NOUN
aiti-9488	113	4	preprocessing	preprocesse	VERB
aiti-9488	113	5	all	all	DET
aiti-9488	113	6	the	the	DET
aiti-9488	113	7	experimental	experimental	ADJ
aiti-9488	113	8	research	research	NOUN
aiti-9488	113	9	was	be	AUX
aiti-9488	113	10	carried	carry	VERB
aiti-9488	113	11	out	out	ADP
aiti-9488	113	12	on	on	ADP
aiti-9488	113	13	the	the	DET
aiti-9488	113	14	isic	isic	PROPN
aiti-9488	113	15	dataset	dataset	NOUN
aiti-9488	113	16	[	[	X
aiti-9488	113	17	4	4	X
aiti-9488	113	18	]	]	PUNCT
aiti-9488	113	19	available	available	ADJ
aiti-9488	113	20	from	from	ADP
aiti-9488	113	21	“	"	PUNCT
aiti-9488	113	22	2017	2017	NUM
aiti-9488	113	23	isbi	isbi	NOUN
aiti-9488	113	24	challenge	challenge	NOUN
aiti-9488	113	25	on	on	ADP
aiti-9488	113	26	skin	skin	NOUN
aiti-9488	113	27	lesion	lesion	NOUN
aiti-9488	113	28	analysis	analysis	NOUN
aiti-9488	113	29	towards	towards	ADP
aiti-9488	113	30	melanoma	melanoma	NOUN
aiti-9488	113	31	detection	detection	NOUN
aiti-9488	113	32	”	"	PUNCT
aiti-9488	113	33	.	.	PUNCT
aiti-9488	114	1	the	the	DET
aiti-9488	114	2	dataset	dataset	NOUN
aiti-9488	114	3	comprised	comprise	VERB
aiti-9488	114	4	3297	3297	NUM
aiti-9488	114	5	images	image	NOUN
aiti-9488	114	6	of	of	ADP
aiti-9488	114	7	benign	benign	ADJ
aiti-9488	114	8	and	and	CCONJ
aiti-9488	114	9	malignant	malignant	ADJ
aiti-9488	114	10	skin	skin	NOUN
aiti-9488	114	11	lesions	lesion	NOUN
aiti-9488	114	12	.	.	PUNCT
aiti-9488	115	1	lesion	lesion	NOUN
aiti-9488	115	2	images	image	NOUN
aiti-9488	115	3	in	in	ADP
aiti-9488	115	4	the	the	DET
aiti-9488	115	5	isic	isic	PROPN
aiti-9488	115	6	dataset	dataset	NOUN
aiti-9488	115	7	are	be	AUX
aiti-9488	115	8	in	in	ADP
aiti-9488	115	9	rgb	rgb	PROPN
aiti-9488	115	10	color	color	NOUN
aiti-9488	115	11	space	space	NOUN
aiti-9488	115	12	with	with	ADP
aiti-9488	115	13	varied	varied	ADJ
aiti-9488	115	14	pixel	pixel	NOUN
aiti-9488	115	15	sizes	size	NOUN
aiti-9488	115	16	in	in	ADP
aiti-9488	115	17	the	the	DET
aiti-9488	115	18	range	range	NOUN
aiti-9488	115	19	540×722	540×722	NUM
aiti-9488	115	20	and	and	CCONJ
aiti-9488	115	21	4499×6748	4499×6748	NOUN
aiti-9488	115	22	.	.	PUNCT
aiti-9488	116	1	fig	fig	NOUN
aiti-9488	116	2	.	.	PUNCT
aiti-9488	117	1	1	1	NUM
aiti-9488	117	2	shows	show	VERB
aiti-9488	117	3	the	the	DET
aiti-9488	117	4	malignant	malignant	ADJ
aiti-9488	117	5	skin	skin	NOUN
aiti-9488	117	6	lesion	lesion	NOUN
aiti-9488	117	7	from	from	ADP
aiti-9488	117	8	the	the	DET
aiti-9488	117	9	isic	isic	PROPN
aiti-9488	117	10	dataset	dataset	NOUN
aiti-9488	117	11	.	.	PUNCT
aiti-9488	118	1	the	the	DET
aiti-9488	118	2	dataset	dataset	NOUN
aiti-9488	118	3	images	image	NOUN
aiti-9488	118	4	consisted	consist	VERB
aiti-9488	118	5	of	of	ADP
aiti-9488	118	6	noise	noise	NOUN
aiti-9488	118	7	artifacts	artifact	NOUN
aiti-9488	118	8	like	like	ADP
aiti-9488	118	9	surgical	surgical	ADJ
aiti-9488	118	10	ink	ink	NOUN
aiti-9488	118	11	markers	marker	NOUN
aiti-9488	118	12	,	,	PUNCT
aiti-9488	118	13	and	and	CCONJ
aiti-9488	118	14	the	the	DET
aiti-9488	118	15	presence	presence	NOUN
aiti-9488	118	16	of	of	ADP
aiti-9488	118	17	hair	hair	NOUN
aiti-9488	118	18	that	that	PRON
aiti-9488	118	19	impedes	impede	VERB
aiti-9488	118	20	accurate	accurate	ADJ
aiti-9488	118	21	lesion	lesion	NOUN
aiti-9488	118	22	classification	classification	NOUN
aiti-9488	118	23	.	.	PUNCT
aiti-9488	119	1	the	the	DET
aiti-9488	119	2	images	image	NOUN
aiti-9488	119	3	are	be	AUX
aiti-9488	119	4	resized	resize	VERB
aiti-9488	119	5	into	into	ADP
aiti-9488	119	6	224×224	224×224	NUM
aiti-9488	119	7	pixels	pixel	NOUN
aiti-9488	119	8	for	for	ADP
aiti-9488	119	9	compatibility	compatibility	NOUN
aiti-9488	119	10	with	with	ADP
aiti-9488	119	11	pre	pre	ADJ
aiti-9488	119	12	-	-	ADJ
aiti-9488	119	13	trained	train	VERB
aiti-9488	119	14	neural	neural	ADJ
aiti-9488	119	15	networks	network	NOUN
aiti-9488	119	16	during	during	ADP
aiti-9488	119	17	the	the	DET
aiti-9488	119	18	model	model	NOUN
aiti-9488	119	19	training	training	NOUN
aiti-9488	119	20	and	and	CCONJ
aiti-9488	119	21	testing	testing	NOUN
aiti-9488	119	22	phase	phase	NOUN
aiti-9488	119	23	.	.	PUNCT
aiti-9488	120	1	the	the	DET
aiti-9488	120	2	images	image	NOUN
aiti-9488	120	3	were	be	AUX
aiti-9488	120	4	down	down	ADV
aiti-9488	120	5	-	-	PUNCT
aiti-9488	120	6	sampled	sample	VERB
aiti-9488	120	7	since	since	SCONJ
aiti-9488	120	8	most	most	ADJ
aiti-9488	120	9	of	of	ADP
aiti-9488	120	10	the	the	DET
aiti-9488	120	11	pre	pre	ADJ
aiti-9488	120	12	-	-	ADJ
aiti-9488	120	13	trained	train	VERB
aiti-9488	120	14	deep	deep	ADJ
aiti-9488	120	15	learning	learning	NOUN
aiti-9488	120	16	networks	network	NOUN
aiti-9488	120	17	take	take	VERB
aiti-9488	120	18	input	input	NOUN
aiti-9488	120	19	images	image	NOUN
aiti-9488	120	20	of	of	ADP
aiti-9488	120	21	fixed	fix	VERB
aiti-9488	120	22	resolution	resolution	NOUN
aiti-9488	120	23	.	.	PUNCT
aiti-9488	121	1	by	by	ADP
aiti-9488	121	2	training	train	VERB
aiti-9488	121	3	raw	raw	ADJ
aiti-9488	121	4	images	image	NOUN
aiti-9488	121	5	of	of	ADP
aiti-9488	121	6	larger	large	ADJ
aiti-9488	121	7	sizes	size	NOUN
aiti-9488	121	8	,	,	PUNCT
aiti-9488	121	9	neural	neural	ADJ
aiti-9488	121	10	networks	network	NOUN
aiti-9488	121	11	will	will	AUX
aiti-9488	121	12	require	require	VERB
aiti-9488	121	13	more	more	ADJ
aiti-9488	121	14	computing	compute	VERB
aiti-9488	121	15	power	power	NOUN
aiti-9488	121	16	to	to	PART
aiti-9488	121	17	handle	handle	VERB
aiti-9488	121	18	higher	high	ADJ
aiti-9488	121	19	parameters	parameter	NOUN
aiti-9488	121	20	that	that	PRON
aiti-9488	121	21	may	may	AUX
aiti-9488	121	22	lead	lead	VERB
aiti-9488	121	23	to	to	ADP
aiti-9488	121	24	model	model	NOUN
aiti-9488	121	25	overfitting	overfitting	NOUN
aiti-9488	121	26	.	.	PUNCT
aiti-9488	122	1	to	to	PART
aiti-9488	122	2	train	train	VERB
aiti-9488	122	3	images	image	NOUN
aiti-9488	122	4	faster	fast	ADV
aiti-9488	122	5	,	,	PUNCT
aiti-9488	122	6	improve	improve	VERB
aiti-9488	122	7	the	the	DET
aiti-9488	122	8	performance	performance	NOUN
aiti-9488	122	9	of	of	ADP
aiti-9488	122	10	neural	neural	ADJ
aiti-9488	122	11	networks	network	NOUN
aiti-9488	122	12	and	and	CCONJ
aiti-9488	122	13	reduce	reduce	VERB
aiti-9488	122	14	model	model	NOUN
aiti-9488	122	15	overfitting	overfitting	NOUN
aiti-9488	122	16	,	,	PUNCT
aiti-9488	122	17	it	it	PRON
aiti-9488	122	18	is	be	AUX
aiti-9488	122	19	important	important	ADJ
aiti-9488	122	20	to	to	PART
aiti-9488	122	21	resize	resize	VERB
aiti-9488	122	22	images	image	NOUN
aiti-9488	122	23	into	into	ADP
aiti-9488	122	24	smaller	small	ADJ
aiti-9488	122	25	resolutions	resolution	NOUN
aiti-9488	122	26	based	base	VERB
aiti-9488	122	27	on	on	ADP
aiti-9488	122	28	the	the	DET
aiti-9488	122	29	architecture	architecture	NOUN
aiti-9488	122	30	of	of	ADP
aiti-9488	122	31	the	the	DET
aiti-9488	122	32	pre	pre	ADJ
aiti-9488	122	33	-	-	ADJ
aiti-9488	122	34	trained	train	VERB
aiti-9488	122	35	network	network	NOUN
aiti-9488	122	36	.	.	PUNCT
aiti-9488	123	1	according	accord	VERB
aiti-9488	123	2	to	to	ADP
aiti-9488	123	3	the	the	DET
aiti-9488	123	4	study	study	NOUN
aiti-9488	123	5	by	by	ADP
aiti-9488	123	6	talebi	talebi	ADJ
aiti-9488	123	7	and	and	CCONJ
aiti-9488	123	8	milanfar	milanfar	ADJ
aiti-9488	123	9	[	[	X
aiti-9488	123	10	17	17	NUM
aiti-9488	123	11	]	]	PUNCT
aiti-9488	123	12	,	,	PUNCT
aiti-9488	123	13	the	the	DET
aiti-9488	123	14	perceptual	perceptual	ADJ
aiti-9488	123	15	quality	quality	NOUN
aiti-9488	123	16	of	of	ADP
aiti-9488	123	17	resized	resize	VERB
aiti-9488	123	18	images	image	NOUN
aiti-9488	123	19	is	be	AUX
aiti-9488	123	20	not	not	PART
aiti-9488	123	21	lost	lose	VERB
aiti-9488	123	22	while	while	SCONJ
aiti-9488	123	23	building	build	VERB
aiti-9488	123	24	computer	computer	NOUN
aiti-9488	123	25	vision	vision	NOUN
aiti-9488	123	26	models	model	NOUN
aiti-9488	123	27	;	;	PUNCT
aiti-9488	123	28	instead	instead	ADV
aiti-9488	123	29	aids	aid	VERB
aiti-9488	123	30	in	in	ADP
aiti-9488	123	31	boosting	boost	VERB
aiti-9488	123	32	the	the	DET
aiti-9488	123	33	performance	performance	NOUN
aiti-9488	123	34	of	of	ADP
aiti-9488	123	35	the	the	DET
aiti-9488	123	36	network	network	NOUN
aiti-9488	123	37	.	.	PUNCT
aiti-9488	124	1	fig	fig	NOUN
aiti-9488	124	2	.	.	PUNCT
aiti-9488	125	1	1	1	NUM
aiti-9488	125	2	skin	skin	NOUN
aiti-9488	125	3	lesion	lesion	NOUN
aiti-9488	125	4	images	image	NOUN
aiti-9488	125	5	from	from	ADP
aiti-9488	125	6	the	the	DET
aiti-9488	125	7	isic	isic	PROPN
aiti-9488	125	8	dataset	dataset	NOUN
aiti-9488	125	9	hair	hair	NOUN
aiti-9488	125	10	artifacts	artifact	NOUN
aiti-9488	125	11	in	in	ADP
aiti-9488	125	12	lesion	lesion	NOUN
aiti-9488	125	13	images	image	NOUN
aiti-9488	125	14	have	have	VERB
aiti-9488	125	15	a	a	DET
aiti-9488	125	16	huge	huge	ADJ
aiti-9488	125	17	impact	impact	NOUN
aiti-9488	125	18	in	in	ADP
aiti-9488	125	19	building	build	VERB
aiti-9488	125	20	a	a	DET
aiti-9488	125	21	computer	computer	NOUN
aiti-9488	125	22	-	-	PUNCT
aiti-9488	125	23	based	base	VERB
aiti-9488	125	24	melanoma	melanoma	NOUN
aiti-9488	125	25	classification	classification	NOUN
aiti-9488	125	26	system	system	NOUN
aiti-9488	125	27	as	as	SCONJ
aiti-9488	125	28	hair	hair	NOUN
aiti-9488	125	29	structures	structure	NOUN
aiti-9488	125	30	tend	tend	VERB
aiti-9488	125	31	to	to	PART
aiti-9488	125	32	block	block	VERB
aiti-9488	125	33	the	the	DET
aiti-9488	125	34	lesion	lesion	NOUN
aiti-9488	125	35	region	region	NOUN
aiti-9488	125	36	.	.	PUNCT
aiti-9488	126	1	color	color	NOUN
aiti-9488	126	2	,	,	PUNCT
aiti-9488	126	3	length	length	NOUN
aiti-9488	126	4	,	,	PUNCT
aiti-9488	126	5	and	and	CCONJ
aiti-9488	126	6	thickness	thickness	NOUN
aiti-9488	126	7	of	of	ADP
aiti-9488	126	8	hair	hair	NOUN
aiti-9488	126	9	are	be	AUX
aiti-9488	126	10	some	some	DET
aiti-9488	126	11	factors	factor	NOUN
aiti-9488	126	12	that	that	PRON
aiti-9488	126	13	need	need	VERB
aiti-9488	126	14	to	to	PART
aiti-9488	126	15	be	be	AUX
aiti-9488	126	16	considered	consider	VERB
aiti-9488	126	17	while	while	SCONJ
aiti-9488	126	18	building	build	VERB
aiti-9488	126	19	an	an	DET
aiti-9488	126	20	automated	automate	VERB
aiti-9488	126	21	image	image	NOUN
aiti-9488	126	22	preprocessing	preprocessing	NOUN
aiti-9488	126	23	system	system	NOUN
aiti-9488	126	24	[	[	X
aiti-9488	126	25	18	18	NUM
aiti-9488	126	26	]	]	PUNCT
aiti-9488	126	27	.	.	PUNCT
aiti-9488	127	1	in	in	ADP
aiti-9488	127	2	this	this	DET
aiti-9488	127	3	study	study	NOUN
aiti-9488	127	4	,	,	PUNCT
aiti-9488	127	5	the	the	DET
aiti-9488	127	6	hair	hair	NOUN
aiti-9488	127	7	artifacts	artifact	VERB
aiti-9488	127	8	removal	removal	NOUN
aiti-9488	127	9	model	model	NOUN
aiti-9488	127	10	is	be	AUX
aiti-9488	127	11	designed	design	VERB
aiti-9488	127	12	to	to	PART
aiti-9488	127	13	efficiently	efficiently	ADV
aiti-9488	127	14	remove	remove	VERB
aiti-9488	127	15	thin	thin	ADJ
aiti-9488	127	16	and	and	CCONJ
aiti-9488	127	17	thick	thick	ADJ
aiti-9488	127	18	hair	hair	NOUN
aiti-9488	127	19	noise	noise	NOUN
aiti-9488	127	20	from	from	ADP
aiti-9488	127	21	dermoscopy	dermoscopy	NOUN
aiti-9488	127	22	images	image	NOUN
aiti-9488	127	23	without	without	ADP
aiti-9488	127	24	impacting	impact	VERB
aiti-9488	127	25	the	the	DET
aiti-9488	127	26	quality	quality	NOUN
aiti-9488	127	27	of	of	ADP
aiti-9488	127	28	the	the	DET
aiti-9488	127	29	image	image	NOUN
aiti-9488	127	30	.	.	PUNCT
aiti-9488	128	1	lesion	lesion	NOUN
aiti-9488	128	2	images	image	NOUN
aiti-9488	128	3	are	be	AUX
aiti-9488	128	4	converted	convert	VERB
aiti-9488	128	5	from	from	ADP
aiti-9488	128	6	rgb	rgb	PROPN
aiti-9488	128	7	color	color	NOUN
aiti-9488	128	8	space	space	NOUN
aiti-9488	128	9	to	to	PART
aiti-9488	128	10	grayscale	grayscale	NOUN
aiti-9488	128	11	images	image	NOUN
aiti-9488	128	12	using	use	VERB
aiti-9488	128	13	the	the	DET
aiti-9488	128	14	weighted	weight	VERB
aiti-9488	128	15	method	method	NOUN
aiti-9488	128	16	.	.	PUNCT
aiti-9488	129	1	images	image	NOUN
aiti-9488	129	2	in	in	ADP
aiti-9488	129	3	grayscale	grayscale	NOUN
aiti-9488	129	4	aid	aid	NOUN
aiti-9488	129	5	in	in	ADP
aiti-9488	129	6	identifying	identify	VERB
aiti-9488	129	7	hair	hair	NOUN
aiti-9488	129	8	advances	advance	NOUN
aiti-9488	129	9	in	in	ADP
aiti-9488	129	10	technology	technology	NOUN
aiti-9488	129	11	innovation	innovation	NOUN
aiti-9488	129	12	,	,	PUNCT
aiti-9488	129	13	vol	vol	NOUN
aiti-9488	129	14	.	.	PROPN
aiti-9488	129	15	8	8	NUM
aiti-9488	129	16	,	,	PUNCT
aiti-9488	129	17	no	no	INTJ
aiti-9488	129	18	.	.	NOUN
aiti-9488	129	19	1	1	NUM
aiti-9488	129	20	,	,	PUNCT
aiti-9488	129	21	2023	2023	NUM
aiti-9488	129	22	,	,	PUNCT
aiti-9488	129	23	pp	pp	ADJ
aiti-9488	129	24	.	.	PUNCT
aiti-9488	130	1	59	59	NUM
aiti-9488	130	2	-	-	SYM
aiti-9488	130	3	72	72	NUM
aiti-9488	130	4	63	63	NUM
aiti-9488	130	5	artifacts	artifact	NOUN
aiti-9488	130	6	from	from	ADP
aiti-9488	130	7	skin	skin	NOUN
aiti-9488	130	8	lesion	lesion	NOUN
aiti-9488	130	9	images	image	NOUN
aiti-9488	130	10	.	.	PUNCT
aiti-9488	131	1	blackhat	blackhat	NOUN
aiti-9488	131	2	filtering	filtering	NOUN
aiti-9488	131	3	technique	technique	NOUN
aiti-9488	131	4	is	be	AUX
aiti-9488	131	5	applied	apply	VERB
aiti-9488	131	6	on	on	ADP
aiti-9488	131	7	these	these	DET
aiti-9488	131	8	grayscale	grayscale	NOUN
aiti-9488	131	9	lesion	lesion	NOUN
aiti-9488	131	10	images	image	NOUN
aiti-9488	131	11	,	,	PUNCT
aiti-9488	131	12	which	which	PRON
aiti-9488	131	13	further	further	ADJ
aiti-9488	131	14	highlights	highlight	NOUN
aiti-9488	131	15	hair	hair	NOUN
aiti-9488	131	16	noise	noise	NOUN
aiti-9488	131	17	against	against	ADP
aiti-9488	131	18	lighter	light	ADJ
aiti-9488	131	19	skin	skin	NOUN
aiti-9488	131	20	backgrounds	background	NOUN
aiti-9488	131	21	.	.	PUNCT
aiti-9488	132	1	to	to	PART
aiti-9488	132	2	effectively	effectively	ADV
aiti-9488	132	3	probe	probe	VERB
aiti-9488	132	4	the	the	DET
aiti-9488	132	5	hair	hair	NOUN
aiti-9488	132	6	structures	structure	NOUN
aiti-9488	132	7	,	,	PUNCT
aiti-9488	132	8	a	a	DET
aiti-9488	132	9	structuring	structuring	NOUN
aiti-9488	132	10	element	element	NOUN
aiti-9488	132	11	of	of	ADP
aiti-9488	132	12	elliptical	elliptical	ADJ
aiti-9488	132	13	shape	shape	NOUN
aiti-9488	132	14	and	and	CCONJ
aiti-9488	132	15	13	13	NUM
aiti-9488	132	16	-	-	PUNCT
aiti-9488	132	17	pixel	pixel	NOUN
aiti-9488	132	18	size	size	NOUN
aiti-9488	132	19	blackhat	blackhat	NOUN
aiti-9488	132	20	filter	filter	NOUN
aiti-9488	132	21	is	be	AUX
aiti-9488	132	22	used	use	VERB
aiti-9488	132	23	[	[	X
aiti-9488	132	24	19	19	NUM
aiti-9488	132	25	]	]	PUNCT
aiti-9488	132	26	.	.	PUNCT
aiti-9488	133	1	a	a	DET
aiti-9488	133	2	binary	binary	ADJ
aiti-9488	133	3	thresholding	thresholding	NOUN
aiti-9488	133	4	function	function	NOUN
aiti-9488	133	5	is	be	AUX
aiti-9488	133	6	exerted	exert	VERB
aiti-9488	133	7	over	over	ADP
aiti-9488	133	8	the	the	DET
aiti-9488	133	9	blackhat	blackhat	NOUN
aiti-9488	133	10	image	image	NOUN
aiti-9488	133	11	to	to	PART
aiti-9488	133	12	create	create	VERB
aiti-9488	133	13	a	a	DET
aiti-9488	133	14	hair	hair	NOUN
aiti-9488	133	15	mask	mask	NOUN
aiti-9488	133	16	that	that	SCONJ
aiti-9488	133	17	further	far	ADV
aiti-9488	133	18	sharpens	sharpen	VERB
aiti-9488	133	19	hair	hair	NOUN
aiti-9488	133	20	structures	structure	NOUN
aiti-9488	133	21	from	from	ADP
aiti-9488	133	22	the	the	DET
aiti-9488	133	23	background	background	NOUN
aiti-9488	133	24	skin	skin	NOUN
aiti-9488	133	25	image	image	NOUN
aiti-9488	133	26	.	.	PUNCT
aiti-9488	134	1	the	the	DET
aiti-9488	134	2	fast	fast	ADJ
aiti-9488	134	3	marching	marching	NOUN
aiti-9488	134	4	restoration	restoration	NOUN
aiti-9488	134	5	inpaint_telea	inpaint_telea	ADJ
aiti-9488	134	6	method	method	NOUN
aiti-9488	134	7	is	be	AUX
aiti-9488	134	8	applied	apply	VERB
aiti-9488	134	9	to	to	ADP
aiti-9488	134	10	the	the	DET
aiti-9488	134	11	masked	mask	VERB
aiti-9488	134	12	image	image	NOUN
aiti-9488	134	13	.	.	PUNCT
aiti-9488	135	1	inpaint_telea	inpaint_telea	ADJ
aiti-9488	135	2	technique	technique	NOUN
aiti-9488	135	3	builds	build	VERB
aiti-9488	135	4	the	the	DET
aiti-9488	135	5	original	original	ADJ
aiti-9488	135	6	image	image	NOUN
aiti-9488	135	7	without	without	ADP
aiti-9488	135	8	any	any	DET
aiti-9488	135	9	hair	hair	NOUN
aiti-9488	135	10	noise	noise	NOUN
aiti-9488	135	11	from	from	ADP
aiti-9488	135	12	the	the	DET
aiti-9488	135	13	masked	mask	VERB
aiti-9488	135	14	image	image	NOUN
aiti-9488	135	15	.	.	PUNCT
aiti-9488	136	1	fig	fig	NOUN
aiti-9488	136	2	.	.	PUNCT
aiti-9488	137	1	2	2	NUM
aiti-9488	137	2	shows	show	VERB
aiti-9488	137	3	the	the	DET
aiti-9488	137	4	proposed	propose	VERB
aiti-9488	137	5	hair	hair	NOUN
aiti-9488	137	6	removal	removal	NOUN
aiti-9488	137	7	process	process	NOUN
aiti-9488	137	8	.	.	PUNCT
aiti-9488	138	1	(	(	PUNCT
aiti-9488	138	2	a	a	X
aiti-9488	138	3	)	)	PUNCT
aiti-9488	138	4	original	original	ADJ
aiti-9488	138	5	image	image	NOUN
aiti-9488	138	6	(	(	PUNCT
aiti-9488	138	7	b	b	NOUN
aiti-9488	138	8	)	)	PUNCT
aiti-9488	138	9	grayscale	grayscale	NOUN
aiti-9488	138	10	image	image	NOUN
aiti-9488	138	11	(	(	PUNCT
aiti-9488	138	12	c	c	NOUN
aiti-9488	138	13	)	)	PUNCT
aiti-9488	138	14	blackhat	blackhat	NOUN
aiti-9488	138	15	filtered	filter	VERB
aiti-9488	138	16	image	image	NOUN
aiti-9488	138	17	(	(	PUNCT
aiti-9488	138	18	d	d	NOUN
aiti-9488	138	19	)	)	PUNCT
aiti-9488	138	20	threshold	threshold	NOUN
aiti-9488	138	21	image	image	NOUN
aiti-9488	138	22	(	(	PUNCT
aiti-9488	138	23	e	e	NOUN
aiti-9488	138	24	)	)	PUNCT
aiti-9488	138	25	hair	hair	NOUN
aiti-9488	138	26	removed	remove	VERB
aiti-9488	138	27	fig	fig	NOUN
aiti-9488	138	28	.	.	PUNCT
aiti-9488	139	1	2	2	NUM
aiti-9488	139	2	hair	hair	NOUN
aiti-9488	139	3	removal	removal	NOUN
aiti-9488	139	4	process	process	NOUN
aiti-9488	139	5	(	(	PUNCT
aiti-9488	139	6	a	a	PRON
aiti-9488	139	7	)	)	PUNCT
aiti-9488	139	8	images	image	NOUN
aiti-9488	139	9	with	with	ADP
aiti-9488	139	10	ink	ink	NOUN
aiti-9488	139	11	markers	marker	NOUN
aiti-9488	139	12	(	(	PUNCT
aiti-9488	139	13	b	b	NOUN
aiti-9488	139	14	)	)	PUNCT
aiti-9488	139	15	images	image	NOUN
aiti-9488	139	16	without	without	ADP
aiti-9488	139	17	ink	ink	NOUN
aiti-9488	139	18	markers	marker	NOUN
aiti-9488	139	19	fig	fig	NOUN
aiti-9488	139	20	.	.	PUNCT
aiti-9488	140	1	3	3	NUM
aiti-9488	140	2	preprocessed	preprocesse	VERB
aiti-9488	140	3	image	image	NOUN
aiti-9488	140	4	without	without	ADP
aiti-9488	140	5	markers	marker	NOUN
aiti-9488	140	6	clinical	clinical	ADJ
aiti-9488	140	7	experts	expert	NOUN
aiti-9488	140	8	mark	mark	VERB
aiti-9488	140	9	out	out	ADP
aiti-9488	140	10	suspicious	suspicious	ADJ
aiti-9488	140	11	skin	skin	NOUN
aiti-9488	140	12	lesions	lesion	NOUN
aiti-9488	140	13	with	with	ADP
aiti-9488	140	14	blue	blue	ADJ
aiti-9488	140	15	or	or	CCONJ
aiti-9488	140	16	violet	violet	ADJ
aiti-9488	140	17	ink	ink	NOUN
aiti-9488	140	18	markers	marker	NOUN
aiti-9488	140	19	.	.	PUNCT
aiti-9488	141	1	these	these	DET
aiti-9488	141	2	ink	ink	NOUN
aiti-9488	141	3	markings	marking	NOUN
aiti-9488	141	4	may	may	AUX
aiti-9488	141	5	be	be	AUX
aiti-9488	141	6	considered	consider	VERB
aiti-9488	141	7	a	a	DET
aiti-9488	141	8	part	part	NOUN
aiti-9488	141	9	of	of	ADP
aiti-9488	141	10	skin	skin	NOUN
aiti-9488	141	11	lesions	lesion	NOUN
aiti-9488	141	12	and	and	CCONJ
aiti-9488	141	13	can	can	AUX
aiti-9488	141	14	cause	cause	VERB
aiti-9488	141	15	false	false	ADJ
aiti-9488	141	16	interpretations	interpretation	NOUN
aiti-9488	141	17	while	while	SCONJ
aiti-9488	141	18	constructing	construct	VERB
aiti-9488	141	19	deep	deep	ADJ
aiti-9488	141	20	learning	learning	NOUN
aiti-9488	141	21	models	model	NOUN
aiti-9488	141	22	[	[	X
aiti-9488	141	23	20	20	NUM
aiti-9488	141	24	]	]	PUNCT
aiti-9488	141	25	.	.	PUNCT
aiti-9488	142	1	therefore	therefore	ADV
aiti-9488	142	2	,	,	PUNCT
aiti-9488	142	3	it	it	PRON
aiti-9488	142	4	is	be	AUX
aiti-9488	142	5	important	important	ADJ
aiti-9488	142	6	to	to	PART
aiti-9488	142	7	eliminate	eliminate	VERB
aiti-9488	142	8	these	these	DET
aiti-9488	142	9	ink	ink	NOUN
aiti-9488	142	10	marker	marker	NOUN
aiti-9488	142	11	artifacts	artifact	NOUN
aiti-9488	142	12	from	from	ADP
aiti-9488	142	13	the	the	DET
aiti-9488	142	14	lesion	lesion	NOUN
aiti-9488	142	15	images	image	NOUN
aiti-9488	142	16	.	.	PUNCT
aiti-9488	143	1	to	to	PART
aiti-9488	143	2	effectively	effectively	ADV
aiti-9488	143	3	remove	remove	VERB
aiti-9488	143	4	ink	ink	NOUN
aiti-9488	143	5	markers	marker	NOUN
aiti-9488	143	6	,	,	PUNCT
aiti-9488	143	7	lesion	lesion	NOUN
aiti-9488	143	8	images	image	NOUN
aiti-9488	143	9	are	be	AUX
aiti-9488	143	10	transmogrified	transmogrify	VERB
aiti-9488	143	11	into	into	ADP
aiti-9488	143	12	hue	hue	NOUN
aiti-9488	143	13	-	-	PUNCT
aiti-9488	143	14	saturation	saturation	NOUN
aiti-9488	143	15	-	-	PUNCT
aiti-9488	143	16	value	value	NOUN
aiti-9488	143	17	(	(	PUNCT
aiti-9488	143	18	hsv	hsv	PROPN
aiti-9488	143	19	)	)	PUNCT
aiti-9488	143	20	color	color	NOUN
aiti-9488	143	21	space	space	NOUN
aiti-9488	143	22	that	that	PRON
aiti-9488	143	23	aids	aid	VERB
aiti-9488	143	24	in	in	ADP
aiti-9488	143	25	color	color	NOUN
aiti-9488	143	26	-	-	PUNCT
aiti-9488	143	27	based	base	VERB
aiti-9488	143	28	segmentation	segmentation	NOUN
aiti-9488	143	29	to	to	PART
aiti-9488	143	30	capture	capture	VERB
aiti-9488	143	31	blue	blue	ADJ
aiti-9488	143	32	or	or	CCONJ
aiti-9488	143	33	violet	violet	ADJ
aiti-9488	143	34	ink	ink	NOUN
aiti-9488	143	35	markers	marker	NOUN
aiti-9488	143	36	.	.	PUNCT
aiti-9488	144	1	to	to	PART
aiti-9488	144	2	create	create	VERB
aiti-9488	144	3	a	a	DET
aiti-9488	144	4	masked	masked	ADJ
aiti-9488	144	5	image	image	NOUN
aiti-9488	144	6	,	,	PUNCT
aiti-9488	144	7	the	the	DET
aiti-9488	144	8	inrange	inrange	ADJ
aiti-9488	144	9	function	function	NOUN
aiti-9488	144	10	is	be	AUX
aiti-9488	144	11	used	use	VERB
aiti-9488	144	12	to	to	PART
aiti-9488	144	13	set	set	VERB
aiti-9488	144	14	up	up	ADP
aiti-9488	144	15	a	a	DET
aiti-9488	144	16	lower	low	ADJ
aiti-9488	144	17	and	and	CCONJ
aiti-9488	144	18	upper	upper	ADJ
aiti-9488	144	19	band	band	NOUN
aiti-9488	144	20	of	of	ADP
aiti-9488	144	21	violet	violet	ADJ
aiti-9488	144	22	color	color	NOUN
aiti-9488	144	23	,	,	PUNCT
aiti-9488	144	24	and	and	CCONJ
aiti-9488	144	25	the	the	DET
aiti-9488	144	26	advances	advance	NOUN
aiti-9488	144	27	in	in	ADP
aiti-9488	144	28	technology	technology	NOUN
aiti-9488	144	29	innovation	innovation	NOUN
aiti-9488	144	30	,	,	PUNCT
aiti-9488	144	31	vol	vol	NOUN
aiti-9488	144	32	.	.	PROPN
aiti-9488	144	33	8	8	NUM
aiti-9488	144	34	,	,	PUNCT
aiti-9488	144	35	no	no	INTJ
aiti-9488	144	36	.	.	NOUN
aiti-9488	144	37	1	1	NUM
aiti-9488	144	38	,	,	PUNCT
aiti-9488	144	39	2023	2023	NUM
aiti-9488	144	40	,	,	PUNCT
aiti-9488	144	41	pp	pp	ADJ
aiti-9488	144	42	.	.	PUNCT
aiti-9488	145	1	59	59	NUM
aiti-9488	145	2	-	-	SYM
aiti-9488	145	3	72	72	NUM
aiti-9488	145	4	64	64	NUM
aiti-9488	145	5	morphological	morphological	ADJ
aiti-9488	145	6	dilation	dilation	NOUN
aiti-9488	145	7	function	function	NOUN
aiti-9488	145	8	is	be	AUX
aiti-9488	145	9	used	use	VERB
aiti-9488	145	10	to	to	PART
aiti-9488	145	11	capture	capture	VERB
aiti-9488	145	12	ink	ink	NOUN
aiti-9488	145	13	markers	marker	NOUN
aiti-9488	145	14	from	from	ADP
aiti-9488	145	15	hsv	hsv	PROPN
aiti-9488	145	16	images	image	NOUN
aiti-9488	145	17	.	.	PUNCT
aiti-9488	146	1	to	to	PART
aiti-9488	146	2	restore	restore	VERB
aiti-9488	146	3	the	the	DET
aiti-9488	146	4	original	original	ADJ
aiti-9488	146	5	image	image	NOUN
aiti-9488	146	6	from	from	ADP
aiti-9488	146	7	the	the	DET
aiti-9488	146	8	masked	mask	VERB
aiti-9488	146	9	image	image	NOUN
aiti-9488	146	10	,	,	PUNCT
aiti-9488	146	11	the	the	DET
aiti-9488	146	12	inpainting	inpainting	ADJ
aiti-9488	146	13	method	method	NOUN
aiti-9488	146	14	is	be	AUX
aiti-9488	146	15	again	again	ADV
aiti-9488	146	16	applied	apply	VERB
aiti-9488	146	17	which	which	PRON
aiti-9488	146	18	produces	produce	VERB
aiti-9488	146	19	lesion	lesion	NOUN
aiti-9488	146	20	images	image	NOUN
aiti-9488	146	21	without	without	ADP
aiti-9488	146	22	ink	ink	NOUN
aiti-9488	146	23	markers	marker	NOUN
aiti-9488	146	24	.	.	PUNCT
aiti-9488	147	1	fig	fig	NOUN
aiti-9488	147	2	.	.	PUNCT
aiti-9488	148	1	3	3	NUM
aiti-9488	148	2	shows	show	VERB
aiti-9488	148	3	a	a	DET
aiti-9488	148	4	comparison	comparison	NOUN
aiti-9488	148	5	of	of	ADP
aiti-9488	148	6	the	the	DET
aiti-9488	148	7	original	original	ADJ
aiti-9488	148	8	image	image	NOUN
aiti-9488	148	9	with	with	ADP
aiti-9488	148	10	ink	ink	NOUN
aiti-9488	148	11	markings	marking	NOUN
aiti-9488	148	12	and	and	CCONJ
aiti-9488	148	13	image	image	NOUN
aiti-9488	148	14	after	after	SCONJ
aiti-9488	148	15	an	an	DET
aiti-9488	148	16	automated	automate	VERB
aiti-9488	148	17	model	model	NOUN
aiti-9488	148	18	is	be	AUX
aiti-9488	148	19	applied	apply	VERB
aiti-9488	148	20	to	to	ADP
aiti-9488	148	21	it	it	PRON
aiti-9488	148	22	.	.	PUNCT
aiti-9488	149	1	3.2	3.2	NUM
aiti-9488	149	2	.	.	PUNCT
aiti-9488	150	1	data	datum	NOUN
aiti-9488	150	2	augmentation	augmentation	NOUN
aiti-9488	150	3	to	to	PART
aiti-9488	150	4	build	build	VERB
aiti-9488	150	5	a	a	DET
aiti-9488	150	6	good	good	ADJ
aiti-9488	150	7	classification	classification	NOUN
aiti-9488	150	8	deep	deep	ADJ
aiti-9488	150	9	learning	learning	NOUN
aiti-9488	150	10	model	model	NOUN
aiti-9488	150	11	,	,	PUNCT
aiti-9488	150	12	it	it	PRON
aiti-9488	150	13	is	be	AUX
aiti-9488	150	14	substantial	substantial	ADJ
aiti-9488	150	15	to	to	PART
aiti-9488	150	16	train	train	VERB
aiti-9488	150	17	the	the	DET
aiti-9488	150	18	neural	neural	ADJ
aiti-9488	150	19	model	model	NOUN
aiti-9488	150	20	with	with	ADP
aiti-9488	150	21	a	a	DET
aiti-9488	150	22	huge	huge	ADJ
aiti-9488	150	23	volume	volume	NOUN
aiti-9488	150	24	of	of	ADP
aiti-9488	150	25	data	data	PROPN
aiti-9488	150	26	.	.	PUNCT
aiti-9488	151	1	the	the	DET
aiti-9488	151	2	majority	majority	NOUN
aiti-9488	151	3	of	of	ADP
aiti-9488	151	4	pre	pre	ADJ
aiti-9488	151	5	-	-	ADJ
aiti-9488	151	6	trained	train	VERB
aiti-9488	151	7	networks	network	NOUN
aiti-9488	151	8	are	be	AUX
aiti-9488	151	9	trained	train	VERB
aiti-9488	151	10	on	on	ADP
aiti-9488	151	11	imagenet	imagenet	NOUN
aiti-9488	151	12	,	,	PUNCT
aiti-9488	151	13	which	which	PRON
aiti-9488	151	14	comprises	comprise	VERB
aiti-9488	151	15	a	a	DET
aiti-9488	151	16	large	large	ADJ
aiti-9488	151	17	set	set	NOUN
aiti-9488	151	18	of	of	ADP
aiti-9488	151	19	data	datum	NOUN
aiti-9488	151	20	with	with	ADP
aiti-9488	151	21	1000	1000	NUM
aiti-9488	151	22	classes	class	NOUN
aiti-9488	151	23	.	.	PUNCT
aiti-9488	152	1	the	the	DET
aiti-9488	152	2	data	data	NOUN
aiti-9488	152	3	augmentation	augmentation	NOUN
aiti-9488	152	4	approach	approach	NOUN
aiti-9488	152	5	is	be	AUX
aiti-9488	152	6	adapted	adapt	VERB
aiti-9488	152	7	to	to	PART
aiti-9488	152	8	expand	expand	VERB
aiti-9488	152	9	the	the	DET
aiti-9488	152	10	size	size	NOUN
aiti-9488	152	11	of	of	ADP
aiti-9488	152	12	the	the	DET
aiti-9488	152	13	training	training	NOUN
aiti-9488	152	14	dataset	dataset	VERB
aiti-9488	152	15	to	to	PART
aiti-9488	152	16	develop	develop	VERB
aiti-9488	152	17	an	an	DET
aiti-9488	152	18	effectual	effectual	ADJ
aiti-9488	152	19	melanoma	melanoma	NOUN
aiti-9488	152	20	lesion	lesion	NOUN
aiti-9488	152	21	classification	classification	NOUN
aiti-9488	152	22	model	model	NOUN
aiti-9488	152	23	.	.	PUNCT
aiti-9488	153	1	in	in	ADP
aiti-9488	153	2	the	the	DET
aiti-9488	153	3	data	data	NOUN
aiti-9488	153	4	augmentation	augmentation	NOUN
aiti-9488	153	5	method	method	NOUN
aiti-9488	153	6	,	,	PUNCT
aiti-9488	153	7	training	training	NOUN
aiti-9488	153	8	data	datum	NOUN
aiti-9488	153	9	is	be	AUX
aiti-9488	153	10	synthetically	synthetically	ADV
aiti-9488	153	11	expanded	expand	VERB
aiti-9488	153	12	by	by	ADP
aiti-9488	153	13	minor	minor	ADJ
aiti-9488	153	14	alterations	alteration	NOUN
aiti-9488	153	15	to	to	ADP
aiti-9488	153	16	existing	exist	VERB
aiti-9488	153	17	original	original	ADJ
aiti-9488	153	18	data	datum	NOUN
aiti-9488	153	19	[	[	X
aiti-9488	153	20	21	21	NUM
aiti-9488	153	21	]	]	PUNCT
aiti-9488	153	22	.	.	PUNCT
aiti-9488	154	1	to	to	PART
aiti-9488	154	2	ameliorate	ameliorate	VERB
aiti-9488	154	3	the	the	DET
aiti-9488	154	4	functioning	functioning	NOUN
aiti-9488	154	5	of	of	ADP
aiti-9488	154	6	the	the	DET
aiti-9488	154	7	melanoma	melanoma	NOUN
aiti-9488	154	8	classification	classification	NOUN
aiti-9488	154	9	model	model	NOUN
aiti-9488	154	10	and	and	CCONJ
aiti-9488	154	11	prevent	prevent	VERB
aiti-9488	154	12	overfitting	overfitting	NOUN
aiti-9488	154	13	of	of	ADP
aiti-9488	154	14	the	the	DET
aiti-9488	154	15	model	model	NOUN
aiti-9488	154	16	,	,	PUNCT
aiti-9488	154	17	data	datum	NOUN
aiti-9488	154	18	augmentation	augmentation	NOUN
aiti-9488	154	19	is	be	AUX
aiti-9488	154	20	added	add	VERB
aiti-9488	154	21	on	on	ADP
aiti-9488	154	22	top	top	NOUN
aiti-9488	154	23	of	of	ADP
aiti-9488	154	24	the	the	DET
aiti-9488	154	25	efficientnetb3	efficientnetb3	PROPN
aiti-9488	154	26	network	network	NOUN
aiti-9488	154	27	.	.	PUNCT
aiti-9488	155	1	the	the	DET
aiti-9488	155	2	training	training	NOUN
aiti-9488	155	3	dataset	dataset	NOUN
aiti-9488	155	4	is	be	AUX
aiti-9488	155	5	expanded	expand	VERB
aiti-9488	155	6	by	by	ADP
aiti-9488	155	7	creating	create	VERB
aiti-9488	155	8	altered	altered	ADJ
aiti-9488	155	9	versions	version	NOUN
aiti-9488	155	10	of	of	ADP
aiti-9488	155	11	images	image	NOUN
aiti-9488	155	12	pertained	pertain	VERB
aiti-9488	155	13	to	to	ADP
aiti-9488	155	14	equivalent	equivalent	ADJ
aiti-9488	155	15	classes	class	NOUN
aiti-9488	155	16	.	.	PUNCT
aiti-9488	156	1	diverse	diverse	ADJ
aiti-9488	156	2	augmentation	augmentation	NOUN
aiti-9488	156	3	techniques	technique	NOUN
aiti-9488	156	4	like	like	ADP
aiti-9488	156	5	random	random	ADJ
aiti-9488	156	6	zoom	zoom	NOUN
aiti-9488	156	7	,	,	PUNCT
aiti-9488	156	8	random	random	ADJ
aiti-9488	156	9	rotation	rotation	NOUN
aiti-9488	156	10	,	,	PUNCT
aiti-9488	156	11	random	random	ADJ
aiti-9488	156	12	flip	flip	NOUN
aiti-9488	156	13	,	,	PUNCT
aiti-9488	156	14	random	random	ADJ
aiti-9488	156	15	shift	shift	NOUN
aiti-9488	156	16	by	by	ADP
aiti-9488	156	17	height	height	NOUN
aiti-9488	156	18	,	,	PUNCT
aiti-9488	156	19	and	and	CCONJ
aiti-9488	156	20	random	random	ADJ
aiti-9488	156	21	shift	shift	NOUN
aiti-9488	156	22	by	by	ADP
aiti-9488	156	23	width	width	NOUN
aiti-9488	156	24	are	be	AUX
aiti-9488	156	25	applied	apply	VERB
aiti-9488	156	26	using	use	VERB
aiti-9488	156	27	keras	keras	PROPN
aiti-9488	156	28	preprocessing	preprocesse	VERB
aiti-9488	156	29	layers	layer	NOUN
aiti-9488	156	30	like	like	ADP
aiti-9488	156	31	keras.layers.resizing	keras.layers.resizing	PROPN
aiti-9488	156	32	,	,	PUNCT
aiti-9488	156	33	keras.layers.randomflip	keras.layers.randomflip	NOUN
aiti-9488	156	34	,	,	PUNCT
aiti-9488	156	35	keras.layers.rescaling	keras.layers.rescaling	NOUN
aiti-9488	156	36	and	and	CCONJ
aiti-9488	156	37	keras.layers.randomrotation	keras.layers.randomrotation	NOUN
aiti-9488	156	38	to	to	PART
aiti-9488	156	39	build	build	VERB
aiti-9488	156	40	keras	keras	PROPN
aiti-9488	156	41	sequential	sequential	ADJ
aiti-9488	156	42	model	model	NOUN
aiti-9488	156	43	.	.	PUNCT
aiti-9488	157	1	table	table	NOUN
aiti-9488	157	2	1	1	NUM
aiti-9488	157	3	indicates	indicate	VERB
aiti-9488	157	4	various	various	ADJ
aiti-9488	157	5	data	datum	NOUN
aiti-9488	157	6	augmentation	augmentation	NOUN
aiti-9488	157	7	methods	method	NOUN
aiti-9488	157	8	.	.	PUNCT
aiti-9488	158	1	fig	fig	NOUN
aiti-9488	158	2	.	.	PUNCT
aiti-9488	159	1	4	4	NUM
aiti-9488	159	2	depicts	depict	VERB
aiti-9488	159	3	the	the	DET
aiti-9488	159	4	augmentation	augmentation	NOUN
aiti-9488	159	5	operation	operation	NOUN
aiti-9488	159	6	applied	apply	VERB
aiti-9488	159	7	to	to	ADP
aiti-9488	159	8	lesion	lesion	NOUN
aiti-9488	159	9	images	image	NOUN
aiti-9488	159	10	.	.	PUNCT
aiti-9488	160	1	(	(	PUNCT
aiti-9488	160	2	a	a	X
aiti-9488	160	3	)	)	PUNCT
aiti-9488	160	4	random	random	ADJ
aiti-9488	160	5	augmented	augment	VERB
aiti-9488	160	6	image	image	NOUN
aiti-9488	160	7	of	of	ADP
aiti-9488	160	8	malignant	malignant	ADJ
aiti-9488	160	9	class	class	NOUN
aiti-9488	160	10	(	(	PUNCT
aiti-9488	160	11	b	b	NOUN
aiti-9488	160	12	)	)	PUNCT
aiti-9488	160	13	random	random	ADJ
aiti-9488	160	14	augmented	augment	VERB
aiti-9488	160	15	image	image	NOUN
aiti-9488	160	16	of	of	ADP
aiti-9488	160	17	benign	benign	ADJ
aiti-9488	160	18	class	class	NOUN
aiti-9488	160	19	fig	fig	NOUN
aiti-9488	160	20	.	.	PUNCT
aiti-9488	161	1	4	4	NUM
aiti-9488	161	2	random	random	ADJ
aiti-9488	161	3	augmented	augment	VERB
aiti-9488	161	4	images	image	NOUN
aiti-9488	161	5	table	table	NOUN
aiti-9488	161	6	1	1	NUM
aiti-9488	161	7	data	datum	NOUN
aiti-9488	161	8	augmentation	augmentation	NOUN
aiti-9488	161	9	approaches	approach	NOUN
aiti-9488	161	10	approach	approach	VERB
aiti-9488	161	11	description	description	NOUN
aiti-9488	161	12	random	random	ADJ
aiti-9488	161	13	width	width	VERB
aiti-9488	161	14	the	the	DET
aiti-9488	161	15	width	width	NOUN
aiti-9488	161	16	of	of	ADP
aiti-9488	161	17	images	image	NOUN
aiti-9488	161	18	arbitrarily	arbitrarily	ADV
aiti-9488	161	19	shifted	shift	VERB
aiti-9488	161	20	by	by	ADP
aiti-9488	161	21	20	20	NUM
aiti-9488	161	22	%	%	NOUN
aiti-9488	161	23	random	random	ADJ
aiti-9488	161	24	rotation	rotation	NOUN
aiti-9488	161	25	images	image	NOUN
aiti-9488	161	26	arbitrarily	arbitrarily	ADV
aiti-9488	161	27	rotated	rotate	VERB
aiti-9488	161	28	by	by	ADP
aiti-9488	161	29	20	20	NUM
aiti-9488	161	30	%	%	NOUN
aiti-9488	161	31	random	random	ADJ
aiti-9488	161	32	flip	flip	NOUN
aiti-9488	161	33	images	image	NOUN
aiti-9488	161	34	arbitrarily	arbitrarily	ADV
aiti-9488	161	35	flipped	flip	VERB
aiti-9488	161	36	random	random	ADJ
aiti-9488	161	37	zoom	zoom	NOUN
aiti-9488	161	38	images	image	NOUN
aiti-9488	161	39	arbitrarily	arbitrarily	ADV
aiti-9488	161	40	zoomed	zoom	VERB
aiti-9488	161	41	by	by	ADP
aiti-9488	161	42	20	20	NUM
aiti-9488	161	43	%	%	NOUN
aiti-9488	161	44	random	random	ADJ
aiti-9488	161	45	height	height	NOUN
aiti-9488	161	46	the	the	DET
aiti-9488	161	47	height	height	NOUN
aiti-9488	161	48	of	of	ADP
aiti-9488	161	49	images	image	NOUN
aiti-9488	161	50	arbitrarily	arbitrarily	ADV
aiti-9488	161	51	shifted	shift	VERB
aiti-9488	161	52	by	by	ADP
aiti-9488	161	53	20	20	NUM
aiti-9488	161	54	%	%	NOUN
aiti-9488	161	55	3.3	3.3	NUM
aiti-9488	161	56	.	.	PUNCT
aiti-9488	162	1	model	model	NOUN
aiti-9488	162	2	architecture	architecture	NOUN
aiti-9488	162	3	efficientnet	efficientnet	NOUN
aiti-9488	162	4	models	model	NOUN
aiti-9488	162	5	include	include	VERB
aiti-9488	162	6	a	a	DET
aiti-9488	162	7	family	family	NOUN
aiti-9488	162	8	of	of	ADP
aiti-9488	162	9	8	8	NUM
aiti-9488	162	10	models	model	NOUN
aiti-9488	162	11	from	from	ADP
aiti-9488	162	12	b0	b0	NOUN
aiti-9488	162	13	-	-	PUNCT
aiti-9488	162	14	b7	b7	NOUN
aiti-9488	162	15	trained	train	VERB
aiti-9488	162	16	over	over	ADP
aiti-9488	162	17	imagenet	imagenet	NOUN
aiti-9488	162	18	.	.	PUNCT
aiti-9488	163	1	efficientnet	efficientnet	NOUN
aiti-9488	163	2	models	model	NOUN
aiti-9488	163	3	are	be	AUX
aiti-9488	163	4	deemed	deem	VERB
aiti-9488	163	5	as	as	ADP
aiti-9488	163	6	the	the	DET
aiti-9488	163	7	uttermost	uttermost	ADJ
aiti-9488	163	8	computationally	computationally	ADV
aiti-9488	163	9	effective	effective	ADJ
aiti-9488	163	10	deep	deep	ADJ
aiti-9488	163	11	learning	learning	NOUN
aiti-9488	163	12	model	model	NOUN
aiti-9488	163	13	that	that	PRON
aiti-9488	163	14	acquires	acquire	VERB
aiti-9488	163	15	top	top	ADJ
aiti-9488	163	16	accuracy	accuracy	NOUN
aiti-9488	163	17	gain	gain	NOUN
aiti-9488	163	18	appertaining	appertain	VERB
aiti-9488	163	19	to	to	ADP
aiti-9488	163	20	the	the	DET
aiti-9488	163	21	compound	compound	NOUN
aiti-9488	163	22	scaling	scale	VERB
aiti-9488	163	23	approach	approach	NOUN
aiti-9488	163	24	.	.	PUNCT
aiti-9488	164	1	in	in	ADP
aiti-9488	164	2	the	the	DET
aiti-9488	164	3	compound	compound	NOUN
aiti-9488	164	4	scaling	scale	VERB
aiti-9488	164	5	approach	approach	NOUN
aiti-9488	164	6	,	,	PUNCT
aiti-9488	164	7	the	the	DET
aiti-9488	164	8	size	size	NOUN
aiti-9488	164	9	of	of	ADP
aiti-9488	164	10	the	the	DET
aiti-9488	164	11	baseline	baseline	PROPN
aiti-9488	164	12	convolution	convolution	NOUN
aiti-9488	164	13	network	network	NOUN
aiti-9488	164	14	model	model	NOUN
aiti-9488	164	15	is	be	AUX
aiti-9488	164	16	expanded	expand	VERB
aiti-9488	164	17	by	by	ADP
aiti-9488	164	18	scaling	scale	VERB
aiti-9488	164	19	the	the	DET
aiti-9488	164	20	network	network	NOUN
aiti-9488	164	21	uniformly	uniformly	ADV
aiti-9488	164	22	across	across	ADP
aiti-9488	164	23	depth	depth	NOUN
aiti-9488	164	24	,	,	PUNCT
aiti-9488	164	25	width	width	ADJ
aiti-9488	164	26	,	,	PUNCT
aiti-9488	164	27	and	and	CCONJ
aiti-9488	164	28	resolution	resolution	NOUN
aiti-9488	164	29	to	to	ADP
aiti-9488	164	30	the	the	DET
aiti-9488	164	31	target	target	NOUN
aiti-9488	164	32	model	model	NOUN
aiti-9488	164	33	size	size	NOUN
aiti-9488	164	34	[	[	X
aiti-9488	164	35	22	22	NUM
aiti-9488	164	36	]	]	PUNCT
aiti-9488	164	37	.	.	PUNCT
aiti-9488	165	1	fig	fig	NOUN
aiti-9488	165	2	.	.	PUNCT
aiti-9488	166	1	5	5	NUM
aiti-9488	166	2	exhibits	exhibit	VERB
aiti-9488	166	3	the	the	DET
aiti-9488	166	4	scaling	scaling	NOUN
aiti-9488	166	5	of	of	ADP
aiti-9488	166	6	advances	advance	NOUN
aiti-9488	166	7	in	in	ADP
aiti-9488	166	8	technology	technology	NOUN
aiti-9488	166	9	innovation	innovation	NOUN
aiti-9488	166	10	,	,	PUNCT
aiti-9488	166	11	vol	vol	NOUN
aiti-9488	166	12	.	.	PROPN
aiti-9488	166	13	8	8	NUM
aiti-9488	166	14	,	,	PUNCT
aiti-9488	166	15	no	no	INTJ
aiti-9488	166	16	.	.	NOUN
aiti-9488	166	17	1	1	NUM
aiti-9488	166	18	,	,	PUNCT
aiti-9488	166	19	2023	2023	NUM
aiti-9488	166	20	,	,	PUNCT
aiti-9488	166	21	pp	pp	ADJ
aiti-9488	166	22	.	.	PUNCT
aiti-9488	167	1	59	59	NUM
aiti-9488	167	2	-	-	SYM
aiti-9488	167	3	72	72	NUM
aiti-9488	167	4	65	65	NUM
aiti-9488	167	5	the	the	DET
aiti-9488	167	6	efficiennet	efficiennet	NOUN
aiti-9488	167	7	model	model	NOUN
aiti-9488	167	8	.	.	PUNCT
aiti-9488	168	1	efficientnet	efficientnet	NOUN
aiti-9488	168	2	models	model	NOUN
aiti-9488	168	3	consist	consist	VERB
aiti-9488	168	4	of	of	ADP
aiti-9488	168	5	inverted	inverted	ADJ
aiti-9488	168	6	residual	residual	ADJ
aiti-9488	168	7	convolutional	convolutional	ADJ
aiti-9488	168	8	blocks	block	NOUN
aiti-9488	168	9	(	(	PUNCT
aiti-9488	168	10	mbconv	mbconv	NOUN
aiti-9488	168	11	)	)	PUNCT
aiti-9488	168	12	originally	originally	ADV
aiti-9488	168	13	based	base	VERB
aiti-9488	168	14	on	on	ADP
aiti-9488	168	15	mobilenetv2	mobilenetv2	PROPN
aiti-9488	169	1	[	[	X
aiti-9488	169	2	23	23	NUM
aiti-9488	169	3	]	]	PUNCT
aiti-9488	169	4	with	with	ADP
aiti-9488	169	5	multiple	multiple	ADJ
aiti-9488	169	6	kernel	kernel	NOUN
aiti-9488	169	7	sizes	size	NOUN
aiti-9488	169	8	of	of	ADP
aiti-9488	169	9	3×3	3×3	NUM
aiti-9488	169	10	and	and	CCONJ
aiti-9488	169	11	5×5	5×5	NUM
aiti-9488	169	12	.	.	PUNCT
aiti-9488	170	1	the	the	DET
aiti-9488	170	2	model	model	NOUN
aiti-9488	170	3	architecture	architecture	NOUN
aiti-9488	170	4	is	be	AUX
aiti-9488	170	5	broadened	broaden	VERB
aiti-9488	170	6	evenly	evenly	ADV
aiti-9488	170	7	through	through	ADP
aiti-9488	170	8	compound	compound	NOUN
aiti-9488	170	9	scaling	scale	VERB
aiti-9488	170	10	coefficient	coefficient	NOUN
aiti-9488	170	11	∅	∅	NOUN
aiti-9488	170	12	by	by	ADP
aiti-9488	170	13	depth	depth	NOUN
aiti-9488	170	14	,	,	PUNCT
aiti-9488	170	15	width	width	ADJ
aiti-9488	170	16	,	,	PUNCT
aiti-9488	170	17	and	and	CCONJ
aiti-9488	170	18	resolution	resolution	NOUN
aiti-9488	170	19	in	in	ADP
aiti-9488	170	20	the	the	DET
aiti-9488	170	21	following	follow	VERB
aiti-9488	170	22	procedure	procedure	NOUN
aiti-9488	170	23	:	:	PUNCT
aiti-9488	170	24	,	,	PUNCT
aiti-9488	170	25	,	,	PUNCT
aiti-9488	171	1	ø	ø	PROPN
aiti-9488	171	2	ø	ø	X
aiti-9488	171	3	ød	ød	NUM
aiti-9488	171	4	r	r	NOUN
aiti-9488	171	5	wα	wα	NOUN
aiti-9488	171	6	γ	γ	X
aiti-9488	171	7	β=	β=	NOUN
aiti-9488	171	8	=	=	PUNCT
aiti-9488	171	9	=	=	SYM
aiti-9488	171	10	(	(	PUNCT
aiti-9488	171	11	1	1	X
aiti-9488	171	12	)	)	PUNCT
aiti-9488	171	13	such	such	ADJ
aiti-9488	171	14	that	that	SCONJ
aiti-9488	171	15	2	2	NUM
aiti-9488	171	16	2	2	NUM
aiti-9488	171	17	2	2	NUM
aiti-9488	171	18	,	,	PUNCT
aiti-9488	171	19	1	1	NUM
aiti-9488	171	20	,	,	PUNCT
aiti-9488	171	21	1	1	NUM
aiti-9488	171	22	,	,	PUNCT
aiti-9488	171	23	1	1	NUM
aiti-9488	171	24	αβ	αβ	NUM
aiti-9488	171	25	γ	γ	X
aiti-9488	171	26	α	α	PROPN
aiti-9488	171	27	γ	γ	PROPN
aiti-9488	171	28	β≈	β≈	NUM
aiti-9488	171	29	≥	≥	NUM
aiti-9488	171	30	≥	≥	PROPN
aiti-9488	171	31	≥	≥	X
aiti-9488	171	32	(	(	PUNCT
aiti-9488	171	33	2	2	NUM
aiti-9488	171	34	)	)	PUNCT
aiti-9488	171	35	where	where	SCONJ
aiti-9488	171	36	d	d	NOUN
aiti-9488	171	37	indicates	indicate	VERB
aiti-9488	171	38	the	the	DET
aiti-9488	171	39	depth	depth	NOUN
aiti-9488	171	40	of	of	ADP
aiti-9488	171	41	the	the	DET
aiti-9488	171	42	network	network	NOUN
aiti-9488	171	43	,	,	PUNCT
aiti-9488	171	44	r	r	NOUN
aiti-9488	171	45	indicates	indicate	VERB
aiti-9488	171	46	the	the	DET
aiti-9488	171	47	resolution	resolution	NOUN
aiti-9488	171	48	of	of	ADP
aiti-9488	171	49	the	the	DET
aiti-9488	171	50	network	network	NOUN
aiti-9488	171	51	,	,	PUNCT
aiti-9488	171	52	w	w	PROPN
aiti-9488	171	53	indicates	indicate	VERB
aiti-9488	171	54	the	the	DET
aiti-9488	171	55	width	width	NOUN
aiti-9488	171	56	of	of	ADP
aiti-9488	171	57	the	the	DET
aiti-9488	171	58	network	network	NOUN
aiti-9488	171	59	,	,	PUNCT
aiti-9488	171	60	and	and	CCONJ
aiti-9488	171	61	α	α	NOUN
aiti-9488	171	62	,	,	PUNCT
aiti-9488	171	63	β	β	NOUN
aiti-9488	171	64	,	,	PUNCT
aiti-9488	171	65	and	and	CCONJ
aiti-9488	171	66	γ	γ	NOUN
aiti-9488	171	67	are	be	AUX
aiti-9488	171	68	constants	constant	NOUN
aiti-9488	171	69	.	.	PUNCT
aiti-9488	172	1	∅	∅	NOUN
aiti-9488	172	2	value	value	NOUN
aiti-9488	172	3	in	in	ADP
aiti-9488	172	4	eq	eq	ADP
aiti-9488	172	5	.	.	PUNCT
aiti-9488	173	1	(	(	PUNCT
aiti-9488	173	2	1	1	X
aiti-9488	173	3	)	)	PUNCT
aiti-9488	173	4	indicates	indicate	VERB
aiti-9488	173	5	the	the	DET
aiti-9488	173	6	level	level	NOUN
aiti-9488	173	7	at	at	ADP
aiti-9488	173	8	which	which	PRON
aiti-9488	173	9	the	the	DET
aiti-9488	173	10	network	network	NOUN
aiti-9488	173	11	can	can	AUX
aiti-9488	173	12	be	be	AUX
aiti-9488	173	13	scaled	scale	VERB
aiti-9488	173	14	up	up	ADP
aiti-9488	173	15	.	.	PUNCT
aiti-9488	174	1	efficientnetb0	efficientnetb0	PROPN
aiti-9488	174	2	baseline	baseline	PROPN
aiti-9488	174	3	model	model	NOUN
aiti-9488	174	4	is	be	AUX
aiti-9488	174	5	constructed	construct	VERB
aiti-9488	174	6	on	on	ADP
aiti-9488	174	7	∅	∅	NOUN
aiti-9488	174	8	value	value	NOUN
aiti-9488	174	9	of	of	ADP
aiti-9488	174	10	0	0	NUM
aiti-9488	174	11	,	,	PUNCT
aiti-9488	174	12	w	w	NOUN
aiti-9488	174	13	value	value	NOUN
aiti-9488	174	14	of	of	ADP
aiti-9488	174	15	1	1	NUM
aiti-9488	174	16	,	,	PUNCT
aiti-9488	174	17	and	and	CCONJ
aiti-9488	174	18	r	r	NOUN
aiti-9488	174	19	value	value	NOUN
aiti-9488	174	20	of	of	ADP
aiti-9488	174	21	1	1	NUM
aiti-9488	174	22	.	.	PUNCT
aiti-9488	175	1	the	the	DET
aiti-9488	175	2	efficientnetb3	efficientnetb3	PROPN
aiti-9488	175	3	model	model	NOUN
aiti-9488	175	4	is	be	AUX
aiti-9488	175	5	established	establish	VERB
aiti-9488	175	6	on	on	ADP
aiti-9488	175	7	∅	∅	NOUN
aiti-9488	175	8	value	value	NOUN
aiti-9488	175	9	of	of	ADP
aiti-9488	175	10	3	3	NUM
aiti-9488	175	11	,	,	PUNCT
aiti-9488	175	12	w	w	NOUN
aiti-9488	175	13	value	value	NOUN
aiti-9488	175	14	of	of	ADP
aiti-9488	175	15	α3	α3	NOUN
aiti-9488	175	16	,	,	PUNCT
aiti-9488	175	17	and	and	CCONJ
aiti-9488	175	18	r	r	NOUN
aiti-9488	175	19	value	value	NOUN
aiti-9488	175	20	of	of	ADP
aiti-9488	175	21	γ3	γ3	NOUN
aiti-9488	175	22	.	.	PUNCT
aiti-9488	176	1	the	the	DET
aiti-9488	176	2	higher	high	ADJ
aiti-9488	176	3	value	value	NOUN
aiti-9488	176	4	of	of	ADP
aiti-9488	176	5	∅	∅	NOUN
aiti-9488	176	6	signifies	signify	VERB
aiti-9488	176	7	extensive	extensive	ADJ
aiti-9488	176	8	resources	resource	NOUN
aiti-9488	176	9	accessible	accessible	ADJ
aiti-9488	176	10	to	to	PART
aiti-9488	176	11	obtain	obtain	VERB
aiti-9488	176	12	superior	superior	ADJ
aiti-9488	176	13	results	result	NOUN
aiti-9488	176	14	.	.	PUNCT
aiti-9488	177	1	(	(	PUNCT
aiti-9488	177	2	a	a	X
aiti-9488	177	3	)	)	PUNCT
aiti-9488	177	4	scaling	scaling	NOUN
aiti-9488	177	5	of	of	ADP
aiti-9488	177	6	baseline	baseline	ADJ
aiti-9488	177	7	efficientnet	efficientnet	NOUN
aiti-9488	177	8	model	model	NOUN
aiti-9488	177	9	(	(	PUNCT
aiti-9488	177	10	b	b	NOUN
aiti-9488	177	11	)	)	PUNCT
aiti-9488	177	12	compound	compound	NOUN
aiti-9488	177	13	scaling	scaling	NOUN
aiti-9488	177	14	of	of	ADP
aiti-9488	177	15	higher	high	ADJ
aiti-9488	177	16	model	model	NOUN
aiti-9488	177	17	fig	fig	NOUN
aiti-9488	177	18	.	.	PUNCT
aiti-9488	178	1	5	5	NUM
aiti-9488	178	2	scaling	scaling	NOUN
aiti-9488	178	3	of	of	ADP
aiti-9488	178	4	efficientnet	efficientnet	ADJ
aiti-9488	178	5	fig	fig	NOUN
aiti-9488	178	6	.	.	PUNCT
aiti-9488	179	1	6	6	NUM
aiti-9488	179	2	efficientnetb3	efficientnetb3	NOUN
aiti-9488	179	3	architecture	architecture	NOUN
aiti-9488	179	4	advances	advance	NOUN
aiti-9488	179	5	in	in	ADP
aiti-9488	179	6	technology	technology	NOUN
aiti-9488	179	7	innovation	innovation	NOUN
aiti-9488	179	8	,	,	PUNCT
aiti-9488	179	9	vol	vol	NOUN
aiti-9488	179	10	.	.	PROPN
aiti-9488	179	11	8	8	NUM
aiti-9488	179	12	,	,	PUNCT
aiti-9488	179	13	no	no	INTJ
aiti-9488	179	14	.	.	NOUN
aiti-9488	179	15	1	1	NUM
aiti-9488	179	16	,	,	PUNCT
aiti-9488	179	17	2023	2023	NUM
aiti-9488	179	18	,	,	PUNCT
aiti-9488	179	19	pp	pp	ADJ
aiti-9488	179	20	.	.	PUNCT
aiti-9488	180	1	59	59	NUM
aiti-9488	180	2	-	-	SYM
aiti-9488	180	3	72	72	NUM
aiti-9488	180	4	66	66	NUM
aiti-9488	180	5	efficientnetb3	efficientnetb3	NOUN
aiti-9488	180	6	consists	consist	VERB
aiti-9488	180	7	of	of	ADP
aiti-9488	180	8	a	a	DET
aiti-9488	180	9	convolution	convolution	NOUN
aiti-9488	180	10	filter	filter	NOUN
aiti-9488	180	11	(	(	PUNCT
aiti-9488	180	12	conv	conv	ADJ
aiti-9488	180	13	)	)	PUNCT
aiti-9488	180	14	block	block	NOUN
aiti-9488	180	15	with	with	ADP
aiti-9488	180	16	a	a	DET
aiti-9488	180	17	kernel	kernel	NOUN
aiti-9488	180	18	size	size	NOUN
aiti-9488	180	19	of	of	ADP
aiti-9488	180	20	3×3	3×3	NUM
aiti-9488	180	21	,	,	PUNCT
aiti-9488	180	22	an	an	DET
aiti-9488	180	23	mbconv1	mbconv1	NOUN
aiti-9488	180	24	block	block	NOUN
aiti-9488	180	25	with	with	ADP
aiti-9488	180	26	a	a	DET
aiti-9488	180	27	kernel	kernel	NOUN
aiti-9488	180	28	size	size	NOUN
aiti-9488	180	29	of	of	ADP
aiti-9488	180	30	3×3	3×3	NUM
aiti-9488	180	31	,	,	PUNCT
aiti-9488	180	32	and	and	CCONJ
aiti-9488	180	33	mbconv6	mbconv6	PROPN
aiti-9488	180	34	blocks	block	VERB
aiti-9488	180	35	with	with	ADP
aiti-9488	180	36	kernel	kernel	PROPN
aiti-9488	180	37	sizes	size	NOUN
aiti-9488	180	38	of	of	ADP
aiti-9488	180	39	3×3	3×3	NUM
aiti-9488	180	40	and	and	CCONJ
aiti-9488	180	41	5×5	5×5	NUM
aiti-9488	180	42	.	.	PUNCT
aiti-9488	181	1	some	some	PRON
aiti-9488	181	2	of	of	ADP
aiti-9488	181	3	the	the	DET
aiti-9488	181	4	mbconv6	mbconv6	PROPN
aiti-9488	181	5	blocks	block	NOUN
aiti-9488	181	6	apply	apply	VERB
aiti-9488	181	7	inverted	inverted	ADJ
aiti-9488	181	8	residual	residual	ADJ
aiti-9488	181	9	connection	connection	NOUN
aiti-9488	181	10	(	(	PUNCT
aiti-9488	181	11	irc	irc	NOUN
aiti-9488	181	12	)	)	PUNCT
aiti-9488	181	13	.	.	PUNCT
aiti-9488	182	1	filter	filter	NOUN
aiti-9488	182	2	kernel	kernel	PROPN
aiti-9488	182	3	sizes	size	NOUN
aiti-9488	182	4	of	of	ADP
aiti-9488	182	5	3×3	3×3	NUM
aiti-9488	182	6	and	and	CCONJ
aiti-9488	182	7	5×5	5×5	NUM
aiti-9488	182	8	are	be	AUX
aiti-9488	182	9	used	use	VERB
aiti-9488	182	10	in	in	ADP
aiti-9488	182	11	the	the	DET
aiti-9488	182	12	efficientnetb3	efficientnetb3	PROPN
aiti-9488	182	13	model	model	NOUN
aiti-9488	182	14	to	to	PART
aiti-9488	182	15	extract	extract	VERB
aiti-9488	182	16	feature	feature	NOUN
aiti-9488	182	17	maps	map	NOUN
aiti-9488	182	18	from	from	ADP
aiti-9488	182	19	the	the	DET
aiti-9488	182	20	input	input	NOUN
aiti-9488	182	21	images	image	NOUN
aiti-9488	182	22	.	.	PUNCT
aiti-9488	183	1	efficientnetb3	efficientnetb3	NOUN
aiti-9488	183	2	comprises	comprise	VERB
aiti-9488	183	3	25	25	NUM
aiti-9488	183	4	mbconv	mbconv	NOUN
aiti-9488	183	5	blocks	block	NOUN
aiti-9488	183	6	differing	differ	VERB
aiti-9488	183	7	in	in	ADP
aiti-9488	183	8	many	many	ADJ
aiti-9488	183	9	characteristics	characteristic	NOUN
aiti-9488	183	10	such	such	ADJ
aiti-9488	183	11	as	as	ADP
aiti-9488	183	12	feature	feature	NOUN
aiti-9488	183	13	maps	map	NOUN
aiti-9488	183	14	expansion	expansion	NOUN
aiti-9488	183	15	ratio	ratio	NOUN
aiti-9488	183	16	,	,	PUNCT
aiti-9488	183	17	resolution	resolution	NOUN
aiti-9488	183	18	,	,	PUNCT
aiti-9488	183	19	output	output	NOUN
aiti-9488	183	20	layers	layer	NOUN
aiti-9488	183	21	kernel	kernel	PROPN
aiti-9488	183	22	size	size	PROPN
aiti-9488	183	23	,	,	PUNCT
aiti-9488	183	24	etc	etc	X
aiti-9488	183	25	.	.	X
aiti-9488	183	26	mbconv1	mbconv1	PROPN
aiti-9488	183	27	with	with	ADP
aiti-9488	183	28	a	a	DET
aiti-9488	183	29	kernel	kernel	NOUN
aiti-9488	183	30	size	size	NOUN
aiti-9488	183	31	of	of	ADP
aiti-9488	183	32	3×3	3×3	NUM
aiti-9488	183	33	and	and	CCONJ
aiti-9488	183	34	mconv6	mconv6	NOUN
aiti-9488	183	35	with	with	ADP
aiti-9488	183	36	kernel	kernel	PROPN
aiti-9488	183	37	sizes	size	NOUN
aiti-9488	183	38	of	of	ADP
aiti-9488	183	39	3×3	3×3	NUM
aiti-9488	183	40	and	and	CCONJ
aiti-9488	183	41	5×5	5×5	NUM
aiti-9488	183	42	employ	employ	NOUN
aiti-9488	183	43	depthwise	depthwise	NOUN
aiti-9488	183	44	convolution	convolution	NOUN
aiti-9488	183	45	along	along	ADP
aiti-9488	183	46	with	with	ADP
aiti-9488	183	47	batch	batch	NOUN
aiti-9488	183	48	normalization	normalization	NOUN
aiti-9488	183	49	and	and	CCONJ
aiti-9488	183	50	activation	activation	NOUN
aiti-9488	183	51	layer	layer	NOUN
aiti-9488	183	52	.	.	PUNCT
aiti-9488	184	1	additionally	additionally	ADV
aiti-9488	184	2	,	,	PUNCT
aiti-9488	184	3	layers	layer	NOUN
aiti-9488	184	4	of	of	ADP
aiti-9488	184	5	dropout	dropout	NOUN
aiti-9488	184	6	and	and	CCONJ
aiti-9488	184	7	skip	skip	ADJ
aiti-9488	184	8	connection	connection	NOUN
aiti-9488	184	9	are	be	AUX
aiti-9488	184	10	integrated	integrate	VERB
aiti-9488	184	11	with	with	ADP
aiti-9488	184	12	mbconv6	mbconv6	PROPN
aiti-9488	184	13	3×3	3×3	NUM
aiti-9488	184	14	,	,	PUNCT
aiti-9488	184	15	and	and	CCONJ
aiti-9488	184	16	mbconv6	mbconv6	PROPN
aiti-9488	184	17	5×5	5×5	NUM
aiti-9488	184	18	but	but	CCONJ
aiti-9488	184	19	omitted	omit	VERB
aiti-9488	184	20	in	in	ADP
aiti-9488	184	21	mbconv1	mbconv1	PROPN
aiti-9488	184	22	.	.	PUNCT
aiti-9488	185	1	in	in	ADP
aiti-9488	185	2	comparison	comparison	NOUN
aiti-9488	185	3	to	to	ADP
aiti-9488	185	4	the	the	DET
aiti-9488	185	5	baseline	baseline	ADJ
aiti-9488	185	6	efficientnetb0	efficientnetb0	PROPN
aiti-9488	185	7	model	model	NOUN
aiti-9488	185	8	,	,	PUNCT
aiti-9488	185	9	eficientnetb3	eficientnetb3	NOUN
aiti-9488	185	10	comprises	comprise	VERB
aiti-9488	185	11	a	a	DET
aiti-9488	185	12	larger	large	ADJ
aiti-9488	185	13	network	network	NOUN
aiti-9488	185	14	that	that	PRON
aiti-9488	185	15	helps	help	VERB
aiti-9488	185	16	to	to	PART
aiti-9488	185	17	pull	pull	VERB
aiti-9488	185	18	out	out	ADP
aiti-9488	185	19	detailed	detailed	ADJ
aiti-9488	185	20	features	feature	NOUN
aiti-9488	185	21	which	which	PRON
aiti-9488	185	22	can	can	AUX
aiti-9488	185	23	infer	infer	VERB
aiti-9488	185	24	better	well	ADV
aiti-9488	185	25	on	on	ADP
aiti-9488	185	26	new	new	ADJ
aiti-9488	185	27	missions	mission	NOUN
aiti-9488	185	28	.	.	PUNCT
aiti-9488	186	1	the	the	DET
aiti-9488	186	2	efficientnetb3	efficientnetb3	PROPN
aiti-9488	186	3	model	model	NOUN
aiti-9488	186	4	has	have	VERB
aiti-9488	186	5	the	the	DET
aiti-9488	186	6	advantage	advantage	NOUN
aiti-9488	186	7	of	of	ADP
aiti-9488	186	8	a	a	DET
aiti-9488	186	9	broader	broad	ADJ
aiti-9488	186	10	network	network	NOUN
aiti-9488	186	11	that	that	PRON
aiti-9488	186	12	abstracts	abstract	VERB
aiti-9488	186	13	superlative	superlative	ADJ
aiti-9488	186	14	features	feature	NOUN
aiti-9488	186	15	and	and	CCONJ
aiti-9488	186	16	patterns	pattern	NOUN
aiti-9488	186	17	employed	employ	VERB
aiti-9488	186	18	for	for	ADP
aiti-9488	186	19	melanoma	melanoma	NOUN
aiti-9488	186	20	classification	classification	NOUN
aiti-9488	186	21	.	.	PUNCT
aiti-9488	187	1	fig	fig	NOUN
aiti-9488	187	2	.	.	PUNCT
aiti-9488	188	1	6	6	NUM
aiti-9488	188	2	shows	show	VERB
aiti-9488	188	3	efficientnetb3	efficientnetb3	NOUN
aiti-9488	188	4	architecture	architecture	NOUN
aiti-9488	188	5	.	.	PUNCT
aiti-9488	189	1	fig	fig	NOUN
aiti-9488	189	2	.	.	PUNCT
aiti-9488	190	1	7	7	NUM
aiti-9488	190	2	shows	show	VERB
aiti-9488	190	3	architecture	architecture	NOUN
aiti-9488	190	4	of	of	ADP
aiti-9488	190	5	proposed	propose	VERB
aiti-9488	190	6	methodology	methodology	NOUN
aiti-9488	190	7	.	.	PUNCT
aiti-9488	191	1	fig	fig	NOUN
aiti-9488	191	2	.	.	PUNCT
aiti-9488	192	1	7	7	NUM
aiti-9488	192	2	proposed	propose	VERB
aiti-9488	192	3	methodology	methodology	NOUN
aiti-9488	192	4	architecture	architecture	NOUN
aiti-9488	192	5	4	4	NUM
aiti-9488	192	6	.	.	PUNCT
aiti-9488	192	7	results	result	VERB
aiti-9488	192	8	the	the	DET
aiti-9488	192	9	proposed	propose	VERB
aiti-9488	192	10	model	model	NOUN
aiti-9488	192	11	amalgamated	amalgamate	VERB
aiti-9488	192	12	data	datum	NOUN
aiti-9488	192	13	preprocessing	preprocessing	NOUN
aiti-9488	192	14	and	and	CCONJ
aiti-9488	192	15	augmentation	augmentation	NOUN
aiti-9488	192	16	techniques	technique	NOUN
aiti-9488	192	17	,	,	PUNCT
aiti-9488	192	18	along	along	ADP
aiti-9488	192	19	with	with	ADP
aiti-9488	192	20	fine	fine	ADV
aiti-9488	192	21	-	-	PUNCT
aiti-9488	192	22	tuned	tune	VERB
aiti-9488	192	23	gap	gap	NOUN
aiti-9488	192	24	layer	layer	NOUN
aiti-9488	192	25	and	and	CCONJ
aiti-9488	192	26	softmax	softmax	NOUN
aiti-9488	192	27	output	output	NOUN
aiti-9488	192	28	layer	layer	NOUN
aiti-9488	192	29	for	for	ADP
aiti-9488	192	30	classification	classification	NOUN
aiti-9488	192	31	,	,	PUNCT
aiti-9488	192	32	to	to	PART
aiti-9488	192	33	improve	improve	VERB
aiti-9488	192	34	the	the	DET
aiti-9488	192	35	efficiency	efficiency	NOUN
aiti-9488	192	36	of	of	ADP
aiti-9488	192	37	lesion	lesion	NOUN
aiti-9488	192	38	classification	classification	NOUN
aiti-9488	192	39	results	result	NOUN
aiti-9488	192	40	.	.	PUNCT
aiti-9488	193	1	the	the	DET
aiti-9488	193	2	isic	isic	PROPN
aiti-9488	193	3	dataset	dataset	NOUN
aiti-9488	193	4	was	be	AUX
aiti-9488	193	5	distributed	distribute	VERB
aiti-9488	193	6	in	in	ADP
aiti-9488	193	7	an	an	DET
aiti-9488	193	8	80:20	80:20	NUM
aiti-9488	193	9	ratio	ratio	NOUN
aiti-9488	193	10	of	of	ADP
aiti-9488	193	11	training	training	NOUN
aiti-9488	193	12	and	and	CCONJ
aiti-9488	193	13	testing	testing	NOUN
aiti-9488	193	14	batches	batch	NOUN
aiti-9488	193	15	.	.	PUNCT
aiti-9488	194	1	the	the	DET
aiti-9488	194	2	training	training	NOUN
aiti-9488	194	3	set	set	NOUN
aiti-9488	194	4	consisted	consist	VERB
aiti-9488	194	5	of	of	ADP
aiti-9488	194	6	2637	2637	NUM
aiti-9488	194	7	images	image	NOUN
aiti-9488	194	8	,	,	PUNCT
aiti-9488	194	9	and	and	CCONJ
aiti-9488	194	10	the	the	DET
aiti-9488	194	11	testing	testing	NOUN
aiti-9488	194	12	set	set	NOUN
aiti-9488	194	13	consisted	consist	VERB
aiti-9488	194	14	of	of	ADP
aiti-9488	194	15	660	660	NUM
aiti-9488	194	16	images	image	NOUN
aiti-9488	194	17	.	.	PUNCT
aiti-9488	195	1	all	all	DET
aiti-9488	195	2	experiments	experiment	NOUN
aiti-9488	195	3	are	be	AUX
aiti-9488	195	4	performed	perform	VERB
aiti-9488	195	5	on	on	ADP
aiti-9488	195	6	a	a	DET
aiti-9488	195	7	google	google	PROPN
aiti-9488	195	8	colab	colab	PROPN
aiti-9488	195	9	notebook	notebook	NOUN
aiti-9488	195	10	that	that	PRON
aiti-9488	195	11	furnished	furnish	VERB
aiti-9488	195	12	usage	usage	NOUN
aiti-9488	195	13	to	to	ADP
aiti-9488	195	14	nvidia	nvidia	PROPN
aiti-9488	195	15	tesla	tesla	PROPN
aiti-9488	195	16	gpu	gpu	PROPN
aiti-9488	195	17	of	of	ADP
aiti-9488	195	18	size	size	NOUN
aiti-9488	195	19	12	12	NUM
aiti-9488	195	20	gb	gb	NOUN
aiti-9488	195	21	k80	k80	PROPN
aiti-9488	195	22	smi	smi	PROPN
aiti-9488	195	23	460.32.03	460.32.03	PROPN
aiti-9488	195	24	.	.	PUNCT
aiti-9488	196	1	all	all	DET
aiti-9488	196	2	the	the	DET
aiti-9488	196	3	models	model	NOUN
aiti-9488	196	4	are	be	AUX
aiti-9488	196	5	compiled	compile	VERB
aiti-9488	196	6	employing	employ	VERB
aiti-9488	196	7	the	the	DET
aiti-9488	196	8	adam	adam	PROPN
aiti-9488	196	9	optimization	optimization	NOUN
aiti-9488	196	10	algorithm	algorithm	NOUN
aiti-9488	196	11	with	with	ADP
aiti-9488	196	12	a	a	DET
aiti-9488	196	13	learning	learn	VERB
aiti-9488	196	14	rate	rate	NOUN
aiti-9488	196	15	of	of	ADP
aiti-9488	196	16	0.001	0.001	NUM
aiti-9488	196	17	.	.	PUNCT
aiti-9488	197	1	the	the	DET
aiti-9488	197	2	models	model	NOUN
aiti-9488	197	3	have	have	AUX
aiti-9488	197	4	been	be	AUX
aiti-9488	197	5	trained	train	VERB
aiti-9488	197	6	for	for	ADP
aiti-9488	197	7	35	35	NUM
aiti-9488	197	8	epochs	epoch	NOUN
aiti-9488	197	9	holding	hold	VERB
aiti-9488	197	10	a	a	DET
aiti-9488	197	11	batch	batch	NOUN
aiti-9488	197	12	size	size	NOUN
aiti-9488	197	13	of	of	ADP
aiti-9488	197	14	32	32	NUM
aiti-9488	197	15	.	.	PUNCT
aiti-9488	198	1	the	the	DET
aiti-9488	198	2	images	image	NOUN
aiti-9488	198	3	are	be	AUX
aiti-9488	198	4	trained	train	VERB
aiti-9488	198	5	batchwise	batchwise	NOUN
aiti-9488	198	6	and	and	CCONJ
aiti-9488	198	7	approximately	approximately	ADV
aiti-9488	198	8	take	take	VERB
aiti-9488	198	9	29	29	NUM
aiti-9488	198	10	to	to	PART
aiti-9488	198	11	33	33	NUM
aiti-9488	198	12	seconds	second	NOUN
aiti-9488	198	13	to	to	PART
aiti-9488	198	14	train	train	VERB
aiti-9488	198	15	per	per	ADP
aiti-9488	198	16	epoch	epoch	NOUN
aiti-9488	198	17	.	.	PUNCT
aiti-9488	199	1	recall	recall	NOUN
aiti-9488	199	2	,	,	PUNCT
aiti-9488	199	3	confusion	confusion	NOUN
aiti-9488	199	4	matrix	matrix	NOUN
aiti-9488	199	5	,	,	PUNCT
aiti-9488	199	6	precision	precision	NOUN
aiti-9488	199	7	,	,	PUNCT
aiti-9488	199	8	accuracy	accuracy	NOUN
aiti-9488	199	9	,	,	PUNCT
aiti-9488	199	10	and	and	CCONJ
aiti-9488	199	11	f1	f1	NOUN
aiti-9488	199	12	-	-	PUNCT
aiti-9488	199	13	score	score	NOUN
aiti-9488	199	14	are	be	AUX
aiti-9488	199	15	computed	compute	VERB
aiti-9488	199	16	to	to	PART
aiti-9488	199	17	probe	probe	VERB
aiti-9488	199	18	model	model	NOUN
aiti-9488	199	19	potency	potency	NOUN
aiti-9488	199	20	[	[	X
aiti-9488	199	21	24	24	NUM
aiti-9488	199	22	]	]	PUNCT
aiti-9488	199	23	.	.	PUNCT
aiti-9488	200	1	these	these	DET
aiti-9488	200	2	metrics	metric	NOUN
aiti-9488	200	3	are	be	AUX
aiti-9488	200	4	calculated	calculate	VERB
aiti-9488	200	5	on	on	ADP
aiti-9488	200	6	true	true	ADJ
aiti-9488	200	7	positive	positive	ADJ
aiti-9488	200	8	(	(	PUNCT
aiti-9488	200	9	tp	tp	NOUN
aiti-9488	200	10	)	)	PUNCT
aiti-9488	200	11	,	,	PUNCT
aiti-9488	200	12	true	true	ADJ
aiti-9488	200	13	negative	negative	ADJ
aiti-9488	200	14	(	(	PUNCT
aiti-9488	200	15	tn	tn	NOUN
aiti-9488	200	16	)	)	PUNCT
aiti-9488	200	17	,	,	PUNCT
aiti-9488	200	18	false	false	ADJ
aiti-9488	200	19	positive	positive	ADJ
aiti-9488	200	20	(	(	PUNCT
aiti-9488	200	21	fp	fp	NOUN
aiti-9488	200	22	)	)	PUNCT
aiti-9488	200	23	,	,	PUNCT
aiti-9488	200	24	and	and	CCONJ
aiti-9488	200	25	false	false	ADJ
aiti-9488	200	26	negative	negative	ADJ
aiti-9488	200	27	(	(	PUNCT
aiti-9488	200	28	fn	fn	NOUN
aiti-9488	200	29	)	)	PUNCT
aiti-9488	201	1	[	[	X
aiti-9488	201	2	25	25	NUM
aiti-9488	201	3	]	]	PUNCT
aiti-9488	201	4	.	.	PUNCT
aiti-9488	202	1	(	(	PUNCT
aiti-9488	202	2	1	1	X
aiti-9488	202	3	)	)	PUNCT
aiti-9488	202	4	true	true	ADJ
aiti-9488	202	5	positive	positive	ADJ
aiti-9488	202	6	(	(	PUNCT
aiti-9488	202	7	tp)the	tp)the	DET
aiti-9488	202	8	correct	correct	ADJ
aiti-9488	202	9	class	class	NOUN
aiti-9488	202	10	is	be	AUX
aiti-9488	202	11	positive	positive	ADJ
aiti-9488	202	12	and	and	CCONJ
aiti-9488	202	13	the	the	DET
aiti-9488	202	14	predicted	predict	VERB
aiti-9488	202	15	class	class	NOUN
aiti-9488	202	16	is	be	AUX
aiti-9488	202	17	positive	positive	ADJ
aiti-9488	202	18	.	.	PUNCT
aiti-9488	203	1	(	(	PUNCT
aiti-9488	203	2	2	2	X
aiti-9488	203	3	)	)	PUNCT
aiti-9488	203	4	false	false	ADJ
aiti-9488	203	5	positive	positive	ADJ
aiti-9488	203	6	(	(	PUNCT
aiti-9488	203	7	fp)the	fp)the	DET
aiti-9488	203	8	correct	correct	ADJ
aiti-9488	203	9	class	class	NOUN
aiti-9488	203	10	is	be	AUX
aiti-9488	203	11	negative	negative	ADJ
aiti-9488	203	12	and	and	CCONJ
aiti-9488	203	13	the	the	DET
aiti-9488	203	14	predicted	predict	VERB
aiti-9488	203	15	class	class	NOUN
aiti-9488	203	16	is	be	AUX
aiti-9488	203	17	positive	positive	ADJ
aiti-9488	203	18	.	.	PUNCT
aiti-9488	204	1	(	(	PUNCT
aiti-9488	204	2	3	3	X
aiti-9488	204	3	)	)	PUNCT
aiti-9488	204	4	true	true	ADJ
aiti-9488	204	5	negative	negative	ADJ
aiti-9488	204	6	(	(	PUNCT
aiti-9488	204	7	tn)the	tn)the	DET
aiti-9488	204	8	correct	correct	ADJ
aiti-9488	204	9	class	class	NOUN
aiti-9488	204	10	is	be	AUX
aiti-9488	204	11	negative	negative	ADJ
aiti-9488	204	12	and	and	CCONJ
aiti-9488	204	13	the	the	DET
aiti-9488	204	14	predicted	predict	VERB
aiti-9488	204	15	class	class	NOUN
aiti-9488	204	16	is	be	AUX
aiti-9488	204	17	negative	negative	ADJ
aiti-9488	204	18	.	.	PUNCT
aiti-9488	205	1	(	(	PUNCT
aiti-9488	205	2	4	4	X
aiti-9488	205	3	)	)	PUNCT
aiti-9488	205	4	false	false	ADJ
aiti-9488	205	5	negative	negative	ADJ
aiti-9488	205	6	(	(	PUNCT
aiti-9488	205	7	fn)the	fn)the	DET
aiti-9488	205	8	correct	correct	ADJ
aiti-9488	205	9	class	class	NOUN
aiti-9488	205	10	is	be	AUX
aiti-9488	205	11	positive	positive	ADJ
aiti-9488	205	12	and	and	CCONJ
aiti-9488	205	13	the	the	DET
aiti-9488	205	14	predicted	predict	VERB
aiti-9488	205	15	class	class	NOUN
aiti-9488	205	16	is	be	AUX
aiti-9488	205	17	negative	negative	ADJ
aiti-9488	205	18	.	.	PUNCT
aiti-9488	206	1	accuracy	accuracy	NOUN
aiti-9488	206	2	:	:	PUNCT
aiti-9488	206	3	it	it	PRON
aiti-9488	206	4	is	be	AUX
aiti-9488	206	5	an	an	DET
aiti-9488	206	6	evaluation	evaluation	NOUN
aiti-9488	206	7	metric	metric	NOUN
aiti-9488	206	8	that	that	PRON
aiti-9488	206	9	finds	find	VERB
aiti-9488	206	10	the	the	DET
aiti-9488	206	11	model	model	NOUN
aiti-9488	206	12	’s	’s	PART
aiti-9488	206	13	performance	performance	NOUN
aiti-9488	206	14	across	across	ADP
aiti-9488	206	15	all	all	DET
aiti-9488	206	16	classes	class	NOUN
aiti-9488	206	17	.	.	PUNCT
aiti-9488	207	1	it	it	PRON
aiti-9488	207	2	is	be	AUX
aiti-9488	207	3	a	a	DET
aiti-9488	207	4	fraction	fraction	NOUN
aiti-9488	207	5	of	of	ADP
aiti-9488	207	6	the	the	DET
aiti-9488	207	7	sum	sum	NOUN
aiti-9488	207	8	of	of	ADP
aiti-9488	207	9	correct	correct	ADJ
aiti-9488	207	10	class	class	NOUN
aiti-9488	207	11	predictions	prediction	NOUN
aiti-9488	207	12	to	to	ADP
aiti-9488	207	13	the	the	DET
aiti-9488	207	14	sum	sum	NOUN
aiti-9488	207	15	of	of	ADP
aiti-9488	207	16	total	total	ADJ
aiti-9488	207	17	predictions	prediction	NOUN
aiti-9488	207	18	.	.	PUNCT
aiti-9488	208	1	advances	advance	NOUN
aiti-9488	208	2	in	in	ADP
aiti-9488	208	3	technology	technology	NOUN
aiti-9488	208	4	innovation	innovation	NOUN
aiti-9488	208	5	,	,	PUNCT
aiti-9488	208	6	vol	vol	NOUN
aiti-9488	208	7	.	.	PROPN
aiti-9488	208	8	8	8	NUM
aiti-9488	208	9	,	,	PUNCT
aiti-9488	208	10	no	no	INTJ
aiti-9488	208	11	.	.	NOUN
aiti-9488	208	12	1	1	NUM
aiti-9488	208	13	,	,	PUNCT
aiti-9488	208	14	2023	2023	NUM
aiti-9488	208	15	,	,	PUNCT
aiti-9488	208	16	pp	pp	ADJ
aiti-9488	208	17	.	.	PUNCT
aiti-9488	209	1	59	59	NUM
aiti-9488	209	2	-	-	SYM
aiti-9488	209	3	72	72	NUM
aiti-9488	209	4	67	67	NUM
aiti-9488	209	5	tp	tp	NOUN
aiti-9488	209	6	tn	tn	PROPN
aiti-9488	209	7	accuracy	accuracy	NOUN
aiti-9488	209	8	fn	fn	PROPN
aiti-9488	209	9	tn	tn	PROPN
aiti-9488	209	10	fp	fp	X
aiti-9488	209	11	tp	tp	ADP
aiti-9488	209	12	+	+	CCONJ
aiti-9488	209	13	=	=	PUNCT
aiti-9488	210	1	+	+	PUNCT
aiti-9488	210	2	+	+	PUNCT
aiti-9488	210	3	+	+	CCONJ
aiti-9488	210	4	(	(	PUNCT
aiti-9488	210	5	3	3	NUM
aiti-9488	210	6	)	)	PUNCT
aiti-9488	210	7	precision	precision	NOUN
aiti-9488	210	8	:	:	PUNCT
aiti-9488	210	9	it	it	PRON
aiti-9488	210	10	computes	compute	VERB
aiti-9488	210	11	the	the	DET
aiti-9488	210	12	ratio	ratio	NOUN
aiti-9488	210	13	of	of	ADP
aiti-9488	210	14	total	total	ADJ
aiti-9488	210	15	positive	positive	ADJ
aiti-9488	210	16	identification	identification	NOUN
aiti-9488	210	17	over	over	ADP
aiti-9488	210	18	the	the	DET
aiti-9488	210	19	sum	sum	NOUN
aiti-9488	210	20	of	of	ADP
aiti-9488	210	21	total	total	ADJ
aiti-9488	210	22	positive	positive	ADJ
aiti-9488	210	23	identification	identification	NOUN
aiti-9488	210	24	that	that	PRON
aiti-9488	210	25	is	be	AUX
aiti-9488	210	26	either	either	CCONJ
aiti-9488	210	27	categorized	categorize	VERB
aiti-9488	210	28	correctly	correctly	ADV
aiti-9488	210	29	or	or	CCONJ
aiti-9488	210	30	incorrectly	incorrectly	ADV
aiti-9488	210	31	.	.	PUNCT
aiti-9488	211	1	tp	tp	AUX
aiti-9488	211	2	precision	precision	VERB
aiti-9488	211	3	fp	fp	INTJ
aiti-9488	211	4	tp	tp	NOUN
aiti-9488	211	5	=	=	PUNCT
aiti-9488	212	1	+	+	CCONJ
aiti-9488	212	2	(	(	PUNCT
aiti-9488	212	3	4	4	X
aiti-9488	212	4	)	)	PUNCT
aiti-9488	212	5	recall	recall	NOUN
aiti-9488	212	6	:	:	PUNCT
aiti-9488	212	7	it	it	PRON
aiti-9488	212	8	computes	compute	VERB
aiti-9488	212	9	the	the	DET
aiti-9488	212	10	ratio	ratio	NOUN
aiti-9488	212	11	of	of	ADP
aiti-9488	212	12	total	total	ADJ
aiti-9488	212	13	positive	positive	ADJ
aiti-9488	212	14	identification	identification	NOUN
aiti-9488	212	15	over	over	ADP
aiti-9488	212	16	the	the	DET
aiti-9488	212	17	sum	sum	NOUN
aiti-9488	212	18	of	of	ADP
aiti-9488	212	19	total	total	ADJ
aiti-9488	212	20	positive	positive	ADJ
aiti-9488	212	21	input	input	NOUN
aiti-9488	212	22	samples	sample	NOUN
aiti-9488	212	23	categorized	categorize	VERB
aiti-9488	212	24	precisely	precisely	ADV
aiti-9488	212	25	.	.	PUNCT
aiti-9488	213	1	tp	tp	PART
aiti-9488	213	2	recall	recall	VERB
aiti-9488	213	3	tp	tp	ADP
aiti-9488	213	4	fn	fn	NOUN
aiti-9488	213	5	=	=	PUNCT
aiti-9488	214	1	+	+	CCONJ
aiti-9488	214	2	(	(	PUNCT
aiti-9488	214	3	5	5	NUM
aiti-9488	214	4	)	)	PUNCT
aiti-9488	214	5	f1	f1	NOUN
aiti-9488	214	6	-	-	PUNCT
aiti-9488	214	7	score	score	NOUN
aiti-9488	214	8	:	:	PUNCT
aiti-9488	214	9	it	it	PRON
aiti-9488	214	10	is	be	AUX
aiti-9488	214	11	computed	compute	VERB
aiti-9488	214	12	from	from	ADP
aiti-9488	214	13	precision	precision	NOUN
aiti-9488	214	14	and	and	CCONJ
aiti-9488	214	15	recall	recall	NOUN
aiti-9488	214	16	metrics	metric	NOUN
aiti-9488	214	17	that	that	PRON
aiti-9488	214	18	calculate	calculate	VERB
aiti-9488	214	19	the	the	DET
aiti-9488	214	20	model	model	NOUN
aiti-9488	214	21	’s	’s	PART
aiti-9488	214	22	accuracy	accuracy	NOUN
aiti-9488	214	23	by	by	ADP
aiti-9488	214	24	giving	give	VERB
aiti-9488	214	25	higher	high	ADJ
aiti-9488	214	26	importance	importance	NOUN
aiti-9488	214	27	to	to	ADP
aiti-9488	214	28	false	false	ADJ
aiti-9488	214	29	negatives	negative	NOUN
aiti-9488	214	30	and	and	CCONJ
aiti-9488	214	31	false	false	ADJ
aiti-9488	214	32	positives	positive	NOUN
aiti-9488	214	33	.	.	PUNCT
aiti-9488	215	1	1	1	NUM
aiti-9488	215	2	2	2	NUM
aiti-9488	215	3	2	2	NUM
aiti-9488	215	4	tp	tp	NOUN
aiti-9488	215	5	f	f	PROPN
aiti-9488	215	6	score	score	NOUN
aiti-9488	216	1	fn	fn	INTJ
aiti-9488	216	2	fp	fp	INTJ
aiti-9488	216	3	tp	tp	ADP
aiti-9488	216	4	−	−	PROPN
aiti-9488	217	1	=	=	PUNCT
aiti-9488	218	1	+	+	PUNCT
aiti-9488	218	2	+	+	CCONJ
aiti-9488	218	3	(	(	PUNCT
aiti-9488	218	4	6	6	NUM
aiti-9488	218	5	)	)	PUNCT
aiti-9488	218	6	validation	validation	NOUN
aiti-9488	218	7	of	of	ADP
aiti-9488	218	8	the	the	DET
aiti-9488	218	9	effectiveness	effectiveness	NOUN
aiti-9488	218	10	of	of	ADP
aiti-9488	218	11	the	the	DET
aiti-9488	218	12	proposed	propose	VERB
aiti-9488	218	13	framework	framework	NOUN
aiti-9488	218	14	is	be	AUX
aiti-9488	218	15	accomplished	accomplish	VERB
aiti-9488	218	16	in	in	ADP
aiti-9488	218	17	two	two	NUM
aiti-9488	218	18	approaches	approach	NOUN
aiti-9488	218	19	.	.	PUNCT
aiti-9488	219	1	primarily	primarily	ADV
aiti-9488	219	2	,	,	PUNCT
aiti-9488	219	3	four	four	NUM
aiti-9488	219	4	models	model	NOUN
aiti-9488	219	5	are	be	AUX
aiti-9488	219	6	compiled	compile	VERB
aiti-9488	219	7	to	to	PART
aiti-9488	219	8	analyze	analyze	VERB
aiti-9488	219	9	the	the	DET
aiti-9488	219	10	outcomes	outcome	NOUN
aiti-9488	219	11	of	of	ADP
aiti-9488	219	12	various	various	ADJ
aiti-9488	219	13	layers	layer	NOUN
aiti-9488	219	14	of	of	ADP
aiti-9488	219	15	efficientnetb3	efficientnetb3	NOUN
aiti-9488	219	16	network	network	NOUN
aiti-9488	219	17	architecture	architecture	NOUN
aiti-9488	219	18	in	in	ADP
aiti-9488	219	19	the	the	DET
aiti-9488	219	20	first	first	ADJ
aiti-9488	219	21	approach	approach	NOUN
aiti-9488	219	22	.	.	PUNCT
aiti-9488	220	1	the	the	DET
aiti-9488	220	2	training	training	NOUN
aiti-9488	220	3	of	of	ADP
aiti-9488	220	4	these	these	DET
aiti-9488	220	5	models	model	NOUN
aiti-9488	220	6	is	be	AUX
aiti-9488	220	7	carried	carry	VERB
aiti-9488	220	8	out	out	ADP
aiti-9488	220	9	on	on	ADP
aiti-9488	220	10	the	the	DET
aiti-9488	220	11	preprocessed	preprocesse	VERB
aiti-9488	220	12	dataset	dataset	NOUN
aiti-9488	220	13	,	,	PUNCT
aiti-9488	220	14	and	and	CCONJ
aiti-9488	220	15	the	the	DET
aiti-9488	220	16	fully	fully	ADV
aiti-9488	220	17	connected	connect	VERB
aiti-9488	220	18	(	(	PUNCT
aiti-9488	220	19	fc	fc	INTJ
aiti-9488	220	20	)	)	PUNCT
aiti-9488	220	21	last	last	ADJ
aiti-9488	220	22	layer	layer	NOUN
aiti-9488	220	23	of	of	ADP
aiti-9488	220	24	classification	classification	NOUN
aiti-9488	220	25	is	be	AUX
aiti-9488	220	26	fine	fine	ADV
aiti-9488	220	27	-	-	PUNCT
aiti-9488	220	28	tuned	tune	VERB
aiti-9488	220	29	to	to	PART
aiti-9488	220	30	adjust	adjust	VERB
aiti-9488	220	31	to	to	ADP
aiti-9488	220	32	the	the	DET
aiti-9488	220	33	binary	binary	ADJ
aiti-9488	220	34	(	(	PUNCT
aiti-9488	220	35	benign	benign	ADJ
aiti-9488	220	36	and	and	CCONJ
aiti-9488	220	37	malignant	malignant	ADJ
aiti-9488	220	38	)	)	PUNCT
aiti-9488	220	39	categorization	categorization	NOUN
aiti-9488	220	40	of	of	ADP
aiti-9488	220	41	the	the	DET
aiti-9488	220	42	isic	isic	PROPN
aiti-9488	220	43	2017	2017	NUM
aiti-9488	220	44	dataset	dataset	NOUN
aiti-9488	220	45	.	.	PUNCT
aiti-9488	221	1	model	model	NOUN
aiti-9488	221	2	1	1	NUM
aiti-9488	221	3	depicts	depict	VERB
aiti-9488	221	4	the	the	DET
aiti-9488	221	5	baseline	baseline	NOUN
aiti-9488	221	6	efficientnetb3	efficientnetb3	PROPN
aiti-9488	221	7	model	model	NOUN
aiti-9488	221	8	with	with	ADP
aiti-9488	221	9	no	no	DET
aiti-9488	221	10	augmentation	augmentation	NOUN
aiti-9488	221	11	and	and	CCONJ
aiti-9488	221	12	no	no	DET
aiti-9488	221	13	gap	gap	NOUN
aiti-9488	221	14	layer	layer	NOUN
aiti-9488	221	15	.	.	PUNCT
aiti-9488	222	1	model	model	NOUN
aiti-9488	222	2	2	2	NUM
aiti-9488	222	3	refers	refer	VERB
aiti-9488	222	4	to	to	ADP
aiti-9488	222	5	efficientnetb3	efficientnetb3	NOUN
aiti-9488	222	6	with	with	SCONJ
aiti-9488	222	7	augmentation	augmentation	NOUN
aiti-9488	222	8	techniques	technique	NOUN
aiti-9488	222	9	applied	apply	VERB
aiti-9488	222	10	to	to	ADP
aiti-9488	222	11	the	the	DET
aiti-9488	222	12	network	network	NOUN
aiti-9488	222	13	with	with	ADP
aiti-9488	222	14	no	no	DET
aiti-9488	222	15	gap	gap	NOUN
aiti-9488	222	16	layer	layer	NOUN
aiti-9488	222	17	.	.	PUNCT
aiti-9488	223	1	model	model	NOUN
aiti-9488	223	2	3	3	NUM
aiti-9488	223	3	depicts	depict	VERB
aiti-9488	223	4	the	the	DET
aiti-9488	223	5	gap	gap	NOUN
aiti-9488	223	6	layer	layer	NOUN
aiti-9488	223	7	added	add	VERB
aiti-9488	223	8	to	to	ADP
aiti-9488	223	9	efficientnetb3	efficientnetb3	PROPN
aiti-9488	223	10	network	network	NOUN
aiti-9488	223	11	with	with	ADP
aiti-9488	223	12	no	no	DET
aiti-9488	223	13	augmentation	augmentation	NOUN
aiti-9488	223	14	techniques	technique	NOUN
aiti-9488	223	15	applied	apply	VERB
aiti-9488	223	16	.	.	PUNCT
aiti-9488	224	1	model	model	NOUN
aiti-9488	224	2	4	4	NUM
aiti-9488	224	3	refers	refer	VERB
aiti-9488	224	4	to	to	ADP
aiti-9488	224	5	the	the	DET
aiti-9488	224	6	fine	fine	ADV
aiti-9488	224	7	-	-	PUNCT
aiti-9488	224	8	tuned	tune	VERB
aiti-9488	224	9	proposed	propose	VERB
aiti-9488	224	10	architecture	architecture	NOUN
aiti-9488	224	11	presented	present	VERB
aiti-9488	224	12	in	in	ADP
aiti-9488	224	13	this	this	DET
aiti-9488	224	14	study	study	NOUN
aiti-9488	224	15	.	.	PUNCT
aiti-9488	225	1	table	table	NOUN
aiti-9488	225	2	2	2	NUM
aiti-9488	225	3	presents	present	VERB
aiti-9488	225	4	the	the	DET
aiti-9488	225	5	methodology	methodology	NOUN
aiti-9488	225	6	of	of	ADP
aiti-9488	225	7	each	each	DET
aiti-9488	225	8	model	model	NOUN
aiti-9488	225	9	.	.	PUNCT
aiti-9488	226	1	table	table	NOUN
aiti-9488	226	2	2	2	NUM
aiti-9488	226	3	approach	approach	NOUN
aiti-9488	226	4	of	of	ADP
aiti-9488	226	5	models	model	NOUN
aiti-9488	226	6	approach	approach	NOUN
aiti-9488	226	7	preprocessing	preprocesse	VERB
aiti-9488	226	8	augment	augment	NOUN
aiti-9488	226	9	gap	gap	NOUN
aiti-9488	226	10	fc	fc	PROPN
aiti-9488	226	11	model	model	NOUN
aiti-9488	226	12	1	1	NUM
aiti-9488	226	13	✓	✓	ADJ
aiti-9488	226	14	✕	✕	NOUN
aiti-9488	226	15	✕	✕	NUM
aiti-9488	226	16	✓	✓	ADJ
aiti-9488	226	17	model	model	NOUN
aiti-9488	226	18	2	2	NUM
aiti-9488	226	19	✓	✓	ADJ
aiti-9488	226	20	✓	✓	ADJ
aiti-9488	226	21	✕	✕	NUM
aiti-9488	226	22	✓	✓	ADJ
aiti-9488	226	23	model	model	NOUN
aiti-9488	226	24	3	3	NUM
aiti-9488	226	25	✓	✓	ADJ
aiti-9488	226	26	✕	✕	NUM
aiti-9488	226	27	✓	✓	ADJ
aiti-9488	226	28	✓	✓	ADJ
aiti-9488	226	29	model	model	NOUN
aiti-9488	226	30	4	4	NUM
aiti-9488	226	31	(	(	PUNCT
aiti-9488	226	32	proposed	propose	VERB
aiti-9488	226	33	model	model	NOUN
aiti-9488	226	34	)	)	PUNCT
aiti-9488	226	35	✓	✓	ADJ
aiti-9488	226	36	✓	✓	ADJ
aiti-9488	226	37	✓	✓	ADJ
aiti-9488	226	38	✓	✓	ADJ
aiti-9488	226	39	table	table	NOUN
aiti-9488	226	40	3	3	NUM
aiti-9488	226	41	result	result	NOUN
aiti-9488	226	42	summary	summary	NOUN
aiti-9488	226	43	of	of	ADP
aiti-9488	226	44	models	model	NOUN
aiti-9488	226	45	approach	approach	VERB
aiti-9488	226	46	recall	recall	PROPN
aiti-9488	226	47	accuracy	accuracy	NOUN
aiti-9488	226	48	f1	f1	ADJ
aiti-9488	226	49	-	-	PUNCT
aiti-9488	226	50	score	score	NOUN
aiti-9488	226	51	precision	precision	NOUN
aiti-9488	226	52	model	model	NOUN
aiti-9488	226	53	1	1	NUM
aiti-9488	226	54	58.00	58.00	NUM
aiti-9488	226	55	56.97	56.97	NUM
aiti-9488	226	56	58.00	58.00	NUM
aiti-9488	226	57	58.00	58.00	NUM
aiti-9488	226	58	model	model	NOUN
aiti-9488	226	59	2	2	NUM
aiti-9488	226	60	57.00	57.00	NUM
aiti-9488	226	61	56.67	56.67	NUM
aiti-9488	226	62	53.00	53.00	NUM
aiti-9488	226	63	56.00	56.00	NUM
aiti-9488	226	64	model	model	NOUN
aiti-9488	226	65	3	3	NUM
aiti-9488	226	66	86.00	86.00	NUM
aiti-9488	226	67	85.75	85.75	NUM
aiti-9488	226	68	85.00	85.00	NUM
aiti-9488	226	69	86.00	86.00	NUM
aiti-9488	226	70	model	model	NOUN
aiti-9488	226	71	4	4	NUM
aiti-9488	226	72	(	(	PUNCT
aiti-9488	226	73	proposed	propose	VERB
aiti-9488	226	74	model	model	NOUN
aiti-9488	226	75	)	)	PUNCT
aiti-9488	226	76	88.00	88.00	NUM
aiti-9488	226	77	88.13	88.13	NUM
aiti-9488	226	78	88.00	88.00	NUM
aiti-9488	226	79	88.00	88.00	NUM
aiti-9488	226	80	table	table	NOUN
aiti-9488	226	81	3	3	NUM
aiti-9488	226	82	provides	provide	VERB
aiti-9488	226	83	the	the	DET
aiti-9488	226	84	result	result	NOUN
aiti-9488	226	85	analyses	analysis	NOUN
aiti-9488	226	86	of	of	ADP
aiti-9488	226	87	the	the	DET
aiti-9488	226	88	above	above	ADJ
aiti-9488	226	89	models	model	NOUN
aiti-9488	226	90	on	on	ADP
aiti-9488	226	91	the	the	DET
aiti-9488	226	92	isic	isic	PROPN
aiti-9488	226	93	dataset	dataset	PROPN
aiti-9488	226	94	.	.	PUNCT
aiti-9488	227	1	from	from	ADP
aiti-9488	227	2	table	table	NOUN
aiti-9488	227	3	3	3	NUM
aiti-9488	227	4	,	,	PUNCT
aiti-9488	227	5	it	it	PRON
aiti-9488	227	6	can	can	AUX
aiti-9488	227	7	be	be	AUX
aiti-9488	227	8	found	find	VERB
aiti-9488	227	9	that	that	SCONJ
aiti-9488	227	10	model	model	NOUN
aiti-9488	227	11	1	1	NUM
aiti-9488	227	12	achieved	achieve	VERB
aiti-9488	227	13	an	an	DET
aiti-9488	227	14	accuracy	accuracy	NOUN
aiti-9488	227	15	of	of	ADP
aiti-9488	227	16	56.97	56.97	NUM
aiti-9488	227	17	%	%	NOUN
aiti-9488	227	18	,	,	PUNCT
aiti-9488	227	19	a	a	DET
aiti-9488	227	20	precision	precision	NOUN
aiti-9488	227	21	of	of	ADP
aiti-9488	227	22	58	58	NUM
aiti-9488	227	23	%	%	NOUN
aiti-9488	227	24	,	,	PUNCT
aiti-9488	227	25	which	which	PRON
aiti-9488	227	26	indicates	indicate	VERB
aiti-9488	227	27	that	that	SCONJ
aiti-9488	227	28	the	the	DET
aiti-9488	227	29	pre	pre	ADJ
aiti-9488	227	30	-	-	ADJ
aiti-9488	227	31	trained	train	VERB
aiti-9488	227	32	baseline	baseline	NOUN
aiti-9488	227	33	efficientnetb3	efficientnetb3	PROPN
aiti-9488	227	34	model	model	NOUN
aiti-9488	227	35	suffered	suffer	VERB
aiti-9488	227	36	from	from	ADP
aiti-9488	227	37	an	an	DET
aiti-9488	227	38	overfitting	overfitte	VERB
aiti-9488	227	39	problem	problem	NOUN
aiti-9488	227	40	.	.	PUNCT
aiti-9488	228	1	fig	fig	NOUN
aiti-9488	228	2	.	.	PUNCT
aiti-9488	229	1	8	8	NUM
aiti-9488	229	2	shows	show	VERB
aiti-9488	229	3	the	the	DET
aiti-9488	229	4	accuracy	accuracy	NOUN
aiti-9488	229	5	curve	curve	NOUN
aiti-9488	229	6	of	of	ADP
aiti-9488	229	7	model	model	NOUN
aiti-9488	229	8	1	1	NUM
aiti-9488	229	9	.	.	PUNCT
aiti-9488	229	10	model	model	PROPN
aiti-9488	229	11	2	2	NUM
aiti-9488	229	12	,	,	PUNCT
aiti-9488	229	13	with	with	ADP
aiti-9488	229	14	augmentation	augmentation	NOUN
aiti-9488	229	15	techniques	technique	NOUN
aiti-9488	229	16	applied	apply	VERB
aiti-9488	229	17	to	to	ADP
aiti-9488	229	18	the	the	DET
aiti-9488	229	19	network	network	NOUN
aiti-9488	229	20	,	,	PUNCT
aiti-9488	229	21	gave	give	VERB
aiti-9488	229	22	poor	poor	ADJ
aiti-9488	229	23	results	result	NOUN
aiti-9488	229	24	with	with	ADP
aiti-9488	229	25	an	an	DET
aiti-9488	229	26	accuracy	accuracy	NOUN
aiti-9488	229	27	of	of	ADP
aiti-9488	229	28	56.67	56.67	NUM
aiti-9488	229	29	%	%	NOUN
aiti-9488	229	30	as	as	ADP
aiti-9488	229	31	compared	compare	VERB
aiti-9488	229	32	to	to	ADP
aiti-9488	229	33	model	model	NOUN
aiti-9488	229	34	1	1	NUM
aiti-9488	229	35	.	.	PUNCT
aiti-9488	229	36	fig	fig	NOUN
aiti-9488	229	37	.	.	PUNCT
aiti-9488	230	1	9	9	NUM
aiti-9488	230	2	shows	show	VERB
aiti-9488	230	3	the	the	DET
aiti-9488	230	4	accuracy	accuracy	NOUN
aiti-9488	230	5	curve	curve	NOUN
aiti-9488	230	6	of	of	ADP
aiti-9488	230	7	model	model	NOUN
aiti-9488	230	8	2	2	NUM
aiti-9488	230	9	.	.	PUNCT
aiti-9488	230	10	model	model	NOUN
aiti-9488	230	11	3	3	NUM
aiti-9488	230	12	gave	give	VERB
aiti-9488	230	13	an	an	DET
aiti-9488	230	14	accuracy	accuracy	NOUN
aiti-9488	230	15	of	of	ADP
aiti-9488	230	16	85.75	85.75	NUM
aiti-9488	230	17	%	%	NOUN
aiti-9488	230	18	,	,	PUNCT
aiti-9488	230	19	which	which	PRON
aiti-9488	230	20	indicated	indicate	VERB
aiti-9488	230	21	that	that	SCONJ
aiti-9488	230	22	the	the	DET
aiti-9488	230	23	gap	gap	NOUN
aiti-9488	230	24	layer	layer	NOUN
aiti-9488	230	25	boosted	boost	VERB
aiti-9488	230	26	the	the	DET
aiti-9488	230	27	performance	performance	NOUN
aiti-9488	230	28	of	of	ADP
aiti-9488	230	29	the	the	DET
aiti-9488	230	30	efficientnetb3	efficientnetb3	PROPN
aiti-9488	230	31	network	network	NOUN
aiti-9488	230	32	.	.	PUNCT
aiti-9488	231	1	fig	fig	NOUN
aiti-9488	231	2	.	.	PUNCT
aiti-9488	232	1	10	10	NUM
aiti-9488	232	2	shows	show	VERB
aiti-9488	232	3	the	the	DET
aiti-9488	232	4	accuracy	accuracy	NOUN
aiti-9488	232	5	curve	curve	NOUN
aiti-9488	232	6	of	of	ADP
aiti-9488	232	7	model	model	NOUN
aiti-9488	232	8	3	3	NUM
aiti-9488	232	9	.	.	PUNCT
aiti-9488	232	10	model	model	NOUN
aiti-9488	232	11	3	3	NUM
aiti-9488	232	12	suffered	suffer	VERB
aiti-9488	232	13	from	from	ADP
aiti-9488	232	14	an	an	DET
aiti-9488	232	15	overfitting	overfitting	ADJ
aiti-9488	232	16	problem	problem	NOUN
aiti-9488	232	17	,	,	PUNCT
aiti-9488	232	18	although	although	SCONJ
aiti-9488	232	19	the	the	DET
aiti-9488	232	20	model	model	NOUN
aiti-9488	232	21	was	be	AUX
aiti-9488	232	22	performing	perform	VERB
aiti-9488	232	23	well	well	ADV
aiti-9488	232	24	on	on	ADP
aiti-9488	232	25	training	training	NOUN
aiti-9488	232	26	data	datum	NOUN
aiti-9488	232	27	.	.	PUNCT
aiti-9488	233	1	whereas	whereas	SCONJ
aiti-9488	233	2	for	for	ADP
aiti-9488	233	3	testing	testing	NOUN
aiti-9488	233	4	data	datum	NOUN
aiti-9488	233	5	,	,	PUNCT
aiti-9488	233	6	the	the	DET
aiti-9488	233	7	model	model	NOUN
aiti-9488	233	8	performed	perform	VERB
aiti-9488	233	9	comparatively	comparatively	ADV
aiti-9488	233	10	less	less	ADJ
aiti-9488	233	11	,	,	PUNCT
aiti-9488	233	12	as	as	ADP
aiti-9488	233	13	observed	observe	VERB
aiti-9488	233	14	advances	advance	NOUN
aiti-9488	233	15	in	in	ADP
aiti-9488	233	16	technology	technology	NOUN
aiti-9488	233	17	innovation	innovation	NOUN
aiti-9488	233	18	,	,	PUNCT
aiti-9488	233	19	vol	vol	NOUN
aiti-9488	233	20	.	.	PROPN
aiti-9488	233	21	8	8	NUM
aiti-9488	233	22	,	,	PUNCT
aiti-9488	233	23	no	no	INTJ
aiti-9488	233	24	.	.	NOUN
aiti-9488	233	25	1	1	NUM
aiti-9488	233	26	,	,	PUNCT
aiti-9488	233	27	2023	2023	NUM
aiti-9488	233	28	,	,	PUNCT
aiti-9488	233	29	pp	pp	ADJ
aiti-9488	233	30	.	.	PUNCT
aiti-9488	234	1	59	59	NUM
aiti-9488	234	2	-	-	SYM
aiti-9488	234	3	72	72	NUM
aiti-9488	234	4	68	68	NUM
aiti-9488	234	5	in	in	ADP
aiti-9488	234	6	fig	fig	NOUN
aiti-9488	234	7	.	.	PUNCT
aiti-9488	235	1	10	10	NUM
aiti-9488	235	2	,	,	PUNCT
aiti-9488	235	3	which	which	PRON
aiti-9488	235	4	represents	represent	VERB
aiti-9488	235	5	the	the	DET
aiti-9488	235	6	accuracy	accuracy	NOUN
aiti-9488	235	7	curve	curve	NOUN
aiti-9488	235	8	of	of	ADP
aiti-9488	235	9	model	model	NOUN
aiti-9488	235	10	3	3	NUM
aiti-9488	235	11	.	.	PUNCT
aiti-9488	235	12	model	model	NOUN
aiti-9488	235	13	4	4	NUM
aiti-9488	235	14	achieved	achieve	VERB
aiti-9488	235	15	the	the	DET
aiti-9488	235	16	best	good	ADJ
aiti-9488	235	17	results	result	NOUN
aiti-9488	235	18	with	with	ADP
aiti-9488	235	19	an	an	DET
aiti-9488	235	20	accuracy	accuracy	NOUN
aiti-9488	235	21	of	of	ADP
aiti-9488	235	22	88.13	88.13	NUM
aiti-9488	235	23	%	%	NOUN
aiti-9488	235	24	and	and	CCONJ
aiti-9488	235	25	also	also	ADV
aiti-9488	235	26	acquired	acquire	VERB
aiti-9488	235	27	a	a	DET
aiti-9488	235	28	balanced	balanced	ADJ
aiti-9488	235	29	precision	precision	NOUN
aiti-9488	235	30	value	value	NOUN
aiti-9488	235	31	of	of	ADP
aiti-9488	235	32	88	88	NUM
aiti-9488	235	33	%	%	NOUN
aiti-9488	235	34	,	,	PUNCT
aiti-9488	235	35	which	which	PRON
aiti-9488	235	36	demonstrates	demonstrate	VERB
aiti-9488	235	37	that	that	SCONJ
aiti-9488	235	38	the	the	DET
aiti-9488	235	39	proposed	propose	VERB
aiti-9488	235	40	framework	framework	NOUN
aiti-9488	235	41	overcomes	overcome	VERB
aiti-9488	235	42	and	and	CCONJ
aiti-9488	235	43	handles	handle	VERB
aiti-9488	235	44	the	the	DET
aiti-9488	235	45	problem	problem	NOUN
aiti-9488	235	46	of	of	ADP
aiti-9488	235	47	model	model	NOUN
aiti-9488	235	48	overfitting	overfitting	NOUN
aiti-9488	235	49	.	.	PUNCT
aiti-9488	236	1	fig	fig	NOUN
aiti-9488	236	2	.	.	PUNCT
aiti-9488	237	1	11	11	NUM
aiti-9488	237	2	represents	represent	VERB
aiti-9488	237	3	the	the	DET
aiti-9488	237	4	accuracy	accuracy	NOUN
aiti-9488	237	5	curve	curve	NOUN
aiti-9488	237	6	of	of	ADP
aiti-9488	237	7	model	model	NOUN
aiti-9488	237	8	4	4	NUM
aiti-9488	237	9	(	(	PUNCT
aiti-9488	237	10	proposed	propose	VERB
aiti-9488	237	11	framework	framework	NOUN
aiti-9488	237	12	)	)	PUNCT
aiti-9488	237	13	.	.	PUNCT
aiti-9488	238	1	fig	fig	NOUN
aiti-9488	238	2	.	.	PUNCT
aiti-9488	239	1	8	8	NUM
aiti-9488	239	2	accuracy	accuracy	NOUN
aiti-9488	239	3	curve	curve	NOUN
aiti-9488	239	4	of	of	ADP
aiti-9488	239	5	model	model	NOUN
aiti-9488	239	6	1	1	NUM
aiti-9488	239	7	fig	fig	NOUN
aiti-9488	239	8	.	.	PUNCT
aiti-9488	240	1	9	9	NUM
aiti-9488	240	2	accuracy	accuracy	NOUN
aiti-9488	240	3	curve	curve	NOUN
aiti-9488	240	4	of	of	ADP
aiti-9488	240	5	model	model	NOUN
aiti-9488	240	6	2	2	NUM
aiti-9488	240	7	fig	fig	NOUN
aiti-9488	240	8	.	.	PUNCT
aiti-9488	241	1	10	10	NUM
aiti-9488	241	2	accuracy	accuracy	NOUN
aiti-9488	241	3	curve	curve	NOUN
aiti-9488	241	4	of	of	ADP
aiti-9488	241	5	model	model	NOUN
aiti-9488	241	6	3	3	NUM
aiti-9488	241	7	fig	fig	NOUN
aiti-9488	241	8	.	.	PUNCT
aiti-9488	242	1	11	11	NUM
aiti-9488	242	2	accuracy	accuracy	NOUN
aiti-9488	242	3	curve	curve	NOUN
aiti-9488	242	4	of	of	ADP
aiti-9488	242	5	model	model	NOUN
aiti-9488	242	6	4	4	NUM
aiti-9488	242	7	in	in	ADP
aiti-9488	242	8	the	the	DET
aiti-9488	242	9	second	second	ADJ
aiti-9488	242	10	approach	approach	NOUN
aiti-9488	242	11	,	,	PUNCT
aiti-9488	242	12	for	for	ADP
aiti-9488	242	13	evaluation	evaluation	NOUN
aiti-9488	242	14	of	of	ADP
aiti-9488	242	15	the	the	DET
aiti-9488	242	16	proposed	propose	VERB
aiti-9488	242	17	model	model	NOUN
aiti-9488	242	18	,	,	PUNCT
aiti-9488	242	19	its	its	PRON
aiti-9488	242	20	result	result	NOUN
aiti-9488	242	21	is	be	AUX
aiti-9488	242	22	compared	compare	VERB
aiti-9488	242	23	to	to	ADP
aiti-9488	242	24	other	other	ADJ
aiti-9488	242	25	advanced	advanced	ADJ
aiti-9488	242	26	pre	pre	ADJ
aiti-9488	242	27	-	-	ADJ
aiti-9488	242	28	trained	train	VERB
aiti-9488	242	29	models	model	NOUN
aiti-9488	242	30	,	,	PUNCT
aiti-9488	242	31	namely	namely	ADV
aiti-9488	242	32	inceptionresnetv2	inceptionresnetv2	PROPN
aiti-9488	243	1	[	[	X
aiti-9488	243	2	26	26	NUM
aiti-9488	243	3	]	]	PUNCT
aiti-9488	243	4	,	,	PUNCT
aiti-9488	243	5	resnet50	resnet50	NOUN
aiti-9488	243	6	[	[	X
aiti-9488	243	7	27	27	NUM
aiti-9488	243	8	]	]	PUNCT
aiti-9488	243	9	,	,	PUNCT
aiti-9488	243	10	efficientnet	efficientnet	PROPN
aiti-9488	243	11	b0	b0	NOUN
aiti-9488	243	12	-	-	PUNCT
aiti-9488	243	13	b2	b2	NOUN
aiti-9488	244	1	[	[	X
aiti-9488	244	2	22	22	NUM
aiti-9488	244	3	]	]	PUNCT
aiti-9488	244	4	,	,	PUNCT
aiti-9488	244	5	and	and	CCONJ
aiti-9488	244	6	inceptionv3	inceptionv3	NOUN
aiti-9488	245	1	[	[	X
aiti-9488	245	2	28	28	NUM
aiti-9488	245	3	]	]	PUNCT
aiti-9488	245	4	over	over	ADP
aiti-9488	245	5	the	the	DET
aiti-9488	245	6	isic	isic	PROPN
aiti-9488	245	7	dataset	dataset	NOUN
aiti-9488	245	8	for	for	ADP
aiti-9488	245	9	the	the	DET
aiti-9488	245	10	melanoma	melanoma	NOUN
aiti-9488	245	11	classification	classification	NOUN
aiti-9488	245	12	task	task	NOUN
aiti-9488	245	13	.	.	PUNCT
aiti-9488	246	1	to	to	PART
aiti-9488	246	2	investigate	investigate	VERB
aiti-9488	246	3	the	the	DET
aiti-9488	246	4	potentiality	potentiality	NOUN
aiti-9488	246	5	of	of	ADP
aiti-9488	246	6	the	the	DET
aiti-9488	246	7	proposed	propose	VERB
aiti-9488	246	8	model	model	NOUN
aiti-9488	246	9	,	,	PUNCT
aiti-9488	246	10	it	it	PRON
aiti-9488	246	11	is	be	AUX
aiti-9488	246	12	compared	compare	VERB
aiti-9488	246	13	with	with	ADP
aiti-9488	246	14	advanced	advanced	ADJ
aiti-9488	246	15	networks	network	NOUN
aiti-9488	246	16	like	like	ADP
aiti-9488	246	17	inceptionresnetv2	inceptionresnetv2	PROPN
aiti-9488	246	18	,	,	PUNCT
aiti-9488	246	19	resnet50	resnet50	NOUN
aiti-9488	246	20	,	,	PUNCT
aiti-9488	246	21	inceptionv3	inceptionv3	NOUN
aiti-9488	246	22	,	,	PUNCT
aiti-9488	246	23	and	and	CCONJ
aiti-9488	246	24	efficientnetb0	efficientnetb0	NOUN
aiti-9488	246	25	-	-	PUNCT
aiti-9488	246	26	b2	b2	NOUN
aiti-9488	246	27	models	model	NOUN
aiti-9488	246	28	.	.	PUNCT
aiti-9488	247	1	these	these	DET
aiti-9488	247	2	advanced	advanced	ADJ
aiti-9488	247	3	neural	neural	ADJ
aiti-9488	247	4	models	model	NOUN
aiti-9488	247	5	are	be	AUX
aiti-9488	247	6	pretrained	pretraine	VERB
aiti-9488	247	7	on	on	ADP
aiti-9488	247	8	the	the	DET
aiti-9488	247	9	imagenet	imagenet	NOUN
aiti-9488	247	10	dataset	dataset	VERB
aiti-9488	247	11	that	that	SCONJ
aiti-9488	247	12	outputs	output	NOUN
aiti-9488	247	13	feature	feature	NOUN
aiti-9488	247	14	vectors	vector	NOUN
aiti-9488	247	15	for	for	ADP
aiti-9488	247	16	1000	1000	NUM
aiti-9488	247	17	categories	category	NOUN
aiti-9488	247	18	.	.	PUNCT
aiti-9488	248	1	therefore	therefore	ADV
aiti-9488	248	2	,	,	PUNCT
aiti-9488	248	3	it	it	PRON
aiti-9488	248	4	is	be	AUX
aiti-9488	248	5	important	important	ADJ
aiti-9488	248	6	to	to	PART
aiti-9488	248	7	adjust	adjust	VERB
aiti-9488	248	8	these	these	DET
aiti-9488	248	9	models	model	NOUN
aiti-9488	248	10	on	on	ADP
aiti-9488	248	11	the	the	DET
aiti-9488	248	12	target	target	NOUN
aiti-9488	248	13	isic	isic	PROPN
aiti-9488	248	14	dataset	dataset	NOUN
aiti-9488	248	15	comprising	comprise	VERB
aiti-9488	248	16	only	only	ADV
aiti-9488	248	17	two	two	NUM
aiti-9488	248	18	types	type	NOUN
aiti-9488	248	19	of	of	ADP
aiti-9488	248	20	benign	benign	ADJ
aiti-9488	248	21	and	and	CCONJ
aiti-9488	248	22	malignant	malignant	ADJ
aiti-9488	248	23	classes	class	NOUN
aiti-9488	248	24	.	.	PUNCT
aiti-9488	249	1	to	to	PART
aiti-9488	249	2	fit	fit	VERB
aiti-9488	249	3	these	these	DET
aiti-9488	249	4	models	model	NOUN
aiti-9488	249	5	over	over	ADP
aiti-9488	249	6	the	the	DET
aiti-9488	249	7	isic	isic	PROPN
aiti-9488	249	8	dataset	dataset	NOUN
aiti-9488	249	9	,	,	PUNCT
aiti-9488	249	10	the	the	DET
aiti-9488	249	11	last	last	ADJ
aiti-9488	249	12	classification	classification	NOUN
aiti-9488	249	13	layer	layer	NOUN
aiti-9488	249	14	of	of	ADP
aiti-9488	249	15	these	these	DET
aiti-9488	249	16	models	model	NOUN
aiti-9488	249	17	is	be	AUX
aiti-9488	249	18	altered	alter	VERB
aiti-9488	249	19	using	use	VERB
aiti-9488	249	20	the	the	DET
aiti-9488	249	21	softmax	softmax	NOUN
aiti-9488	249	22	layer	layer	NOUN
aiti-9488	249	23	with	with	ADP
aiti-9488	249	24	two	two	NUM
aiti-9488	249	25	classes	class	NOUN
aiti-9488	249	26	.	.	PUNCT
aiti-9488	250	1	table	table	NOUN
aiti-9488	250	2	4	4	NUM
aiti-9488	250	3	comparative	comparative	ADJ
aiti-9488	250	4	analyses	analysis	NOUN
aiti-9488	250	5	of	of	ADP
aiti-9488	250	6	various	various	ADJ
aiti-9488	250	7	evaluation	evaluation	NOUN
aiti-9488	250	8	metrics	metric	NOUN
aiti-9488	250	9	approach	approach	NOUN
aiti-9488	250	10	dataset	dataset	VERB
aiti-9488	250	11	recall	recall	PROPN
aiti-9488	250	12	accuracy	accuracy	PROPN
aiti-9488	250	13	f1	f1	ADJ
aiti-9488	250	14	-	-	PUNCT
aiti-9488	250	15	score	score	NOUN
aiti-9488	250	16	precision	precision	NOUN
aiti-9488	250	17	proposed	propose	VERB
aiti-9488	250	18	model	model	PROPN
aiti-9488	250	19	isic	isic	PROPN
aiti-9488	250	20	2017	2017	NUM
aiti-9488	250	21	88.00	88.00	NUM
aiti-9488	250	22	88.13	88.13	NUM
aiti-9488	250	23	88.00	88.00	NUM
aiti-9488	250	24	88.00	88.00	NUM
aiti-9488	250	25	inceptionv3	inceptionv3	NOUN
aiti-9488	250	26	isic	isic	NOUN
aiti-9488	250	27	2017	2017	NUM
aiti-9488	250	28	83.00	83.00	NUM
aiti-9488	250	29	82.73	82.73	NUM
aiti-9488	250	30	83.00	83.00	NUM
aiti-9488	250	31	83.00	83.00	NUM
aiti-9488	250	32	efficientnetb0	efficientnetb0	NOUN
aiti-9488	250	33	isic	isic	PROPN
aiti-9488	250	34	2017	2017	NUM
aiti-9488	250	35	60.00	60.00	NUM
aiti-9488	250	36	60.15	60.15	NUM
aiti-9488	250	37	59.00	59.00	NUM
aiti-9488	250	38	65.00	65.00	NUM
aiti-9488	250	39	resnet50	resnet50	NOUN
aiti-9488	250	40	isic	isic	PROPN
aiti-9488	250	41	2017	2017	NUM
aiti-9488	250	42	85.00	85.00	NUM
aiti-9488	250	43	84.85	84.85	NUM
aiti-9488	250	44	85.00	85.00	NUM
aiti-9488	250	45	85.00	85.00	NUM
aiti-9488	250	46	efficientnetb1	efficientnetb1	PROPN
aiti-9488	250	47	isic	isic	PROPN
aiti-9488	250	48	2017	2017	NUM
aiti-9488	250	49	56.00	56.00	NUM
aiti-9488	250	50	55.75	55.75	NUM
aiti-9488	250	51	55.00	55.00	NUM
aiti-9488	250	52	55.00	55.00	NUM
aiti-9488	250	53	efficientnetb2	efficientnetb2	X
aiti-9488	251	1	isic	isic	NOUN
aiti-9488	251	2	2017	2017	NUM
aiti-9488	251	3	66.00	66.00	NUM
aiti-9488	251	4	58.48	58.48	NUM
aiti-9488	251	5	66.00	66.00	NUM
aiti-9488	251	6	66.00	66.00	NUM
aiti-9488	251	7	inceptionresnetv2	inceptionresnetv2	PROPN
aiti-9488	251	8	isic	isic	PROPN
aiti-9488	251	9	2017	2017	NUM
aiti-9488	251	10	83.00	83.00	NUM
aiti-9488	251	11	83.33	83.33	NUM
aiti-9488	251	12	83.00	83.00	NUM
aiti-9488	251	13	83.00	83.00	NUM
aiti-9488	251	14	advances	advance	NOUN
aiti-9488	251	15	in	in	ADP
aiti-9488	251	16	technology	technology	NOUN
aiti-9488	251	17	innovation	innovation	NOUN
aiti-9488	251	18	,	,	PUNCT
aiti-9488	251	19	vol	vol	NOUN
aiti-9488	251	20	.	.	PROPN
aiti-9488	251	21	8	8	NUM
aiti-9488	251	22	,	,	PUNCT
aiti-9488	251	23	no	no	INTJ
aiti-9488	251	24	.	.	NOUN
aiti-9488	251	25	1	1	NUM
aiti-9488	251	26	,	,	PUNCT
aiti-9488	251	27	2023	2023	NUM
aiti-9488	251	28	,	,	PUNCT
aiti-9488	251	29	pp	pp	ADJ
aiti-9488	251	30	.	.	PUNCT
aiti-9488	252	1	59	59	NUM
aiti-9488	252	2	-	-	SYM
aiti-9488	252	3	72	72	NUM
aiti-9488	252	4	69	69	NUM
aiti-9488	252	5	table	table	NOUN
aiti-9488	252	6	4	4	NUM
aiti-9488	252	7	indicates	indicate	VERB
aiti-9488	252	8	the	the	DET
aiti-9488	252	9	comparative	comparative	ADJ
aiti-9488	252	10	metrics	metric	NOUN
aiti-9488	252	11	assessment	assessment	NOUN
aiti-9488	252	12	of	of	ADP
aiti-9488	252	13	the	the	DET
aiti-9488	252	14	proposed	propose	VERB
aiti-9488	252	15	model	model	NOUN
aiti-9488	252	16	with	with	ADP
aiti-9488	252	17	other	other	ADJ
aiti-9488	252	18	advanced	advanced	ADJ
aiti-9488	252	19	pre	pre	ADJ
aiti-9488	252	20	-	-	ADJ
aiti-9488	252	21	trained	train	VERB
aiti-9488	252	22	networks	network	NOUN
aiti-9488	252	23	.	.	PUNCT
aiti-9488	253	1	the	the	DET
aiti-9488	253	2	proposed	propose	VERB
aiti-9488	253	3	design	design	NOUN
aiti-9488	253	4	excelled	excel	VERB
aiti-9488	253	5	over	over	ADP
aiti-9488	253	6	other	other	ADJ
aiti-9488	253	7	models	model	NOUN
aiti-9488	253	8	and	and	CCONJ
aiti-9488	253	9	gave	give	VERB
aiti-9488	253	10	an	an	DET
aiti-9488	253	11	accuracy	accuracy	NOUN
aiti-9488	253	12	of	of	ADP
aiti-9488	253	13	88.13	88.13	NUM
aiti-9488	253	14	%	%	NOUN
aiti-9488	253	15	,	,	PUNCT
aiti-9488	253	16	recall	recall	NOUN
aiti-9488	253	17	of	of	ADP
aiti-9488	253	18	88	88	NUM
aiti-9488	253	19	%	%	NOUN
aiti-9488	253	20	,	,	PUNCT
aiti-9488	253	21	precision	precision	NOUN
aiti-9488	253	22	of	of	ADP
aiti-9488	253	23	88	88	NUM
aiti-9488	253	24	%	%	NOUN
aiti-9488	253	25	,	,	PUNCT
aiti-9488	253	26	and	and	CCONJ
aiti-9488	253	27	f1score	f1score	NOUN
aiti-9488	253	28	of	of	ADP
aiti-9488	253	29	88	88	NUM
aiti-9488	253	30	%	%	NOUN
aiti-9488	253	31	.	.	PUNCT
aiti-9488	254	1	resnet50	resnet50	PROPN
aiti-9488	254	2	model	model	NOUN
aiti-9488	254	3	achieved	achieve	VERB
aiti-9488	254	4	an	an	DET
aiti-9488	254	5	accuracy	accuracy	NOUN
aiti-9488	254	6	of	of	ADP
aiti-9488	254	7	84.85	84.85	NUM
aiti-9488	254	8	%	%	NOUN
aiti-9488	254	9	and	and	CCONJ
aiti-9488	254	10	performed	perform	VERB
aiti-9488	254	11	well	well	ADV
aiti-9488	254	12	compared	compare	VERB
aiti-9488	254	13	to	to	ADP
aiti-9488	254	14	inceptionv3	inceptionv3	NOUN
aiti-9488	254	15	,	,	PUNCT
aiti-9488	254	16	inceptionresnetv2	inceptionresnetv2	PROPN
aiti-9488	254	17	,	,	PUNCT
aiti-9488	254	18	and	and	CCONJ
aiti-9488	254	19	efficientnetb0	efficientnetb0	NOUN
aiti-9488	254	20	-	-	PUNCT
aiti-9488	254	21	b2	b2	NOUN
aiti-9488	254	22	models	model	NOUN
aiti-9488	254	23	.	.	PUNCT
aiti-9488	255	1	fig	fig	NOUN
aiti-9488	255	2	.	.	PUNCT
aiti-9488	256	1	12	12	NUM
aiti-9488	256	2	shows	show	VERB
aiti-9488	256	3	the	the	DET
aiti-9488	256	4	accuracy	accuracy	NOUN
aiti-9488	256	5	curve	curve	NOUN
aiti-9488	256	6	of	of	ADP
aiti-9488	256	7	resnet50	resnet50	NOUN
aiti-9488	256	8	.	.	PUNCT
aiti-9488	257	1	efficientnetb1	efficientnetb1	PROPN
aiti-9488	257	2	achieved	achieve	VERB
aiti-9488	257	3	the	the	DET
aiti-9488	257	4	lowest	low	ADJ
aiti-9488	257	5	accuracy	accuracy	NOUN
aiti-9488	257	6	of	of	ADP
aiti-9488	257	7	55.75	55.75	NUM
aiti-9488	257	8	%	%	NOUN
aiti-9488	257	9	,	,	PUNCT
aiti-9488	257	10	with	with	ADP
aiti-9488	257	11	an	an	DET
aiti-9488	257	12	f1	f1	NOUN
aiti-9488	257	13	-	-	PUNCT
aiti-9488	257	14	score	score	NOUN
aiti-9488	257	15	of	of	ADP
aiti-9488	257	16	55	55	NUM
aiti-9488	257	17	%	%	NOUN
aiti-9488	257	18	.	.	PUNCT
aiti-9488	258	1	fig	fig	NOUN
aiti-9488	258	2	.	.	PUNCT
aiti-9488	259	1	13	13	NUM
aiti-9488	259	2	shows	show	VERB
aiti-9488	259	3	the	the	DET
aiti-9488	259	4	accuracy	accuracy	NOUN
aiti-9488	259	5	curve	curve	NOUN
aiti-9488	259	6	of	of	ADP
aiti-9488	259	7	inceptionresnetv2	inceptionresnetv2	PROPN
aiti-9488	259	8	.	.	PUNCT
aiti-9488	260	1	inceptionv3	inceptionv3	NOUN
aiti-9488	260	2	,	,	PUNCT
aiti-9488	260	3	efficientnetb0	efficientnetb0	NOUN
aiti-9488	260	4	,	,	PUNCT
aiti-9488	260	5	efficientnetb2	efficientnetb2	NOUN
aiti-9488	260	6	and	and	CCONJ
aiti-9488	260	7	inceptionresnetv2	inceptionresnetv2	PROPN
aiti-9488	260	8	achieved	achieve	VERB
aiti-9488	260	9	an	an	DET
aiti-9488	260	10	accuracy	accuracy	NOUN
aiti-9488	260	11	of	of	ADP
aiti-9488	260	12	82.73	82.73	NUM
aiti-9488	260	13	%	%	NOUN
aiti-9488	260	14	,	,	PUNCT
aiti-9488	260	15	60.15	60.15	NUM
aiti-9488	260	16	%	%	NOUN
aiti-9488	260	17	,	,	PUNCT
aiti-9488	260	18	58.48	58.48	NUM
aiti-9488	260	19	%	%	NOUN
aiti-9488	260	20	,	,	PUNCT
aiti-9488	260	21	and	and	CCONJ
aiti-9488	260	22	83.33	83.33	NUM
aiti-9488	260	23	%	%	NOUN
aiti-9488	260	24	respectively	respectively	ADV
aiti-9488	260	25	.	.	PUNCT
aiti-9488	261	1	confusion	confusion	NOUN
aiti-9488	261	2	matrices	matrix	NOUN
aiti-9488	261	3	of	of	ADP
aiti-9488	261	4	each	each	DET
aiti-9488	261	5	model	model	NOUN
aiti-9488	261	6	are	be	AUX
aiti-9488	261	7	also	also	ADV
aiti-9488	261	8	examined	examine	VERB
aiti-9488	261	9	to	to	PART
aiti-9488	261	10	check	check	VERB
aiti-9488	261	11	false	false	ADJ
aiti-9488	261	12	negative	negative	ADJ
aiti-9488	261	13	and	and	CCONJ
aiti-9488	261	14	false	false	ADJ
aiti-9488	261	15	positive	positive	ADJ
aiti-9488	261	16	class	class	NOUN
aiti-9488	261	17	counts	count	NOUN
aiti-9488	261	18	.	.	PUNCT
aiti-9488	262	1	fig	fig	NOUN
aiti-9488	262	2	.	.	PUNCT
aiti-9488	263	1	14	14	NUM
aiti-9488	263	2	depicts	depict	VERB
aiti-9488	263	3	the	the	DET
aiti-9488	263	4	confusion	confusion	NOUN
aiti-9488	263	5	matrix	matrix	NOUN
aiti-9488	263	6	of	of	ADP
aiti-9488	263	7	fine	fine	ADV
aiti-9488	263	8	-	-	PUNCT
aiti-9488	263	9	tuned	tune	VERB
aiti-9488	263	10	proposed	propose	VERB
aiti-9488	263	11	model	model	NOUN
aiti-9488	263	12	in	in	ADP
aiti-9488	263	13	comparison	comparison	NOUN
aiti-9488	263	14	with	with	ADP
aiti-9488	263	15	inceptionv3	inceptionv3	NOUN
aiti-9488	263	16	,	,	PUNCT
aiti-9488	263	17	resnet50	resnet50	NOUN
aiti-9488	263	18	,	,	PUNCT
aiti-9488	263	19	inceptionresnetv2	inceptionresnetv2	PROPN
aiti-9488	263	20	.	.	PUNCT
aiti-9488	264	1	fig	fig	NOUN
aiti-9488	264	2	.	.	PUNCT
aiti-9488	265	1	12	12	NUM
aiti-9488	265	2	resnet50	resnet50	NOUN
aiti-9488	265	3	accuracy	accuracy	NOUN
aiti-9488	265	4	curve	curve	PROPN
aiti-9488	265	5	fig	fig	NOUN
aiti-9488	265	6	.	.	PUNCT
aiti-9488	266	1	13	13	NUM
aiti-9488	266	2	inceptionresnetv2	inceptionresnetv2	ADJ
aiti-9488	266	3	accuracy	accuracy	NOUN
aiti-9488	266	4	curve	curve	NOUN
aiti-9488	266	5	(	(	PUNCT
aiti-9488	266	6	a	a	NOUN
aiti-9488	266	7	)	)	PUNCT
aiti-9488	266	8	proposed	propose	VERB
aiti-9488	266	9	model	model	NOUN
aiti-9488	266	10	confusion	confusion	NOUN
aiti-9488	266	11	matrix	matrix	NOUN
aiti-9488	266	12	(	(	PUNCT
aiti-9488	266	13	b	b	NOUN
aiti-9488	266	14	)	)	PUNCT
aiti-9488	266	15	inceptionv3	inceptionv3	NOUN
aiti-9488	266	16	model	model	NOUN
aiti-9488	266	17	confusion	confusion	NOUN
aiti-9488	266	18	matrix	matrix	NOUN
aiti-9488	266	19	(	(	PUNCT
aiti-9488	266	20	c	c	NOUN
aiti-9488	266	21	)	)	PUNCT
aiti-9488	266	22	inceptionresnetv2	inceptionresnetv2	ADJ
aiti-9488	266	23	model	model	NOUN
aiti-9488	266	24	confusion	confusion	NOUN
aiti-9488	266	25	matrix	matrix	NOUN
aiti-9488	266	26	(	(	PUNCT
aiti-9488	266	27	d	d	X
aiti-9488	266	28	)	)	PUNCT
aiti-9488	266	29	resnet50	resnet50	NOUN
aiti-9488	266	30	model	model	NOUN
aiti-9488	266	31	confusion	confusion	NOUN
aiti-9488	266	32	matrix	matrix	NOUN
aiti-9488	266	33	fig	fig	NOUN
aiti-9488	266	34	.	.	PUNCT
aiti-9488	267	1	14	14	NUM
aiti-9488	267	2	confusion	confusion	NOUN
aiti-9488	267	3	matrix	matrix	NOUN
aiti-9488	267	4	comparison	comparison	NOUN
aiti-9488	267	5	advances	advance	VERB
aiti-9488	267	6	in	in	ADP
aiti-9488	267	7	technology	technology	NOUN
aiti-9488	267	8	innovation	innovation	NOUN
aiti-9488	267	9	,	,	PUNCT
aiti-9488	267	10	vol	vol	NOUN
aiti-9488	267	11	.	.	PROPN
aiti-9488	267	12	8	8	NUM
aiti-9488	267	13	,	,	PUNCT
aiti-9488	267	14	no	no	INTJ
aiti-9488	267	15	.	.	NOUN
aiti-9488	267	16	1	1	NUM
aiti-9488	267	17	,	,	PUNCT
aiti-9488	267	18	2023	2023	NUM
aiti-9488	267	19	,	,	PUNCT
aiti-9488	267	20	pp	pp	ADJ
aiti-9488	267	21	.	.	PUNCT
aiti-9488	268	1	59	59	NUM
aiti-9488	268	2	-	-	SYM
aiti-9488	268	3	72	72	NUM
aiti-9488	268	4	70	70	NUM
aiti-9488	268	5	5	5	NUM
aiti-9488	268	6	.	.	PUNCT
aiti-9488	268	7	discussion	discussion	NOUN
aiti-9488	268	8	to	to	PART
aiti-9488	268	9	further	far	ADV
aiti-9488	268	10	validate	validate	VERB
aiti-9488	268	11	the	the	DET
aiti-9488	268	12	efficacy	efficacy	NOUN
aiti-9488	268	13	of	of	ADP
aiti-9488	268	14	the	the	DET
aiti-9488	268	15	proposed	propose	VERB
aiti-9488	268	16	model	model	NOUN
aiti-9488	268	17	,	,	PUNCT
aiti-9488	268	18	a	a	DET
aiti-9488	268	19	comparative	comparative	ADJ
aiti-9488	268	20	analysis	analysis	NOUN
aiti-9488	268	21	is	be	AUX
aiti-9488	268	22	carried	carry	VERB
aiti-9488	268	23	out	out	ADP
aiti-9488	268	24	with	with	ADP
aiti-9488	268	25	other	other	ADJ
aiti-9488	268	26	state	state	NOUN
aiti-9488	268	27	-	-	PUNCT
aiti-9488	268	28	of	of	ADP
aiti-9488	268	29	-	-	PUNCT
aiti-9488	268	30	art	art	NOUN
aiti-9488	268	31	methods	method	NOUN
aiti-9488	268	32	.	.	PUNCT
aiti-9488	269	1	table	table	NOUN
aiti-9488	269	2	5	5	NUM
aiti-9488	269	3	provides	provide	VERB
aiti-9488	269	4	a	a	DET
aiti-9488	269	5	comparative	comparative	ADJ
aiti-9488	269	6	evaluation	evaluation	NOUN
aiti-9488	269	7	of	of	ADP
aiti-9488	269	8	the	the	DET
aiti-9488	269	9	proposed	propose	VERB
aiti-9488	269	10	model	model	NOUN
aiti-9488	269	11	with	with	ADP
aiti-9488	269	12	other	other	ADJ
aiti-9488	269	13	methods	method	NOUN
aiti-9488	269	14	.	.	PUNCT
aiti-9488	270	1	the	the	DET
aiti-9488	270	2	experimental	experimental	ADJ
aiti-9488	270	3	study	study	NOUN
aiti-9488	270	4	provided	provide	VERB
aiti-9488	270	5	by	by	ADP
aiti-9488	270	6	naronglerdrit	naronglerdrit	PROPN
aiti-9488	270	7	et	et	PROPN
aiti-9488	270	8	al	al	PROPN
aiti-9488	270	9	.	.	PUNCT
aiti-9488	271	1	[	[	X
aiti-9488	271	2	5	5	NUM
aiti-9488	271	3	]	]	PUNCT
aiti-9488	271	4	over	over	ADP
aiti-9488	271	5	the	the	DET
aiti-9488	271	6	ham10000	ham10000	PROPN
aiti-9488	271	7	dataset	dataset	NOUN
aiti-9488	271	8	provided	provide	VERB
aiti-9488	271	9	an	an	DET
aiti-9488	271	10	accuracy	accuracy	NOUN
aiti-9488	271	11	of	of	ADP
aiti-9488	271	12	97.12	97.12	NUM
aiti-9488	271	13	%	%	NOUN
aiti-9488	271	14	,	,	PUNCT
aiti-9488	271	15	but	but	CCONJ
aiti-9488	271	16	less	less	ADJ
aiti-9488	271	17	recall	recall	NOUN
aiti-9488	271	18	value	value	NOUN
aiti-9488	271	19	of	of	ADP
aiti-9488	271	20	85.18	85.18	NUM
aiti-9488	271	21	%	%	NOUN
aiti-9488	271	22	using	use	VERB
aiti-9488	271	23	the	the	DET
aiti-9488	271	24	resnet-101	resnet-101	NOUN
aiti-9488	271	25	deep	deep	ADJ
aiti-9488	271	26	learning	learning	NOUN
aiti-9488	271	27	model	model	NOUN
aiti-9488	271	28	indicates	indicate	VERB
aiti-9488	271	29	the	the	DET
aiti-9488	271	30	model	model	NOUN
aiti-9488	271	31	suffers	suffer	VERB
aiti-9488	271	32	from	from	ADP
aiti-9488	271	33	the	the	DET
aiti-9488	271	34	problem	problem	NOUN
aiti-9488	271	35	of	of	ADP
aiti-9488	271	36	overfitting	overfitte	VERB
aiti-9488	271	37	.	.	PUNCT
aiti-9488	272	1	categorical	categorical	ADJ
aiti-9488	272	2	accuracy	accuracy	NOUN
aiti-9488	272	3	of	of	ADP
aiti-9488	272	4	the	the	DET
aiti-9488	272	5	mobilenet	mobilenet	NOUN
aiti-9488	272	6	model	model	NOUN
aiti-9488	272	7	with	with	ADP
aiti-9488	272	8	data	datum	NOUN
aiti-9488	272	9	augmentation	augmentation	NOUN
aiti-9488	272	10	proposed	propose	VERB
aiti-9488	272	11	by	by	ADP
aiti-9488	272	12	chaturvedi	chaturvedi	PROPN
aiti-9488	272	13	et	et	PROPN
aiti-9488	272	14	al	al	PROPN
aiti-9488	272	15	.	.	PUNCT
aiti-9488	273	1	[	[	X
aiti-9488	273	2	7	7	NUM
aiti-9488	273	3	]	]	PUNCT
aiti-9488	273	4	provided	provide	VERB
aiti-9488	273	5	an	an	DET
aiti-9488	273	6	overall	overall	ADJ
aiti-9488	273	7	accuracy	accuracy	NOUN
aiti-9488	273	8	of	of	ADP
aiti-9488	273	9	83.15	83.15	NUM
aiti-9488	273	10	%	%	NOUN
aiti-9488	273	11	,	,	PUNCT
aiti-9488	273	12	precision	precision	NOUN
aiti-9488	273	13	of	of	ADP
aiti-9488	273	14	89	89	NUM
aiti-9488	273	15	%	%	NOUN
aiti-9488	273	16	,	,	PUNCT
aiti-9488	273	17	recall	recall	NOUN
aiti-9488	273	18	of	of	ADP
aiti-9488	273	19	83	83	NUM
aiti-9488	273	20	%	%	NOUN
aiti-9488	273	21	,	,	PUNCT
aiti-9488	273	22	and	and	CCONJ
aiti-9488	273	23	f1	f1	NOUN
aiti-9488	273	24	-	-	PUNCT
aiti-9488	273	25	score	score	NOUN
aiti-9488	273	26	of	of	ADP
aiti-9488	273	27	83	83	NUM
aiti-9488	273	28	%	%	NOUN
aiti-9488	273	29	only	only	ADV
aiti-9488	273	30	.	.	PUNCT
aiti-9488	274	1	ashim	ashim	PRON
aiti-9488	274	2	et	et	PROPN
aiti-9488	274	3	al	al	PROPN
aiti-9488	274	4	.	.	PUNCT
aiti-9488	275	1	[	[	X
aiti-9488	275	2	10	10	NUM
aiti-9488	275	3	]	]	PUNCT
aiti-9488	275	4	,	,	PUNCT
aiti-9488	275	5	presented	present	VERB
aiti-9488	275	6	an	an	DET
aiti-9488	275	7	experimental	experimental	ADJ
aiti-9488	275	8	analysis	analysis	NOUN
aiti-9488	275	9	of	of	ADP
aiti-9488	275	10	pre	pre	ADJ
aiti-9488	275	11	-	-	ADJ
aiti-9488	275	12	trained	train	VERB
aiti-9488	275	13	networks	network	NOUN
aiti-9488	275	14	such	such	ADJ
aiti-9488	275	15	as	as	ADP
aiti-9488	275	16	vgg16	vgg16	PROPN
aiti-9488	275	17	,	,	PUNCT
aiti-9488	275	18	resnet50	resnet50	NOUN
aiti-9488	275	19	,	,	PUNCT
aiti-9488	275	20	efficientnet	efficientnet	NOUN
aiti-9488	275	21	,	,	PUNCT
aiti-9488	275	22	densenet	densenet	NOUN
aiti-9488	275	23	,	,	PUNCT
aiti-9488	275	24	and	and	CCONJ
aiti-9488	275	25	xception	xception	NOUN
aiti-9488	275	26	for	for	ADP
aiti-9488	275	27	melanoma	melanoma	NOUN
aiti-9488	275	28	classification	classification	NOUN
aiti-9488	275	29	over	over	ADP
aiti-9488	275	30	the	the	DET
aiti-9488	275	31	kaggle	kaggle	ADJ
aiti-9488	275	32	dataset	dataset	NOUN
aiti-9488	275	33	consisting	consist	VERB
aiti-9488	275	34	of	of	ADP
aiti-9488	275	35	only	only	ADV
aiti-9488	275	36	660	660	NUM
aiti-9488	275	37	images	image	NOUN
aiti-9488	275	38	of	of	ADP
aiti-9488	275	39	benign	benign	ADJ
aiti-9488	275	40	and	and	CCONJ
aiti-9488	275	41	malignant	malignant	ADJ
aiti-9488	275	42	classes	class	NOUN
aiti-9488	275	43	.	.	PUNCT
aiti-9488	276	1	xception	xception	PROPN
aiti-9488	276	2	,	,	PUNCT
aiti-9488	276	3	efficientnetb0	efficientnetb0	PROPN
aiti-9488	276	4	,	,	PUNCT
aiti-9488	276	5	densenet	densenet	NOUN
aiti-9488	276	6	,	,	PUNCT
aiti-9488	276	7	vgg16	vgg16	NOUN
aiti-9488	276	8	,	,	PUNCT
aiti-9488	276	9	and	and	CCONJ
aiti-9488	276	10	resnet	resnet	NOUN
aiti-9488	276	11	models	model	NOUN
aiti-9488	276	12	furnished	furnish	VERB
aiti-9488	276	13	training	training	NOUN
aiti-9488	276	14	accuracy	accuracy	NOUN
aiti-9488	276	15	of	of	ADP
aiti-9488	276	16	78.41	78.41	NUM
aiti-9488	276	17	,	,	PUNCT
aiti-9488	276	18	78.44	78.44	NUM
aiti-9488	276	19	%	%	NOUN
aiti-9488	276	20	,	,	PUNCT
aiti-9488	276	21	81.94	81.94	NUM
aiti-9488	276	22	%	%	NOUN
aiti-9488	276	23	,	,	PUNCT
aiti-9488	276	24	72.37	72.37	NUM
aiti-9488	276	25	%	%	NOUN
aiti-9488	276	26	,	,	PUNCT
aiti-9488	276	27	and	and	CCONJ
aiti-9488	276	28	88.61	88.61	NUM
aiti-9488	276	29	%	%	NOUN
aiti-9488	276	30	respectively	respectively	ADV
aiti-9488	276	31	.	.	PUNCT
aiti-9488	277	1	le	le	PROPN
aiti-9488	277	2	et	et	PROPN
aiti-9488	277	3	al	al	PROPN
aiti-9488	277	4	.	.	PUNCT
aiti-9488	278	1	[	[	X
aiti-9488	278	2	12	12	NUM
aiti-9488	278	3	]	]	PUNCT
aiti-9488	278	4	,	,	PUNCT
aiti-9488	278	5	presented	present	VERB
aiti-9488	278	6	a	a	DET
aiti-9488	278	7	resnet50	resnet50	NOUN
aiti-9488	278	8	-	-	PUNCT
aiti-9488	278	9	based	base	VERB
aiti-9488	278	10	deep	deep	ADJ
aiti-9488	278	11	learning	learning	NOUN
aiti-9488	278	12	classifier	classifier	NOUN
aiti-9488	278	13	over	over	ADP
aiti-9488	278	14	the	the	DET
aiti-9488	278	15	ham10000	ham10000	NOUN
aiti-9488	278	16	dataset	dataset	NOUN
aiti-9488	278	17	and	and	CCONJ
aiti-9488	278	18	achieved	achieve	VERB
aiti-9488	278	19	an	an	DET
aiti-9488	278	20	average	average	ADJ
aiti-9488	278	21	accuracy	accuracy	NOUN
aiti-9488	278	22	of	of	ADP
aiti-9488	278	23	93	93	NUM
aiti-9488	278	24	%	%	NOUN
aiti-9488	278	25	,	,	PUNCT
aiti-9488	278	26	precision	precision	NOUN
aiti-9488	278	27	of	of	ADP
aiti-9488	278	28	81	81	NUM
aiti-9488	278	29	%	%	NOUN
aiti-9488	278	30	,	,	PUNCT
aiti-9488	278	31	recall	recall	NOUN
aiti-9488	278	32	,	,	PUNCT
aiti-9488	278	33	and	and	CCONJ
aiti-9488	278	34	f1	f1	NOUN
aiti-9488	278	35	-	-	PUNCT
aiti-9488	278	36	score	score	NOUN
aiti-9488	278	37	of	of	ADP
aiti-9488	278	38	80	80	NUM
aiti-9488	278	39	%	%	NOUN
aiti-9488	278	40	only	only	ADV
aiti-9488	278	41	.	.	PUNCT
aiti-9488	279	1	the	the	DET
aiti-9488	279	2	low	low	ADJ
aiti-9488	279	3	value	value	NOUN
aiti-9488	279	4	of	of	ADP
aiti-9488	279	5	other	other	ADJ
aiti-9488	279	6	evaluation	evaluation	NOUN
aiti-9488	279	7	metrics	metric	NOUN
aiti-9488	279	8	indicates	indicate	VERB
aiti-9488	279	9	that	that	SCONJ
aiti-9488	279	10	the	the	DET
aiti-9488	279	11	model	model	NOUN
aiti-9488	279	12	proposed	propose	VERB
aiti-9488	279	13	by	by	ADP
aiti-9488	279	14	[	[	X
aiti-9488	279	15	12	12	NUM
aiti-9488	279	16	]	]	PUNCT
aiti-9488	279	17	suffers	suffer	VERB
aiti-9488	279	18	from	from	ADP
aiti-9488	279	19	model	model	NOUN
aiti-9488	279	20	overfitting	overfitting	NOUN
aiti-9488	279	21	problem	problem	NOUN
aiti-9488	279	22	.	.	PUNCT
aiti-9488	280	1	acosta	acosta	PROPN
aiti-9488	280	2	et	et	PROPN
aiti-9488	280	3	al	al	PROPN
aiti-9488	280	4	.	.	PUNCT
aiti-9488	281	1	[	[	X
aiti-9488	281	2	15	15	NUM
aiti-9488	281	3	]	]	PUNCT
aiti-9488	281	4	,	,	PUNCT
aiti-9488	281	5	proposed	propose	VERB
aiti-9488	281	6	a	a	DET
aiti-9488	281	7	deep	deep	ADJ
aiti-9488	281	8	learning	learning	NOUN
aiti-9488	281	9	model	model	NOUN
aiti-9488	281	10	based	base	VERB
aiti-9488	281	11	on	on	ADP
aiti-9488	281	12	resnet152	resnet152	PROPN
aiti-9488	281	13	and	and	CCONJ
aiti-9488	281	14	also	also	ADV
aiti-9488	281	15	presented	present	VERB
aiti-9488	281	16	a	a	DET
aiti-9488	281	17	comparison	comparison	NOUN
aiti-9488	281	18	of	of	ADP
aiti-9488	281	19	20	20	NUM
aiti-9488	281	20	other	other	ADJ
aiti-9488	281	21	state	state	NOUN
aiti-9488	281	22	-	-	PUNCT
aiti-9488	281	23	of	of	ADP
aiti-9488	281	24	-	-	PUNCT
aiti-9488	281	25	art	art	NOUN
aiti-9488	281	26	models	model	NOUN
aiti-9488	281	27	trained	train	VERB
aiti-9488	281	28	and	and	CCONJ
aiti-9488	281	29	tested	test	VERB
aiti-9488	281	30	over	over	ADP
aiti-9488	281	31	the	the	DET
aiti-9488	281	32	isic	isic	PROPN
aiti-9488	281	33	2017	2017	NUM
aiti-9488	281	34	dataset	dataset	NOUN
aiti-9488	281	35	.	.	PUNCT
aiti-9488	282	1	according	accord	VERB
aiti-9488	282	2	to	to	ADP
aiti-9488	282	3	the	the	DET
aiti-9488	282	4	study	study	NOUN
aiti-9488	282	5	carried	carry	VERB
aiti-9488	282	6	out	out	ADP
aiti-9488	282	7	by	by	ADP
aiti-9488	282	8	[	[	X
aiti-9488	282	9	15	15	NUM
aiti-9488	282	10	]	]	PUNCT
aiti-9488	282	11	,	,	PUNCT
aiti-9488	282	12	the	the	DET
aiti-9488	282	13	proposed	propose	VERB
aiti-9488	282	14	resnet152	resnet152	PROPN
aiti-9488	282	15	achieved	achieve	VERB
aiti-9488	282	16	the	the	DET
aiti-9488	282	17	highest	high	ADJ
aiti-9488	282	18	accuracy	accuracy	NOUN
aiti-9488	282	19	of	of	ADP
aiti-9488	282	20	87.2	87.2	NUM
aiti-9488	282	21	%	%	NOUN
aiti-9488	282	22	,	,	PUNCT
aiti-9488	282	23	the	the	DET
aiti-9488	282	24	sensitivity	sensitivity	NOUN
aiti-9488	282	25	of	of	ADP
aiti-9488	282	26	82	82	NUM
aiti-9488	282	27	%	%	NOUN
aiti-9488	282	28	,	,	PUNCT
aiti-9488	282	29	and	and	CCONJ
aiti-9488	282	30	the	the	DET
aiti-9488	282	31	f1	f1	NOUN
aiti-9488	282	32	-	-	PUNCT
aiti-9488	282	33	score	score	NOUN
aiti-9488	282	34	of	of	ADP
aiti-9488	282	35	84.8	84.8	NUM
aiti-9488	282	36	%	%	NOUN
aiti-9488	282	37	.	.	PUNCT
aiti-9488	283	1	rezaoana	rezaoana	PROPN
aiti-9488	283	2	et	et	PROPN
aiti-9488	283	3	al	al	PROPN
aiti-9488	283	4	.	.	PUNCT
aiti-9488	284	1	[	[	X
aiti-9488	284	2	16	16	NUM
aiti-9488	284	3	]	]	PUNCT
aiti-9488	284	4	performed	perform	VERB
aiti-9488	284	5	an	an	DET
aiti-9488	284	6	exploratory	exploratory	ADJ
aiti-9488	284	7	analysis	analysis	NOUN
aiti-9488	284	8	of	of	ADP
aiti-9488	284	9	the	the	DET
aiti-9488	284	10	proposed	propose	VERB
aiti-9488	284	11	cnn	cnn	PROPN
aiti-9488	284	12	model	model	NOUN
aiti-9488	284	13	with	with	ADP
aiti-9488	284	14	other	other	ADJ
aiti-9488	284	15	networks	network	NOUN
aiti-9488	284	16	like	like	ADP
aiti-9488	284	17	vgg-16	vgg-16	NOUN
aiti-9488	284	18	and	and	CCONJ
aiti-9488	284	19	vgg-19	vgg-19	NOUN
aiti-9488	284	20	.	.	PUNCT
aiti-9488	285	1	the	the	DET
aiti-9488	285	2	proposed	propose	VERB
aiti-9488	285	3	approach	approach	NOUN
aiti-9488	285	4	by	by	ADP
aiti-9488	285	5	[	[	X
aiti-9488	285	6	16	16	NUM
aiti-9488	285	7	]	]	PUNCT
aiti-9488	285	8	achieved	achieve	VERB
aiti-9488	285	9	an	an	DET
aiti-9488	285	10	f1	f1	NOUN
aiti-9488	285	11	-	-	PUNCT
aiti-9488	285	12	score	score	NOUN
aiti-9488	285	13	of	of	ADP
aiti-9488	285	14	76.92	76.92	NUM
aiti-9488	285	15	%	%	NOUN
aiti-9488	285	16	,	,	PUNCT
aiti-9488	285	17	precision	precision	NOUN
aiti-9488	285	18	of	of	ADP
aiti-9488	285	19	76.16	76.16	NUM
aiti-9488	285	20	%	%	NOUN
aiti-9488	285	21	,	,	PUNCT
aiti-9488	285	22	and	and	CCONJ
aiti-9488	285	23	recall	recall	NOUN
aiti-9488	285	24	of	of	ADP
aiti-9488	285	25	78.15	78.15	NUM
aiti-9488	285	26	%	%	NOUN
aiti-9488	285	27	.	.	PUNCT
aiti-9488	286	1	it	it	PRON
aiti-9488	286	2	can	can	AUX
aiti-9488	286	3	be	be	AUX
aiti-9488	286	4	noticed	notice	VERB
aiti-9488	286	5	from	from	ADP
aiti-9488	286	6	table	table	NOUN
aiti-9488	286	7	5	5	NUM
aiti-9488	286	8	that	that	SCONJ
aiti-9488	286	9	the	the	DET
aiti-9488	286	10	proposed	propose	VERB
aiti-9488	286	11	efficientnetb3	efficientnetb3	PROPN
aiti-9488	286	12	model	model	PROPN
aiti-9488	286	13	accomplished	accomplish	VERB
aiti-9488	286	14	better	well	ADJ
aiti-9488	286	15	results	result	NOUN
aiti-9488	286	16	compared	compare	VERB
aiti-9488	286	17	to	to	ADP
aiti-9488	286	18	other	other	ADJ
aiti-9488	286	19	methods	method	NOUN
aiti-9488	286	20	concerning	concern	VERB
aiti-9488	286	21	metrics	metric	NOUN
aiti-9488	286	22	such	such	ADJ
aiti-9488	286	23	as	as	ADP
aiti-9488	286	24	recall	recall	NOUN
aiti-9488	286	25	,	,	PUNCT
aiti-9488	286	26	precision	precision	NOUN
aiti-9488	286	27	,	,	PUNCT
aiti-9488	286	28	and	and	CCONJ
aiti-9488	286	29	f1	f1	NOUN
aiti-9488	286	30	-	-	PUNCT
aiti-9488	286	31	score	score	NOUN
aiti-9488	286	32	.	.	PUNCT
aiti-9488	287	1	these	these	DET
aiti-9488	287	2	metrics	metric	NOUN
aiti-9488	287	3	are	be	AUX
aiti-9488	287	4	consistent	consistent	ADJ
aiti-9488	287	5	with	with	ADP
aiti-9488	287	6	the	the	DET
aiti-9488	287	7	accuracy	accuracy	NOUN
aiti-9488	287	8	of	of	ADP
aiti-9488	287	9	the	the	DET
aiti-9488	287	10	proposed	propose	VERB
aiti-9488	287	11	model	model	NOUN
aiti-9488	287	12	and	and	CCONJ
aiti-9488	287	13	thus	thus	ADV
aiti-9488	287	14	handle	handle	VERB
aiti-9488	287	15	the	the	DET
aiti-9488	287	16	problem	problem	NOUN
aiti-9488	287	17	of	of	ADP
aiti-9488	287	18	model	model	NOUN
aiti-9488	287	19	overfitting	overfitting	NOUN
aiti-9488	287	20	.	.	PUNCT
aiti-9488	288	1	table	table	NOUN
aiti-9488	288	2	5	5	NUM
aiti-9488	288	3	comparative	comparative	ADJ
aiti-9488	288	4	analyses	analysis	NOUN
aiti-9488	288	5	of	of	ADP
aiti-9488	288	6	the	the	DET
aiti-9488	288	7	proposed	propose	VERB
aiti-9488	288	8	methodology	methodology	NOUN
aiti-9488	288	9	with	with	ADP
aiti-9488	288	10	other	other	ADJ
aiti-9488	288	11	methods	method	NOUN
aiti-9488	288	12	reference	reference	NOUN
aiti-9488	288	13	approach	approach	NOUN
aiti-9488	288	14	dataset	dataset	NOUN
aiti-9488	288	15	accuracy	accuracy	NOUN
aiti-9488	288	16	recall	recall	NOUN
aiti-9488	288	17	f1	f1	NOUN
aiti-9488	288	18	-	-	PUNCT
aiti-9488	288	19	score	score	NOUN
aiti-9488	288	20	precision	precision	NOUN
aiti-9488	288	21	proposed	propose	VERB
aiti-9488	288	22	model	model	NOUN
aiti-9488	289	1	efficientnetb3	efficientnetb3	PROPN
aiti-9488	289	2	isic	isic	PROPN
aiti-9488	289	3	2017	2017	NUM
aiti-9488	289	4	88.13	88.13	NUM
aiti-9488	289	5	88.00	88.00	NUM
aiti-9488	289	6	88.00	88.00	NUM
aiti-9488	289	7	88.00	88.00	NUM
aiti-9488	289	8	naronglerdrit	naronglerdrit	NOUN
aiti-9488	289	9	et	et	PROPN
aiti-9488	289	10	al	al	PROPN
aiti-9488	289	11	.	.	PUNCT
aiti-9488	290	1	[	[	X
aiti-9488	290	2	5	5	X
aiti-9488	290	3	]	]	PUNCT
aiti-9488	290	4	resnet-101	resnet-101	NOUN
aiti-9488	290	5	ham10000	ham10000	PROPN
aiti-9488	290	6	97.12	97.12	NUM
aiti-9488	290	7	85.18	85.18	NUM
aiti-9488	290	8	chaturvedi	chaturvedi	PROPN
aiti-9488	290	9	et	et	PROPN
aiti-9488	290	10	al	al	PROPN
aiti-9488	290	11	.	.	PUNCT
aiti-9488	291	1	[	[	X
aiti-9488	291	2	7	7	X
aiti-9488	291	3	]	]	X
aiti-9488	291	4	mobilenet	mobilenet	NOUN
aiti-9488	291	5	ham10000	ham10000	VERB
aiti-9488	291	6	83.15	83.15	NUM
aiti-9488	291	7	83.00	83.00	NUM
aiti-9488	291	8	83.00	83.00	NUM
aiti-9488	291	9	89.00	89.00	NUM
aiti-9488	291	10	ashim	ashim	NOUN
aiti-9488	291	11	et	et	PROPN
aiti-9488	291	12	al	al	PROPN
aiti-9488	291	13	.	.	PUNCT
aiti-9488	292	1	[	[	X
aiti-9488	292	2	10	10	NUM
aiti-9488	292	3	]	]	X
aiti-9488	292	4	efficientnetb0	efficientnetb0	NOUN
aiti-9488	292	5	kaggle	kaggle	VERB
aiti-9488	292	6	78.41	78.41	NUM
aiti-9488	292	7	resnet	resnet	VERB
aiti-9488	292	8	88.61	88.61	NUM
aiti-9488	292	9	le	le	X
aiti-9488	292	10	et	et	PROPN
aiti-9488	292	11	al	al	PROPN
aiti-9488	292	12	.	.	PUNCT
aiti-9488	293	1	[	[	X
aiti-9488	293	2	12	12	NUM
aiti-9488	293	3	]	]	PUNCT
aiti-9488	293	4	resnet50	resnet50	NOUN
aiti-9488	293	5	ham10000	ham10000	PROPN
aiti-9488	293	6	93.00	93.00	NUM
aiti-9488	293	7	81.00	81.00	NUM
aiti-9488	293	8	80.00	80.00	NUM
aiti-9488	293	9	80.00	80.00	NUM
aiti-9488	293	10	acosta	acosta	NOUN
aiti-9488	293	11	et	et	PROPN
aiti-9488	293	12	al	al	PROPN
aiti-9488	293	13	.	.	PUNCT
aiti-9488	294	1	[	[	X
aiti-9488	294	2	15	15	NUM
aiti-9488	294	3	]	]	X
aiti-9488	294	4	resnet152	resnet152	PROPN
aiti-9488	294	5	isic	isic	PROPN
aiti-9488	294	6	2017	2017	NUM
aiti-9488	294	7	87.20	87.20	NUM
aiti-9488	294	8	82.00	82.00	NUM
aiti-9488	294	9	84.80	84.80	NUM
aiti-9488	294	10	rezaoana	rezaoana	PROPN
aiti-9488	294	11	et	et	PROPN
aiti-9488	294	12	al	al	PROPN
aiti-9488	294	13	.	.	PUNCT
aiti-9488	295	1	[	[	X
aiti-9488	295	2	16	16	NUM
aiti-9488	295	3	]	]	PUNCT
aiti-9488	295	4	proposed	propose	VERB
aiti-9488	295	5	cnn	cnn	PROPN
aiti-9488	295	6	kaggle	kaggle	PROPN
aiti-9488	295	7	isic	isic	PROPN
aiti-9488	295	8	79.45	79.45	NUM
aiti-9488	295	9	78.15	78.15	NUM
aiti-9488	295	10	76.92	76.92	NUM
aiti-9488	295	11	76.16	76.16	NUM
aiti-9488	295	12	vgg-16	vgg-16	X
aiti-9488	295	13	69.57	69.57	NUM
aiti-9488	295	14	68.89	68.89	NUM
aiti-9488	295	15	67.77	67.77	NUM
aiti-9488	295	16	65.67	65.67	NUM
aiti-9488	295	17	vgg-19	vgg-19	NUM
aiti-9488	295	18	71.19	71.19	NUM
aiti-9488	295	19	69.45	69.45	NUM
aiti-9488	295	20	68.95	68.95	NUM
aiti-9488	295	21	68.54	68.54	NUM
aiti-9488	295	22	6	6	NUM
aiti-9488	295	23	.	.	PUNCT
aiti-9488	295	24	conclusion	conclusion	NOUN
aiti-9488	295	25	in	in	ADP
aiti-9488	295	26	this	this	DET
aiti-9488	295	27	study	study	NOUN
aiti-9488	295	28	,	,	PUNCT
aiti-9488	295	29	an	an	DET
aiti-9488	295	30	automated	automate	VERB
aiti-9488	295	31	computer	computer	NOUN
aiti-9488	295	32	-	-	PUNCT
aiti-9488	295	33	aided	aid	VERB
aiti-9488	295	34	diagnostic	diagnostic	ADJ
aiti-9488	295	35	system	system	NOUN
aiti-9488	295	36	using	use	VERB
aiti-9488	295	37	fine	fine	ADV
aiti-9488	295	38	-	-	PUNCT
aiti-9488	295	39	tuned	tune	VERB
aiti-9488	295	40	efficientnetb3	efficientnetb3	NOUN
aiti-9488	295	41	deep	deep	ADJ
aiti-9488	295	42	neural	neural	ADJ
aiti-9488	295	43	model	model	NOUN
aiti-9488	295	44	for	for	ADP
aiti-9488	295	45	melanoma	melanoma	NOUN
aiti-9488	295	46	classification	classification	NOUN
aiti-9488	295	47	is	be	AUX
aiti-9488	295	48	proposed	propose	VERB
aiti-9488	295	49	that	that	SCONJ
aiti-9488	295	50	efficiently	efficiently	ADV
aiti-9488	295	51	classifies	classify	VERB
aiti-9488	295	52	lesion	lesion	NOUN
aiti-9488	295	53	images	image	NOUN
aiti-9488	295	54	into	into	ADP
aiti-9488	295	55	benign	benign	ADJ
aiti-9488	295	56	and	and	CCONJ
aiti-9488	295	57	malignant	malignant	ADJ
aiti-9488	295	58	classes	class	NOUN
aiti-9488	295	59	.	.	PUNCT
aiti-9488	296	1	skeptical	skeptical	ADJ
aiti-9488	296	2	skin	skin	NOUN
aiti-9488	296	3	lesions	lesion	NOUN
aiti-9488	296	4	are	be	AUX
aiti-9488	296	5	routinely	routinely	ADV
aiti-9488	296	6	marked	mark	VERB
aiti-9488	296	7	with	with	ADP
aiti-9488	296	8	surgical	surgical	ADJ
aiti-9488	296	9	ink	ink	NOUN
aiti-9488	296	10	markers	marker	NOUN
aiti-9488	296	11	,	,	PUNCT
aiti-9488	296	12	and	and	CCONJ
aiti-9488	296	13	the	the	DET
aiti-9488	296	14	presence	presence	NOUN
aiti-9488	296	15	of	of	ADP
aiti-9488	296	16	hair	hair	NOUN
aiti-9488	296	17	artifacts	artifact	NOUN
aiti-9488	296	18	often	often	ADV
aiti-9488	296	19	influences	influence	VERB
aiti-9488	296	20	the	the	DET
aiti-9488	296	21	classification	classification	NOUN
aiti-9488	296	22	analysis	analysis	NOUN
aiti-9488	296	23	.	.	PUNCT
aiti-9488	297	1	an	an	DET
aiti-9488	297	2	automated	automate	VERB
aiti-9488	297	3	preprocessing	preprocessing	NOUN
aiti-9488	297	4	model	model	NOUN
aiti-9488	297	5	is	be	AUX
aiti-9488	297	6	employed	employ	VERB
aiti-9488	297	7	,	,	PUNCT
aiti-9488	297	8	and	and	CCONJ
aiti-9488	297	9	it	it	PRON
aiti-9488	297	10	can	can	AUX
aiti-9488	297	11	effectively	effectively	ADV
aiti-9488	297	12	remove	remove	VERB
aiti-9488	297	13	surgical	surgical	ADJ
aiti-9488	297	14	ink	ink	NOUN
aiti-9488	297	15	markers	marker	NOUN
aiti-9488	297	16	and	and	CCONJ
aiti-9488	297	17	hair	hair	NOUN
aiti-9488	297	18	artifacts	artifact	NOUN
aiti-9488	297	19	.	.	PUNCT
aiti-9488	298	1	a	a	DET
aiti-9488	298	2	broad	broad	ADJ
aiti-9488	298	3	variety	variety	NOUN
aiti-9488	298	4	of	of	ADP
aiti-9488	298	5	data	datum	NOUN
aiti-9488	298	6	augmentation	augmentation	NOUN
aiti-9488	298	7	schemes	scheme	NOUN
aiti-9488	298	8	are	be	AUX
aiti-9488	298	9	utilized	utilize	VERB
aiti-9488	298	10	to	to	PART
aiti-9488	298	11	prevent	prevent	VERB
aiti-9488	298	12	overfitting	overfitting	NOUN
aiti-9488	298	13	and	and	CCONJ
aiti-9488	298	14	improve	improve	VERB
aiti-9488	298	15	the	the	DET
aiti-9488	298	16	overall	overall	ADJ
aiti-9488	298	17	performance	performance	NOUN
aiti-9488	298	18	of	of	ADP
aiti-9488	298	19	the	the	DET
aiti-9488	298	20	proposed	propose	VERB
aiti-9488	298	21	model	model	NOUN
aiti-9488	298	22	.	.	PUNCT
aiti-9488	299	1	the	the	DET
aiti-9488	299	2	weights	weight	NOUN
aiti-9488	299	3	of	of	ADP
aiti-9488	299	4	efficientnetb3	efficientnetb3	NOUN
aiti-9488	299	5	models	model	NOUN
aiti-9488	299	6	are	be	AUX
aiti-9488	299	7	fine	fine	ADV
aiti-9488	299	8	-	-	PUNCT
aiti-9488	299	9	tuned	tune	VERB
aiti-9488	299	10	by	by	ADP
aiti-9488	299	11	appending	append	VERB
aiti-9488	299	12	an	an	DET
aiti-9488	299	13	additional	additional	ADJ
aiti-9488	299	14	layer	layer	NOUN
aiti-9488	299	15	of	of	ADP
aiti-9488	299	16	gap	gap	NOUN
aiti-9488	299	17	,	,	PUNCT
aiti-9488	299	18	and	and	CCONJ
aiti-9488	299	19	softmax	softmax	NOUN
aiti-9488	299	20	layer	layer	NOUN
aiti-9488	299	21	to	to	PART
aiti-9488	299	22	adjust	adjust	VERB
aiti-9488	299	23	to	to	ADP
aiti-9488	299	24	the	the	DET
aiti-9488	299	25	isic	isic	PROPN
aiti-9488	299	26	2017	2017	NUM
aiti-9488	299	27	dataset	dataset	NOUN
aiti-9488	299	28	.	.	PUNCT
aiti-9488	300	1	extensive	extensive	ADJ
aiti-9488	300	2	experimental	experimental	ADJ
aiti-9488	300	3	analyses	analysis	NOUN
aiti-9488	300	4	are	be	AUX
aiti-9488	300	5	carried	carry	VERB
aiti-9488	300	6	out	out	ADP
aiti-9488	300	7	with	with	ADP
aiti-9488	300	8	other	other	ADJ
aiti-9488	300	9	popular	popular	ADJ
aiti-9488	300	10	pre	pre	ADJ
aiti-9488	300	11	-	-	ADJ
aiti-9488	300	12	trained	train	VERB
aiti-9488	300	13	cnn	cnn	PROPN
aiti-9488	300	14	networks	network	NOUN
aiti-9488	300	15	like	like	ADP
aiti-9488	300	16	inceptionresnetv2	inceptionresnetv2	PROPN
aiti-9488	300	17	,	,	PUNCT
aiti-9488	300	18	inceptionv3	inceptionv3	NOUN
aiti-9488	300	19	,	,	PUNCT
aiti-9488	300	20	efficientnetb0	efficientnetb0	NOUN
aiti-9488	300	21	-	-	PUNCT
aiti-9488	300	22	b2	b2	NOUN
aiti-9488	300	23	,	,	PUNCT
aiti-9488	300	24	and	and	CCONJ
aiti-9488	300	25	resnet50	resnet50	NOUN
aiti-9488	300	26	.	.	PUNCT
aiti-9488	301	1	the	the	DET
aiti-9488	301	2	analytic	analytic	ADJ
aiti-9488	301	3	results	result	NOUN
aiti-9488	301	4	indicate	indicate	VERB
aiti-9488	301	5	that	that	SCONJ
aiti-9488	301	6	the	the	DET
aiti-9488	301	7	proposed	propose	VERB
aiti-9488	301	8	model	model	NOUN
aiti-9488	301	9	achieves	achieve	VERB
aiti-9488	301	10	robust	robust	ADJ
aiti-9488	301	11	and	and	CCONJ
aiti-9488	301	12	higher	high	ADJ
aiti-9488	301	13	classification	classification	NOUN
aiti-9488	301	14	results	result	NOUN
aiti-9488	301	15	.	.	PUNCT
aiti-9488	302	1	empirical	empirical	ADJ
aiti-9488	302	2	findings	finding	NOUN
aiti-9488	302	3	demonstrated	demonstrate	VERB
aiti-9488	302	4	the	the	DET
aiti-9488	302	5	effectiveness	effectiveness	NOUN
aiti-9488	302	6	of	of	ADP
aiti-9488	302	7	the	the	DET
aiti-9488	302	8	proposed	propose	VERB
aiti-9488	302	9	model	model	NOUN
aiti-9488	302	10	in	in	ADP
aiti-9488	302	11	the	the	DET
aiti-9488	302	12	malignant	malignant	ADJ
aiti-9488	302	13	melanoma	melanoma	NOUN
aiti-9488	302	14	classification	classification	NOUN
aiti-9488	302	15	task	task	NOUN
aiti-9488	302	16	.	.	PUNCT
aiti-9488	303	1	advances	advance	NOUN
aiti-9488	303	2	in	in	ADP
aiti-9488	303	3	technology	technology	NOUN
aiti-9488	303	4	innovation	innovation	NOUN
aiti-9488	303	5	,	,	PUNCT
aiti-9488	303	6	vol	vol	NOUN
aiti-9488	303	7	.	.	PROPN
aiti-9488	303	8	8	8	NUM
aiti-9488	303	9	,	,	PUNCT
aiti-9488	303	10	no	no	INTJ
aiti-9488	303	11	.	.	NOUN
aiti-9488	303	12	1	1	NUM
aiti-9488	303	13	,	,	PUNCT
aiti-9488	303	14	2023	2023	NUM
aiti-9488	303	15	,	,	PUNCT
aiti-9488	303	16	pp	pp	ADJ
aiti-9488	303	17	.	.	PUNCT
aiti-9488	304	1	59	59	NUM
aiti-9488	304	2	-	-	SYM
aiti-9488	304	3	72	72	NUM
aiti-9488	304	4	71	71	NUM
aiti-9488	304	5	the	the	DET
aiti-9488	304	6	future	future	ADJ
aiti-9488	304	7	study	study	NOUN
aiti-9488	304	8	might	might	AUX
aiti-9488	304	9	be	be	AUX
aiti-9488	304	10	to	to	PART
aiti-9488	304	11	employ	employ	VERB
aiti-9488	304	12	the	the	DET
aiti-9488	304	13	proposed	propose	VERB
aiti-9488	304	14	model	model	NOUN
aiti-9488	304	15	on	on	ADP
aiti-9488	304	16	diverse	diverse	ADJ
aiti-9488	304	17	repositories	repository	NOUN
aiti-9488	304	18	of	of	ADP
aiti-9488	304	19	isic	isic	PROPN
aiti-9488	304	20	datasets	dataset	NOUN
aiti-9488	304	21	,	,	PUNCT
aiti-9488	304	22	such	such	ADJ
aiti-9488	304	23	as	as	ADP
aiti-9488	304	24	the	the	DET
aiti-9488	304	25	ham10000	ham10000	PROPN
aiti-9488	304	26	dataset	dataset	PROPN
aiti-9488	304	27	,	,	PUNCT
aiti-9488	304	28	isic	isic	PROPN
aiti-9488	304	29	2019	2019	NUM
aiti-9488	304	30	,	,	PUNCT
aiti-9488	304	31	etc	etc	X
aiti-9488	304	32	.	.	X
aiti-9488	304	33	,	,	PUNCT
aiti-9488	304	34	consisting	consist	VERB
aiti-9488	304	35	of	of	ADP
aiti-9488	304	36	more	more	ADJ
aiti-9488	304	37	than	than	ADP
aiti-9488	304	38	10000	10000	NUM
aiti-9488	304	39	images	image	NOUN
aiti-9488	304	40	of	of	ADP
aiti-9488	304	41	pigmented	pigment	VERB
aiti-9488	304	42	skin	skin	NOUN
aiti-9488	304	43	lesions	lesion	NOUN
aiti-9488	304	44	.	.	PUNCT
aiti-9488	305	1	it	it	PRON
aiti-9488	305	2	can	can	AUX
aiti-9488	305	3	also	also	ADV
aiti-9488	305	4	include	include	VERB
aiti-9488	305	5	testing	test	VERB
aiti-9488	305	6	the	the	DET
aiti-9488	305	7	proposed	propose	VERB
aiti-9488	305	8	model	model	NOUN
aiti-9488	305	9	on	on	ADP
aiti-9488	305	10	multi	multi	ADJ
aiti-9488	305	11	-	-	ADJ
aiti-9488	305	12	labeled	label	VERB
aiti-9488	305	13	lesion	lesion	NOUN
aiti-9488	305	14	classification	classification	NOUN
aiti-9488	305	15	dataset	dataset	NOUN
aiti-9488	305	16	and	and	CCONJ
aiti-9488	305	17	building	build	VERB
aiti-9488	305	18	fine	fine	ADV
aiti-9488	305	19	-	-	PUNCT
aiti-9488	305	20	grained	grain	VERB
aiti-9488	305	21	neural	neural	ADJ
aiti-9488	305	22	networks	network	NOUN
aiti-9488	305	23	for	for	ADP
aiti-9488	305	24	melanoma	melanoma	NOUN
aiti-9488	305	25	classification	classification	NOUN
aiti-9488	305	26	with	with	ADP
aiti-9488	305	27	improved	improved	ADJ
aiti-9488	305	28	accuracy	accuracy	NOUN
aiti-9488	305	29	.	.	PUNCT
aiti-9488	306	1	the	the	DET
aiti-9488	306	2	study	study	NOUN
aiti-9488	306	3	can	can	AUX
aiti-9488	306	4	be	be	AUX
aiti-9488	306	5	extended	extend	VERB
aiti-9488	306	6	to	to	PART
aiti-9488	306	7	develop	develop	VERB
aiti-9488	306	8	an	an	DET
aiti-9488	306	9	efficient	efficient	ADJ
aiti-9488	306	10	lightweight	lightweight	ADJ
aiti-9488	306	11	smartphone	smartphone	NOUN
aiti-9488	306	12	application	application	NOUN
aiti-9488	306	13	integrating	integrate	VERB
aiti-9488	306	14	the	the	DET
aiti-9488	306	15	proposed	propose	VERB
aiti-9488	306	16	methodology	methodology	NOUN
aiti-9488	306	17	for	for	ADP
aiti-9488	306	18	skin	skin	NOUN
aiti-9488	306	19	lesion	lesion	NOUN
aiti-9488	306	20	classification	classification	NOUN
aiti-9488	306	21	with	with	ADP
aiti-9488	306	22	a	a	DET
aiti-9488	306	23	deep	deep	ADJ
aiti-9488	306	24	learning	learning	NOUN
aiti-9488	306	25	-	-	PUNCT
aiti-9488	306	26	based	base	VERB
aiti-9488	306	27	segmentation	segmentation	NOUN
aiti-9488	306	28	model	model	NOUN
aiti-9488	306	29	.	.	PUNCT
aiti-9488	307	1	conflicts	conflict	NOUN
aiti-9488	307	2	of	of	ADP
aiti-9488	307	3	interest	interest	NOUN
aiti-9488	307	4	the	the	DET
aiti-9488	307	5	authors	author	NOUN
aiti-9488	307	6	declare	declare	VERB
aiti-9488	307	7	no	no	DET
aiti-9488	307	8	conflict	conflict	NOUN
aiti-9488	307	9	of	of	ADP
aiti-9488	307	10	interest	interest	NOUN
aiti-9488	307	11	.	.	PUNCT
aiti-9488	308	1	statement	statement	NOUN
aiti-9488	308	2	of	of	ADP
aiti-9488	308	3	ethical	ethical	ADJ
aiti-9488	308	4	approval	approval	NOUN
aiti-9488	308	5	for	for	ADP
aiti-9488	308	6	this	this	DET
aiti-9488	308	7	type	type	NOUN
aiti-9488	308	8	of	of	ADP
aiti-9488	308	9	study	study	NOUN
aiti-9488	308	10	,	,	PUNCT
aiti-9488	308	11	statement	statement	NOUN
aiti-9488	308	12	of	of	ADP
aiti-9488	308	13	human	human	ADJ
aiti-9488	308	14	rights	right	NOUN
aiti-9488	308	15	is	be	AUX
aiti-9488	308	16	not	not	PART
aiti-9488	308	17	required	require	VERB
aiti-9488	308	18	.	.	PUNCT
aiti-9488	309	1	statement	statement	NOUN
aiti-9488	309	2	of	of	ADP
aiti-9488	309	3	informed	informed	ADJ
aiti-9488	309	4	consent	consent	NOUN
aiti-9488	309	5	for	for	ADP
aiti-9488	309	6	this	this	DET
aiti-9488	309	7	type	type	NOUN
aiti-9488	309	8	of	of	ADP
aiti-9488	309	9	study	study	NOUN
aiti-9488	309	10	,	,	PUNCT
aiti-9488	309	11	informed	inform	VERB
aiti-9488	309	12	consent	consent	NOUN
aiti-9488	309	13	is	be	AUX
aiti-9488	309	14	not	not	PART
aiti-9488	309	15	required	require	VERB
aiti-9488	309	16	.	.	PUNCT
aiti-9488	310	1	references	reference	NOUN
aiti-9488	310	2	[	[	X
aiti-9488	310	3	1	1	X
aiti-9488	310	4	]	]	PUNCT
aiti-9488	310	5	“	"	PUNCT
aiti-9488	310	6	cancer	cancer	NOUN
aiti-9488	310	7	facts	fact	NOUN
aiti-9488	310	8	and	and	CCONJ
aiti-9488	310	9	figures	figure	NOUN
aiti-9488	310	10	2021	2021	NUM
aiti-9488	310	11	,	,	PUNCT
aiti-9488	310	12	”	"	PUNCT
aiti-9488	310	13	https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-andstatistics/annual-cancer-facts-and-figures/2021/cancer-facts-and-figures-2021.html	https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-andstatistics/annual-cancer-facts-and-figures/2021/cancer-facts-and-figures-2021.html	NOUN
aiti-9488	310	14	,	,	PUNCT
aiti-9488	310	15	december	december	PROPN
aiti-9488	310	16	01	01	NUM
aiti-9488	310	17	,	,	PUNCT
aiti-9488	310	18	2021	2021	NUM
aiti-9488	310	19	.	.	PUNCT
aiti-9488	311	1	[	[	X
aiti-9488	311	2	2	2	X
aiti-9488	311	3	]	]	PUNCT
aiti-9488	311	4	s.	s.	PROPN
aiti-9488	311	5	sonthalia	sonthalia	PROPN
aiti-9488	311	6	,	,	PUNCT
aiti-9488	311	7	s.	s.	PROPN
aiti-9488	311	8	yumeen	yumeen	PROPN
aiti-9488	311	9	,	,	PUNCT
aiti-9488	311	10	and	and	CCONJ
aiti-9488	311	11	f.	f.	PROPN
aiti-9488	311	12	kaliyadan	kaliyadan	PROPN
aiti-9488	311	13	,	,	PUNCT
aiti-9488	311	14	“	"	PUNCT
aiti-9488	311	15	dermoscopy	dermoscopy	VERB
aiti-9488	311	16	overview	overview	NOUN
aiti-9488	311	17	and	and	CCONJ
aiti-9488	311	18	extradiagnostic	extradiagnostic	ADJ
aiti-9488	311	19	applications	application	NOUN
aiti-9488	311	20	,	,	PUNCT
aiti-9488	311	21	”	"	PUNCT
aiti-9488	311	22	https://www.ncbi.nlm.nih.gov/books/nbk537131/	https://www.ncbi.nlm.nih.gov/books/nbk537131/	PROPN
aiti-9488	311	23	,	,	PUNCT
aiti-9488	311	24	august	august	PROPN
aiti-9488	311	25	13	13	NUM
aiti-9488	311	26	,	,	PUNCT
aiti-9488	311	27	2021	2021	NUM
aiti-9488	311	28	.	.	PUNCT
aiti-9488	312	1	[	[	X
aiti-9488	312	2	3	3	X
aiti-9488	312	3	]	]	PUNCT
aiti-9488	312	4	k.	k.	PROPN
aiti-9488	312	5	munir	munir	PROPN
aiti-9488	312	6	,	,	PUNCT
aiti-9488	312	7	h.	h.	PROPN
aiti-9488	312	8	elahi	elahi	PROPN
aiti-9488	312	9	,	,	PUNCT
aiti-9488	312	10	a.	a.	NOUN
aiti-9488	312	11	ayub	ayub	PROPN
aiti-9488	312	12	,	,	PUNCT
aiti-9488	312	13	f.	f.	PROPN
aiti-9488	312	14	frezza	frezza	PROPN
aiti-9488	312	15	,	,	PUNCT
aiti-9488	312	16	and	and	CCONJ
aiti-9488	312	17	a.	a.	NOUN
aiti-9488	312	18	rizzi	rizzi	PROPN
aiti-9488	312	19	,	,	PUNCT
aiti-9488	312	20	“	"	PUNCT
aiti-9488	312	21	cancer	cancer	NOUN
aiti-9488	312	22	diagnosis	diagnosis	NOUN
aiti-9488	312	23	using	use	VERB
aiti-9488	312	24	deep	deep	ADJ
aiti-9488	312	25	learning	learning	NOUN
aiti-9488	312	26	:	:	PUNCT
aiti-9488	312	27	a	a	DET
aiti-9488	312	28	bibliographic	bibliographic	ADJ
aiti-9488	312	29	review	review	NOUN
aiti-9488	312	30	,	,	PUNCT
aiti-9488	312	31	”	"	PUNCT
aiti-9488	312	32	cancers	cancer	NOUN
aiti-9488	312	33	(	(	PUNCT
aiti-9488	312	34	basel	basel	PROPN
aiti-9488	312	35	)	)	PUNCT
aiti-9488	312	36	,	,	PUNCT
aiti-9488	312	37	vol	vol	NOUN
aiti-9488	312	38	.	.	PROPN
aiti-9488	312	39	11	11	NUM
aiti-9488	312	40	,	,	PUNCT
aiti-9488	312	41	no	no	INTJ
aiti-9488	312	42	.	.	NOUN
aiti-9488	312	43	9	9	NUM
aiti-9488	312	44	,	,	PUNCT
aiti-9488	312	45	article	article	NOUN
aiti-9488	312	46	no	no	NOUN
aiti-9488	312	47	.	.	PROPN
aiti-9488	312	48	1235	1235	NUM
aiti-9488	312	49	,	,	PUNCT
aiti-9488	312	50	august	august	PROPN
aiti-9488	312	51	2019	2019	NUM
aiti-9488	312	52	.	.	PUNCT
aiti-9488	313	1	[	[	X
aiti-9488	313	2	4	4	X
aiti-9488	313	3	]	]	PUNCT
aiti-9488	313	4	“	"	PUNCT
aiti-9488	313	5	isic	isic	PROPN
aiti-9488	313	6	challenge	challenge	NOUN
aiti-9488	313	7	datasets	dataset	NOUN
aiti-9488	313	8	,	,	PUNCT
aiti-9488	313	9	”	"	PUNCT
aiti-9488	313	10	https://challenge.isic-archive.com/data/	https://challenge.isic-archive.com/data/	PROPN
aiti-9488	313	11	,	,	PUNCT
aiti-9488	313	12	december	december	PROPN
aiti-9488	313	13	01	01	NUM
aiti-9488	313	14	,	,	PUNCT
aiti-9488	313	15	2021	2021	NUM
aiti-9488	313	16	.	.	PUNCT
aiti-9488	314	1	[	[	X
aiti-9488	314	2	5	5	NUM
aiti-9488	314	3	]	]	PUNCT
aiti-9488	314	4	p.	p.	NOUN
aiti-9488	314	5	naronglerdrit	naronglerdrit	PROPN
aiti-9488	314	6	,	,	PUNCT
aiti-9488	314	7	i.	i.	PROPN
aiti-9488	314	8	mporas	mporas	PROPN
aiti-9488	314	9	,	,	PUNCT
aiti-9488	314	10	m.	m.	NOUN
aiti-9488	314	11	paraskevas	paraskevas	PROPN
aiti-9488	314	12	,	,	PUNCT
aiti-9488	314	13	and	and	CCONJ
aiti-9488	314	14	v.	v.	ADP
aiti-9488	314	15	kapoulas	kapoula	NOUN
aiti-9488	314	16	,	,	PUNCT
aiti-9488	314	17	“	"	PUNCT
aiti-9488	314	18	melanoma	melanoma	NOUN
aiti-9488	314	19	detection	detection	NOUN
aiti-9488	314	20	from	from	ADP
aiti-9488	314	21	dermatoscopic	dermatoscopic	NOUN
aiti-9488	314	22	images	image	NOUN
aiti-9488	314	23	using	use	VERB
aiti-9488	314	24	deep	deep	ADJ
aiti-9488	314	25	convolutional	convolutional	ADJ
aiti-9488	314	26	neural	neural	ADJ
aiti-9488	314	27	networks	network	NOUN
aiti-9488	314	28	,	,	PUNCT
aiti-9488	314	29	”	"	PUNCT
aiti-9488	314	30	international	international	ADJ
aiti-9488	314	31	conference	conference	NOUN
aiti-9488	314	32	on	on	ADP
aiti-9488	314	33	biomedical	biomedical	ADJ
aiti-9488	314	34	innovations	innovation	NOUN
aiti-9488	314	35	and	and	CCONJ
aiti-9488	314	36	applications	application	NOUN
aiti-9488	314	37	(	(	PUNCT
aiti-9488	314	38	bia	bia	PROPN
aiti-9488	314	39	)	)	PUNCT
aiti-9488	314	40	,	,	PUNCT
aiti-9488	314	41	pp	pp	PROPN
aiti-9488	314	42	.	.	PUNCT
aiti-9488	315	1	13	13	NUM
aiti-9488	315	2	-	-	SYM
aiti-9488	315	3	16	16	NUM
aiti-9488	315	4	,	,	PUNCT
aiti-9488	315	5	november	november	PROPN
aiti-9488	315	6	2020	2020	NUM
aiti-9488	315	7	.	.	PUNCT
aiti-9488	316	1	[	[	X
aiti-9488	316	2	6	6	NUM
aiti-9488	316	3	]	]	X
aiti-9488	316	4	n.	n.	NOUN
aiti-9488	316	5	siddique	siddique	PROPN
aiti-9488	316	6	,	,	PUNCT
aiti-9488	316	7	s.	s.	PROPN
aiti-9488	316	8	paheding	paheding	PROPN
aiti-9488	316	9	,	,	PUNCT
aiti-9488	316	10	md	md	PROPN
aiti-9488	316	11	.	.	PROPN
aiti-9488	316	12	z.	z.	PROPN
aiti-9488	316	13	alom	alom	PROPN
aiti-9488	316	14	,	,	PUNCT
aiti-9488	316	15	and	and	CCONJ
aiti-9488	316	16	v.	v.	ADP
aiti-9488	316	17	devabhaktuni	devabhaktuni	NOUN
aiti-9488	316	18	,	,	PUNCT
aiti-9488	316	19	“	"	PUNCT
aiti-9488	316	20	recurrent	recurrent	VERB
aiti-9488	316	21	residual	residual	ADJ
aiti-9488	316	22	u	u	NOUN
aiti-9488	316	23	-	-	NOUN
aiti-9488	316	24	net	net	ADJ
aiti-9488	316	25	with	with	ADP
aiti-9488	316	26	efficientnet	efficientnet	ADJ
aiti-9488	316	27	encoder	encoder	NOUN
aiti-9488	316	28	for	for	ADP
aiti-9488	316	29	medical	medical	ADJ
aiti-9488	316	30	image	image	NOUN
aiti-9488	316	31	segmentation	segmentation	NOUN
aiti-9488	316	32	,	,	PUNCT
aiti-9488	316	33	”	"	PUNCT
aiti-9488	316	34	pattern	pattern	NOUN
aiti-9488	316	35	recognition	recognition	NOUN
aiti-9488	316	36	and	and	CCONJ
aiti-9488	316	37	tracking	track	VERB
aiti-9488	316	38	xxxii	xxxii	ADV
aiti-9488	316	39	,	,	PUNCT
aiti-9488	316	40	pp	pp	ADJ
aiti-9488	316	41	.	.	PUNCT
aiti-9488	317	1	1	1	NUM
aiti-9488	317	2	-	-	SYM
aiti-9488	317	3	10	10	NUM
aiti-9488	317	4	,	,	PUNCT
aiti-9488	317	5	april	april	PROPN
aiti-9488	317	6	2021	2021	NUM
aiti-9488	317	7	.	.	PUNCT
aiti-9488	318	1	[	[	X
aiti-9488	318	2	7	7	X
aiti-9488	318	3	]	]	PUNCT
aiti-9488	318	4	s.	s.	PROPN
aiti-9488	318	5	s.	s.	PROPN
aiti-9488	318	6	chaturvedi	chaturvedi	PROPN
aiti-9488	318	7	,	,	PUNCT
aiti-9488	318	8	k.	k.	PROPN
aiti-9488	318	9	gupta	gupta	PROPN
aiti-9488	318	10	,	,	PUNCT
aiti-9488	318	11	and	and	CCONJ
aiti-9488	318	12	p.	p.	PROPN
aiti-9488	318	13	s.	s.	PROPN
aiti-9488	318	14	prasad	prasad	PROPN
aiti-9488	318	15	,	,	PUNCT
aiti-9488	318	16	“	"	PUNCT
aiti-9488	318	17	skin	skin	NOUN
aiti-9488	318	18	lesion	lesion	NOUN
aiti-9488	318	19	analyser	analyser	NOUN
aiti-9488	318	20	:	:	PUNCT
aiti-9488	318	21	an	an	DET
aiti-9488	318	22	efficient	efficient	ADJ
aiti-9488	318	23	seven	seven	NUM
aiti-9488	318	24	-	-	PUNCT
aiti-9488	318	25	way	way	NOUN
aiti-9488	318	26	multi	multi	ADJ
aiti-9488	318	27	-	-	ADJ
aiti-9488	318	28	class	class	ADJ
aiti-9488	318	29	skin	skin	NOUN
aiti-9488	318	30	cancer	cancer	NOUN
aiti-9488	318	31	classification	classification	NOUN
aiti-9488	318	32	using	use	VERB
aiti-9488	318	33	mobilenet	mobilenet	NOUN
aiti-9488	318	34	,	,	PUNCT
aiti-9488	318	35	”	"	PUNCT
aiti-9488	318	36	international	international	ADJ
aiti-9488	318	37	conference	conference	NOUN
aiti-9488	318	38	on	on	ADP
aiti-9488	318	39	advanced	advanced	ADJ
aiti-9488	318	40	machine	machine	NOUN
aiti-9488	318	41	learning	learning	NOUN
aiti-9488	318	42	technologies	technology	NOUN
aiti-9488	318	43	and	and	CCONJ
aiti-9488	318	44	applications	application	NOUN
aiti-9488	318	45	,	,	PUNCT
aiti-9488	318	46	pp	pp	ADJ
aiti-9488	318	47	.	.	PUNCT
aiti-9488	319	1	165	165	NUM
aiti-9488	319	2	-	-	SYM
aiti-9488	319	3	176	176	NUM
aiti-9488	319	4	,	,	PUNCT
aiti-9488	319	5	february	february	PROPN
aiti-9488	319	6	2020	2020	NUM
aiti-9488	319	7	.	.	PUNCT
aiti-9488	320	1	[	[	X
aiti-9488	320	2	8	8	NUM
aiti-9488	320	3	]	]	PUNCT
aiti-9488	320	4	r.	r.	PROPN
aiti-9488	320	5	zhang	zhang	PROPN
aiti-9488	320	6	,	,	PUNCT
aiti-9488	320	7	“	"	PUNCT
aiti-9488	320	8	melanoma	melanoma	NOUN
aiti-9488	320	9	detection	detection	NOUN
aiti-9488	320	10	using	use	VERB
aiti-9488	320	11	convolutional	convolutional	ADJ
aiti-9488	320	12	neural	neural	ADJ
aiti-9488	320	13	network	network	NOUN
aiti-9488	320	14	,	,	PUNCT
aiti-9488	320	15	”	"	PUNCT
aiti-9488	320	16	ieee	ieee	PROPN
aiti-9488	320	17	international	international	ADJ
aiti-9488	320	18	conference	conference	NOUN
aiti-9488	320	19	on	on	ADP
aiti-9488	320	20	consumer	consumer	NOUN
aiti-9488	320	21	electronics	electronic	NOUN
aiti-9488	320	22	and	and	CCONJ
aiti-9488	320	23	computer	computer	NOUN
aiti-9488	320	24	engineering	engineering	NOUN
aiti-9488	320	25	(	(	PUNCT
aiti-9488	320	26	iccece	iccece	PROPN
aiti-9488	320	27	)	)	PUNCT
aiti-9488	320	28	,	,	PUNCT
aiti-9488	320	29	pp	pp	ADJ
aiti-9488	320	30	.	.	PUNCT
aiti-9488	320	31	75	75	NUM
aiti-9488	320	32	-	-	SYM
aiti-9488	320	33	78	78	NUM
aiti-9488	320	34	,	,	PUNCT
aiti-9488	320	35	january	january	NOUN
aiti-9488	320	36	2021	2021	NUM
aiti-9488	320	37	.	.	PUNCT
aiti-9488	321	1	[	[	X
aiti-9488	321	2	9	9	NUM
aiti-9488	321	3	]	]	X
aiti-9488	321	4	y.	y.	PROPN
aiti-9488	321	5	zhang	zhang	PROPN
aiti-9488	321	6	and	and	CCONJ
aiti-9488	321	7	c.	c.	PROPN
aiti-9488	321	8	wang	wang	PROPN
aiti-9488	321	9	,	,	PUNCT
aiti-9488	321	10	“	"	PUNCT
aiti-9488	321	11	siim	siim	NOUN
aiti-9488	321	12	-	-	PUNCT
aiti-9488	321	13	isic	isic	NOUN
aiti-9488	321	14	melanoma	melanoma	PROPN
aiti-9488	321	15	classification	classification	NOUN
aiti-9488	321	16	with	with	ADP
aiti-9488	321	17	densenet	densenet	NOUN
aiti-9488	321	18	,	,	PUNCT
aiti-9488	321	19	”	"	PUNCT
aiti-9488	321	20	ieee	ieee	NOUN
aiti-9488	321	21	2nd	2nd	PROPN
aiti-9488	321	22	international	international	ADJ
aiti-9488	321	23	conference	conference	NOUN
aiti-9488	321	24	on	on	ADP
aiti-9488	321	25	big	big	ADJ
aiti-9488	321	26	data	datum	NOUN
aiti-9488	321	27	,	,	PUNCT
aiti-9488	321	28	artificial	artificial	ADJ
aiti-9488	321	29	intelligence	intelligence	NOUN
aiti-9488	321	30	and	and	CCONJ
aiti-9488	321	31	internet	internet	NOUN
aiti-9488	321	32	of	of	ADP
aiti-9488	321	33	things	thing	NOUN
aiti-9488	321	34	engineering	engineering	NOUN
aiti-9488	321	35	(	(	PUNCT
aiti-9488	321	36	icbaie	icbaie	PROPN
aiti-9488	321	37	)	)	PUNCT
aiti-9488	321	38	,	,	PUNCT
aiti-9488	321	39	pp	pp	PROPN
aiti-9488	321	40	.	.	PUNCT
aiti-9488	322	1	14	14	NUM
aiti-9488	322	2	-	-	SYM
aiti-9488	322	3	17	17	NUM
aiti-9488	322	4	,	,	PUNCT
aiti-9488	322	5	march	march	NOUN
aiti-9488	322	6	2021	2021	NUM
aiti-9488	322	7	.	.	PUNCT
aiti-9488	323	1	[	[	X
aiti-9488	323	2	10	10	NUM
aiti-9488	323	3	]	]	PUNCT
aiti-9488	323	4	l.	l.	PROPN
aiti-9488	323	5	k.	k.	PROPN
aiti-9488	323	6	ashim	ashim	PROPN
aiti-9488	323	7	,	,	PUNCT
aiti-9488	323	8	n.	n.	PROPN
aiti-9488	323	9	suresh	suresh	PROPN
aiti-9488	323	10	,	,	PUNCT
aiti-9488	323	11	and	and	CCONJ
aiti-9488	323	12	c.	c.	PROPN
aiti-9488	323	13	v.	v.	PROPN
aiti-9488	323	14	prasannakumar	prasannakumar	PROPN
aiti-9488	323	15	,	,	PUNCT
aiti-9488	323	16	“	"	PUNCT
aiti-9488	323	17	a	a	DET
aiti-9488	323	18	comparative	comparative	ADJ
aiti-9488	323	19	analysis	analysis	NOUN
aiti-9488	323	20	of	of	ADP
aiti-9488	323	21	various	various	ADJ
aiti-9488	323	22	transfer	transfer	NOUN
aiti-9488	323	23	learning	learning	NOUN
aiti-9488	323	24	approaches	approach	VERB
aiti-9488	323	25	skin	skin	NOUN
aiti-9488	323	26	cancer	cancer	NOUN
aiti-9488	323	27	detection	detection	NOUN
aiti-9488	323	28	,	,	PUNCT
aiti-9488	323	29	”	"	PUNCT
aiti-9488	323	30	5th	5th	ADJ
aiti-9488	323	31	international	international	ADJ
aiti-9488	323	32	conference	conference	NOUN
aiti-9488	323	33	on	on	ADP
aiti-9488	323	34	trends	trend	NOUN
aiti-9488	323	35	in	in	ADP
aiti-9488	323	36	electronics	electronic	NOUN
aiti-9488	323	37	and	and	CCONJ
aiti-9488	323	38	informatics	informatic	NOUN
aiti-9488	323	39	(	(	PUNCT
aiti-9488	323	40	icoei	icoei	NOUN
aiti-9488	323	41	)	)	PUNCT
aiti-9488	323	42	,	,	PUNCT
aiti-9488	323	43	pp	pp	PROPN
aiti-9488	323	44	.	.	PUNCT
aiti-9488	323	45	1379	1379	NUM
aiti-9488	323	46	-	-	SYM
aiti-9488	323	47	1385	1385	NUM
aiti-9488	323	48	,	,	PUNCT
aiti-9488	323	49	june	june	PROPN
aiti-9488	323	50	2021	2021	NUM
aiti-9488	323	51	.	.	PUNCT
aiti-9488	324	1	[	[	X
aiti-9488	324	2	11	11	NUM
aiti-9488	324	3	]	]	X
aiti-9488	324	4	y.	y.	PROPN
aiti-9488	324	5	chen	chen	PROPN
aiti-9488	324	6	,	,	PUNCT
aiti-9488	324	7	y.	y.	PROPN
aiti-9488	324	8	zhu	zhu	PROPN
aiti-9488	324	9	,	,	PUNCT
aiti-9488	324	10	and	and	CCONJ
aiti-9488	324	11	y.	y.	PROPN
aiti-9488	324	12	chang	chang	PROPN
aiti-9488	324	13	,	,	PUNCT
aiti-9488	324	14	“	"	PUNCT
aiti-9488	324	15	cyclegan	cyclegan	NOUN
aiti-9488	324	16	based	base	VERB
aiti-9488	324	17	data	datum	NOUN
aiti-9488	324	18	augmentation	augmentation	NOUN
aiti-9488	324	19	for	for	ADP
aiti-9488	324	20	melanoma	melanoma	NOUN
aiti-9488	324	21	images	image	NOUN
aiti-9488	324	22	classification	classification	NOUN
aiti-9488	324	23	,	,	PUNCT
aiti-9488	324	24	”	"	PUNCT
aiti-9488	324	25	proceedings	proceeding	NOUN
aiti-9488	324	26	of	of	ADP
aiti-9488	324	27	the	the	DET
aiti-9488	324	28	3rd	3rd	ADJ
aiti-9488	324	29	international	international	ADJ
aiti-9488	324	30	conference	conference	NOUN
aiti-9488	324	31	on	on	ADP
aiti-9488	324	32	artificial	artificial	ADJ
aiti-9488	324	33	intelligence	intelligence	NOUN
aiti-9488	324	34	and	and	CCONJ
aiti-9488	324	35	pattern	pattern	NOUN
aiti-9488	324	36	recognition	recognition	NOUN
aiti-9488	324	37	,	,	PUNCT
aiti-9488	324	38	pp	pp	PROPN
aiti-9488	324	39	.	.	PUNCT
aiti-9488	325	1	115	115	NUM
aiti-9488	325	2	-	-	SYM
aiti-9488	325	3	119	119	NUM
aiti-9488	325	4	,	,	PUNCT
aiti-9488	325	5	june	june	PROPN
aiti-9488	325	6	2020	2020	NUM
aiti-9488	325	7	.	.	PUNCT
aiti-9488	326	1	[	[	X
aiti-9488	326	2	12	12	NUM
aiti-9488	326	3	]	]	X
aiti-9488	326	4	d.	d.	PROPN
aiti-9488	326	5	n.	n.	PROPN
aiti-9488	326	6	t.	t.	PROPN
aiti-9488	326	7	le	le	PROPN
aiti-9488	326	8	,	,	PUNCT
aiti-9488	326	9	h.	h.	PROPN
aiti-9488	326	10	x.	x.	PROPN
aiti-9488	326	11	le	le	PROPN
aiti-9488	326	12	,	,	PUNCT
aiti-9488	326	13	l.	l.	PROPN
aiti-9488	326	14	t.	t.	PROPN
aiti-9488	326	15	ngo	ngo	PROPN
aiti-9488	326	16	,	,	PUNCT
aiti-9488	326	17	and	and	CCONJ
aiti-9488	326	18	h.	h.	PROPN
aiti-9488	326	19	t.	t.	PROPN
aiti-9488	326	20	ngo	ngo	PROPN
aiti-9488	326	21	,	,	PUNCT
aiti-9488	326	22	“	"	PUNCT
aiti-9488	326	23	transfer	transfer	NOUN
aiti-9488	326	24	learning	learning	NOUN
aiti-9488	326	25	with	with	ADP
aiti-9488	326	26	class	class	NOUN
aiti-9488	326	27	-	-	PUNCT
aiti-9488	326	28	weighted	weight	VERB
aiti-9488	326	29	and	and	CCONJ
aiti-9488	326	30	focal	focal	ADJ
aiti-9488	326	31	loss	loss	NOUN
aiti-9488	326	32	function	function	NOUN
aiti-9488	326	33	for	for	ADP
aiti-9488	326	34	automatic	automatic	ADJ
aiti-9488	326	35	skin	skin	NOUN
aiti-9488	326	36	cancer	cancer	NOUN
aiti-9488	326	37	classification	classification	NOUN
aiti-9488	326	38	,	,	PUNCT
aiti-9488	326	39	”	"	PUNCT
aiti-9488	326	40	https://arxiv.org/abs/2009.05977	https://arxiv.org/abs/2009.05977	PROPN
aiti-9488	326	41	,	,	PUNCT
aiti-9488	326	42	september	september	PROPN
aiti-9488	326	43	13	13	NUM
aiti-9488	326	44	,	,	PUNCT
aiti-9488	326	45	2020	2020	NUM
aiti-9488	326	46	.	.	PUNCT
aiti-9488	327	1	[	[	X
aiti-9488	327	2	13	13	NUM
aiti-9488	327	3	]	]	PUNCT
aiti-9488	327	4	m.	m.	NOUN
aiti-9488	327	5	manzo	manzo	PROPN
aiti-9488	327	6	and	and	CCONJ
aiti-9488	327	7	s.	s.	PROPN
aiti-9488	327	8	pellino	pellino	PROPN
aiti-9488	327	9	,	,	PUNCT
aiti-9488	327	10	“	"	PUNCT
aiti-9488	327	11	bucket	bucket	NOUN
aiti-9488	327	12	of	of	ADP
aiti-9488	327	13	deep	deep	ADJ
aiti-9488	327	14	transfer	transfer	NOUN
aiti-9488	327	15	learning	learn	VERB
aiti-9488	327	16	features	feature	NOUN
aiti-9488	327	17	and	and	CCONJ
aiti-9488	327	18	classification	classification	NOUN
aiti-9488	327	19	models	model	NOUN
aiti-9488	327	20	for	for	ADP
aiti-9488	327	21	melanoma	melanoma	NOUN
aiti-9488	327	22	detection	detection	NOUN
aiti-9488	327	23	,	,	PUNCT
aiti-9488	327	24	”	"	PUNCT
aiti-9488	327	25	journal	journal	NOUN
aiti-9488	327	26	of	of	ADP
aiti-9488	327	27	imaging	imaging	NOUN
aiti-9488	327	28	,	,	PUNCT
aiti-9488	327	29	vol	vol	NOUN
aiti-9488	327	30	.	.	PROPN
aiti-9488	327	31	6	6	NUM
aiti-9488	327	32	,	,	PUNCT
aiti-9488	327	33	no	no	INTJ
aiti-9488	327	34	.	.	NOUN
aiti-9488	327	35	12	12	NUM
aiti-9488	327	36	,	,	PUNCT
aiti-9488	327	37	pp	pp	ADJ
aiti-9488	327	38	.	.	PUNCT
aiti-9488	328	1	1	1	NUM
aiti-9488	328	2	-	-	SYM
aiti-9488	328	3	15	15	NUM
aiti-9488	328	4	,	,	PUNCT
aiti-9488	328	5	november	november	PROPN
aiti-9488	328	6	2020	2020	NUM
aiti-9488	328	7	.	.	PUNCT
aiti-9488	329	1	[	[	X
aiti-9488	329	2	14	14	NUM
aiti-9488	329	3	]	]	PUNCT
aiti-9488	329	4	m.	m.	NOUN
aiti-9488	329	5	a.	a.	NOUN
aiti-9488	329	6	kadampur	kadampur	PROPN
aiti-9488	329	7	and	and	CCONJ
aiti-9488	329	8	s.	s.	PROPN
aiti-9488	329	9	a.	a.	PROPN
aiti-9488	329	10	riyaee	riyaee	PROPN
aiti-9488	329	11	,	,	PUNCT
aiti-9488	329	12	“	"	PUNCT
aiti-9488	329	13	skin	skin	NOUN
aiti-9488	329	14	cancer	cancer	NOUN
aiti-9488	329	15	detection	detection	NOUN
aiti-9488	329	16	:	:	PUNCT
aiti-9488	329	17	applying	apply	VERB
aiti-9488	329	18	a	a	DET
aiti-9488	329	19	deep	deep	ADJ
aiti-9488	329	20	learning	learning	NOUN
aiti-9488	329	21	based	base	VERB
aiti-9488	329	22	model	model	NOUN
aiti-9488	329	23	driven	drive	VERB
aiti-9488	329	24	architecture	architecture	NOUN
aiti-9488	329	25	in	in	ADP
aiti-9488	329	26	the	the	DET
aiti-9488	329	27	cloud	cloud	NOUN
aiti-9488	329	28	for	for	ADP
aiti-9488	329	29	classifying	classify	VERB
aiti-9488	329	30	dermal	dermal	ADJ
aiti-9488	329	31	cell	cell	NOUN
aiti-9488	329	32	images	image	NOUN
aiti-9488	329	33	,	,	PUNCT
aiti-9488	329	34	”	"	PUNCT
aiti-9488	329	35	informatics	informatic	NOUN
aiti-9488	329	36	in	in	ADP
aiti-9488	329	37	medicine	medicine	NOUN
aiti-9488	329	38	unlocked	unlock	VERB
aiti-9488	329	39	,	,	PUNCT
aiti-9488	329	40	vol	vol	NOUN
aiti-9488	329	41	.	.	PROPN
aiti-9488	330	1	18	18	NUM
aiti-9488	330	2	,	,	PUNCT
aiti-9488	330	3	no	no	INTJ
aiti-9488	330	4	.	.	NOUN
aiti-9488	330	5	100282	100282	NUM
aiti-9488	330	6	,	,	PUNCT
aiti-9488	330	7	pp	pp	ADJ
aiti-9488	330	8	.	.	PUNCT
aiti-9488	331	1	1	1	NUM
aiti-9488	331	2	-	-	SYM
aiti-9488	331	3	6	6	NUM
aiti-9488	331	4	,	,	PUNCT
aiti-9488	331	5	2020	2020	NUM
aiti-9488	331	6	.	.	PUNCT
aiti-9488	332	1	advances	advance	NOUN
aiti-9488	332	2	in	in	ADP
aiti-9488	332	3	technology	technology	NOUN
aiti-9488	332	4	innovation	innovation	NOUN
aiti-9488	332	5	,	,	PUNCT
aiti-9488	332	6	vol	vol	NOUN
aiti-9488	332	7	.	.	PROPN
aiti-9488	332	8	8	8	NUM
aiti-9488	332	9	,	,	PUNCT
aiti-9488	332	10	no	no	INTJ
aiti-9488	332	11	.	.	NOUN
aiti-9488	332	12	1	1	NUM
aiti-9488	332	13	,	,	PUNCT
aiti-9488	332	14	2023	2023	NUM
aiti-9488	332	15	,	,	PUNCT
aiti-9488	332	16	pp	pp	ADJ
aiti-9488	332	17	.	.	PUNCT
aiti-9488	333	1	59	59	NUM
aiti-9488	333	2	-	-	SYM
aiti-9488	333	3	72	72	NUM
aiti-9488	333	4	72	72	NUM
aiti-9488	334	1	[	[	X
aiti-9488	334	2	15	15	NUM
aiti-9488	334	3	]	]	X
aiti-9488	334	4	m.	m.	PROPN
aiti-9488	334	5	f.	f.	PROPN
aiti-9488	334	6	j.	j.	PROPN
aiti-9488	334	7	acosta	acosta	PROPN
aiti-9488	334	8	,	,	PUNCT
aiti-9488	334	9	l.	l.	PROPN
aiti-9488	334	10	y.	y.	PROPN
aiti-9488	334	11	c.	c.	PROPN
aiti-9488	334	12	tovar	tovar	PROPN
aiti-9488	334	13	,	,	PUNCT
aiti-9488	334	14	m.	m.	PROPN
aiti-9488	334	15	b.	b.	PROPN
aiti-9488	334	16	g.	g.	PROPN
aiti-9488	334	17	zapirain	zapirain	PROPN
aiti-9488	334	18	,	,	PUNCT
aiti-9488	334	19	and	and	CCONJ
aiti-9488	334	20	w.	w.	PROPN
aiti-9488	334	21	s.	s.	PROPN
aiti-9488	334	22	percybrooks	percybrooks	PROPN
aiti-9488	334	23	,	,	PUNCT
aiti-9488	334	24	“	"	PUNCT
aiti-9488	334	25	melanoma	melanoma	ADJ
aiti-9488	334	26	diagnosis	diagnosis	NOUN
aiti-9488	334	27	using	use	VERB
aiti-9488	334	28	deep	deep	ADJ
aiti-9488	334	29	learning	learning	NOUN
aiti-9488	334	30	techniques	technique	NOUN
aiti-9488	334	31	on	on	ADP
aiti-9488	334	32	dermatoscopic	dermatoscopic	NOUN
aiti-9488	334	33	images	image	NOUN
aiti-9488	334	34	,	,	PUNCT
aiti-9488	334	35	”	"	PUNCT
aiti-9488	334	36	bmc	bmc	PROPN
aiti-9488	334	37	medical	medical	ADJ
aiti-9488	334	38	imaging	imaging	NOUN
aiti-9488	334	39	,	,	PUNCT
aiti-9488	334	40	vol	vol	NOUN
aiti-9488	334	41	.	.	PROPN
aiti-9488	334	42	21	21	NUM
aiti-9488	334	43	,	,	PUNCT
aiti-9488	334	44	no	no	INTJ
aiti-9488	334	45	.	.	NOUN
aiti-9488	334	46	1	1	NUM
aiti-9488	334	47	,	,	PUNCT
aiti-9488	334	48	pp	pp	ADJ
aiti-9488	334	49	.	.	PUNCT
aiti-9488	335	1	1	1	NUM
aiti-9488	335	2	-	-	SYM
aiti-9488	335	3	11	11	NUM
aiti-9488	335	4	,	,	PUNCT
aiti-9488	335	5	january	january	NOUN
aiti-9488	335	6	2021	2021	NUM
aiti-9488	335	7	.	.	PUNCT
aiti-9488	336	1	[	[	X
aiti-9488	336	2	16	16	NUM
aiti-9488	336	3	]	]	X
aiti-9488	336	4	n.	n.	NOUN
aiti-9488	336	5	rezaoana	rezaoana	PROPN
aiti-9488	336	6	,	,	PUNCT
aiti-9488	336	7	m.	m.	PROPN
aiti-9488	336	8	s.	s.	PROPN
aiti-9488	336	9	hossain	hossain	PROPN
aiti-9488	336	10	,	,	PUNCT
aiti-9488	336	11	and	and	CCONJ
aiti-9488	336	12	k.	k.	PROPN
aiti-9488	336	13	andersson	andersson	PROPN
aiti-9488	336	14	,	,	PUNCT
aiti-9488	336	15	“	"	PUNCT
aiti-9488	336	16	detection	detection	NOUN
aiti-9488	336	17	and	and	CCONJ
aiti-9488	336	18	classification	classification	NOUN
aiti-9488	336	19	of	of	ADP
aiti-9488	336	20	skin	skin	NOUN
aiti-9488	336	21	cancer	cancer	NOUN
aiti-9488	336	22	by	by	ADP
aiti-9488	336	23	using	use	VERB
aiti-9488	336	24	a	a	DET
aiti-9488	336	25	parallel	parallel	ADJ
aiti-9488	336	26	cnn	cnn	PROPN
aiti-9488	336	27	model	model	NOUN
aiti-9488	336	28	,	,	PUNCT
aiti-9488	336	29	”	"	PUNCT
aiti-9488	336	30	ieee	ieee	NOUN
aiti-9488	336	31	international	international	ADJ
aiti-9488	336	32	women	woman	NOUN
aiti-9488	336	33	in	in	ADP
aiti-9488	336	34	engineering	engineering	NOUN
aiti-9488	336	35	(	(	PUNCT
aiti-9488	336	36	wie	wie	PROPN
aiti-9488	336	37	)	)	PUNCT
aiti-9488	336	38	conference	conference	NOUN
aiti-9488	336	39	on	on	ADP
aiti-9488	336	40	electrical	electrical	ADJ
aiti-9488	336	41	and	and	CCONJ
aiti-9488	336	42	computer	computer	NOUN
aiti-9488	336	43	engineering	engineering	NOUN
aiti-9488	336	44	(	(	PUNCT
aiti-9488	336	45	wiecon	wiecon	PROPN
aiti-9488	336	46	-	-	PUNCT
aiti-9488	336	47	ece	ece	PROPN
aiti-9488	336	48	)	)	PUNCT
aiti-9488	336	49	,	,	PUNCT
aiti-9488	336	50	pp	pp	PROPN
aiti-9488	336	51	.	.	PUNCT
aiti-9488	337	1	380	380	NUM
aiti-9488	337	2	-	-	SYM
aiti-9488	337	3	386	386	NUM
aiti-9488	337	4	,	,	PUNCT
aiti-9488	337	5	december	december	PROPN
aiti-9488	337	6	2020	2020	NUM
aiti-9488	337	7	.	.	PUNCT
aiti-9488	338	1	[	[	X
aiti-9488	338	2	17	17	NUM
aiti-9488	338	3	]	]	PUNCT
aiti-9488	338	4	h.	h.	PROPN
aiti-9488	338	5	talebi	talebi	PROPN
aiti-9488	338	6	and	and	CCONJ
aiti-9488	338	7	p.	p.	PROPN
aiti-9488	338	8	milanfar	milanfar	PROPN
aiti-9488	338	9	,	,	PUNCT
aiti-9488	338	10	“	"	PUNCT
aiti-9488	338	11	learning	learn	VERB
aiti-9488	338	12	to	to	PART
aiti-9488	338	13	resize	resize	VERB
aiti-9488	338	14	images	image	NOUN
aiti-9488	338	15	for	for	ADP
aiti-9488	338	16	computer	computer	NOUN
aiti-9488	338	17	vision	vision	NOUN
aiti-9488	338	18	tasks	task	NOUN
aiti-9488	338	19	,	,	PUNCT
aiti-9488	338	20	”	"	PUNCT
aiti-9488	338	21	ieee	ieee	NOUN
aiti-9488	338	22	/	/	SYM
aiti-9488	338	23	cvf	cvf	NOUN
aiti-9488	338	24	international	international	ADJ
aiti-9488	338	25	conference	conference	NOUN
aiti-9488	338	26	on	on	ADP
aiti-9488	338	27	computer	computer	NOUN
aiti-9488	338	28	vision	vision	NOUN
aiti-9488	338	29	(	(	PUNCT
aiti-9488	338	30	iccv	iccv	PROPN
aiti-9488	338	31	)	)	PUNCT
aiti-9488	338	32	,	,	PUNCT
aiti-9488	338	33	pp	pp	ADP
aiti-9488	338	34	.	.	PUNCT
aiti-9488	339	1	497	497	NUM
aiti-9488	339	2	-	-	SYM
aiti-9488	339	3	506	506	NUM
aiti-9488	339	4	,	,	PUNCT
aiti-9488	339	5	october	october	PROPN
aiti-9488	339	6	2021	2021	NUM
aiti-9488	339	7	.	.	PUNCT
aiti-9488	340	1	[	[	X
aiti-9488	340	2	18	18	NUM
aiti-9488	340	3	]	]	PUNCT
aiti-9488	340	4	m.	m.	NOUN
aiti-9488	340	5	k.	k.	PROPN
aiti-9488	340	6	tekleyohannes	tekleyohannes	PROPN
aiti-9488	340	7	,	,	PUNCT
aiti-9488	340	8	c.	c.	PROPN
aiti-9488	340	9	weis	weis	PROPN
aiti-9488	340	10	,	,	PUNCT
aiti-9488	340	11	n.	n.	NOUN
aiti-9488	340	12	wehn	wehn	NOUN
aiti-9488	340	13	,	,	PUNCT
aiti-9488	340	14	m.	m.	NOUN
aiti-9488	340	15	klein	klein	PROPN
aiti-9488	340	16	,	,	PUNCT
aiti-9488	340	17	and	and	CCONJ
aiti-9488	340	18	m.	m.	NOUN
aiti-9488	340	19	siegrist	siegrist	NOUN
aiti-9488	340	20	,	,	PUNCT
aiti-9488	340	21	“	"	PUNCT
aiti-9488	340	22	a	a	DET
aiti-9488	340	23	reconfigurable	reconfigurable	ADJ
aiti-9488	340	24	accelerator	accelerator	NOUN
aiti-9488	340	25	for	for	ADP
aiti-9488	340	26	morphological	morphological	ADJ
aiti-9488	340	27	operations	operation	NOUN
aiti-9488	340	28	,	,	PUNCT
aiti-9488	340	29	”	"	PUNCT
aiti-9488	340	30	ieee	ieee	NOUN
aiti-9488	340	31	international	international	PROPN
aiti-9488	340	32	parallel	parallel	PROPN
aiti-9488	340	33	and	and	CCONJ
aiti-9488	340	34	distributed	distributed	ADJ
aiti-9488	340	35	processing	processing	NOUN
aiti-9488	340	36	symposium	symposium	NOUN
aiti-9488	340	37	workshops	workshop	NOUN
aiti-9488	340	38	(	(	PUNCT
aiti-9488	340	39	ipdpsw	ipdpsw	NOUN
aiti-9488	340	40	)	)	PUNCT
aiti-9488	340	41	,	,	PUNCT
aiti-9488	340	42	pp	pp	ADP
aiti-9488	340	43	.	.	PUNCT
aiti-9488	341	1	186	186	NUM
aiti-9488	341	2	-	-	SYM
aiti-9488	341	3	193	193	NUM
aiti-9488	341	4	,	,	PUNCT
aiti-9488	341	5	may	may	PROPN
aiti-9488	341	6	2018	2018	NUM
aiti-9488	341	7	.	.	PUNCT
aiti-9488	342	1	[	[	X
aiti-9488	342	2	19	19	NUM
aiti-9488	342	3	]	]	PUNCT
aiti-9488	342	4	s.	s.	PROPN
aiti-9488	342	5	chatterjee	chatterjee	PROPN
aiti-9488	342	6	,	,	PUNCT
aiti-9488	342	7	d.	d.	PROPN
aiti-9488	342	8	dey	dey	PROPN
aiti-9488	342	9	,	,	PUNCT
aiti-9488	342	10	and	and	CCONJ
aiti-9488	342	11	s.	s.	PROPN
aiti-9488	342	12	munshi	munshi	PROPN
aiti-9488	342	13	,	,	PUNCT
aiti-9488	342	14	“	"	PUNCT
aiti-9488	342	15	integration	integration	NOUN
aiti-9488	342	16	of	of	ADP
aiti-9488	342	17	morphological	morphological	ADJ
aiti-9488	342	18	preprocessing	preprocessing	NOUN
aiti-9488	342	19	and	and	CCONJ
aiti-9488	342	20	fractal	fractal	ADJ
aiti-9488	342	21	based	base	VERB
aiti-9488	342	22	feature	feature	NOUN
aiti-9488	342	23	extraction	extraction	NOUN
aiti-9488	342	24	with	with	ADP
aiti-9488	342	25	recursive	recursive	ADJ
aiti-9488	342	26	feature	feature	NOUN
aiti-9488	342	27	elimination	elimination	NOUN
aiti-9488	342	28	for	for	ADP
aiti-9488	342	29	skin	skin	NOUN
aiti-9488	342	30	lesion	lesion	NOUN
aiti-9488	342	31	types	type	NOUN
aiti-9488	342	32	classification	classification	NOUN
aiti-9488	342	33	,	,	PUNCT
aiti-9488	342	34	”	"	PUNCT
aiti-9488	342	35	computer	computer	NOUN
aiti-9488	342	36	methods	method	NOUN
aiti-9488	342	37	programs	program	NOUN
aiti-9488	342	38	in	in	ADP
aiti-9488	342	39	biomedicine	biomedicine	NOUN
aiti-9488	342	40	,	,	PUNCT
aiti-9488	342	41	vol	vol	NOUN
aiti-9488	342	42	.	.	PROPN
aiti-9488	342	43	178	178	NUM
aiti-9488	342	44	,	,	PUNCT
aiti-9488	342	45	pp	pp	ADJ
aiti-9488	342	46	.	.	PUNCT
aiti-9488	342	47	201	201	NUM
aiti-9488	342	48	-	-	SYM
aiti-9488	342	49	218	218	NUM
aiti-9488	342	50	,	,	PUNCT
aiti-9488	342	51	september	september	PROPN
aiti-9488	342	52	2019	2019	NUM
aiti-9488	342	53	.	.	PUNCT
aiti-9488	343	1	[	[	X
aiti-9488	343	2	20	20	NUM
aiti-9488	343	3	]	]	PUNCT
aiti-9488	343	4	j.	j.	PROPN
aiti-9488	343	5	k.	k.	PROPN
aiti-9488	343	6	winkler	winkler	PROPN
aiti-9488	343	7	,	,	PUNCT
aiti-9488	343	8	c.	c.	PROPN
aiti-9488	343	9	fink	fink	PROPN
aiti-9488	343	10	,	,	PUNCT
aiti-9488	343	11	f.	f.	PROPN
aiti-9488	343	12	toberer	toberer	PROPN
aiti-9488	343	13	,	,	PUNCT
aiti-9488	343	14	a.	a.	PROPN
aiti-9488	343	15	enk	enk	PROPN
aiti-9488	343	16	,	,	PUNCT
aiti-9488	343	17	t.	t.	PROPN
aiti-9488	343	18	deinlein	deinlein	PROPN
aiti-9488	343	19	,	,	PUNCT
aiti-9488	343	20	r.	r.	PROPN
aiti-9488	343	21	hofmann	hofmann	PROPN
aiti-9488	343	22	-	-	PUNCT
aiti-9488	343	23	wellenhof	wellenhof	NOUN
aiti-9488	343	24	,	,	PUNCT
aiti-9488	343	25	et	et	PROPN
aiti-9488	343	26	al	al	PROPN
aiti-9488	343	27	.	.	PROPN
aiti-9488	343	28	,	,	PUNCT
aiti-9488	343	29	“	"	PUNCT
aiti-9488	343	30	association	association	NOUN
aiti-9488	343	31	between	between	ADP
aiti-9488	343	32	surgical	surgical	ADJ
aiti-9488	343	33	skin	skin	NOUN
aiti-9488	343	34	markings	marking	NOUN
aiti-9488	343	35	in	in	ADP
aiti-9488	343	36	dermoscopic	dermoscopic	ADJ
aiti-9488	343	37	images	image	NOUN
aiti-9488	343	38	and	and	CCONJ
aiti-9488	343	39	diagnostic	diagnostic	ADJ
aiti-9488	343	40	performance	performance	NOUN
aiti-9488	343	41	of	of	ADP
aiti-9488	343	42	a	a	DET
aiti-9488	343	43	deep	deep	ADJ
aiti-9488	343	44	learning	learning	NOUN
aiti-9488	343	45	convolutional	convolutional	ADJ
aiti-9488	343	46	neural	neural	ADJ
aiti-9488	343	47	network	network	NOUN
aiti-9488	343	48	for	for	ADP
aiti-9488	343	49	melanoma	melanoma	NOUN
aiti-9488	343	50	recognition	recognition	NOUN
aiti-9488	343	51	,	,	PUNCT
aiti-9488	343	52	”	"	PUNCT
aiti-9488	343	53	jama	jama	X
aiti-9488	343	54	dermatology	dermatology	NOUN
aiti-9488	343	55	,	,	PUNCT
aiti-9488	343	56	vol	vol	NOUN
aiti-9488	343	57	.	.	PROPN
aiti-9488	343	58	155	155	NUM
aiti-9488	343	59	,	,	PUNCT
aiti-9488	343	60	no	no	INTJ
aiti-9488	343	61	.	.	NOUN
aiti-9488	343	62	10	10	NUM
aiti-9488	343	63	,	,	PUNCT
aiti-9488	343	64	pp	pp	ADJ
aiti-9488	343	65	.	.	PUNCT
aiti-9488	344	1	1135	1135	NUM
aiti-9488	344	2	-	-	SYM
aiti-9488	344	3	1141	1141	NUM
aiti-9488	344	4	,	,	PUNCT
aiti-9488	344	5	october	october	PROPN
aiti-9488	344	6	2019	2019	NUM
aiti-9488	344	7	.	.	PUNCT
aiti-9488	345	1	[	[	X
aiti-9488	345	2	21	21	NUM
aiti-9488	345	3	]	]	PUNCT
aiti-9488	345	4	a.	a.	NOUN
aiti-9488	345	5	mikołajczyk	mikołajczyk	PROPN
aiti-9488	345	6	and	and	CCONJ
aiti-9488	345	7	m.	m.	NOUN
aiti-9488	345	8	grochowski	grochowski	PROPN
aiti-9488	345	9	,	,	PUNCT
aiti-9488	345	10	“	"	PUNCT
aiti-9488	345	11	data	datum	NOUN
aiti-9488	345	12	augmentation	augmentation	NOUN
aiti-9488	345	13	for	for	ADP
aiti-9488	345	14	improving	improve	VERB
aiti-9488	345	15	deep	deep	ADJ
aiti-9488	345	16	learning	learning	NOUN
aiti-9488	345	17	in	in	ADP
aiti-9488	345	18	image	image	NOUN
aiti-9488	345	19	classification	classification	NOUN
aiti-9488	345	20	problem	problem	NOUN
aiti-9488	345	21	,	,	PUNCT
aiti-9488	345	22	”	"	PUNCT
aiti-9488	345	23	ieee	ieee	PROPN
aiti-9488	345	24	international	international	ADJ
aiti-9488	345	25	interdisciplinary	interdisciplinary	ADJ
aiti-9488	345	26	phd	phd	NOUN
aiti-9488	345	27	workshop	workshop	NOUN
aiti-9488	345	28	(	(	PUNCT
aiti-9488	345	29	iiphdw	iiphdw	NOUN
aiti-9488	345	30	)	)	PUNCT
aiti-9488	345	31	,	,	PUNCT
aiti-9488	345	32	pp	pp	PROPN
aiti-9488	345	33	.	.	PUNCT
aiti-9488	346	1	117	117	NUM
aiti-9488	346	2	-	-	SYM
aiti-9488	346	3	122	122	NUM
aiti-9488	346	4	,	,	PUNCT
aiti-9488	346	5	may	may	PROPN
aiti-9488	346	6	2018	2018	NUM
aiti-9488	346	7	.	.	PUNCT
aiti-9488	347	1	[	[	X
aiti-9488	347	2	22	22	NUM
aiti-9488	347	3	]	]	PUNCT
aiti-9488	347	4	m.	m.	NOUN
aiti-9488	347	5	tan	tan	PROPN
aiti-9488	347	6	and	and	CCONJ
aiti-9488	347	7	q.	q.	PROPN
aiti-9488	347	8	le	le	PROPN
aiti-9488	347	9	,	,	PUNCT
aiti-9488	347	10	“	"	PUNCT
aiti-9488	347	11	efficientnet	efficientnet	NOUN
aiti-9488	347	12	:	:	PUNCT
aiti-9488	347	13	rethinking	rethink	VERB
aiti-9488	347	14	model	model	NOUN
aiti-9488	347	15	scaling	scale	VERB
aiti-9488	347	16	for	for	ADP
aiti-9488	347	17	convolutional	convolutional	ADJ
aiti-9488	347	18	neural	neural	ADJ
aiti-9488	347	19	networks	network	NOUN
aiti-9488	347	20	,	,	PUNCT
aiti-9488	347	21	”	"	PUNCT
aiti-9488	347	22	international	international	ADJ
aiti-9488	347	23	conference	conference	NOUN
aiti-9488	347	24	on	on	ADP
aiti-9488	347	25	machine	machine	NOUN
aiti-9488	347	26	learning	learning	NOUN
aiti-9488	347	27	,	,	PUNCT
aiti-9488	347	28	pp	pp	ADJ
aiti-9488	347	29	.	.	PUNCT
aiti-9488	348	1	6105	6105	NUM
aiti-9488	348	2	-	-	PUNCT
aiti-9488	348	3	6114	6114	NUM
aiti-9488	348	4	,	,	PUNCT
aiti-9488	348	5	june	june	PROPN
aiti-9488	348	6	2019	2019	NUM
aiti-9488	348	7	.	.	PUNCT
aiti-9488	349	1	[	[	X
aiti-9488	349	2	23	23	NUM
aiti-9488	349	3	]	]	PUNCT
aiti-9488	349	4	m.	m.	NOUN
aiti-9488	349	5	sandler	sandler	PROPN
aiti-9488	349	6	,	,	PUNCT
aiti-9488	349	7	a.	a.	PROPN
aiti-9488	349	8	howard	howard	PROPN
aiti-9488	349	9	,	,	PUNCT
aiti-9488	349	10	m.	m.	PROPN
aiti-9488	349	11	zhu	zhu	PROPN
aiti-9488	349	12	,	,	PUNCT
aiti-9488	349	13	a.	a.	NOUN
aiti-9488	349	14	zhmoginov	zhmoginov	PROPN
aiti-9488	349	15	,	,	PUNCT
aiti-9488	349	16	and	and	CCONJ
aiti-9488	349	17	l.	l.	PROPN
aiti-9488	349	18	c.	c.	PROPN
aiti-9488	349	19	chen	chen	PROPN
aiti-9488	349	20	,	,	PUNCT
aiti-9488	349	21	“	"	PUNCT
aiti-9488	349	22	mobilenetv2	mobilenetv2	NOUN
aiti-9488	349	23	:	:	PUNCT
aiti-9488	349	24	inverted	inverted	ADJ
aiti-9488	349	25	residuals	residual	NOUN
aiti-9488	349	26	and	and	CCONJ
aiti-9488	349	27	linear	linear	ADJ
aiti-9488	349	28	bottlenecks	bottleneck	NOUN
aiti-9488	349	29	,	,	PUNCT
aiti-9488	349	30	”	"	PUNCT
aiti-9488	349	31	ieee	ieee	NOUN
aiti-9488	349	32	/	/	SYM
aiti-9488	349	33	cvf	cvf	NOUN
aiti-9488	349	34	conference	conference	NOUN
aiti-9488	349	35	on	on	ADP
aiti-9488	349	36	computer	computer	NOUN
aiti-9488	349	37	vision	vision	NOUN
aiti-9488	349	38	and	and	CCONJ
aiti-9488	349	39	pattern	pattern	NOUN
aiti-9488	349	40	recognition	recognition	NOUN
aiti-9488	349	41	,	,	PUNCT
aiti-9488	349	42	pp	pp	ADP
aiti-9488	349	43	.	.	PUNCT
aiti-9488	349	44	4510	4510	NUM
aiti-9488	349	45	-	-	SYM
aiti-9488	349	46	4520	4520	NUM
aiti-9488	349	47	,	,	PUNCT
aiti-9488	349	48	june	june	PROPN
aiti-9488	349	49	2018	2018	NUM
aiti-9488	349	50	.	.	PUNCT
aiti-9488	350	1	[	[	X
aiti-9488	350	2	24	24	NUM
aiti-9488	350	3	]	]	PUNCT
aiti-9488	350	4	m.	m.	NOUN
aiti-9488	350	5	lin	lin	PROPN
aiti-9488	350	6	,	,	PUNCT
aiti-9488	350	7	q.	q.	PROPN
aiti-9488	350	8	chen	chen	PROPN
aiti-9488	350	9	,	,	PUNCT
aiti-9488	350	10	and	and	CCONJ
aiti-9488	350	11	s.	s.	PROPN
aiti-9488	350	12	yan	yan	PROPN
aiti-9488	350	13	,	,	PUNCT
aiti-9488	350	14	“	"	PUNCT
aiti-9488	350	15	network	network	NOUN
aiti-9488	350	16	in	in	ADP
aiti-9488	350	17	network	network	NOUN
aiti-9488	350	18	,	,	PUNCT
aiti-9488	350	19	”	"	PUNCT
aiti-9488	350	20	https://arxiv.org/abs/1312.4400	https://arxiv.org/abs/1312.4400	NOUN
aiti-9488	350	21	,	,	PUNCT
aiti-9488	350	22	december	december	PROPN
aiti-9488	350	23	16	16	NUM
aiti-9488	350	24	,	,	PUNCT
aiti-9488	350	25	2013	2013	NUM
aiti-9488	350	26	.	.	PUNCT
aiti-9488	351	1	[	[	X
aiti-9488	351	2	25	25	NUM
aiti-9488	351	3	]	]	X
aiti-9488	351	4	s.	s.	PROPN
aiti-9488	351	5	r.	r.	PROPN
aiti-9488	351	6	salian	salian	PROPN
aiti-9488	351	7	and	and	CCONJ
aiti-9488	351	8	s.	s.	PROPN
aiti-9488	351	9	d.	d.	PROPN
aiti-9488	351	10	sawarkar	sawarkar	PROPN
aiti-9488	351	11	,	,	PUNCT
aiti-9488	351	12	“	"	PUNCT
aiti-9488	351	13	melanoma	melanoma	NOUN
aiti-9488	351	14	skin	skin	NOUN
aiti-9488	351	15	lesion	lesion	NOUN
aiti-9488	351	16	classification	classification	NOUN
aiti-9488	351	17	using	use	VERB
aiti-9488	351	18	improved	improve	VERB
aiti-9488	351	19	efficientnetb3	efficientnetb3	NOUN
aiti-9488	351	20	,	,	PUNCT
aiti-9488	351	21	”	"	PUNCT
aiti-9488	351	22	jordanian	jordanian	ADJ
aiti-9488	351	23	journal	journal	NOUN
aiti-9488	351	24	of	of	ADP
aiti-9488	351	25	computers	computer	NOUN
aiti-9488	351	26	and	and	CCONJ
aiti-9488	351	27	information	information	NOUN
aiti-9488	351	28	technology	technology	NOUN
aiti-9488	351	29	(	(	PUNCT
aiti-9488	351	30	jjcit	jjcit	NOUN
aiti-9488	351	31	)	)	PUNCT
aiti-9488	351	32	,	,	PUNCT
aiti-9488	351	33	vol	vol	NOUN
aiti-9488	351	34	.	.	PROPN
aiti-9488	351	35	8	8	NUM
aiti-9488	351	36	,	,	PUNCT
aiti-9488	351	37	no	no	INTJ
aiti-9488	351	38	.	.	NOUN
aiti-9488	351	39	1	1	NUM
aiti-9488	351	40	,	,	PUNCT
aiti-9488	351	41	pp	pp	ADJ
aiti-9488	351	42	.	.	PUNCT
aiti-9488	352	1	45	45	NUM
aiti-9488	352	2	-	-	SYM
aiti-9488	352	3	56	56	NUM
aiti-9488	352	4	,	,	PUNCT
aiti-9488	352	5	march	march	PROPN
aiti-9488	352	6	2022	2022	NUM
aiti-9488	352	7	.	.	PUNCT
aiti-9488	353	1	[	[	X
aiti-9488	353	2	26	26	NUM
aiti-9488	353	3	]	]	X
aiti-9488	353	4	c.	c.	PROPN
aiti-9488	353	5	szegedy	szegedy	PROPN
aiti-9488	353	6	,	,	PUNCT
aiti-9488	353	7	s.	s.	PROPN
aiti-9488	353	8	ioffe	ioffe	PROPN
aiti-9488	353	9	,	,	PUNCT
aiti-9488	353	10	v.	v.	PROPN
aiti-9488	353	11	vanhoucke	vanhoucke	PROPN
aiti-9488	353	12	,	,	PUNCT
aiti-9488	353	13	and	and	CCONJ
aiti-9488	353	14	a.	a.	NOUN
aiti-9488	353	15	a.	a.	PROPN
aiti-9488	353	16	alemi	alemi	PROPN
aiti-9488	353	17	,	,	PUNCT
aiti-9488	353	18	“	"	PUNCT
aiti-9488	353	19	inception	inception	NOUN
aiti-9488	353	20	-	-	PUNCT
aiti-9488	353	21	v4	v4	NOUN
aiti-9488	353	22	,	,	PUNCT
aiti-9488	353	23	inception	inception	NOUN
aiti-9488	353	24	-	-	PUNCT
aiti-9488	353	25	resnet	resnet	NOUN
aiti-9488	353	26	and	and	CCONJ
aiti-9488	353	27	the	the	DET
aiti-9488	353	28	impact	impact	NOUN
aiti-9488	353	29	of	of	ADP
aiti-9488	353	30	residual	residual	ADJ
aiti-9488	353	31	connections	connection	NOUN
aiti-9488	353	32	on	on	ADP
aiti-9488	353	33	learning	learn	VERB
aiti-9488	353	34	,	,	PUNCT
aiti-9488	353	35	”	"	PUNCT
aiti-9488	353	36	31st	31st	NOUN
aiti-9488	353	37	aaai	aaai	PROPN
aiti-9488	353	38	conference	conference	PROPN
aiti-9488	353	39	on	on	ADP
aiti-9488	353	40	artificial	artificial	ADJ
aiti-9488	353	41	intelligence	intelligence	NOUN
aiti-9488	353	42	(	(	PUNCT
aiti-9488	353	43	aaai'17	aaai'17	PROPN
aiti-9488	353	44	)	)	PUNCT
aiti-9488	353	45	,	,	PUNCT
aiti-9488	353	46	pp	pp	PROPN
aiti-9488	353	47	.	.	PUNCT
aiti-9488	353	48	4278	4278	NUM
aiti-9488	353	49	-	-	SYM
aiti-9488	353	50	4284	4284	NUM
aiti-9488	353	51	,	,	PUNCT
aiti-9488	353	52	february	february	PROPN
aiti-9488	353	53	2017	2017	NUM
aiti-9488	353	54	.	.	PUNCT
aiti-9488	354	1	[	[	X
aiti-9488	354	2	27	27	NUM
aiti-9488	354	3	]	]	PUNCT
aiti-9488	354	4	k.	k.	NOUN
aiti-9488	355	1	he	he	PROPN
aiti-9488	355	2	,	,	PUNCT
aiti-9488	355	3	x.	x.	PROPN
aiti-9488	355	4	zhang	zhang	PROPN
aiti-9488	355	5	,	,	PUNCT
aiti-9488	355	6	s.	s.	PROPN
aiti-9488	355	7	ren	ren	PROPN
aiti-9488	355	8	,	,	PUNCT
aiti-9488	355	9	and	and	CCONJ
aiti-9488	355	10	j.	j.	PROPN
aiti-9488	355	11	sun	sun	PROPN
aiti-9488	355	12	,	,	PUNCT
aiti-9488	355	13	“	"	PUNCT
aiti-9488	355	14	deep	deep	ADJ
aiti-9488	355	15	residual	residual	ADJ
aiti-9488	355	16	learning	learning	NOUN
aiti-9488	355	17	for	for	ADP
aiti-9488	355	18	image	image	NOUN
aiti-9488	355	19	recognition	recognition	NOUN
aiti-9488	355	20	,	,	PUNCT
aiti-9488	355	21	”	"	PUNCT
aiti-9488	355	22	ieee	ieee	NOUN
aiti-9488	355	23	conference	conference	NOUN
aiti-9488	355	24	on	on	ADP
aiti-9488	355	25	computer	computer	NOUN
aiti-9488	355	26	vision	vision	NOUN
aiti-9488	355	27	and	and	CCONJ
aiti-9488	355	28	pattern	pattern	NOUN
aiti-9488	355	29	recognition	recognition	NOUN
aiti-9488	355	30	(	(	PUNCT
aiti-9488	355	31	cvpr	cvpr	NOUN
aiti-9488	355	32	)	)	PUNCT
aiti-9488	355	33	,	,	PUNCT
aiti-9488	355	34	pp	pp	ADJ
aiti-9488	355	35	.	.	PUNCT
aiti-9488	356	1	770	770	NUM
aiti-9488	356	2	-	-	SYM
aiti-9488	356	3	778	778	NUM
aiti-9488	356	4	,	,	PUNCT
aiti-9488	356	5	june	june	PROPN
aiti-9488	356	6	2016	2016	NUM
aiti-9488	356	7	.	.	PUNCT
aiti-9488	357	1	[	[	X
aiti-9488	357	2	28	28	NUM
aiti-9488	357	3	]	]	X
aiti-9488	357	4	c.	c.	PROPN
aiti-9488	357	5	szegedy	szegedy	PROPN
aiti-9488	357	6	,	,	PUNCT
aiti-9488	357	7	v.	v.	PROPN
aiti-9488	357	8	vanhoucke	vanhoucke	PROPN
aiti-9488	357	9	,	,	PUNCT
aiti-9488	357	10	s.	s.	PROPN
aiti-9488	357	11	ioffe	ioffe	PROPN
aiti-9488	357	12	,	,	PUNCT
aiti-9488	357	13	j.	j.	PROPN
aiti-9488	357	14	shlens	shlens	PROPN
aiti-9488	357	15	,	,	PUNCT
aiti-9488	357	16	and	and	CCONJ
aiti-9488	357	17	z.	z.	PROPN
aiti-9488	357	18	wojna	wojna	PROPN
aiti-9488	357	19	,	,	PUNCT
aiti-9488	357	20	“	"	PUNCT
aiti-9488	357	21	rethinking	rethink	VERB
aiti-9488	357	22	the	the	DET
aiti-9488	357	23	inception	inception	ADJ
aiti-9488	357	24	architecture	architecture	NOUN
aiti-9488	357	25	for	for	ADP
aiti-9488	357	26	computer	computer	NOUN
aiti-9488	357	27	vision	vision	NOUN
aiti-9488	357	28	,	,	PUNCT
aiti-9488	357	29	”	"	PUNCT
aiti-9488	357	30	proceedings	proceeding	NOUN
aiti-9488	357	31	of	of	ADP
aiti-9488	357	32	the	the	DET
aiti-9488	357	33	ieee	ieee	NOUN
aiti-9488	357	34	conference	conference	NOUN
aiti-9488	357	35	on	on	ADP
aiti-9488	357	36	computer	computer	NOUN
aiti-9488	357	37	vision	vision	NOUN
aiti-9488	357	38	and	and	CCONJ
aiti-9488	357	39	pattern	pattern	NOUN
aiti-9488	357	40	recognition	recognition	NOUN
aiti-9488	357	41	(	(	PUNCT
aiti-9488	357	42	cvpr	cvpr	NOUN
aiti-9488	357	43	)	)	PUNCT
aiti-9488	357	44	,	,	PUNCT
aiti-9488	357	45	pp	pp	PROPN
aiti-9488	357	46	.	.	PUNCT
aiti-9488	358	1	2818	2818	NUM
aiti-9488	358	2	-	-	SYM
aiti-9488	358	3	2826	2826	NUM
aiti-9488	358	4	,	,	PUNCT
aiti-9488	358	5	june	june	PROPN
aiti-9488	358	6	2016	2016	NUM
aiti-9488	358	7	.	.	PUNCT
aiti-9488	359	1	copyright	copyright	NOUN
aiti-9488	359	2	©	©	PROPN
aiti-9488	359	3	by	by	ADP
aiti-9488	359	4	the	the	DET
aiti-9488	359	5	authors	author	NOUN
aiti-9488	359	6	.	.	PUNCT
aiti-9488	360	1	licensee	licensee	PROPN
aiti-9488	360	2	taeti	taeti	PROPN
aiti-9488	360	3	,	,	PUNCT
aiti-9488	360	4	taiwan	taiwan	PROPN
aiti-9488	360	5	.	.	PUNCT
aiti-9488	361	1	this	this	DET
aiti-9488	361	2	article	article	NOUN
aiti-9488	361	3	is	be	AUX
aiti-9488	361	4	an	an	DET
aiti-9488	361	5	open	open	ADJ
aiti-9488	361	6	access	access	NOUN
aiti-9488	361	7	article	article	NOUN
aiti-9488	361	8	distributed	distribute	VERB
aiti-9488	361	9	under	under	ADP
aiti-9488	361	10	the	the	DET
aiti-9488	361	11	terms	term	NOUN
aiti-9488	361	12	and	and	CCONJ
aiti-9488	361	13	conditions	condition	NOUN
aiti-9488	361	14	of	of	ADP
aiti-9488	361	15	the	the	DET
aiti-9488	361	16	creative	creative	ADJ
aiti-9488	361	17	commons	common	NOUN
aiti-9488	361	18	attribution	attribution	NOUN
aiti-9488	361	19	(	(	PUNCT
aiti-9488	361	20	cc	cc	NOUN
aiti-9488	361	21	by	by	ADP
aiti-9488	361	22	-	-	PUNCT
aiti-9488	361	23	nc	nc	NOUN
aiti-9488	361	24	)	)	PUNCT
aiti-9488	361	25	license	license	NOUN
aiti-9488	361	26	(	(	PUNCT
aiti-9488	361	27	https://creativecommons.org/licenses/by-nc/4.0/	https://creativecommons.org/licenses/by-nc/4.0/	NOUN
aiti-9488	361	28	)	)	PUNCT
aiti-9488	361	29	.	.	PUNCT