id	sid	tid	token	lemma	pos
cana-5708	1	1	communications	communication	NOUN
cana-5708	1	2	on	on	ADP
cana-5708	1	3	applied	apply	VERB
cana-5708	1	4	nonlinear	nonlinear	ADJ
cana-5708	1	5	analysis	analysis	NOUN
cana-5708	1	6	issn	issn	NOUN
cana-5708	1	7	:	:	PUNCT
cana-5708	1	8	1074	1074	NUM
cana-5708	1	9	-	-	PUNCT
cana-5708	1	10	133x	133x	NUM
cana-5708	1	11	vol	vol	VERB
cana-5708	1	12	32	32	NUM
cana-5708	1	13	no	no	NOUN
cana-5708	1	14	.	.	PUNCT
cana-5708	2	1	10s	10	NOUN
cana-5708	2	2	(	(	PUNCT
cana-5708	2	3	2025	2025	NUM
cana-5708	2	4	)	)	PUNCT
cana-5708	2	5	2667	2667	NUM
cana-5708	2	6	https://internationalpubls.com	https://internationalpubls.com	NUM
cana-5708	2	7	automated	automate	VERB
cana-5708	2	8	skin	skin	NOUN
cana-5708	2	9	cancer	cancer	NOUN
cana-5708	2	10	detection	detection	NOUN
cana-5708	2	11	:	:	PUNCT
cana-5708	2	12	leveraging	leverage	VERB
cana-5708	2	13	hyperband	hyperband	NOUN
cana-5708	2	14	for	for	ADP
cana-5708	2	15	optimized	optimize	VERB
cana-5708	2	16	convolutional	convolutional	ADJ
cana-5708	2	17	neural	neural	ADJ
cana-5708	2	18	networks	network	NOUN
cana-5708	2	19	1	1	NUM
cana-5708	2	20	sakshi	sakshi	PROPN
cana-5708	2	21	gupta	gupta	PROPN
cana-5708	2	22	,	,	PUNCT
cana-5708	2	23	2	2	NUM
cana-5708	2	24	sonam	sonam	PROPN
cana-5708	2	25	juneja	juneja	PROPN
cana-5708	2	26	,	,	PUNCT
cana-5708	2	27	3	3	NUM
cana-5708	2	28	shikha	shikha	NOUN
cana-5708	2	29	atwal	atwal	NOUN
cana-5708	2	30	and	and	CCONJ
cana-5708	2	31	4jagdeep	4jagdeep	NUM
cana-5708	2	32	walia	walia	PROPN
cana-5708	2	33	1,2,3	1,2,3	NUM
cana-5708	2	34	department	department	NOUN
cana-5708	2	35	of	of	ADP
cana-5708	2	36	computer	computer	NOUN
cana-5708	2	37	science	science	NOUN
cana-5708	2	38	and	and	CCONJ
cana-5708	2	39	engineering	engineering	NOUN
cana-5708	2	40	,	,	PUNCT
cana-5708	2	41	chandigarh	chandigarh	PROPN
cana-5708	2	42	university	university	NOUN
cana-5708	2	43	,	,	PUNCT
cana-5708	2	44	gharuan	gharuan	PROPN
cana-5708	2	45	,	,	PUNCT
cana-5708	2	46	mohali	mohali	PROPN
cana-5708	2	47	,	,	PUNCT
cana-5708	2	48	punjab	punjab	ADJ
cana-5708	2	49	,	,	PUNCT
cana-5708	2	50	140301	140301	NUM
cana-5708	2	51	,	,	PUNCT
cana-5708	2	52	india	india	PROPN
cana-5708	2	53	4	4	NUM
cana-5708	2	54	chandigarh	chandigarh	PROPN
cana-5708	2	55	group	group	NOUN
cana-5708	2	56	of	of	ADP
cana-5708	2	57	colleges	college	NOUN
cana-5708	2	58	,	,	PUNCT
cana-5708	2	59	jhanjeri	jhanjeri	NOUN
cana-5708	2	60	,	,	PUNCT
cana-5708	2	61	mohali	mohali	PROPN
cana-5708	2	62	,	,	PUNCT
cana-5708	2	63	punjab	punjab	PROPN
cana-5708	2	64	,	,	PUNCT
cana-5708	2	65	india	india	PROPN
cana-5708	2	66	140307	140307	NUM
cana-5708	2	67	,	,	PUNCT
cana-5708	2	68	chandigarh	chandigarh	NOUN
cana-5708	2	69	engineering	engineering	NOUN
cana-5708	2	70	college	college	NOUN
cana-5708	2	71	,	,	PUNCT
cana-5708	2	72	department	department	NOUN
cana-5708	2	73	of	of	ADP
cana-5708	2	74	applied	apply	VERB
cana-5708	2	75	sciences	science	NOUN
cana-5708	2	76	1	1	NUM
cana-5708	2	77	sakshigupta0982@gmail.com	sakshigupta0982@gmail.com	NOUN
cana-5708	2	78	,	,	PUNCT
cana-5708	2	79	2	2	NUM
cana-5708	2	80	sonam.december@gmail.com	sonam.december@gmail.com	NOUN
cana-5708	2	81	,	,	PUNCT
cana-5708	2	82	3	3	NUM
cana-5708	2	83	shikhaatwal780@gmail.com	shikhaatwal780@gmail.com	NUM
cana-5708	2	84	,	,	PUNCT
cana-5708	2	85	4	4	NUM
cana-5708	2	86	jagdeep.j822@cgc.ac.in	jagdeep.j822@cgc.ac.in	PROPN
cana-5708	2	87	article	article	NOUN
cana-5708	2	88	history	history	NOUN
cana-5708	2	89	:	:	PUNCT
cana-5708	2	90	received	receive	VERB
cana-5708	2	91	:	:	PUNCT
cana-5708	2	92	12	12	NUM
cana-5708	2	93	-	-	SYM
cana-5708	2	94	01	01	NUM
cana-5708	2	95	-	-	PUNCT
cana-5708	2	96	2025	2025	NUM
cana-5708	2	97	revised	revise	VERB
cana-5708	2	98	:	:	PUNCT
cana-5708	2	99	15	15	NUM
cana-5708	2	100	-	-	NUM
cana-5708	2	101	02	02	NUM
cana-5708	2	102	-	-	PUNCT
cana-5708	2	103	2025	2025	NUM
cana-5708	2	104	accepted	accept	VERB
cana-5708	2	105	:	:	PUNCT
cana-5708	2	106	01	01	NUM
cana-5708	2	107	-	-	SYM
cana-5708	2	108	03	03	NUM
cana-5708	2	109	-	-	PUNCT
cana-5708	2	110	2025	2025	NUM
cana-5708	2	111	abstract	abstract	NOUN
cana-5708	2	112	:	:	PUNCT
cana-5708	2	113	skin	skin	NOUN
cana-5708	2	114	cancer	cancer	NOUN
cana-5708	2	115	is	be	AUX
cana-5708	2	116	an	an	DET
cana-5708	2	117	extremely	extremely	ADV
cana-5708	2	118	common	common	ADJ
cana-5708	2	119	disease	disease	NOUN
cana-5708	2	120	around	around	ADP
cana-5708	2	121	the	the	DET
cana-5708	2	122	world	world	NOUN
cana-5708	2	123	and	and	CCONJ
cana-5708	2	124	proactive	proactive	ADJ
cana-5708	2	125	identification	identification	NOUN
cana-5708	2	126	is	be	AUX
cana-5708	2	127	key	key	ADJ
cana-5708	2	128	to	to	ADP
cana-5708	2	129	raising	raise	VERB
cana-5708	2	130	survival	survival	NOUN
cana-5708	2	131	rates	rate	NOUN
cana-5708	2	132	.	.	PUNCT
cana-5708	3	1	the	the	DET
cana-5708	3	2	area	area	NOUN
cana-5708	3	3	of	of	ADP
cana-5708	3	4	study	study	NOUN
cana-5708	3	5	of	of	ADP
cana-5708	3	6	skin	skin	NOUN
cana-5708	3	7	cancer	cancer	NOUN
cana-5708	3	8	detection	detection	NOUN
cana-5708	3	9	offers	offer	VERB
cana-5708	3	10	a	a	DET
cana-5708	3	11	compelling	compelling	ADJ
cana-5708	3	12	use	use	NOUN
cana-5708	3	13	case	case	NOUN
cana-5708	3	14	for	for	ADP
cana-5708	3	15	the	the	DET
cana-5708	3	16	application	application	NOUN
cana-5708	3	17	of	of	ADP
cana-5708	3	18	artificial	artificial	ADJ
cana-5708	3	19	intelligence	intelligence	NOUN
cana-5708	3	20	(	(	PUNCT
cana-5708	3	21	ai	ai	NOUN
cana-5708	3	22	)	)	PUNCT
cana-5708	3	23	within	within	ADP
cana-5708	3	24	the	the	DET
cana-5708	3	25	domain	domain	NOUN
cana-5708	3	26	of	of	ADP
cana-5708	3	27	image	image	NOUN
cana-5708	3	28	-	-	PUNCT
cana-5708	3	29	based	base	VERB
cana-5708	3	30	diagnosis	diagnosis	NOUN
cana-5708	3	31	..	..	PUNCT
cana-5708	3	32	through	through	ADP
cana-5708	3	33	the	the	DET
cana-5708	3	34	analysis	analysis	NOUN
cana-5708	3	35	of	of	ADP
cana-5708	3	36	large	large	ADJ
cana-5708	3	37	datasets	dataset	NOUN
cana-5708	3	38	,	,	PUNCT
cana-5708	3	39	ai	ai	VERB
cana-5708	3	40	algorithms	algorithm	NOUN
cana-5708	3	41	have	have	VERB
cana-5708	3	42	the	the	DET
cana-5708	3	43	capacity	capacity	NOUN
cana-5708	3	44	to	to	PART
cana-5708	3	45	classify	classify	VERB
cana-5708	3	46	clinical	clinical	ADJ
cana-5708	3	47	images	image	NOUN
cana-5708	3	48	with	with	ADP
cana-5708	3	49	remarkable	remarkable	ADJ
cana-5708	3	50	accuracy	accuracy	NOUN
cana-5708	3	51	.	.	PUNCT
cana-5708	4	1	convolutional	convolutional	ADJ
cana-5708	4	2	neural	neural	ADJ
cana-5708	4	3	networks	network	NOUN
cana-5708	4	4	(	(	PUNCT
cana-5708	4	5	cnns	cnns	PROPN
cana-5708	4	6	)	)	PUNCT
cana-5708	4	7	have	have	AUX
cana-5708	4	8	proven	prove	VERB
cana-5708	4	9	to	to	PART
cana-5708	4	10	be	be	AUX
cana-5708	4	11	effective	effective	ADJ
cana-5708	4	12	as	as	ADP
cana-5708	4	13	dermatologists	dermatologist	NOUN
cana-5708	4	14	in	in	ADP
cana-5708	4	15	diagnosing	diagnose	VERB
cana-5708	4	16	skin	skin	NOUN
cana-5708	4	17	lesions	lesion	NOUN
cana-5708	4	18	.	.	PUNCT
cana-5708	5	1	however	however	ADV
cana-5708	5	2	,	,	PUNCT
cana-5708	5	3	conventional	conventional	ADJ
cana-5708	5	4	hyperparameter	hyperparameter	NOUN
cana-5708	5	5	tuning	tune	VERB
cana-5708	5	6	techniques	technique	NOUN
cana-5708	5	7	like	like	ADP
cana-5708	5	8	manual	manual	ADJ
cana-5708	5	9	selection	selection	NOUN
cana-5708	5	10	or	or	CCONJ
cana-5708	5	11	grid	grid	NOUN
cana-5708	5	12	search	search	NOUN
cana-5708	5	13	are	be	AUX
cana-5708	5	14	computationally	computationally	ADV
cana-5708	5	15	intensive	intensive	ADJ
cana-5708	5	16	and	and	CCONJ
cana-5708	5	17	time	time	NOUN
cana-5708	5	18	consuming	consume	VERB
cana-5708	5	19	.	.	PUNCT
cana-5708	6	1	this	this	DET
cana-5708	6	2	work	work	NOUN
cana-5708	6	3	utilizes	utilize	VERB
cana-5708	6	4	hyperband	hyperband	NOUN
cana-5708	6	5	which	which	PRON
cana-5708	6	6	is	be	AUX
cana-5708	6	7	an	an	DET
cana-5708	6	8	adaptive	adaptive	ADJ
cana-5708	6	9	optimization	optimization	NOUN
cana-5708	6	10	algorithm	algorithm	NOUN
cana-5708	6	11	to	to	PART
cana-5708	6	12	optimize	optimize	VERB
cana-5708	6	13	significant	significant	ADJ
cana-5708	6	14	parameters	parameter	NOUN
cana-5708	6	15	like	like	ADP
cana-5708	6	16	learning	learn	VERB
cana-5708	6	17	rate	rate	NOUN
cana-5708	6	18	,	,	PUNCT
cana-5708	6	19	dropout	dropout	NOUN
cana-5708	6	20	rate	rate	NOUN
cana-5708	6	21	,	,	PUNCT
cana-5708	6	22	and	and	CCONJ
cana-5708	6	23	batch	batch	VERB
cana-5708	6	24	size	size	NOUN
cana-5708	6	25	efficiently	efficiently	ADV
cana-5708	6	26	on	on	ADP
cana-5708	6	27	datasets	dataset	NOUN
cana-5708	6	28	like	like	ADP
cana-5708	6	29	ham10000	ham10000	PROPN
cana-5708	6	30	,	,	PUNCT
cana-5708	6	31	isic	isic	PROPN
cana-5708	6	32	archive	archive	NOUN
cana-5708	6	33	,	,	PUNCT
cana-5708	6	34	and	and	CCONJ
cana-5708	6	35	kaggle	kaggle	PROPN
cana-5708	6	36	's	's	PART
cana-5708	6	37	skin	skin	NOUN
cana-5708	6	38	cancer	cancer	NOUN
cana-5708	6	39	dataset	dataset	NOUN
cana-5708	6	40	.	.	PUNCT
cana-5708	7	1	the	the	DET
cana-5708	7	2	proposed	propose	VERB
cana-5708	7	3	framework	framework	NOUN
cana-5708	7	4	of	of	ADP
cana-5708	7	5	hyperband	hyperband	ADJ
cana-5708	7	6	optimization	optimization	NOUN
cana-5708	7	7	has	have	AUX
cana-5708	7	8	resulted	result	VERB
cana-5708	7	9	in	in	ADP
cana-5708	7	10	an	an	DET
cana-5708	7	11	increased	increase	VERB
cana-5708	7	12	accuracy	accuracy	NOUN
cana-5708	7	13	of	of	ADP
cana-5708	7	14	92.8	92.8	NUM
cana-5708	7	15	%	%	NOUN
cana-5708	7	16	in	in	ADP
cana-5708	7	17	terms	term	NOUN
cana-5708	7	18	of	of	ADP
cana-5708	7	19	skin	skin	NOUN
cana-5708	7	20	cancer	cancer	NOUN
cana-5708	7	21	detection	detection	NOUN
cana-5708	7	22	.	.	PUNCT
cana-5708	8	1	hyperband	hyperband	ADJ
cana-5708	8	2	-	-	PUNCT
cana-5708	8	3	optimized	optimize	VERB
cana-5708	8	4	cnns	cnn	NOUN
cana-5708	8	5	outperformed	outperform	VERB
cana-5708	8	6	baseline	baseline	NOUN
cana-5708	8	7	models	model	NOUN
cana-5708	8	8	by	by	ADP
cana-5708	8	9	dynamically	dynamically	ADV
cana-5708	8	10	allocating	allocate	VERB
cana-5708	8	11	resources	resource	NOUN
cana-5708	8	12	and	and	CCONJ
cana-5708	8	13	removing	remove	VERB
cana-5708	8	14	poor	poor	ADJ
cana-5708	8	15	configurations	configuration	NOUN
cana-5708	8	16	in	in	ADP
cana-5708	8	17	a	a	DET
cana-5708	8	18	quick	quick	ADJ
cana-5708	8	19	time	time	NOUN
cana-5708	8	20	.	.	PUNCT
cana-5708	9	1	the	the	DET
cana-5708	9	2	work	work	NOUN
cana-5708	9	3	presents	present	VERB
cana-5708	9	4	the	the	DET
cana-5708	9	5	hyperband	hyperband	ADJ
cana-5708	9	6	model	model	NOUN
cana-5708	9	7	with	with	ADP
cana-5708	9	8	improved	improved	ADJ
cana-5708	9	9	accuracy	accuracy	NOUN
cana-5708	9	10	,	,	PUNCT
cana-5708	9	11	precision	precision	NOUN
cana-5708	9	12	,	,	PUNCT
cana-5708	9	13	recall	recall	NOUN
cana-5708	9	14	,	,	PUNCT
cana-5708	9	15	f1	f1	NOUN
cana-5708	9	16	-	-	PUNCT
cana-5708	9	17	score	score	NOUN
cana-5708	9	18	,	,	PUNCT
cana-5708	9	19	and	and	CCONJ
cana-5708	9	20	auc	auc	NOUN
cana-5708	9	21	-	-	PUNCT
cana-5708	9	22	roc	roc	NOUN
cana-5708	9	23	scores	score	NOUN
cana-5708	9	24	and	and	CCONJ
cana-5708	9	25	significantly	significantly	ADV
cana-5708	9	26	lower	low	ADJ
cana-5708	9	27	computational	computational	ADJ
cana-5708	9	28	overhead	overhead	NOUN
cana-5708	9	29	.	.	PUNCT
cana-5708	10	1	these	these	DET
cana-5708	10	2	results	result	NOUN
cana-5708	10	3	prove	prove	VERB
cana-5708	10	4	the	the	DET
cana-5708	10	5	revolutionizing	revolutionize	VERB
cana-5708	10	6	potential	potential	NOUN
cana-5708	10	7	of	of	ADP
cana-5708	10	8	automated	automate	VERB
cana-5708	10	9	hyperparameter	hyperparameter	NOUN
cana-5708	10	10	optimization	optimization	NOUN
cana-5708	10	11	platforms	platform	NOUN
cana-5708	10	12	in	in	ADP
cana-5708	10	13	enhancing	enhance	VERB
cana-5708	10	14	aiassisted	aiassiste	VERB
cana-5708	10	15	medical	medical	ADJ
cana-5708	10	16	diagnosis	diagnosis	NOUN
cana-5708	10	17	,	,	PUNCT
cana-5708	10	18	and	and	CCONJ
cana-5708	10	19	providing	provide	VERB
cana-5708	10	20	scalable	scalable	ADJ
cana-5708	10	21	and	and	CCONJ
cana-5708	10	22	highly	highly	ADV
cana-5708	10	23	efficient	efficient	ADJ
cana-5708	10	24	solutions	solution	NOUN
cana-5708	10	25	for	for	ADP
cana-5708	10	26	early	early	ADJ
cana-5708	10	27	skin	skin	NOUN
cana-5708	10	28	cancer	cancer	NOUN
cana-5708	10	29	detection	detection	NOUN
cana-5708	10	30	.	.	PUNCT
cana-5708	11	1	keywords	keyword	NOUN
cana-5708	11	2	:	:	PUNCT
cana-5708	11	3	convolutional	convolutional	ADJ
cana-5708	11	4	neural	neural	ADJ
cana-5708	11	5	networks	network	NOUN
cana-5708	11	6	,	,	PUNCT
cana-5708	11	7	hyperband	hyperband	NOUN
cana-5708	11	8	optimization	optimization	NOUN
cana-5708	11	9	,	,	PUNCT
cana-5708	11	10	medical	medical	ADJ
cana-5708	11	11	image	image	NOUN
cana-5708	11	12	classification	classification	NOUN
cana-5708	11	13	,	,	PUNCT
cana-5708	11	14	automated	automate	VERB
cana-5708	11	15	hyperparameter	hyperparameter	NOUN
cana-5708	11	16	tuning	tuning	NOUN
cana-5708	11	17	,	,	PUNCT
cana-5708	11	18	deep	deep	ADJ
cana-5708	11	19	learning	learn	VERB
cana-5708	11	20	1.introduction	1.introduction	NUM
cana-5708	11	21	one	one	NUM
cana-5708	11	22	of	of	ADP
cana-5708	11	23	the	the	DET
cana-5708	11	24	greatest	great	ADJ
cana-5708	11	25	health	health	NOUN
cana-5708	11	26	risks	risk	NOUN
cana-5708	11	27	to	to	ADP
cana-5708	11	28	the	the	DET
cana-5708	11	29	general	general	ADJ
cana-5708	11	30	population	population	NOUN
cana-5708	11	31	is	be	AUX
cana-5708	11	32	skin	skin	NOUN
cana-5708	11	33	cancer	cancer	NOUN
cana-5708	11	34	.	.	PUNCT
cana-5708	12	1	it	it	PRON
cana-5708	12	2	requires	require	VERB
cana-5708	12	3	utmost	utmost	ADJ
cana-5708	12	4	care	care	NOUN
cana-5708	12	5	and	and	CCONJ
cana-5708	12	6	caution	caution	NOUN
cana-5708	12	7	should	should	AUX
cana-5708	12	8	be	be	AUX
cana-5708	12	9	taken	take	VERB
cana-5708	12	10	because	because	SCONJ
cana-5708	12	11	there	there	PRON
cana-5708	12	12	has	have	AUX
cana-5708	12	13	been	be	AUX
cana-5708	12	14	a	a	DET
cana-5708	12	15	steep	steep	ADJ
cana-5708	12	16	rise	rise	NOUN
cana-5708	12	17	in	in	ADP
cana-5708	12	18	the	the	DET
cana-5708	12	19	incidence	incidence	NOUN
cana-5708	12	20	rate	rate	NOUN
cana-5708	12	21	all	all	ADV
cana-5708	12	22	over	over	ADP
cana-5708	12	23	the	the	DET
cana-5708	12	24	world	world	NOUN
cana-5708	12	25	.	.	PUNCT
cana-5708	13	1	it	it	PRON
cana-5708	13	2	is	be	AUX
cana-5708	13	3	the	the	DET
cana-5708	13	4	most	most	ADV
cana-5708	13	5	prevalent	prevalent	ADJ
cana-5708	13	6	type	type	NOUN
cana-5708	13	7	of	of	ADP
cana-5708	13	8	cancer	cancer	NOUN
cana-5708	13	9	with	with	ADP
cana-5708	13	10	different	different	ADJ
cana-5708	13	11	types	type	NOUN
cana-5708	13	12	like	like	ADP
cana-5708	13	13	melanoma	melanoma	NOUN
cana-5708	13	14	,	,	PUNCT
cana-5708	13	15	basal	basal	ADJ
cana-5708	13	16	cell	cell	NOUN
cana-5708	13	17	carcinoma	carcinoma	NOUN
cana-5708	13	18	,	,	PUNCT
cana-5708	13	19	and	and	CCONJ
cana-5708	13	20	squamous	squamous	ADJ
cana-5708	13	21	cell	cell	NOUN
cana-5708	13	22	carcinoma	carcinoma	NOUN
cana-5708	13	23	.	.	PUNCT
cana-5708	14	1	since	since	SCONJ
cana-5708	14	2	this	this	DET
cana-5708	14	3	cancer	cancer	NOUN
cana-5708	14	4	is	be	AUX
cana-5708	14	5	highly	highly	ADV
cana-5708	14	6	curable	curable	ADJ
cana-5708	14	7	if	if	SCONJ
cana-5708	14	8	a	a	DET
cana-5708	14	9	person	person	NOUN
cana-5708	14	10	gets	get	AUX
cana-5708	14	11	diagnosed	diagnose	VERB
cana-5708	14	12	early	early	ADV
cana-5708	14	13	,	,	PUNCT
cana-5708	14	14	it	it	PRON
cana-5708	14	15	is	be	AUX
cana-5708	14	16	suggested	suggest	VERB
cana-5708	14	17	to	to	PART
cana-5708	14	18	be	be	AUX
cana-5708	14	19	diagnosed	diagnose	VERB
cana-5708	14	20	early	early	ADV
cana-5708	14	21	so	so	SCONJ
cana-5708	14	22	that	that	SCONJ
cana-5708	14	23	one	one	PRON
cana-5708	14	24	can	can	AUX
cana-5708	14	25	be	be	AUX
cana-5708	14	26	given	give	VERB
cana-5708	14	27	the	the	DET
cana-5708	14	28	best	good	ADJ
cana-5708	14	29	treatment	treatment	NOUN
cana-5708	14	30	.	.	PUNCT
cana-5708	15	1	at	at	ADP
cana-5708	15	2	the	the	DET
cana-5708	15	3	same	same	ADJ
cana-5708	15	4	time	time	NOUN
cana-5708	15	5	,	,	PUNCT
cana-5708	15	6	it	it	PRON
cana-5708	15	7	relies	rely	VERB
cana-5708	15	8	on	on	ADP
cana-5708	15	9	very	very	ADV
cana-5708	15	10	well	well	ADV
cana-5708	15	11	-	-	PUNCT
cana-5708	15	12	trained	train	VERB
cana-5708	15	13	dermatologists	dermatologist	NOUN
cana-5708	15	14	to	to	PART
cana-5708	15	15	undertake	undertake	VERB
cana-5708	15	16	a	a	DET
cana-5708	15	17	visual	visual	ADJ
cana-5708	15	18	examination	examination	NOUN
cana-5708	15	19	of	of	ADP
cana-5708	15	20	skin	skin	NOUN
cana-5708	15	21	lesions	lesion	NOUN
cana-5708	15	22	.	.	PUNCT
cana-5708	16	1	such	such	DET
cana-5708	16	2	a	a	DET
cana-5708	16	3	process	process	NOUN
cana-5708	16	4	is	be	AUX
cana-5708	16	5	thus	thus	ADV
cana-5708	16	6	time	time	NOUN
cana-5708	16	7	-	-	PUNCT
cana-5708	16	8	consuming	consume	VERB
cana-5708	16	9	and	and	CCONJ
cana-5708	16	10	prone	prone	ADJ
cana-5708	16	11	to	to	ADP
cana-5708	16	12	errors	error	NOUN
cana-5708	16	13	,	,	PUNCT
cana-5708	16	14	especially	especially	ADV
cana-5708	16	15	in	in	ADP
cana-5708	16	16	peak	peak	ADJ
cana-5708	16	17	seasons	season	NOUN
cana-5708	16	18	.	.	PUNCT
cana-5708	17	1	connected	connect	VERB
cana-5708	17	2	to	to	ADP
cana-5708	17	3	this	this	PRON
cana-5708	17	4	is	be	AUX
cana-5708	17	5	a	a	DET
cana-5708	17	6	major	major	ADJ
cana-5708	17	7	disabler	disabler	NOUN
cana-5708	17	8	to	to	ADP
cana-5708	17	9	early	early	ADJ
cana-5708	17	10	screening	screening	NOUN
cana-5708	17	11	:	:	PUNCT
cana-5708	17	12	dermatologists	dermatologist	NOUN
cana-5708	17	13	are	be	AUX
cana-5708	17	14	few	few	ADJ
cana-5708	17	15	and	and	CCONJ
cana-5708	17	16	thin	thin	ADJ
cana-5708	17	17	on	on	ADP
cana-5708	17	18	the	the	DET
cana-5708	17	19	ground	ground	NOUN
cana-5708	17	20	in	in	ADP
cana-5708	17	21	remote	remote	ADJ
cana-5708	17	22	rural	rural	ADJ
cana-5708	17	23	or	or	CCONJ
cana-5708	17	24	underserved	underserved	ADJ
cana-5708	17	25	areas	area	NOUN
cana-5708	17	26	[	[	X
cana-5708	17	27	1][11	1][11	X
cana-5708	17	28	]	]	PUNCT
cana-5708	17	29	.	.	PUNCT
cana-5708	18	1	however	however	ADV
cana-5708	18	2	,	,	PUNCT
cana-5708	18	3	the	the	DET
cana-5708	18	4	combination	combination	NOUN
cana-5708	18	5	of	of	ADP
cana-5708	18	6	machine	machine	NOUN
cana-5708	18	7	learning	learn	VERB
cana-5708	18	8	with	with	ADP
cana-5708	18	9	medicine	medicine	NOUN
cana-5708	18	10	is	be	AUX
cana-5708	18	11	endowing	endow	VERB
cana-5708	18	12	novel	novel	ADJ
cana-5708	18	13	avenues	avenue	NOUN
cana-5708	18	14	to	to	PART
cana-5708	18	15	tackle	tackle	VERB
cana-5708	18	16	these	these	DET
cana-5708	18	17	issues	issue	NOUN
cana-5708	18	18	.	.	PUNCT
cana-5708	19	1	deep	deep	ADJ
cana-5708	19	2	learning	learn	VERB
cana-5708	19	3	algorithms	algorithm	NOUN
cana-5708	19	4	have	have	VERB
cana-5708	19	5	the	the	DET
cana-5708	19	6	empowering	empowering	ADJ
cana-5708	19	7	abilities	ability	NOUN
cana-5708	19	8	of	of	ADP
cana-5708	19	9	automation	automation	NOUN
cana-5708	19	10	in	in	ADP
cana-5708	19	11	diagnosis	diagnosis	NOUN
cana-5708	19	12	by	by	ADP
cana-5708	19	13	pairing	pair	VERB
cana-5708	19	14	the	the	DET
cana-5708	19	15	expertise	expertise	NOUN
cana-5708	19	16	of	of	ADP
cana-5708	19	17	the	the	DET
cana-5708	19	18	dermatologist	dermatologist	NOUN
cana-5708	19	19	with	with	ADP
cana-5708	19	20	timely	timely	ADJ
cana-5708	19	21	accuracy	accuracy	NOUN
cana-5708	19	22	in	in	ADP
cana-5708	19	23	diagnosing	diagnose	VERB
cana-5708	19	24	skin	skin	NOUN
cana-5708	19	25	mailto:sakshigupta0982@gmail.com	mailto:sakshigupta0982@gmail.com	X
cana-5708	19	26	mailto:sonam.december@gmail.com	mailto:sonam.december@gmail.com	X
cana-5708	20	1	mailto:shikhaatwal780@gmail.com	mailto:shikhaatwal780@gmail.com	X
cana-5708	21	1	mailto:jagdeep.j822@cgc.ac.in	mailto:jagdeep.j822@cgc.ac.in	X
cana-5708	22	1	communications	communication	NOUN
cana-5708	22	2	on	on	ADP
cana-5708	22	3	applied	apply	VERB
cana-5708	22	4	nonlinear	nonlinear	ADJ
cana-5708	22	5	analysis	analysis	NOUN
cana-5708	22	6	issn	issn	NOUN
cana-5708	22	7	:	:	PUNCT
cana-5708	22	8	1074	1074	NUM
cana-5708	22	9	-	-	PUNCT
cana-5708	22	10	133x	133x	NUM
cana-5708	22	11	vol	vol	VERB
cana-5708	22	12	32	32	NUM
cana-5708	22	13	no	no	NOUN
cana-5708	22	14	.	.	PUNCT
cana-5708	23	1	10s	10	NOUN
cana-5708	23	2	(	(	PUNCT
cana-5708	23	3	2025	2025	NUM
cana-5708	23	4	)	)	PUNCT
cana-5708	23	5	2668	2668	NUM
cana-5708	23	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5708	23	7	lesion	lesion	NOUN
cana-5708	23	8	diagnosis	diagnosis	NOUN
cana-5708	23	9	[	[	X
cana-5708	23	10	22][2	22][2	NUM
cana-5708	23	11	]	]	PUNCT
cana-5708	23	12	.	.	PUNCT
cana-5708	24	1	such	such	ADJ
cana-5708	24	2	deep	deep	ADJ
cana-5708	24	3	networks	network	NOUN
cana-5708	24	4	such	such	ADJ
cana-5708	24	5	as	as	ADP
cana-5708	24	6	convolutional	convolutional	ADJ
cana-5708	24	7	neural	neural	ADJ
cana-5708	24	8	networks	network	NOUN
cana-5708	24	9	,	,	PUNCT
cana-5708	24	10	beside	beside	ADP
cana-5708	24	11	their	their	PRON
cana-5708	24	12	application	application	NOUN
cana-5708	24	13	to	to	ADP
cana-5708	24	14	such	such	ADJ
cana-5708	24	15	tasks	task	NOUN
cana-5708	24	16	as	as	ADP
cana-5708	24	17	skin	skin	NOUN
cana-5708	24	18	lesion	lesion	NOUN
cana-5708	24	19	classification	classification	NOUN
cana-5708	24	20	and	and	CCONJ
cana-5708	24	21	detection	detection	NOUN
cana-5708	24	22	,	,	PUNCT
cana-5708	24	23	have	have	AUX
cana-5708	24	24	further	far	ADV
cana-5708	24	25	shown	show	VERB
cana-5708	24	26	great	great	ADJ
cana-5708	24	27	usefulness	usefulness	NOUN
cana-5708	24	28	in	in	ADP
cana-5708	24	29	medical	medical	ADJ
cana-5708	24	30	image	image	NOUN
cana-5708	24	31	analysis	analysis	NOUN
cana-5708	24	32	[	[	X
cana-5708	24	33	1][7	1][7	NUM
cana-5708	24	34	]	]	PUNCT
cana-5708	24	35	.	.	PUNCT
cana-5708	25	1	such	such	ADJ
cana-5708	25	2	technologies	technology	NOUN
cana-5708	25	3	promise	promise	VERB
cana-5708	25	4	to	to	PART
cana-5708	25	5	democratize	democratize	VERB
cana-5708	25	6	health	health	NOUN
cana-5708	25	7	care	care	NOUN
cana-5708	25	8	by	by	ADP
cana-5708	25	9	bringing	bring	VERB
cana-5708	25	10	skin	skin	NOUN
cana-5708	25	11	cancer	cancer	NOUN
cana-5708	25	12	diagnosis	diagnosis	NOUN
cana-5708	25	13	equivalent	equivalent	ADJ
cana-5708	25	14	to	to	ADP
cana-5708	25	15	the	the	DET
cana-5708	25	16	very	very	ADV
cana-5708	25	17	poor	poor	ADJ
cana-5708	25	18	communities	community	NOUN
cana-5708	25	19	.	.	PUNCT
cana-5708	26	1	some	some	PRON
cana-5708	26	2	of	of	ADP
cana-5708	26	3	the	the	DET
cana-5708	26	4	databases	database	NOUN
cana-5708	26	5	used	use	VERB
cana-5708	26	6	to	to	PART
cana-5708	26	7	provide	provide	VERB
cana-5708	26	8	diverse	diverse	ADJ
cana-5708	26	9	dermatological	dermatological	ADJ
cana-5708	26	10	image	image	NOUN
cana-5708	26	11	resources	resource	NOUN
cana-5708	26	12	for	for	ADP
cana-5708	26	13	ai	ai	VERB
cana-5708	26	14	 	 	SPACE
cana-5708	26	15	training	training	NOUN
cana-5708	26	16	include	include	VERB
cana-5708	26	17	ham10000	ham10000	PROPN
cana-5708	27	1	[	[	X
cana-5708	27	2	11	11	NUM
cana-5708	27	3	]	]	PUNCT
cana-5708	27	4	,	,	PUNCT
cana-5708	27	5	isic	isic	PROPN
cana-5708	27	6	archive	archive	NOUN
cana-5708	27	7	[	[	X
cana-5708	27	8	14	14	NUM
cana-5708	27	9	]	]	PUNCT
cana-5708	27	10	,	,	PUNCT
cana-5708	27	11	and	and	CCONJ
cana-5708	27	12	kaggle	kaggle	PROPN
cana-5708	27	13	's	's	PART
cana-5708	27	14	skin	skin	NOUN
cana-5708	27	15	cancer	cancer	NOUN
cana-5708	27	16	mnist	mnist	NOUN
cana-5708	27	17	dataset	dataset	VERB
cana-5708	28	1	[	[	PUNCT
cana-5708	28	2	13	13	NUM
cana-5708	28	3	]	]	PUNCT
cana-5708	28	4	.	.	PUNCT
cana-5708	29	1	architectures	architecture	NOUN
cana-5708	29	2	like	like	ADP
cana-5708	29	3	tensorflow	tensorflow	NOUN
cana-5708	29	4	[	[	X
cana-5708	29	5	19	19	NUM
cana-5708	29	6	]	]	PUNCT
cana-5708	29	7	and	and	CCONJ
cana-5708	29	8	pytorch	pytorch	NOUN
cana-5708	30	1	[	[	X
cana-5708	30	2	21	21	NUM
cana-5708	30	3	]	]	PUNCT
cana-5708	30	4	have	have	AUX
cana-5708	30	5	efficiently	efficiently	ADV
cana-5708	30	6	propelled	propel	VERB
cana-5708	30	7	progress	progress	NOUN
cana-5708	30	8	in	in	ADP
cana-5708	30	9	the	the	DET
cana-5708	30	10	model	model	NOUN
cana-5708	30	11	's	's	PART
cana-5708	30	12	propagation	propagation	NOUN
cana-5708	30	13	in	in	ADP
cana-5708	30	14	addition	addition	NOUN
cana-5708	30	15	to	to	ADP
cana-5708	30	16	the	the	DET
cana-5708	30	17	development	development	NOUN
cana-5708	30	18	and	and	CCONJ
cana-5708	30	19	deployment	deployment	NOUN
cana-5708	30	20	of	of	ADP
cana-5708	30	21	such	such	ADJ
cana-5708	30	22	systems.cnn	systems.cnn	PROPN
cana-5708	30	23	models	model	NOUN
cana-5708	30	24	such	such	ADJ
cana-5708	30	25	as	as	ADP
cana-5708	30	26	resnet	resnet	NOUN
cana-5708	30	27	[	[	X
cana-5708	30	28	7	7	NUM
cana-5708	30	29	]	]	PUNCT
cana-5708	30	30	,	,	PUNCT
cana-5708	30	31	and	and	CCONJ
cana-5708	30	32	inception	inception	NOUN
cana-5708	30	33	have	have	AUX
cana-5708	30	34	greatly	greatly	ADV
cana-5708	30	35	enhanced	enhance	VERB
cana-5708	30	36	diagnostic	diagnostic	ADJ
cana-5708	30	37	accuracy	accuracy	NOUN
cana-5708	30	38	.	.	PUNCT
cana-5708	31	1	such	such	ADJ
cana-5708	31	2	models	model	NOUN
cana-5708	31	3	thereby	thereby	ADV
cana-5708	31	4	utilize	utilize	VERB
cana-5708	31	5	deep	deep	ADJ
cana-5708	31	6	hierarchical	hierarchical	ADJ
cana-5708	31	7	representation	representation	NOUN
cana-5708	31	8	of	of	ADP
cana-5708	31	9	features	feature	NOUN
cana-5708	31	10	to	to	PART
cana-5708	31	11	try	try	VERB
cana-5708	31	12	and	and	CCONJ
cana-5708	31	13	understand	understand	VERB
cana-5708	31	14	very	very	ADV
cana-5708	31	15	faint	faint	ADJ
cana-5708	31	16	patterns	pattern	NOUN
cana-5708	31	17	from	from	ADP
cana-5708	31	18	medical	medical	ADJ
cana-5708	31	19	images	image	NOUN
cana-5708	31	20	making	make	VERB
cana-5708	31	21	them	they	PRON
cana-5708	31	22	more	more	ADV
cana-5708	31	23	precise	precise	ADJ
cana-5708	31	24	.	.	PUNCT
cana-5708	32	1	in	in	ADP
cana-5708	32	2	addition	addition	NOUN
cana-5708	32	3	,	,	PUNCT
cana-5708	32	4	model	model	NOUN
cana-5708	32	5	training	training	NOUN
cana-5708	32	6	and	and	CCONJ
cana-5708	32	7	the	the	DET
cana-5708	32	8	model	model	NOUN
cana-5708	32	9	architecture	architecture	NOUN
cana-5708	32	10	have	have	AUX
cana-5708	32	11	been	be	AUX
cana-5708	32	12	enabled	enable	VERB
cana-5708	32	13	by	by	ADP
cana-5708	32	14	libraries	library	NOUN
cana-5708	32	15	such	such	ADJ
cana-5708	32	16	as	as	ADP
cana-5708	32	17	keras	keras	PROPN
cana-5708	32	18	[	[	X
cana-5708	32	19	20	20	NUM
cana-5708	32	20	]	]	PUNCT
cana-5708	32	21	and	and	CCONJ
cana-5708	32	22	optimizers	optimizer	NOUN
cana-5708	32	23	like	like	ADP
cana-5708	32	24	adam	adam	PROPN
cana-5708	33	1	[	[	X
cana-5708	33	2	10	10	NUM
cana-5708	33	3	]	]	PUNCT
cana-5708	33	4	,	,	PUNCT
cana-5708	33	5	and	and	CCONJ
cana-5708	33	6	therefore	therefore	ADV
cana-5708	33	7	they	they	PRON
cana-5708	33	8	are	be	AUX
cana-5708	33	9	more	more	ADV
cana-5708	33	10	effective	effective	ADJ
cana-5708	33	11	.	.	PUNCT
cana-5708	34	1	the	the	DET
cana-5708	34	2	ai	ai	ADJ
cana-5708	34	3	solutions	solution	NOUN
cana-5708	34	4	supported	support	VERB
cana-5708	34	5	by	by	ADP
cana-5708	34	6	big	big	ADJ
cana-5708	34	7	datasets	dataset	NOUN
cana-5708	34	8	and	and	CCONJ
cana-5708	34	9	cutting	cutting	NOUN
cana-5708	34	10	-	-	PUNCT
cana-5708	34	11	edge	edge	NOUN
cana-5708	34	12	deep	deep	ADJ
cana-5708	34	13	learning	learning	NOUN
cana-5708	34	14	technology	technology	NOUN
cana-5708	34	15	are	be	AUX
cana-5708	34	16	an	an	DET
cana-5708	34	17	economical	economical	ADJ
cana-5708	34	18	solution	solution	NOUN
cana-5708	34	19	to	to	ADP
cana-5708	34	20	the	the	DET
cana-5708	34	21	plight	plight	NOUN
cana-5708	34	22	of	of	ADP
cana-5708	34	23	skin	skin	NOUN
cana-5708	34	24	cancer	cancer	NOUN
cana-5708	34	25	detection	detection	NOUN
cana-5708	34	26	.	.	PUNCT
cana-5708	35	1	through	through	ADP
cana-5708	35	2	their	their	PRON
cana-5708	35	3	implementation	implementation	NOUN
cana-5708	35	4	,	,	PUNCT
cana-5708	35	5	the	the	DET
cana-5708	35	6	healthcare	healthcare	NOUN
cana-5708	35	7	systems	system	NOUN
cana-5708	35	8	have	have	VERB
cana-5708	35	9	the	the	DET
cana-5708	35	10	capability	capability	NOUN
cana-5708	35	11	to	to	PART
cana-5708	35	12	provide	provide	VERB
cana-5708	35	13	early	early	ADJ
cana-5708	35	14	and	and	CCONJ
cana-5708	35	15	precise	precise	ADJ
cana-5708	35	16	diagnosis	diagnosis	NOUN
cana-5708	35	17	and	and	CCONJ
cana-5708	35	18	further	far	ADV
cana-5708	35	19	reduce	reduce	VERB
cana-5708	35	20	the	the	DET
cana-5708	35	21	burden	burden	NOUN
cana-5708	35	22	of	of	ADP
cana-5708	35	23	global	global	ADJ
cana-5708	35	24	skin	skin	NOUN
cana-5708	35	25	cancer	cancer	NOUN
cana-5708	35	26	and	and	CCONJ
cana-5708	35	27	enhance	enhance	VERB
cana-5708	35	28	outcomes	outcome	NOUN
cana-5708	35	29	for	for	ADP
cana-5708	35	30	patients	patient	NOUN
cana-5708	35	31	.	.	PUNCT
cana-5708	36	1	the	the	DET
cana-5708	36	2	research	research	NOUN
cana-5708	36	3	tackles	tackle	VERB
cana-5708	36	4	the	the	DET
cana-5708	36	5	hyperparameter	hyperparameter	NOUN
cana-5708	36	6	optimization	optimization	NOUN
cana-5708	36	7	problem	problem	NOUN
cana-5708	36	8	in	in	ADP
cana-5708	36	9	cnns	cnn	NOUN
cana-5708	36	10	related	relate	VERB
cana-5708	36	11	to	to	ADP
cana-5708	36	12	skin	skin	NOUN
cana-5708	36	13	cancer	cancer	NOUN
cana-5708	36	14	detection	detection	NOUN
cana-5708	36	15	with	with	ADP
cana-5708	36	16	the	the	DET
cana-5708	36	17	hyperband	hyperband	ADJ
cana-5708	36	18	technique	technique	NOUN
cana-5708	36	19	.	.	PUNCT
cana-5708	37	1	hyperband	hyperband	PROPN
cana-5708	37	2	is	be	AUX
cana-5708	37	3	a	a	DET
cana-5708	37	4	very	very	ADV
cana-5708	37	5	robust	robust	ADJ
cana-5708	37	6	methodology	methodology	NOUN
cana-5708	37	7	for	for	ADP
cana-5708	37	8	hyperparameter	hyperparameter	NOUN
cana-5708	37	9	optimization	optimization	NOUN
cana-5708	37	10	.	.	PUNCT
cana-5708	38	1	hyperband	hyperband	PROPN
cana-5708	38	2	is	be	AUX
cana-5708	38	3	a	a	DET
cana-5708	38	4	fast	fast	ADJ
cana-5708	38	5	algorithm	algorithm	NOUN
cana-5708	38	6	to	to	PART
cana-5708	38	7	optimize	optimize	VERB
cana-5708	38	8	appearances	appearance	NOUN
cana-5708	38	9	of	of	ADP
cana-5708	38	10	cnn	cnn	PROPN
cana-5708	38	11	because	because	SCONJ
cana-5708	38	12	of	of	ADP
cana-5708	38	13	its	its	PRON
cana-5708	38	14	higher	high	ADJ
cana-5708	38	15	efficiency	efficiency	NOUN
cana-5708	38	16	over	over	ADP
cana-5708	38	17	other	other	ADJ
cana-5708	38	18	broad	broad	ADJ
cana-5708	38	19	approaches	approach	NOUN
cana-5708	38	20	.	.	PUNCT
cana-5708	39	1	this	this	DET
cana-5708	39	2	current	current	ADJ
cana-5708	39	3	piece	piece	NOUN
cana-5708	39	4	of	of	ADP
cana-5708	39	5	work	work	NOUN
cana-5708	39	6	is	be	AUX
cana-5708	39	7	aimed	aim	VERB
cana-5708	39	8	at	at	ADP
cana-5708	39	9	the	the	DET
cana-5708	39	10	following	following	NOUN
cana-5708	39	11	:	:	PUNCT
cana-5708	39	12	i	i	PRON
cana-5708	39	13	)	)	PUNCT
cana-5708	39	14	hyperparameter	hyperparameter	NOUN
cana-5708	39	15	optimization	optimization	NOUN
cana-5708	39	16	using	use	VERB
cana-5708	39	17	hyperband	hyperband	NOUN
cana-5708	39	18	to	to	PART
cana-5708	39	19	optimize	optimize	VERB
cana-5708	39	20	significant	significant	ADJ
cana-5708	39	21	hyper	hyper	ADJ
cana-5708	39	22	parameters	parameter	NOUN
cana-5708	39	23	,	,	PUNCT
cana-5708	39	24	including	include	VERB
cana-5708	39	25	batch	batch	NOUN
cana-5708	39	26	size	size	NOUN
cana-5708	39	27	and	and	CCONJ
cana-5708	39	28	dropout	dropout	NOUN
cana-5708	39	29	rates	rate	NOUN
cana-5708	39	30	,	,	PUNCT
cana-5708	39	31	for	for	ADP
cana-5708	39	32	cnn	cnn	PROPN
cana-5708	39	33	models	model	NOUN
cana-5708	39	34	used	use	VERB
cana-5708	39	35	in	in	ADP
cana-5708	39	36	skin	skin	NOUN
cana-5708	39	37	lesion	lesion	NOUN
cana-5708	39	38	classification	classification	NOUN
cana-5708	39	39	.	.	PUNCT
cana-5708	40	1	ii)performance	ii)performance	NOUN
cana-5708	40	2	evaluation	evaluation	NOUN
cana-5708	40	3	and	and	CCONJ
cana-5708	40	4	comparison	comparison	NOUN
cana-5708	40	5	cnn	cnn	PROPN
cana-5708	40	6	models	model	NOUN
cana-5708	40	7	optimized	optimize	VERB
cana-5708	40	8	with	with	ADP
cana-5708	40	9	hyperband	hyperband	NOUN
cana-5708	40	10	will	will	AUX
cana-5708	40	11	be	be	AUX
cana-5708	40	12	compared	compare	VERB
cana-5708	40	13	against	against	ADP
cana-5708	40	14	baseline	baseline	NOUN
cana-5708	40	15	models	model	NOUN
cana-5708	40	16	,	,	PUNCT
cana-5708	40	17	which	which	PRON
cana-5708	40	18	are	be	AUX
cana-5708	40	19	those	those	PRON
cana-5708	40	20	with	with	ADP
cana-5708	40	21	handpicked	handpicke	VERB
cana-5708	40	22	hyperparameters	hyperparameter	NOUN
cana-5708	40	23	.	.	PUNCT
cana-5708	41	1	iii)diverse	iii)diverse	NOUN
cana-5708	41	2	datasets	dataset	NOUN
cana-5708	41	3	will	will	AUX
cana-5708	41	4	be	be	AUX
cana-5708	41	5	employed	employ	VERB
cana-5708	41	6	trained	train	VERB
cana-5708	41	7	cnn	cnn	PROPN
cana-5708	41	8	models	model	NOUN
cana-5708	41	9	on	on	ADP
cana-5708	41	10	contrasting	contrast	VERB
cana-5708	41	11	datasets	dataset	NOUN
cana-5708	41	12	like	like	ADP
cana-5708	41	13	the	the	DET
cana-5708	41	14	ham10000	ham10000	NOUN
cana-5708	41	15	,	,	PUNCT
cana-5708	41	16	the	the	DET
cana-5708	41	17	isic	isic	PROPN
cana-5708	41	18	archive	archive	NOUN
cana-5708	41	19	,	,	PUNCT
cana-5708	41	20	and	and	CCONJ
cana-5708	41	21	the	the	DET
cana-5708	41	22	skin	skin	NOUN
cana-5708	41	23	cancer	cancer	NOUN
cana-5708	41	24	dataset	dataset	VERB
cana-5708	41	25	from	from	ADP
cana-5708	41	26	kaggle	kaggle	PROPN
cana-5708	41	27	.	.	PUNCT
cana-5708	42	1	iv)exhibit	iv)exhibit	VERB
cana-5708	42	2	the	the	DET
cana-5708	42	3	impact	impact	NOUN
cana-5708	42	4	of	of	ADP
cana-5708	42	5	hyperband	hyperband	NOUN
cana-5708	42	6	contrive	contrive	VERB
cana-5708	42	7	examples	example	NOUN
cana-5708	42	8	explaining	explain	VERB
cana-5708	42	9	how	how	SCONJ
cana-5708	42	10	hyperband	hyperband	NOUN
cana-5708	42	11	's	's	PART
cana-5708	42	12	automation	automation	NOUN
cana-5708	42	13	-	-	PUNCT
cana-5708	42	14	based	base	VERB
cana-5708	42	15	optimization	optimization	NOUN
cana-5708	42	16	approach	approach	NOUN
cana-5708	42	17	dramatically	dramatically	ADV
cana-5708	42	18	impacts	impact	VERB
cana-5708	42	19	cnn	cnn	PROPN
cana-5708	42	20	performance	performance	NOUN
cana-5708	42	21	with	with	ADP
cana-5708	42	22	significantly	significantly	ADV
cana-5708	42	23	lowered	lower	VERB
cana-5708	42	24	computation	computation	NOUN
cana-5708	42	25	cost	cost	NOUN
cana-5708	42	26	.	.	PUNCT
cana-5708	43	1	with	with	ADP
cana-5708	43	2	these	these	DET
cana-5708	43	3	aims	aim	NOUN
cana-5708	43	4	,	,	PUNCT
cana-5708	43	5	the	the	DET
cana-5708	43	6	research	research	NOUN
cana-5708	43	7	will	will	AUX
cana-5708	43	8	push	push	VERB
cana-5708	43	9	the	the	DET
cana-5708	43	10	frontier	frontier	NOUN
cana-5708	43	11	of	of	ADP
cana-5708	43	12	ai	ai	PROPN
cana-5708	43	13	dermatology	dermatology	NOUN
cana-5708	43	14	through	through	ADP
cana-5708	43	15	an	an	DET
cana-5708	43	16	efficient	efficient	ADJ
cana-5708	43	17	,	,	PUNCT
cana-5708	43	18	scalable	scalable	ADJ
cana-5708	43	19	,	,	PUNCT
cana-5708	43	20	and	and	CCONJ
cana-5708	43	21	accurate	accurate	ADJ
cana-5708	43	22	skin	skin	NOUN
cana-5708	43	23	cancer	cancer	NOUN
cana-5708	43	24	diagnosis	diagnosis	NOUN
cana-5708	43	25	solution	solution	NOUN
cana-5708	43	26	.	.	PUNCT
cana-5708	44	1	for	for	ADP
cana-5708	44	2	a	a	DET
cana-5708	44	3	long	long	ADJ
cana-5708	44	4	-	-	PUNCT
cana-5708	44	5	term	term	NOUN
cana-5708	44	6	impact	impact	NOUN
cana-5708	44	7	,	,	PUNCT
cana-5708	44	8	the	the	DET
cana-5708	44	9	research	research	NOUN
cana-5708	44	10	aspires	aspire	VERB
cana-5708	44	11	to	to	PART
cana-5708	44	12	connect	connect	VERB
cana-5708	44	13	science	science	NOUN
cana-5708	44	14	innovation	innovation	NOUN
cana-5708	44	15	and	and	CCONJ
cana-5708	44	16	clinical	clinical	ADJ
cana-5708	44	17	practice	practice	NOUN
cana-5708	44	18	to	to	PART
cana-5708	44	19	enhance	enhance	VERB
cana-5708	44	20	healthcare	healthcare	NOUN
cana-5708	44	21	outcomes	outcome	NOUN
cana-5708	44	22	globally	globally	ADV
cana-5708	44	23	and	and	CCONJ
cana-5708	44	24	ease	ease	VERB
cana-5708	44	25	the	the	DET
cana-5708	44	26	workload	workload	NOUN
cana-5708	44	27	of	of	ADP
cana-5708	44	28	healthcare	healthcare	NOUN
cana-5708	44	29	systems	system	NOUN
cana-5708	44	30	and	and	CCONJ
cana-5708	44	31	dermatologists	dermatologist	NOUN
cana-5708	44	32	.	.	PUNCT
cana-5708	45	1	communications	communication	NOUN
cana-5708	45	2	on	on	ADP
cana-5708	45	3	applied	apply	VERB
cana-5708	45	4	nonlinear	nonlinear	ADJ
cana-5708	45	5	analysis	analysis	NOUN
cana-5708	45	6	issn	issn	NOUN
cana-5708	45	7	:	:	PUNCT
cana-5708	45	8	1074	1074	NUM
cana-5708	45	9	-	-	PUNCT
cana-5708	45	10	133x	133x	NUM
cana-5708	45	11	vol	vol	VERB
cana-5708	45	12	32	32	NUM
cana-5708	45	13	no	no	NOUN
cana-5708	45	14	.	.	PUNCT
cana-5708	46	1	10s	10	NOUN
cana-5708	46	2	(	(	PUNCT
cana-5708	46	3	2025	2025	NUM
cana-5708	46	4	)	)	PUNCT
cana-5708	46	5	2669	2669	NUM
cana-5708	46	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5708	46	7	2	2	X
cana-5708	46	8	.	.	X
cana-5708	46	9	literature	literature	NOUN
cana-5708	46	10	review	review	VERB
cana-5708	46	11	the	the	DET
cana-5708	46	12	domain	domain	NOUN
cana-5708	46	13	of	of	ADP
cana-5708	46	14	skin	skin	NOUN
cana-5708	46	15	cancer	cancer	NOUN
cana-5708	46	16	diagnosis	diagnosis	NOUN
cana-5708	46	17	has	have	AUX
cana-5708	46	18	witnessed	witness	VERB
cana-5708	46	19	enormous	enormous	ADJ
cana-5708	46	20	progress	progress	NOUN
cana-5708	46	21	through	through	ADP
cana-5708	46	22	convolutional	convolutional	ADJ
cana-5708	46	23	neural	neural	ADJ
cana-5708	46	24	networks	network	NOUN
cana-5708	46	25	(	(	PUNCT
cana-5708	46	26	cnns	cnns	PROPN
cana-5708	46	27	)	)	PUNCT
cana-5708	46	28	.	.	PUNCT
cana-5708	47	1	they	they	PRON
cana-5708	47	2	have	have	AUX
cana-5708	47	3	particularly	particularly	ADV
cana-5708	47	4	been	be	AUX
cana-5708	47	5	outstanding	outstanding	ADJ
cana-5708	47	6	as	as	ADP
cana-5708	47	7	medical	medical	ADJ
cana-5708	47	8	image	image	NOUN
cana-5708	47	9	classifiers	classifier	NOUN
cana-5708	47	10	.	.	PUNCT
cana-5708	48	1	best	good	ADJ
cana-5708	48	2	performance	performance	NOUN
cana-5708	48	3	is	be	AUX
cana-5708	48	4	accompanied	accompany	VERB
cana-5708	48	5	by	by	ADP
cana-5708	48	6	the	the	DET
cana-5708	48	7	requirement	requirement	NOUN
cana-5708	48	8	for	for	ADP
cana-5708	48	9	hyperparameter	hyperparameter	NOUN
cana-5708	48	10	optimization	optimization	NOUN
cana-5708	48	11	,	,	PUNCT
cana-5708	48	12	which	which	PRON
cana-5708	48	13	is	be	AUX
cana-5708	48	14	notoriously	notoriously	ADV
cana-5708	48	15	inefficient	inefficient	ADJ
cana-5708	48	16	.	.	PUNCT
cana-5708	49	1	this	this	DET
cana-5708	49	2	section	section	NOUN
cana-5708	49	3	encapsulates	encapsulate	VERB
cana-5708	49	4	major	major	ADJ
cana-5708	49	5	literature	literature	NOUN
cana-5708	49	6	on	on	ADP
cana-5708	49	7	cnn	cnn	PROPN
cana-5708	49	8	skin	skin	NOUN
cana-5708	49	9	cancer	cancer	NOUN
cana-5708	49	10	diagnosis	diagnosis	NOUN
cana-5708	49	11	,	,	PUNCT
cana-5708	49	12	hyperparameter	hyperparameter	NOUN
cana-5708	49	13	optimization	optimization	NOUN
cana-5708	49	14	methods	method	NOUN
cana-5708	49	15	,	,	PUNCT
cana-5708	49	16	and	and	CCONJ
cana-5708	49	17	hyperband	hyperband	ADJ
cana-5708	49	18	optimization	optimization	NOUN
cana-5708	49	19	process	process	NOUN
cana-5708	49	20	potential	potential	NOUN
cana-5708	49	21	.	.	PUNCT
cana-5708	50	1	conventional	conventional	ADJ
cana-5708	50	2	means	mean	NOUN
cana-5708	50	3	of	of	ADP
cana-5708	50	4	skin	skin	NOUN
cana-5708	50	5	cancer	cancer	NOUN
cana-5708	50	6	diagnosis	diagnosis	NOUN
cana-5708	50	7	have	have	AUX
cana-5708	50	8	long	long	ADV
cana-5708	50	9	relied	rely	VERB
cana-5708	50	10	on	on	ADP
cana-5708	50	11	clinical	clinical	ADJ
cana-5708	50	12	expert	expert	NOUN
cana-5708	50	13	experience	experience	NOUN
cana-5708	50	14	and	and	CCONJ
cana-5708	50	15	human	human	ADJ
cana-5708	50	16	visual	visual	ADJ
cana-5708	50	17	interpretation	interpretation	NOUN
cana-5708	50	18	of	of	ADP
cana-5708	50	19	dermatoscopic	dermatoscopic	NOUN
cana-5708	50	20	images	image	NOUN
cana-5708	50	21	.	.	PUNCT
cana-5708	51	1	good	good	ADJ
cana-5708	51	2	as	as	SCONJ
cana-5708	51	3	it	it	PRON
cana-5708	51	4	is	be	AUX
cana-5708	51	5	with	with	ADP
cana-5708	51	6	the	the	DET
cana-5708	51	7	experts	expert	NOUN
cana-5708	51	8	,	,	PUNCT
cana-5708	51	9	any	any	DET
cana-5708	51	10	such	such	ADJ
cana-5708	51	11	approach	approach	NOUN
cana-5708	51	12	is	be	AUX
cana-5708	51	13	liable	liable	ADJ
cana-5708	51	14	to	to	ADP
cana-5708	51	15	human	human	ADJ
cana-5708	51	16	fallibility	fallibility	NOUN
cana-5708	51	17	and	and	CCONJ
cana-5708	51	18	subject	subject	ADJ
cana-5708	51	19	to	to	ADP
cana-5708	51	20	subjective	subjective	ADJ
cana-5708	51	21	clinical	clinical	ADJ
cana-5708	51	22	interpretation	interpretation	NOUN
cana-5708	51	23	with	with	ADP
cana-5708	51	24	the	the	DET
cana-5708	51	25	potential	potential	NOUN
cana-5708	51	26	for	for	ADP
cana-5708	51	27	false	false	ADJ
cana-5708	51	28	negatives	negative	NOUN
cana-5708	51	29	or	or	CCONJ
cana-5708	51	30	misdiagnosis	misdiagnosis	NOUN
cana-5708	51	31	.	.	PUNCT
cana-5708	52	1	deep	deep	ADJ
cana-5708	52	2	learning	learning	NOUN
cana-5708	52	3	has	have	AUX
cana-5708	52	4	changed	change	VERB
cana-5708	52	5	the	the	DET
cana-5708	52	6	scene	scene	NOUN
cana-5708	52	7	with	with	ADP
cana-5708	52	8	convolutional	convolutional	ADJ
cana-5708	52	9	neural	neural	ADJ
cana-5708	52	10	networks	network	NOUN
cana-5708	52	11	(	(	PUNCT
cana-5708	52	12	cnns	cnns	PROPN
cana-5708	52	13	)	)	PUNCT
cana-5708	52	14	as	as	ADP
cana-5708	52	15	the	the	DET
cana-5708	52	16	first	first	ADJ
cana-5708	52	17	choice	choice	NOUN
cana-5708	52	18	for	for	ADP
cana-5708	52	19	image	image	NOUN
cana-5708	52	20	classification	classification	NOUN
cana-5708	52	21	automation	automation	NOUN
cana-5708	52	22	,	,	PUNCT
cana-5708	52	23	i.e.	i.e.	X
cana-5708	52	24	,	,	PUNCT
cana-5708	52	25	skin	skin	NOUN
cana-5708	52	26	cancer	cancer	NOUN
cana-5708	52	27	detection	detection	NOUN
cana-5708	52	28	.	.	PUNCT
cana-5708	53	1	the	the	DET
cana-5708	53	2	extraction	extraction	NOUN
cana-5708	53	3	of	of	ADP
cana-5708	53	4	very	very	ADV
cana-5708	53	5	small	small	ADJ
cana-5708	53	6	details	detail	NOUN
cana-5708	53	7	from	from	ADP
cana-5708	53	8	images	image	NOUN
cana-5708	53	9	of	of	ADP
cana-5708	53	10	medical	medical	ADJ
cana-5708	53	11	cases	case	NOUN
cana-5708	53	12	is	be	AUX
cana-5708	53	13	one	one	NUM
cana-5708	53	14	of	of	ADP
cana-5708	53	15	the	the	DET
cana-5708	53	16	many	many	ADJ
cana-5708	53	17	advantages	advantage	NOUN
cana-5708	53	18	cnns	cnn	NOUN
cana-5708	53	19	have	have	VERB
cana-5708	53	20	for	for	ADP
cana-5708	53	21	recognition	recognition	NOUN
cana-5708	53	22	,	,	PUNCT
cana-5708	53	23	since	since	SCONJ
cana-5708	53	24	it	it	PRON
cana-5708	53	25	assists	assist	VERB
cana-5708	53	26	in	in	ADP
cana-5708	53	27	the	the	DET
cana-5708	53	28	identification	identification	NOUN
cana-5708	53	29	of	of	ADP
cana-5708	53	30	patterns	pattern	NOUN
cana-5708	53	31	that	that	PRON
cana-5708	53	32	might	might	AUX
cana-5708	53	33	simply	simply	ADV
cana-5708	53	34	go	go	VERB
cana-5708	53	35	unnoticed	unnoticed	ADJ
cana-5708	53	36	by	by	ADP
cana-5708	53	37	the	the	DET
cana-5708	53	38	human	human	ADJ
cana-5708	53	39	eye	eye	NOUN
cana-5708	53	40	[	[	X
cana-5708	53	41	3	3	NUM
cana-5708	53	42	]	]	PUNCT
cana-5708	53	43	.	.	PUNCT
cana-5708	54	1	esteva	esteva	PROPN
cana-5708	54	2	et	et	PROPN
cana-5708	54	3	al	al	PROPN
cana-5708	54	4	.	.	PROPN
cana-5708	54	5	were	be	AUX
cana-5708	54	6	among	among	ADP
cana-5708	54	7	the	the	DET
cana-5708	54	8	very	very	ADV
cana-5708	54	9	first	first	ADJ
cana-5708	54	10	to	to	PART
cana-5708	54	11	demonstrate	demonstrate	VERB
cana-5708	54	12	that	that	SCONJ
cana-5708	54	13	cnns	cnns	PROPN
cana-5708	54	14	could	could	AUX
cana-5708	54	15	detect	detect	VERB
cana-5708	54	16	the	the	DET
cana-5708	54	17	presence	presence	NOUN
cana-5708	54	18	of	of	ADP
cana-5708	54	19	skin	skin	NOUN
cana-5708	54	20	cancer	cancer	NOUN
cana-5708	54	21	lesions	lesion	NOUN
cana-5708	54	22	with	with	ADP
cana-5708	54	23	as	as	ADV
cana-5708	54	24	much	much	ADJ
cana-5708	54	25	efficiency	efficiency	NOUN
cana-5708	54	26	as	as	ADP
cana-5708	54	27	a	a	DET
cana-5708	54	28	dermatologist	dermatologist	NOUN
cana-5708	55	1	[	[	X
cana-5708	55	2	1	1	NUM
cana-5708	55	3	]	]	PUNCT
cana-5708	55	4	.	.	PUNCT
cana-5708	56	1	this	this	DET
cana-5708	56	2	achievement	achievement	NOUN
cana-5708	56	3	made	make	VERB
cana-5708	56	4	cnn	cnn	PROPN
cana-5708	56	5	a	a	DET
cana-5708	56	6	scourge	scourge	NOUN
cana-5708	56	7	in	in	ADP
cana-5708	56	8	ai	ai	ADJ
cana-5708	56	9	dermatology	dermatology	NOUN
cana-5708	56	10	.	.	PUNCT
cana-5708	57	1	liu	liu	PROPN
cana-5708	57	2	et	et	PROPN
cana-5708	57	3	al	al	PROPN
cana-5708	57	4	.	.	PROPN
cana-5708	57	5	progressed	progress	VERB
cana-5708	57	6	this	this	DET
cana-5708	57	7	work	work	NOUN
cana-5708	57	8	further	far	ADV
cana-5708	57	9	by	by	ADP
cana-5708	57	10	combining	combine	VERB
cana-5708	57	11	into	into	ADP
cana-5708	57	12	theirs	theirs	PRON
cana-5708	57	13	a	a	DET
cana-5708	57	14	pretty	pretty	ADV
cana-5708	57	15	sturdy	sturdy	ADJ
cana-5708	57	16	deep	deep	ADJ
cana-5708	57	17	learning	learning	NOUN
cana-5708	57	18	model	model	NOUN
cana-5708	57	19	capable	capable	ADJ
cana-5708	57	20	of	of	ADP
cana-5708	57	21	distinguishing	distinguish	VERB
cana-5708	57	22	malignant	malignant	NOUN
cana-5708	57	23	from	from	ADP
cana-5708	57	24	benign	benign	ADJ
cana-5708	57	25	skin	skin	NOUN
cana-5708	57	26	lesions	lesion	NOUN
cana-5708	57	27	,	,	PUNCT
cana-5708	57	28	very	very	ADV
cana-5708	57	29	well	well	ADV
cana-5708	57	30	opening	open	VERB
cana-5708	57	31	the	the	DET
cana-5708	57	32	doors	door	NOUN
cana-5708	57	33	for	for	ADP
cana-5708	57	34	the	the	DET
cana-5708	57	35	use	use	NOUN
cana-5708	57	36	of	of	ADP
cana-5708	57	37	cnn	cnn	PROPN
cana-5708	57	38	in	in	ADP
cana-5708	57	39	dermatology	dermatology	NOUN
cana-5708	58	1	[	[	X
cana-5708	58	2	2	2	NUM
cana-5708	58	3	]	]	PUNCT
cana-5708	58	4	.	.	PUNCT
cana-5708	59	1	the	the	DET
cana-5708	59	2	advent	advent	NOUN
cana-5708	59	3	of	of	ADP
cana-5708	59	4	large	large	ADJ
cana-5708	59	5	and	and	CCONJ
cana-5708	59	6	high	high	ADJ
cana-5708	59	7	-	-	PUNCT
cana-5708	59	8	quality	quality	NOUN
cana-5708	59	9	databases	database	NOUN
cana-5708	59	10	has	have	AUX
cana-5708	59	11	paved	pave	VERB
cana-5708	59	12	the	the	DET
cana-5708	59	13	way	way	NOUN
cana-5708	59	14	for	for	ADP
cana-5708	59	15	the	the	DET
cana-5708	59	16	employment	employment	NOUN
cana-5708	59	17	of	of	ADP
cana-5708	59	18	cnns	cnn	NOUN
cana-5708	59	19	in	in	ADP
cana-5708	59	20	dermatology	dermatology	NOUN
cana-5708	59	21	.	.	PUNCT
cana-5708	60	1	it	it	PRON
cana-5708	60	2	is	be	AUX
cana-5708	60	3	the	the	DET
cana-5708	60	4	ham10000	ham10000	NOUN
cana-5708	60	5	[	[	X
cana-5708	60	6	11	11	NUM
cana-5708	60	7	]	]	PUNCT
cana-5708	60	8	dataset	dataset	VERB
cana-5708	60	9	with	with	ADP
cana-5708	60	10	its	its	PRON
cana-5708	60	11	large	large	ADJ
cana-5708	60	12	patch	patch	NOUN
cana-5708	60	13	of	of	ADP
cana-5708	60	14	dermatoscopic	dermatoscopic	NOUN
cana-5708	60	15	images	image	NOUN
cana-5708	60	16	that	that	PRON
cana-5708	60	17	has	have	AUX
cana-5708	60	18	become	become	VERB
cana-5708	60	19	essential	essential	ADJ
cana-5708	60	20	in	in	ADP
cana-5708	60	21	the	the	DET
cana-5708	60	22	model	model	NOUN
cana-5708	60	23	training	training	NOUN
cana-5708	60	24	and	and	CCONJ
cana-5708	60	25	testing	testing	NOUN
cana-5708	60	26	process	process	NOUN
cana-5708	60	27	of	of	ADP
cana-5708	60	28	cnn	cnn	PROPN
cana-5708	60	29	models	model	NOUN
cana-5708	60	30	.	.	PUNCT
cana-5708	61	1	isic	isic	PROPN
cana-5708	61	2	archive	archive	NOUN
cana-5708	62	1	[	[	X
cana-5708	62	2	12	12	NUM
cana-5708	62	3	]	]	PUNCT
cana-5708	62	4	has	have	AUX
cana-5708	62	5	also	also	ADV
cana-5708	62	6	been	be	AUX
cana-5708	62	7	of	of	ADP
cana-5708	62	8	thorough	thorough	ADJ
cana-5708	62	9	importance	importance	NOUN
cana-5708	62	10	,	,	PUNCT
cana-5708	62	11	often	often	ADV
cana-5708	62	12	employed	employ	VERB
cana-5708	62	13	for	for	ADP
cana-5708	62	14	machine	machine	NOUN
cana-5708	62	15	learning	learning	NOUN
cana-5708	62	16	competitions	competition	NOUN
cana-5708	62	17	and	and	CCONJ
cana-5708	62	18	skin	skin	NOUN
cana-5708	62	19	lesion	lesion	NOUN
cana-5708	62	20	analysis	analysis	NOUN
cana-5708	62	21	.	.	PUNCT
cana-5708	63	1	such	such	ADJ
cana-5708	63	2	data	datum	NOUN
cana-5708	63	3	are	be	AUX
cana-5708	63	4	core	core	NOUN
cana-5708	63	5	grounds	ground	NOUN
cana-5708	63	6	for	for	ADP
cana-5708	63	7	improving	improve	VERB
cana-5708	63	8	cnn	cnn	PROPN
cana-5708	63	9	-	-	PUNCT
cana-5708	63	10	based	base	VERB
cana-5708	63	11	systems	system	NOUN
cana-5708	63	12	'	'	PART
cana-5708	63	13	performances	performance	NOUN
cana-5708	63	14	and	and	CCONJ
cana-5708	63	15	adaptability	adaptability	NOUN
cana-5708	63	16	,	,	PUNCT
cana-5708	63	17	providing	provide	VERB
cana-5708	63	18	better	well	ADJ
cana-5708	63	19	accuracy	accuracy	NOUN
cana-5708	63	20	to	to	ADP
cana-5708	63	21	them	they	PRON
cana-5708	63	22	in	in	ADP
cana-5708	63	23	real	real	ADJ
cana-5708	63	24	diagnosis	diagnosis	NOUN
cana-5708	63	25	applications	application	NOUN
cana-5708	63	26	.	.	PUNCT
cana-5708	64	1	2.1	2.1	NUM
cana-5708	64	2	skin	skin	NOUN
cana-5708	64	3	cancer	cancer	NOUN
cana-5708	64	4	detection	detection	NOUN
cana-5708	64	5	with	with	ADP
cana-5708	64	6	cnns	cnns	PROPN
cana-5708	64	7	cnns	cnn	NOUN
cana-5708	64	8	have	have	AUX
cana-5708	64	9	become	become	VERB
cana-5708	64	10	a	a	DET
cana-5708	64	11	foundation	foundation	NOUN
cana-5708	64	12	for	for	ADP
cana-5708	64	13	computerized	computerized	ADJ
cana-5708	64	14	skin	skin	NOUN
cana-5708	64	15	lesion	lesion	NOUN
cana-5708	64	16	classification	classification	NOUN
cana-5708	64	17	,	,	PUNCT
cana-5708	64	18	owing	owe	VERB
cana-5708	64	19	to	to	ADP
cana-5708	64	20	their	their	PRON
cana-5708	64	21	ability	ability	NOUN
cana-5708	64	22	to	to	PART
cana-5708	64	23	extract	extract	VERB
cana-5708	64	24	complex	complex	ADJ
cana-5708	64	25	structured	structured	ADJ
cana-5708	64	26	patterns	pattern	NOUN
cana-5708	64	27	from	from	ADP
cana-5708	64	28	dermatoscopic	dermatoscopic	ADJ
cana-5708	64	29	images	image	NOUN
cana-5708	64	30	.	.	PUNCT
cana-5708	65	1	this	this	DET
cana-5708	65	2	feature	feature	NOUN
cana-5708	65	3	eliminates	eliminate	VERB
cana-5708	65	4	the	the	DET
cana-5708	65	5	need	need	NOUN
cana-5708	65	6	for	for	ADP
cana-5708	65	7	handcrafted	handcrafted	ADJ
cana-5708	65	8	features	feature	NOUN
cana-5708	65	9	,	,	PUNCT
cana-5708	65	10	enabling	enable	VERB
cana-5708	65	11	the	the	DET
cana-5708	65	12	identification	identification	NOUN
cana-5708	65	13	of	of	ADP
cana-5708	65	14	fine	fine	ADJ
cana-5708	65	15	patterns	pattern	NOUN
cana-5708	65	16	in	in	ADP
cana-5708	65	17	dermatoscopic	dermatoscopic	NOUN
cana-5708	65	18	images	image	NOUN
cana-5708	65	19	with	with	ADP
cana-5708	65	20	high	high	ADJ
cana-5708	65	21	precision	precision	NOUN
cana-5708	65	22	.	.	PUNCT
cana-5708	66	1	numerous	numerous	ADJ
cana-5708	66	2	studies	study	NOUN
cana-5708	66	3	have	have	AUX
cana-5708	66	4	highlighted	highlight	VERB
cana-5708	66	5	the	the	DET
cana-5708	66	6	effectiveness	effectiveness	NOUN
cana-5708	66	7	of	of	ADP
cana-5708	66	8	cnn	cnn	PROPN
cana-5708	66	9	architectures	architecture	NOUN
cana-5708	66	10	,	,	PUNCT
cana-5708	66	11	occasionally	occasionally	ADV
cana-5708	66	12	matching	match	VERB
cana-5708	66	13	the	the	DET
cana-5708	66	14	expertise	expertise	NOUN
cana-5708	66	15	of	of	ADP
cana-5708	66	16	seasoned	seasoned	ADJ
cana-5708	66	17	dermatologists	dermatologist	NOUN
cana-5708	66	18	and	and	CCONJ
cana-5708	66	19	emphasizing	emphasize	VERB
cana-5708	66	20	their	their	PRON
cana-5708	66	21	transformative	transformative	ADJ
cana-5708	66	22	impact	impact	NOUN
cana-5708	66	23	on	on	ADP
cana-5708	66	24	dermatology	dermatology	NOUN
cana-5708	66	25	.	.	PUNCT
cana-5708	67	1	here	here	ADV
cana-5708	67	2	,	,	PUNCT
cana-5708	67	3	we	we	PRON
cana-5708	67	4	provide	provide	VERB
cana-5708	67	5	an	an	DET
cana-5708	67	6	overview	overview	NOUN
cana-5708	67	7	of	of	ADP
cana-5708	67	8	significant	significant	ADJ
cana-5708	67	9	work	work	NOUN
cana-5708	67	10	on	on	ADP
cana-5708	67	11	skin	skin	NOUN
cana-5708	67	12	cancer	cancer	NOUN
cana-5708	67	13	detection	detection	NOUN
cana-5708	67	14	using	use	VERB
cana-5708	67	15	cnns	cnn	NOUN
cana-5708	67	16	.	.	PUNCT
cana-5708	68	1	the	the	DET
cana-5708	68	2	table	table	NOUN
cana-5708	68	3	presents	present	VERB
cana-5708	68	4	information	information	NOUN
cana-5708	68	5	regarding	regard	VERB
cana-5708	68	6	datasets	dataset	NOUN
cana-5708	68	7	,	,	PUNCT
cana-5708	68	8	cnn	cnn	PROPN
cana-5708	68	9	architectures	architecture	NOUN
cana-5708	68	10	,	,	PUNCT
cana-5708	68	11	and	and	CCONJ
cana-5708	68	12	key	key	ADJ
cana-5708	68	13	discoveries	discovery	NOUN
cana-5708	68	14	,	,	PUNCT
cana-5708	68	15	offering	offer	VERB
cana-5708	68	16	an	an	DET
cana-5708	68	17	overview	overview	NOUN
cana-5708	68	18	of	of	ADP
cana-5708	68	19	major	major	ADJ
cana-5708	68	20	advancements	advancement	NOUN
cana-5708	68	21	in	in	ADP
cana-5708	68	22	this	this	DET
cana-5708	68	23	domain	domain	NOUN
cana-5708	68	24	.	.	PUNCT
cana-5708	69	1	essential	essential	ADJ
cana-5708	69	2	cnn	cnn	PROPN
cana-5708	69	3	research	research	NOUN
cana-5708	69	4	insights	insight	NOUN
cana-5708	69	5	are	be	AUX
cana-5708	69	6	compiled	compile	VERB
cana-5708	69	7	in	in	ADP
cana-5708	69	8	table	table	NOUN
cana-5708	69	9	i.	i.	NOUN
cana-5708	69	10	communications	communication	NOUN
cana-5708	69	11	on	on	ADP
cana-5708	69	12	applied	apply	VERB
cana-5708	69	13	nonlinear	nonlinear	ADJ
cana-5708	69	14	analysis	analysis	NOUN
cana-5708	69	15	issn	issn	NOUN
cana-5708	69	16	:	:	PUNCT
cana-5708	69	17	1074	1074	NUM
cana-5708	69	18	-	-	PUNCT
cana-5708	69	19	133x	133x	NUM
cana-5708	69	20	vol	vol	VERB
cana-5708	69	21	32	32	NUM
cana-5708	69	22	no	no	NOUN
cana-5708	69	23	.	.	PUNCT
cana-5708	70	1	10s	10	NOUN
cana-5708	70	2	(	(	PUNCT
cana-5708	70	3	2025	2025	NUM
cana-5708	70	4	)	)	PUNCT
cana-5708	70	5	2670	2670	NUM
cana-5708	70	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5708	70	7	table	table	NOUN
cana-5708	71	1	i	i	PRON
cana-5708	71	2	:	:	PUNCT
cana-5708	71	3	cnns	cnns	PROPN
cana-5708	71	4	major	major	ADJ
cana-5708	71	5	research	research	NOUN
cana-5708	71	6	findings	finding	NOUN
cana-5708	71	7	study	study	NOUN
cana-5708	71	8	dataset	dataset	VERB
cana-5708	71	9	used	use	VERB
cana-5708	71	10	cnn	cnn	PROPN
cana-5708	71	11	architecture	architecture	NOUN
cana-5708	71	12	key	key	ADJ
cana-5708	71	13	findings	finding	NOUN
cana-5708	71	14	esteva	esteva	PROPN
cana-5708	71	15	et	et	PROPN
cana-5708	71	16	al	al	PROPN
cana-5708	71	17	.	.	PROPN
cana-5708	72	1	(	(	PUNCT
cana-5708	72	2	2017	2017	NUM
cana-5708	72	3	)	)	PUNCT
cana-5708	72	4	isic	isic	PROPN
cana-5708	72	5	2017	2017	NUM
cana-5708	72	6	dataset	dataset	NOUN
cana-5708	72	7	inception	inception	PROPN
cana-5708	72	8	-	-	PUNCT
cana-5708	72	9	v3	v3	NOUN
cana-5708	72	10	achieved	achieve	VERB
cana-5708	72	11	dermatologist	dermatologist	NOUN
cana-5708	72	12	-	-	PUNCT
cana-5708	72	13	level	level	NOUN
cana-5708	72	14	accuracy	accuracy	NOUN
cana-5708	72	15	,	,	PUNCT
cana-5708	72	16	exceeding	exceed	VERB
cana-5708	72	17	70	70	NUM
cana-5708	72	18	%	%	NOUN
cana-5708	72	19	in	in	ADP
cana-5708	72	20	classification	classification	NOUN
cana-5708	72	21	tasks	task	NOUN
cana-5708	72	22	.	.	PUNCT
cana-5708	73	1	haenssle	haenssle	NOUN
cana-5708	73	2	et	et	PROPN
cana-5708	73	3	al	al	PROPN
cana-5708	73	4	.	.	PROPN
cana-5708	74	1	(	(	PUNCT
cana-5708	74	2	2018	2018	NUM
cana-5708	74	3	)	)	PUNCT
cana-5708	74	4	ham10000	ham10000	PROPN
cana-5708	74	5	dataset	dataset	PROPN
cana-5708	74	6	resnet-50	resnet-50	PROPN
cana-5708	74	7	achieved	achieve	VERB
cana-5708	74	8	high	high	ADJ
cana-5708	74	9	sensitivity	sensitivity	NOUN
cana-5708	74	10	(	(	PUNCT
cana-5708	74	11	91	91	NUM
cana-5708	74	12	%	%	NOUN
cana-5708	74	13	)	)	PUNCT
cana-5708	74	14	and	and	CCONJ
cana-5708	74	15	specificity	specificity	NOUN
cana-5708	74	16	(	(	PUNCT
cana-5708	74	17	88	88	NUM
cana-5708	74	18	%	%	NOUN
cana-5708	74	19	)	)	PUNCT
cana-5708	74	20	for	for	ADP
cana-5708	74	21	melanoma	melanoma	NOUN
cana-5708	74	22	detection	detection	NOUN
cana-5708	74	23	.	.	PUNCT
cana-5708	75	1	tschandl	tschandl	PROPN
cana-5708	75	2	et	et	PROPN
cana-5708	75	3	al	al	PROPN
cana-5708	75	4	.	.	PROPN
cana-5708	76	1	(	(	PUNCT
cana-5708	76	2	2019	2019	NUM
cana-5708	76	3	)	)	PUNCT
cana-5708	76	4	isic	isic	PROPN
cana-5708	76	5	archive	archive	PROPN
cana-5708	76	6	densenet-121	densenet-121	NOUN
cana-5708	76	7	demonstrated	demonstrate	VERB
cana-5708	76	8	superior	superior	ADJ
cana-5708	76	9	diagnostic	diagnostic	ADJ
cana-5708	76	10	accuracy	accuracy	NOUN
cana-5708	76	11	for	for	ADP
cana-5708	76	12	melanoma	melanoma	NOUN
cana-5708	76	13	compared	compare	VERB
cana-5708	76	14	to	to	ADP
cana-5708	76	15	clinicians	clinician	NOUN
cana-5708	76	16	.	.	PUNCT
cana-5708	77	1	liu	liu	PROPN
cana-5708	77	2	et	et	PROPN
cana-5708	77	3	al	al	PROPN
cana-5708	77	4	.	.	PROPN
cana-5708	77	5	(	(	PUNCT
cana-5708	77	6	2020	2020	NUM
cana-5708	77	7	)	)	PUNCT
cana-5708	77	8	various	various	ADJ
cana-5708	77	9	public	public	ADJ
cana-5708	77	10	datasets	dataset	NOUN
cana-5708	77	11	vggnet	vggnet	NOUN
cana-5708	77	12	and	and	CCONJ
cana-5708	77	13	resnet	resnet	NOUN
cana-5708	77	14	presented	present	VERB
cana-5708	77	15	a	a	DET
cana-5708	77	16	framework	framework	NOUN
cana-5708	77	17	capable	capable	ADJ
cana-5708	77	18	of	of	ADP
cana-5708	77	19	differential	differential	ADJ
cana-5708	77	20	diagnosis	diagnosis	NOUN
cana-5708	77	21	across	across	ADP
cana-5708	77	22	multiple	multiple	ADJ
cana-5708	77	23	conditions	condition	NOUN
cana-5708	77	24	.	.	PUNCT
cana-5708	78	1	2.2	2.2	NUM
cana-5708	78	2	hyperparameter	hyperparameter	NOUN
cana-5708	78	3	tuning	tune	VERB
cana-5708	78	4	techniques	technique	NOUN
cana-5708	78	5	hyperparameter	hyperparameter	NOUN
cana-5708	78	6	tuning	tune	VERB
cana-5708	78	7	plays	play	VERB
cana-5708	78	8	a	a	DET
cana-5708	78	9	critical	critical	ADJ
cana-5708	78	10	role	role	NOUN
cana-5708	78	11	in	in	ADP
cana-5708	78	12	maximizing	maximize	VERB
cana-5708	78	13	the	the	DET
cana-5708	78	14	performance	performance	NOUN
cana-5708	78	15	of	of	ADP
cana-5708	78	16	cnns	cnn	NOUN
cana-5708	78	17	in	in	ADP
cana-5708	78	18	applications	application	NOUN
cana-5708	78	19	like	like	ADP
cana-5708	78	20	skin	skin	NOUN
cana-5708	78	21	cancer	cancer	NOUN
cana-5708	78	22	detection	detection	NOUN
cana-5708	78	23	.	.	PUNCT
cana-5708	79	1	proper	proper	ADJ
cana-5708	79	2	tuning	tuning	NOUN
cana-5708	79	3	can	can	AUX
cana-5708	79	4	enhance	enhance	VERB
cana-5708	79	5	model	model	NOUN
cana-5708	79	6	accuracy	accuracy	NOUN
cana-5708	79	7	,	,	PUNCT
cana-5708	79	8	computational	computational	ADJ
cana-5708	79	9	efficiency	efficiency	NOUN
cana-5708	79	10	,	,	PUNCT
cana-5708	79	11	and	and	CCONJ
cana-5708	79	12	generalizability	generalizability	NOUN
cana-5708	79	13	.	.	PUNCT
cana-5708	80	1	however	however	ADV
cana-5708	80	2	,	,	PUNCT
cana-5708	80	3	selecting	select	VERB
cana-5708	80	4	the	the	DET
cana-5708	80	5	optimal	optimal	ADJ
cana-5708	80	6	set	set	NOUN
cana-5708	80	7	of	of	ADP
cana-5708	80	8	hyper	hyper	ADJ
cana-5708	80	9	parameters	parameter	NOUN
cana-5708	80	10	is	be	AUX
cana-5708	80	11	a	a	DET
cana-5708	80	12	challenging	challenging	ADJ
cana-5708	80	13	task	task	NOUN
cana-5708	80	14	that	that	PRON
cana-5708	80	15	requires	require	VERB
cana-5708	80	16	balancing	balance	VERB
cana-5708	80	17	computational	computational	ADJ
cana-5708	80	18	efficiency	efficiency	NOUN
cana-5708	80	19	with	with	ADP
cana-5708	80	20	thorough	thorough	ADJ
cana-5708	80	21	parameter	parameter	NOUN
cana-5708	80	22	exploration	exploration	NOUN
cana-5708	80	23	.	.	PUNCT
cana-5708	81	1	below	below	ADV
cana-5708	81	2	,	,	PUNCT
cana-5708	81	3	we	we	PRON
cana-5708	81	4	examine	examine	VERB
cana-5708	81	5	three	three	NUM
cana-5708	81	6	prominent	prominent	ADJ
cana-5708	81	7	techniques	technique	NOUN
cana-5708	81	8	for	for	ADP
cana-5708	81	9	optimizing	optimize	VERB
cana-5708	81	10	hyper	hyper	ADJ
cana-5708	81	11	parameters	parameter	NOUN
cana-5708	81	12	—	—	PUNCT
cana-5708	81	13	manual	manual	ADJ
cana-5708	81	14	tuning	tuning	NOUN
cana-5708	81	15	,	,	PUNCT
cana-5708	81	16	grid	grid	NOUN
cana-5708	81	17	search	search	NOUN
cana-5708	81	18	,	,	PUNCT
cana-5708	81	19	and	and	CCONJ
cana-5708	81	20	random	random	ADJ
cana-5708	81	21	search	search	NOUN
cana-5708	81	22	—	—	PUNCT
cana-5708	81	23	while	while	SCONJ
cana-5708	81	24	discussing	discuss	VERB
cana-5708	81	25	their	their	PRON
cana-5708	81	26	strengths	strength	NOUN
cana-5708	81	27	and	and	CCONJ
cana-5708	81	28	limitations	limitation	NOUN
cana-5708	81	29	.	.	PUNCT
cana-5708	82	1	key	key	ADJ
cana-5708	82	2	benefits	benefit	NOUN
cana-5708	82	3	and	and	CCONJ
cana-5708	82	4	drawbacks	drawback	NOUN
cana-5708	82	5	of	of	ADP
cana-5708	82	6	these	these	DET
cana-5708	82	7	traditional	traditional	ADJ
cana-5708	82	8	hyper	hyper	ADJ
cana-5708	82	9	parameter	parameter	NOUN
cana-5708	82	10	optimization	optimization	NOUN
cana-5708	82	11	techniques	technique	NOUN
cana-5708	82	12	have	have	AUX
cana-5708	82	13	been	be	AUX
cana-5708	82	14	outlined	outline	VERB
cana-5708	82	15	in	in	ADP
cana-5708	82	16	table	table	NOUN
cana-5708	82	17	ii	ii	PROPN
cana-5708	82	18	.	.	PROPN
cana-5708	82	19	table	table	PROPN
cana-5708	82	20	ii	ii	PROPN
cana-5708	82	21	:	:	PUNCT
cana-5708	82	22	key	key	ADJ
cana-5708	82	23	strengths	strength	NOUN
cana-5708	82	24	and	and	CCONJ
cana-5708	82	25	limitations	limitation	NOUN
cana-5708	82	26	of	of	ADP
cana-5708	82	27	three	three	NUM
cana-5708	82	28	traditional	traditional	ADJ
cana-5708	82	29	hyper	hyper	ADJ
cana-5708	82	30	parameter	parameter	NOUN
cana-5708	82	31	optimization	optimization	NOUN
cana-5708	82	32	methods	method	NOUN
cana-5708	82	33	.	.	PUNCT
cana-5708	83	1	tuning	tune	VERB
cana-5708	83	2	method	method	NOUN
cana-5708	83	3	advantages	advantage	NOUN
cana-5708	83	4	disadvantages	disadvantage	VERB
cana-5708	83	5	manual	manual	ADJ
cana-5708	83	6	tuning	tune	VERB
cana-5708	83	7	simple	simple	ADJ
cana-5708	83	8	to	to	PART
cana-5708	83	9	implement	implement	VERB
cana-5708	83	10	.	.	PUNCT
cana-5708	84	1	low	low	ADJ
cana-5708	84	2	computational	computational	ADJ
cana-5708	84	3	cost	cost	NOUN
cana-5708	84	4	.	.	PUNCT
cana-5708	85	1	time	time	NOUN
cana-5708	85	2	-	-	PUNCT
cana-5708	85	3	consuming	consume	VERB
cana-5708	85	4	.	.	PUNCT
cana-5708	86	1	prone	prone	ADJ
cana-5708	86	2	to	to	ADP
cana-5708	86	3	human	human	ADJ
cana-5708	86	4	error	error	NOUN
cana-5708	86	5	.	.	PUNCT
cana-5708	87	1	inefficient	inefficient	ADJ
cana-5708	87	2	.	.	PUNCT
cana-5708	88	1	grid	grid	NOUN
cana-5708	88	2	search	search	NOUN
cana-5708	88	3	systematic	systematic	ADJ
cana-5708	88	4	and	and	CCONJ
cana-5708	88	5	exhaustive	exhaustive	ADJ
cana-5708	88	6	search	search	NOUN
cana-5708	88	7	.	.	PUNCT
cana-5708	89	1	easy	easy	ADJ
cana-5708	89	2	to	to	PART
cana-5708	89	3	apply	apply	VERB
cana-5708	89	4	with	with	ADP
cana-5708	89	5	tools	tool	NOUN
cana-5708	89	6	like	like	ADP
cana-5708	89	7	scikit	scikit	NOUN
cana-5708	89	8	-	-	PUNCT
cana-5708	89	9	learn	learn	VERB
cana-5708	89	10	.	.	PUNCT
cana-5708	90	1	computationally	computationally	ADV
cana-5708	90	2	expensive	expensive	ADJ
cana-5708	90	3	.	.	PUNCT
cana-5708	91	1	inefficient	inefficient	ADJ
cana-5708	91	2	for	for	ADP
cana-5708	91	3	large	large	ADJ
cana-5708	91	4	parameter	parameter	NOUN
cana-5708	91	5	spaces	space	NOUN
cana-5708	91	6	.	.	PUNCT
cana-5708	92	1	random	random	ADJ
cana-5708	92	2	search	search	NOUN
cana-5708	92	3	covers	cover	VERB
cana-5708	92	4	a	a	DET
cana-5708	92	5	broader	broad	ADJ
cana-5708	92	6	range	range	NOUN
cana-5708	92	7	of	of	ADP
cana-5708	92	8	hyperparameter	hyperparameter	NOUN
cana-5708	92	9	space	space	NOUN
cana-5708	92	10	.	.	PUNCT
cana-5708	93	1	may	may	AUX
cana-5708	93	2	not	not	PART
cana-5708	93	3	find	find	VERB
cana-5708	93	4	the	the	DET
cana-5708	93	5	optimal	optimal	ADJ
cana-5708	93	6	hyperparameters	hyperparameter	NOUN
cana-5708	93	7	.	.	PUNCT
cana-5708	94	1	communications	communication	NOUN
cana-5708	94	2	on	on	ADP
cana-5708	94	3	applied	apply	VERB
cana-5708	94	4	nonlinear	nonlinear	ADJ
cana-5708	94	5	analysis	analysis	NOUN
cana-5708	94	6	issn	issn	NOUN
cana-5708	94	7	:	:	PUNCT
cana-5708	94	8	1074	1074	NUM
cana-5708	94	9	-	-	PUNCT
cana-5708	94	10	133x	133x	NUM
cana-5708	94	11	vol	vol	VERB
cana-5708	94	12	32	32	NUM
cana-5708	94	13	no	no	NOUN
cana-5708	94	14	.	.	PUNCT
cana-5708	95	1	10s	10	NOUN
cana-5708	95	2	(	(	PUNCT
cana-5708	95	3	2025	2025	NUM
cana-5708	95	4	)	)	PUNCT
cana-5708	95	5	2671	2671	NUM
cana-5708	95	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5708	95	7	tuning	tune	VERB
cana-5708	95	8	method	method	NOUN
cana-5708	95	9	advantages	advantage	NOUN
cana-5708	95	10	disadvantages	disadvantage	VERB
cana-5708	95	11	more	more	ADV
cana-5708	95	12	efficient	efficient	ADJ
cana-5708	95	13	than	than	ADP
cana-5708	95	14	grid	grid	NOUN
cana-5708	95	15	search	search	NOUN
cana-5708	95	16	.	.	PUNCT
cana-5708	96	1	computationally	computationally	ADV
cana-5708	96	2	demanding	demanding	ADJ
cana-5708	96	3	.	.	PUNCT
cana-5708	97	1	2.3	2.3	NUM
cana-5708	97	2	hyperband	hyperband	NOUN
cana-5708	97	3	in	in	ADP
cana-5708	97	4	machine	machine	NOUN
cana-5708	97	5	learning	learning	NOUN
cana-5708	97	6	hyperband	hyperband	PROPN
cana-5708	97	7	is	be	AUX
cana-5708	97	8	a	a	DET
cana-5708	97	9	new	new	ADJ
cana-5708	97	10	hyperparameter	hyperparameter	NOUN
cana-5708	97	11	optimization	optimization	NOUN
cana-5708	97	12	algorithm	algorithm	NOUN
cana-5708	97	13	that	that	PRON
cana-5708	97	14	seeks	seek	VERB
cana-5708	97	15	to	to	PART
cana-5708	97	16	rid	rid	VERB
cana-5708	97	17	us	we	PRON
cana-5708	97	18	of	of	ADP
cana-5708	97	19	such	such	ADJ
cana-5708	97	20	classical	classical	ADJ
cana-5708	97	21	methods	method	NOUN
cana-5708	97	22	as	as	ADP
cana-5708	97	23	random	random	ADJ
cana-5708	97	24	search	search	NOUN
cana-5708	97	25	and	and	CCONJ
cana-5708	97	26	grid	grid	NOUN
cana-5708	97	27	search	search	NOUN
cana-5708	97	28	of	of	ADP
cana-5708	97	29	their	their	PRON
cana-5708	97	30	inefficiencies	inefficiency	NOUN
cana-5708	97	31	.	.	PUNCT
cana-5708	98	1	through	through	ADP
cana-5708	98	2	a	a	DET
cana-5708	98	3	larger	large	ADJ
cana-5708	98	4	resource	resource	NOUN
cana-5708	98	5	investment	investment	NOUN
cana-5708	98	6	in	in	ADP
cana-5708	98	7	high	high	ADJ
cana-5708	98	8	-	-	PUNCT
cana-5708	98	9	potential	potential	ADJ
cana-5708	98	10	configurations	configuration	NOUN
cana-5708	98	11	and	and	CCONJ
cana-5708	98	12	drastic	drastic	ADJ
cana-5708	98	13	elimination	elimination	NOUN
cana-5708	98	14	of	of	ADP
cana-5708	98	15	poor	poor	ADJ
cana-5708	98	16	performers	performer	NOUN
cana-5708	98	17	,	,	PUNCT
cana-5708	98	18	hyperband	hyperband	NOUN
cana-5708	98	19	enables	enable	VERB
cana-5708	98	20	effective	effective	ADJ
cana-5708	98	21	exploration	exploration	NOUN
cana-5708	98	22	of	of	ADP
cana-5708	98	23	the	the	DET
cana-5708	98	24	hyperparameter	hyperparameter	NOUN
cana-5708	98	25	space	space	NOUN
cana-5708	98	26	.	.	PUNCT
cana-5708	99	1	the	the	DET
cana-5708	99	2	approach	approach	NOUN
cana-5708	99	3	is	be	AUX
cana-5708	99	4	grounded	ground	VERB
cana-5708	99	5	on	on	ADP
cana-5708	99	6	successive	successive	ADJ
cana-5708	99	7	halving	halving	NOUN
cana-5708	99	8	,	,	PUNCT
cana-5708	99	9	a	a	DET
cana-5708	99	10	technique	technique	NOUN
cana-5708	99	11	that	that	PRON
cana-5708	99	12	iteratively	iteratively	ADV
cana-5708	99	13	screens	screen	NOUN
cana-5708	99	14	and	and	CCONJ
cana-5708	99	15	prunes	prune	NOUN
cana-5708	99	16	suboptimal	suboptimal	ADJ
cana-5708	99	17	configurations	configuration	NOUN
cana-5708	99	18	,	,	PUNCT
cana-5708	99	19	with	with	ADP
cana-5708	99	20	the	the	DET
cana-5708	99	21	available	available	ADJ
cana-5708	99	22	computational	computational	ADJ
cana-5708	99	23	resources	resource	NOUN
cana-5708	99	24	devoted	devote	VERB
cana-5708	99	25	to	to	ADP
cana-5708	99	26	the	the	DET
cana-5708	99	27	highest	high	ADJ
cana-5708	99	28	-	-	PUNCT
cana-5708	99	29	potential	potential	ADJ
cana-5708	99	30	ones	one	NOUN
cana-5708	99	31	.	.	PUNCT
cana-5708	100	1	applications	application	NOUN
cana-5708	100	2	and	and	CCONJ
cana-5708	100	3	results	result	NOUN
cana-5708	100	4	of	of	ADP
cana-5708	100	5	these	these	DET
cana-5708	100	6	machine	machine	NOUN
cana-5708	100	7	learning	learn	VERB
cana-5708	100	8	hyper	hyper	ADJ
cana-5708	100	9	parameter	parameter	NOUN
cana-5708	100	10	optimization	optimization	NOUN
cana-5708	100	11	techniques	technique	NOUN
cana-5708	100	12	have	have	AUX
cana-5708	100	13	been	be	AUX
cana-5708	100	14	outlined	outline	VERB
cana-5708	100	15	in	in	ADP
cana-5708	100	16	table	table	NOUN
cana-5708	100	17	iii	iii	PROPN
cana-5708	100	18	.	.	PUNCT
cana-5708	100	19	fig	fig	NOUN
cana-5708	100	20	.	.	PUNCT
cana-5708	101	1	i	i	PRON
cana-5708	101	2	overview	overview	VERB
cana-5708	101	3	of	of	ADP
cana-5708	101	4	cnn	cnn	PROPN
cana-5708	101	5	architecture	architecture	NOUN
cana-5708	101	6	optimized	optimize	VERB
cana-5708	101	7	via	via	ADP
cana-5708	101	8	hyperband	hyperband	NOUN
cana-5708	102	1	[	[	X
cana-5708	102	2	23	23	NUM
cana-5708	102	3	]	]	PUNCT
cana-5708	102	4	this	this	DET
cana-5708	102	5	figure	figure	NOUN
cana-5708	102	6	i	i	PRON
cana-5708	102	7	,	,	PUNCT
cana-5708	102	8	illustrates	illustrate	VERB
cana-5708	102	9	an	an	DET
cana-5708	102	10	overview	overview	NOUN
cana-5708	102	11	of	of	ADP
cana-5708	102	12	an	an	DET
cana-5708	102	13	optimized	optimize	VERB
cana-5708	102	14	convolutional	convolutional	ADJ
cana-5708	102	15	neural	neural	ADJ
cana-5708	102	16	network	network	NOUN
cana-5708	102	17	(	(	PUNCT
cana-5708	102	18	cnn	cnn	PROPN
cana-5708	102	19	)	)	PUNCT
cana-5708	102	20	architecture	architecture	NOUN
cana-5708	102	21	for	for	ADP
cana-5708	102	22	skin	skin	NOUN
cana-5708	102	23	cancer	cancer	NOUN
cana-5708	102	24	detection	detection	NOUN
cana-5708	102	25	using	use	VERB
cana-5708	102	26	the	the	DET
cana-5708	102	27	hyperband	hyperband	ADJ
cana-5708	102	28	algorithm	algorithm	NOUN
cana-5708	102	29	.	.	PUNCT
cana-5708	103	1	the	the	DET
cana-5708	103	2	architecture	architecture	NOUN
cana-5708	103	3	consists	consist	VERB
cana-5708	103	4	of	of	ADP
cana-5708	103	5	two	two	NUM
cana-5708	103	6	main	main	ADJ
cana-5708	103	7	entities	entity	NOUN
cana-5708	103	8	:	:	PUNCT
cana-5708	103	9	customized	customize	VERB
cana-5708	103	10	models	model	NOUN
cana-5708	103	11	and	and	CCONJ
cana-5708	103	12	transferred	transfer	VERB
cana-5708	103	13	models	model	NOUN
cana-5708	103	14	.	.	PUNCT
cana-5708	104	1	●	●	NUM
cana-5708	104	2	customized	customize	VERB
cana-5708	104	3	models	model	NOUN
cana-5708	104	4	:	:	PUNCT
cana-5708	104	5	the	the	DET
cana-5708	104	6	input	input	NOUN
cana-5708	104	7	is	be	AUX
cana-5708	104	8	passed	pass	VERB
cana-5708	104	9	through	through	ADP
cana-5708	104	10	multiple	multiple	ADJ
cana-5708	104	11	convolutional	convolutional	ADJ
cana-5708	104	12	layers	layer	NOUN
cana-5708	104	13	(	(	PUNCT
cana-5708	104	14	conv	conv	PROPN
cana-5708	104	15	b1	b1	PROPN
cana-5708	104	16	to	to	ADP
cana-5708	104	17	conv	conv	ADJ
cana-5708	104	18	b5	b5	PROPN
cana-5708	104	19	)	)	PUNCT
cana-5708	104	20	,	,	PUNCT
cana-5708	104	21	and	and	CCONJ
cana-5708	104	22	then	then	ADV
cana-5708	104	23	fully	fully	ADV
cana-5708	104	24	connected	connected	ADJ
cana-5708	104	25	layers	layer	NOUN
cana-5708	104	26	(	(	PUNCT
cana-5708	104	27	fc1	fc1	PROPN
cana-5708	104	28	,	,	PUNCT
cana-5708	104	29	fc2	fc2	PROPN
cana-5708	104	30	,	,	PUNCT
cana-5708	104	31	fc3	fc3	PROPN
cana-5708	104	32	)	)	PUNCT
cana-5708	104	33	in	in	ADP
cana-5708	104	34	order	order	NOUN
cana-5708	104	35	to	to	PART
cana-5708	104	36	classify	classify	VERB
cana-5708	104	37	the	the	DET
cana-5708	104	38	skin	skin	NOUN
cana-5708	104	39	lesion	lesion	NOUN
cana-5708	104	40	.	.	PUNCT
cana-5708	105	1	hyperband	hyperband	PROPN
cana-5708	105	2	optimizes	optimize	VERB
cana-5708	105	3	key	key	ADJ
cana-5708	105	4	hyperparameters	hyperparameter	NOUN
cana-5708	105	5	like	like	ADP
cana-5708	105	6	dropout	dropout	NOUN
cana-5708	105	7	rates	rate	NOUN
cana-5708	105	8	,	,	PUNCT
cana-5708	105	9	learning	learn	VERB
cana-5708	105	10	rates	rate	NOUN
cana-5708	105	11	,	,	PUNCT
cana-5708	105	12	as	as	ADV
cana-5708	105	13	well	well	ADV
cana-5708	105	14	as	as	ADP
cana-5708	105	15	layer	layer	NOUN
cana-5708	105	16	count	count	NOUN
cana-5708	105	17	to	to	PART
cana-5708	105	18	enhance	enhance	VERB
cana-5708	105	19	performance	performance	NOUN
cana-5708	105	20	and	and	CCONJ
cana-5708	105	21	efficiency	efficiency	NOUN
cana-5708	105	22	in	in	ADP
cana-5708	105	23	the	the	DET
cana-5708	105	24	model	model	NOUN
cana-5708	105	25	.	.	PUNCT
cana-5708	106	1	●	●	NUM
cana-5708	106	2	transferred	transfer	VERB
cana-5708	106	3	models	model	NOUN
cana-5708	106	4	:	:	PUNCT
cana-5708	106	5	transferred	transfer	VERB
cana-5708	106	6	models	model	NOUN
cana-5708	106	7	like	like	ADP
cana-5708	106	8	resnet50	resnet50	NOUN
cana-5708	106	9	and	and	CCONJ
cana-5708	106	10	xception	xception	NOUN
cana-5708	106	11	are	be	AUX
cana-5708	106	12	used	use	VERB
cana-5708	106	13	in	in	ADP
cana-5708	106	14	the	the	DET
cana-5708	106	15	model	model	NOUN
cana-5708	106	16	,	,	PUNCT
cana-5708	106	17	which	which	PRON
cana-5708	106	18	are	be	AUX
cana-5708	106	19	once	once	ADV
cana-5708	106	20	again	again	ADV
cana-5708	106	21	fine	fine	ADV
cana-5708	106	22	-	-	PUNCT
cana-5708	106	23	tuned	tune	VERB
cana-5708	106	24	with	with	ADP
cana-5708	106	25	global	global	ADJ
cana-5708	106	26	average	average	ADJ
cana-5708	106	27	pooling	pooling	NOUN
cana-5708	106	28	for	for	ADP
cana-5708	106	29	classification	classification	NOUN
cana-5708	106	30	.	.	PUNCT
cana-5708	107	1	communications	communication	NOUN
cana-5708	107	2	on	on	ADP
cana-5708	107	3	applied	apply	VERB
cana-5708	107	4	nonlinear	nonlinear	ADJ
cana-5708	107	5	analysis	analysis	NOUN
cana-5708	107	6	issn	issn	NOUN
cana-5708	107	7	:	:	PUNCT
cana-5708	107	8	1074	1074	NUM
cana-5708	107	9	-	-	PUNCT
cana-5708	107	10	133x	133x	NUM
cana-5708	107	11	vol	vol	VERB
cana-5708	107	12	32	32	NUM
cana-5708	107	13	no	no	NOUN
cana-5708	107	14	.	.	PUNCT
cana-5708	108	1	10s	10	NOUN
cana-5708	108	2	(	(	PUNCT
cana-5708	108	3	2025	2025	NUM
cana-5708	108	4	)	)	PUNCT
cana-5708	108	5	2672	2672	NUM
cana-5708	108	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5708	108	7	table	table	NOUN
cana-5708	108	8	iii	iii	NOUN
cana-5708	108	9	:	:	PUNCT
cana-5708	108	10	hyperband	hyperband	NOUN
cana-5708	108	11	in	in	ADP
cana-5708	108	12	ai	ai	PROPN
cana-5708	108	13	-	-	PUNCT
cana-5708	108	14	driven	drive	VERB
cana-5708	108	15	applications	application	NOUN
cana-5708	108	16	study	study	NOUN
cana-5708	108	17	application	application	NOUN
cana-5708	108	18	results	result	NOUN
cana-5708	108	19	li	li	PROPN
cana-5708	108	20	et	et	PROPN
cana-5708	108	21	al	al	PROPN
cana-5708	108	22	.	.	PROPN
cana-5708	109	1	(	(	PUNCT
cana-5708	109	2	2017	2017	NUM
cana-5708	109	3	)	)	PUNCT
cana-5708	109	4	neural	neural	ADJ
cana-5708	109	5	network	network	NOUN
cana-5708	109	6	hyperparameter	hyperparameter	NOUN
cana-5708	109	7	optimization	optimization	NOUN
cana-5708	109	8	demonstrated	demonstrate	VERB
cana-5708	109	9	superior	superior	ADJ
cana-5708	109	10	performance	performance	NOUN
cana-5708	109	11	over	over	ADP
cana-5708	109	12	random	random	ADJ
cana-5708	109	13	search	search	NOUN
cana-5708	109	14	in	in	ADP
cana-5708	109	15	image	image	NOUN
cana-5708	109	16	classification	classification	NOUN
cana-5708	109	17	tasks	task	NOUN
cana-5708	109	18	.	.	PUNCT
cana-5708	110	1	zoph	zoph	NOUN
cana-5708	110	2	et	et	PROPN
cana-5708	110	3	al	al	PROPN
cana-5708	110	4	.	.	PROPN
cana-5708	111	1	(	(	PUNCT
cana-5708	111	2	2018	2018	NUM
cana-5708	111	3	)	)	PUNCT
cana-5708	111	4	neural	neural	ADJ
cana-5708	111	5	architecture	architecture	NOUN
cana-5708	111	6	search	search	NOUN
cana-5708	111	7	efficiently	efficiently	ADV
cana-5708	111	8	identified	identify	VERB
cana-5708	111	9	high	high	ADV
cana-5708	111	10	-	-	PUNCT
cana-5708	111	11	performing	perform	VERB
cana-5708	111	12	architectures	architecture	NOUN
cana-5708	111	13	with	with	ADP
cana-5708	111	14	fewer	few	ADJ
cana-5708	111	15	computational	computational	ADJ
cana-5708	111	16	resources	resource	NOUN
cana-5708	111	17	compared	compare	VERB
cana-5708	111	18	to	to	ADP
cana-5708	111	19	traditional	traditional	ADJ
cana-5708	111	20	methods	method	NOUN
cana-5708	111	21	.	.	PUNCT
cana-5708	112	1	chen	chen	PROPN
cana-5708	112	2	et	et	PROPN
cana-5708	112	3	al	al	PROPN
cana-5708	112	4	.	.	PROPN
cana-5708	112	5	(	(	PUNCT
cana-5708	112	6	2020	2020	NUM
cana-5708	112	7	)	)	PUNCT
cana-5708	112	8	image	image	NOUN
cana-5708	112	9	classification	classification	NOUN
cana-5708	112	10	in	in	ADP
cana-5708	112	11	medical	medical	ADJ
cana-5708	112	12	imaging	imaging	NOUN
cana-5708	112	13	reduced	reduce	VERB
cana-5708	112	14	computational	computational	ADJ
cana-5708	112	15	costs	cost	NOUN
cana-5708	112	16	while	while	SCONJ
cana-5708	112	17	improving	improve	VERB
cana-5708	112	18	accuracy	accuracy	NOUN
cana-5708	112	19	in	in	ADP
cana-5708	112	20	cnn	cnn	PROPN
cana-5708	112	21	models	model	NOUN
cana-5708	112	22	for	for	ADP
cana-5708	112	23	skin	skin	NOUN
cana-5708	112	24	cancer	cancer	NOUN
cana-5708	112	25	detection	detection	NOUN
cana-5708	112	26	.	.	PUNCT
cana-5708	113	1	3	3	X
cana-5708	113	2	.	.	X
cana-5708	113	3	resources	resource	NOUN
cana-5708	113	4	and	and	CCONJ
cana-5708	113	5	techniques	technique	NOUN
cana-5708	113	6	the	the	DET
cana-5708	113	7	study	study	NOUN
cana-5708	113	8	presents	present	VERB
cana-5708	113	9	the	the	DET
cana-5708	113	10	tools	tool	NOUN
cana-5708	113	11	used	use	VERB
cana-5708	113	12	in	in	ADP
cana-5708	113	13	the	the	DET
cana-5708	113	14	research	research	NOUN
cana-5708	113	15	.	.	PUNCT
cana-5708	114	1	it	it	PRON
cana-5708	114	2	includes	include	VERB
cana-5708	114	3	the	the	DET
cana-5708	114	4	dataset	dataset	NOUN
cana-5708	114	5	,	,	PUNCT
cana-5708	114	6	cnn	cnn	PROPN
cana-5708	114	7	architecture	architecture	NOUN
cana-5708	114	8	,	,	PUNCT
cana-5708	114	9	optimization	optimization	NOUN
cana-5708	114	10	and	and	CCONJ
cana-5708	114	11	hyperparameter	hyperparameter	NOUN
cana-5708	114	12	tuning	tune	VERB
cana-5708	114	13	.	.	PUNCT
cana-5708	115	1	3.1	3.1	NUM
cana-5708	115	2	experiment	experiment	NOUN
cana-5708	115	3	configuration	configuration	NOUN
cana-5708	115	4	the	the	DET
cana-5708	115	5	model	model	NOUN
cana-5708	115	6	was	be	AUX
cana-5708	115	7	trained	train	VERB
cana-5708	115	8	on	on	ADP
cana-5708	115	9	a	a	DET
cana-5708	115	10	cloud	cloud	NOUN
cana-5708	115	11	-	-	PUNCT
cana-5708	115	12	building	building	NOUN
cana-5708	115	13	flexible	flexible	ADJ
cana-5708	115	14	platform	platform	NOUN
cana-5708	115	15	,	,	PUNCT
cana-5708	115	16	with	with	ADP
cana-5708	115	17	nvidia	nvidia	PROPN
cana-5708	115	18	tesla	tesla	PROPN
cana-5708	115	19	t4	t4	PROPN
cana-5708	115	20	gpus	gpus	PROPN
cana-5708	115	21	for	for	ADP
cana-5708	115	22	computational	computational	ADJ
cana-5708	115	23	efficiency	efficiency	NOUN
cana-5708	115	24	that	that	PRON
cana-5708	115	25	provided	provide	VERB
cana-5708	115	26	a	a	DET
cana-5708	115	27	costeffective	costeffective	ADJ
cana-5708	115	28	training	training	NOUN
cana-5708	115	29	sandbox	sandbox	NOUN
cana-5708	115	30	for	for	ADP
cana-5708	115	31	very	very	ADV
cana-5708	115	32	large	large	ADJ
cana-5708	115	33	models	model	NOUN
cana-5708	115	34	.	.	PUNCT
cana-5708	116	1	to	to	PART
cana-5708	116	2	ensure	ensure	VERB
cana-5708	116	3	reproducibility	reproducibility	NOUN
cana-5708	116	4	,	,	PUNCT
cana-5708	116	5	fixed	fix	VERB
cana-5708	116	6	random	random	ADJ
cana-5708	116	7	seeds	seed	NOUN
cana-5708	116	8	were	be	AUX
cana-5708	116	9	used	use	VERB
cana-5708	116	10	for	for	ADP
cana-5708	116	11	each	each	DET
cana-5708	116	12	experiment	experiment	NOUN
cana-5708	116	13	.	.	PUNCT
cana-5708	117	1	model	model	NOUN
cana-5708	117	2	performance	performance	NOUN
cana-5708	117	3	was	be	AUX
cana-5708	117	4	evaluated	evaluate	VERB
cana-5708	117	5	using	use	VERB
cana-5708	117	6	standard	standard	ADJ
cana-5708	117	7	metrics	metric	NOUN
cana-5708	117	8	,	,	PUNCT
cana-5708	117	9	including	include	VERB
cana-5708	117	10	accuracy	accuracy	NOUN
cana-5708	117	11	,	,	PUNCT
cana-5708	117	12	precision	precision	NOUN
cana-5708	117	13	,	,	PUNCT
cana-5708	117	14	recall	recall	NOUN
cana-5708	117	15	,	,	PUNCT
cana-5708	117	16	f1score	f1score	NOUN
cana-5708	117	17	,	,	PUNCT
cana-5708	117	18	and	and	CCONJ
cana-5708	117	19	area	area	NOUN
cana-5708	117	20	under	under	ADP
cana-5708	117	21	the	the	DET
cana-5708	117	22	roc	roc	PROPN
cana-5708	117	23	curve	curve	NOUN
cana-5708	117	24	(	(	PUNCT
cana-5708	117	25	auc	auc	NOUN
cana-5708	117	26	)	)	PUNCT
cana-5708	117	27	,	,	PUNCT
cana-5708	117	28	calculated	calculate	VERB
cana-5708	117	29	using	use	VERB
cana-5708	117	30	scikit	scikit	NOUN
cana-5708	117	31	-	-	PUNCT
cana-5708	117	32	learn	learn	VERB
cana-5708	117	33	[	[	X
cana-5708	117	34	21	21	NUM
cana-5708	117	35	]	]	X
cana-5708	117	36	,	,	PUNCT
cana-5708	117	37	a	a	DET
cana-5708	117	38	python	python	NOUN
cana-5708	117	39	machine	machine	NOUN
cana-5708	117	40	learning	learn	VERB
cana-5708	117	41	learning	learning	NOUN
cana-5708	117	42	and	and	CCONJ
cana-5708	117	43	statistical	statistical	ADJ
cana-5708	117	44	computing	computing	NOUN
cana-5708	117	45	.	.	PUNCT
cana-5708	118	1	the	the	DET
cana-5708	118	2	adam	adam	PROPN
cana-5708	118	3	optimizer	optimizer	NOUN
cana-5708	118	4	[	[	X
cana-5708	118	5	10	10	NUM
cana-5708	118	6	]	]	PUNCT
cana-5708	118	7	was	be	AUX
cana-5708	118	8	employed	employ	VERB
cana-5708	118	9	during	during	ADP
cana-5708	118	10	training	training	NOUN
cana-5708	118	11	since	since	SCONJ
cana-5708	118	12	it	it	PRON
cana-5708	118	13	helps	help	VERB
cana-5708	118	14	adapt	adapt	VERB
cana-5708	118	15	the	the	DET
cana-5708	118	16	learning	learning	NOUN
cana-5708	118	17	rate	rate	NOUN
cana-5708	118	18	nicely	nicely	ADV
cana-5708	118	19	by	by	ADP
cana-5708	118	20	taking	take	VERB
cana-5708	118	21	into	into	ADP
cana-5708	118	22	account	account	NOUN
cana-5708	118	23	the	the	DET
cana-5708	118	24	first	first	ADJ
cana-5708	118	25	two	two	NUM
cana-5708	118	26	moments	moment	NOUN
cana-5708	118	27	of	of	ADP
cana-5708	118	28	the	the	DET
cana-5708	118	29	gradient	gradient	NOUN
cana-5708	118	30	.	.	PUNCT
cana-5708	119	1	generalization	generalization	NOUN
cana-5708	119	2	was	be	AUX
cana-5708	119	3	assessed	assess	VERB
cana-5708	119	4	on	on	ADP
cana-5708	119	5	held	hold	VERB
cana-5708	119	6	-	-	PUNCT
cana-5708	119	7	out	out	ADP
cana-5708	119	8	test	test	NOUN
cana-5708	119	9	data	datum	NOUN
cana-5708	119	10	.	.	PUNCT
cana-5708	120	1	3.2	3.2	NUM
cana-5708	120	2	dataset	dataset	NOUN
cana-5708	120	3	overview	overview	NOUN
cana-5708	120	4	here	here	ADV
cana-5708	120	5	is	be	AUX
cana-5708	120	6	discussed	discuss	VERB
cana-5708	120	7	the	the	DET
cana-5708	120	8	training	training	NOUN
cana-5708	120	9	and	and	CCONJ
cana-5708	120	10	validation	validation	NOUN
cana-5708	120	11	data	datum	NOUN
cana-5708	120	12	,	,	PUNCT
cana-5708	120	13	as	as	ADV
cana-5708	120	14	well	well	ADV
cana-5708	120	15	as	as	ADP
cana-5708	120	16	pre	pre	ADJ
cana-5708	120	17	-	-	ADJ
cana-5708	120	18	processing	processing	ADJ
cana-5708	120	19	methods	method	NOUN
cana-5708	120	20	and	and	CCONJ
cana-5708	120	21	augmentation	augmentation	NOUN
cana-5708	120	22	strategies	strategy	NOUN
cana-5708	120	23	applied	apply	VERB
cana-5708	120	24	to	to	PART
cana-5708	120	25	enhance	enhance	VERB
cana-5708	120	26	the	the	DET
cana-5708	120	27	effectiveness	effectiveness	NOUN
cana-5708	120	28	of	of	ADP
cana-5708	120	29	the	the	DET
cana-5708	120	30	model	model	NOUN
cana-5708	120	31	.	.	PUNCT
cana-5708	121	1	the	the	DET
cana-5708	121	2	datasets	dataset	NOUN
cana-5708	121	3	employed	employ	VERB
cana-5708	121	4	in	in	ADP
cana-5708	121	5	this	this	DET
cana-5708	121	6	work	work	NOUN
cana-5708	121	7	is	be	AUX
cana-5708	121	8	given	give	VERB
cana-5708	121	9	in	in	ADP
cana-5708	121	10	figure	figure	NOUN
cana-5708	121	11	ii	ii	PROPN
cana-5708	121	12	:	:	PUNCT
cana-5708	121	13	fig	fig	NOUN
cana-5708	121	14	.	.	PUNCT
cana-5708	122	1	ii	ii	PROPN
cana-5708	122	2	image	image	NOUN
cana-5708	122	3	dataset	dataset	NOUN
cana-5708	122	4	representation	representation	NOUN
cana-5708	122	5	of	of	ADP
cana-5708	122	6	various	various	ADJ
cana-5708	122	7	skin	skin	NOUN
cana-5708	122	8	cancer	cancer	NOUN
cana-5708	122	9	images	image	VERB
cana-5708	122	10	communications	communication	NOUN
cana-5708	122	11	on	on	ADP
cana-5708	122	12	applied	apply	VERB
cana-5708	122	13	nonlinear	nonlinear	ADJ
cana-5708	122	14	analysis	analysis	NOUN
cana-5708	122	15	issn	issn	NOUN
cana-5708	122	16	:	:	PUNCT
cana-5708	122	17	1074	1074	NUM
cana-5708	122	18	-	-	PUNCT
cana-5708	122	19	133x	133x	NUM
cana-5708	122	20	vol	vol	VERB
cana-5708	122	21	32	32	NUM
cana-5708	122	22	no	no	NOUN
cana-5708	122	23	.	.	PUNCT
cana-5708	123	1	10s	10	NOUN
cana-5708	123	2	(	(	PUNCT
cana-5708	123	3	2025	2025	NUM
cana-5708	123	4	)	)	PUNCT
cana-5708	123	5	2673	2673	NUM
cana-5708	123	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5708	124	1	this	this	DET
cana-5708	124	2	figure	figure	NOUN
cana-5708	124	3	ii	ii	PROPN
cana-5708	124	4	,	,	PUNCT
cana-5708	124	5	shows	show	VERB
cana-5708	124	6	the	the	DET
cana-5708	124	7	varied	varied	ADJ
cana-5708	124	8	kinds	kind	NOUN
cana-5708	124	9	of	of	ADP
cana-5708	124	10	images	image	NOUN
cana-5708	124	11	of	of	ADP
cana-5708	124	12	skin	skin	NOUN
cana-5708	124	13	cancer	cancer	NOUN
cana-5708	124	14	utilized	utilize	VERB
cana-5708	124	15	in	in	ADP
cana-5708	124	16	the	the	DET
cana-5708	124	17	study	study	NOUN
cana-5708	124	18	,	,	PUNCT
cana-5708	124	19	comprising	comprise	VERB
cana-5708	124	20	both	both	CCONJ
cana-5708	124	21	dermoscopic	dermoscopic	ADJ
cana-5708	124	22	and	and	CCONJ
cana-5708	124	23	macroscopic	macroscopic	ADJ
cana-5708	124	24	images	image	NOUN
cana-5708	124	25	,	,	PUNCT
cana-5708	124	26	from	from	ADP
cana-5708	124	27	various	various	ADJ
cana-5708	124	28	datasets	dataset	NOUN
cana-5708	124	29	.	.	PUNCT
cana-5708	125	1	the	the	DET
cana-5708	125	2	datasets	dataset	NOUN
cana-5708	125	3	comprise	comprise	VERB
cana-5708	125	4	different	different	ADJ
cana-5708	125	5	skin	skin	NOUN
cana-5708	125	6	ailments	ailment	NOUN
cana-5708	125	7	,	,	PUNCT
cana-5708	125	8	such	such	ADJ
cana-5708	125	9	as	as	ADP
cana-5708	125	10	melanocytic	melanocytic	ADJ
cana-5708	125	11	lesions	lesion	NOUN
cana-5708	125	12	,	,	PUNCT
cana-5708	125	13	dangerous	dangerous	ADJ
cana-5708	125	14	skin	skin	NOUN
cana-5708	125	15	cancers	cancer	NOUN
cana-5708	125	16	,	,	PUNCT
cana-5708	125	17	and	and	CCONJ
cana-5708	125	18	other	other	ADJ
cana-5708	125	19	dermatological	dermatological	ADJ
cana-5708	125	20	abnormalities	abnormality	NOUN
cana-5708	125	21	.	.	PUNCT
cana-5708	126	1	the	the	DET
cana-5708	126	2	images	image	NOUN
cana-5708	126	3	go	go	VERB
cana-5708	126	4	through	through	ADP
cana-5708	126	5	pre	pre	ADJ
cana-5708	126	6	-	-	ADJ
cana-5708	126	7	processing	processing	NOUN
cana-5708	126	8	and	and	CCONJ
cana-5708	126	9	augmentation	augmentation	NOUN
cana-5708	126	10	,	,	PUNCT
cana-5708	126	11	making	make	VERB
cana-5708	126	12	the	the	DET
cana-5708	126	13	deep	deep	ADJ
cana-5708	126	14	learning	learning	NOUN
cana-5708	126	15	models	model	NOUN
cana-5708	126	16	more	more	ADV
cana-5708	126	17	adaptable	adaptable	ADJ
cana-5708	126	18	and	and	CCONJ
cana-5708	126	19	robust	robust	ADJ
cana-5708	126	20	.	.	PUNCT
cana-5708	127	1	preprocessing	preprocessing	NOUN
cana-5708	127	2	and	and	CCONJ
cana-5708	127	3	augmentation	augmentation	NOUN
cana-5708	127	4	steps	step	NOUN
cana-5708	127	5	images	image	NOUN
cana-5708	127	6	underwent	undergo	VERB
cana-5708	127	7	standard	standard	ADJ
cana-5708	127	8	preprocessing	preprocessing	NOUN
cana-5708	127	9	,	,	PUNCT
cana-5708	127	10	including	include	VERB
cana-5708	127	11	resizing	resizing	NOUN
cana-5708	127	12	,	,	PUNCT
cana-5708	127	13	normalization	normalization	NOUN
cana-5708	127	14	,	,	PUNCT
cana-5708	127	15	and	and	CCONJ
cana-5708	127	16	removal	removal	NOUN
cana-5708	127	17	of	of	ADP
cana-5708	127	18	artifacts	artifact	NOUN
cana-5708	127	19	.	.	PUNCT
cana-5708	128	1	augmentation	augmentation	NOUN
cana-5708	128	2	techniques	technique	NOUN
cana-5708	128	3	were	be	AUX
cana-5708	128	4	applied	apply	VERB
cana-5708	128	5	to	to	PART
cana-5708	128	6	improve	improve	VERB
cana-5708	128	7	generalization	generalization	NOUN
cana-5708	128	8	and	and	CCONJ
cana-5708	128	9	avoid	avoid	VERB
cana-5708	128	10	overfitting	overfitte	VERB
cana-5708	128	11	.	.	PUNCT
cana-5708	129	1	the	the	DET
cana-5708	129	2	dataset	dataset	NOUN
cana-5708	129	3	composition	composition	NOUN
cana-5708	129	4	and	and	CCONJ
cana-5708	129	5	split	split	ADJ
cana-5708	129	6	parameters	parameter	NOUN
cana-5708	129	7	are	be	AUX
cana-5708	129	8	covered	cover	VERB
cana-5708	129	9	in	in	ADP
cana-5708	129	10	table	table	NOUN
cana-5708	129	11	iv	iv	NOUN
cana-5708	129	12	,	,	PUNCT
cana-5708	129	13	while	while	SCONJ
cana-5708	129	14	the	the	DET
cana-5708	129	15	image	image	NOUN
cana-5708	129	16	preprocessing	preprocesse	VERB
cana-5708	129	17	along	along	ADP
cana-5708	129	18	with	with	ADP
cana-5708	129	19	enhancement	enhancement	NOUN
cana-5708	129	20	methods	method	NOUN
cana-5708	129	21	are	be	AUX
cana-5708	129	22	detailed	detail	VERB
cana-5708	129	23	within	within	ADP
cana-5708	129	24	table	table	NOUN
cana-5708	129	25	v.	v.	ADP
cana-5708	129	26	table	table	NOUN
cana-5708	130	1	iv	iv	NUM
cana-5708	130	2	:	:	PUNCT
cana-5708	130	3	dataset	dataset	NOUN
cana-5708	130	4	composition	composition	NOUN
cana-5708	130	5	and	and	CCONJ
cana-5708	130	6	split	split	VERB
cana-5708	130	7	datase	datase	PROPN
cana-5708	130	8	t	t	PROPN
cana-5708	130	9	total	total	NOUN
cana-5708	130	10	images	image	NOUN
cana-5708	130	11	trainin	trainin	PROPN
cana-5708	130	12	g	g	PROPN
cana-5708	130	13	split	split	VERB
cana-5708	130	14	validati	validati	NOUN
cana-5708	130	15	on	on	ADP
cana-5708	130	16	split	split	ADJ
cana-5708	130	17	testin	testin	PROPN
cana-5708	130	18	g	g	PROPN
cana-5708	130	19	split	split	VERB
cana-5708	130	20	isic	isic	PROPN
cana-5708	130	21	archiv	archiv	PROPN
cana-5708	130	22	e	e	PROPN
cana-5708	130	23	25,000	25,000	NUM
cana-5708	130	24	70	70	NUM
cana-5708	130	25	%	%	NOUN
cana-5708	130	26	15	15	NUM
cana-5708	130	27	%	%	NOUN
cana-5708	130	28	15	15	NUM
cana-5708	130	29	%	%	NOUN
cana-5708	130	30	dermi	dermi	NOUN
cana-5708	130	31	s	s	VERB
cana-5708	130	32	10,000	10,000	NUM
cana-5708	130	33	70	70	NUM
cana-5708	130	34	%	%	NOUN
cana-5708	130	35	15	15	NUM
cana-5708	130	36	%	%	NOUN
cana-5708	130	37	15	15	NUM
cana-5708	130	38	%	%	NOUN
cana-5708	130	39	ph2	ph2	NOUN
cana-5708	130	40	1,000	1,000	NUM
cana-5708	130	41	70	70	NUM
cana-5708	130	42	%	%	NOUN
cana-5708	130	43	15	15	NUM
cana-5708	130	44	%	%	NOUN
cana-5708	130	45	15	15	NUM
cana-5708	130	46	%	%	NOUN
cana-5708	130	47	table	table	NOUN
cana-5708	130	48	v	v	NOUN
cana-5708	130	49	:	:	PUNCT
cana-5708	130	50	image	image	NOUN
cana-5708	130	51	preprocessing	preprocessing	NOUN
cana-5708	130	52	and	and	CCONJ
cana-5708	130	53	augmentation	augmentation	NOUN
cana-5708	130	54	techniques	technique	NOUN
cana-5708	130	55	method	method	NOUN
cana-5708	130	56	overview	overview	NOUN
cana-5708	130	57	description	description	NOUN
cana-5708	130	58	image	image	NOUN
cana-5708	130	59	resizing	resizing	NOUN
cana-5708	130	60	adjusting	adjust	VERB
cana-5708	130	61	all	all	DET
cana-5708	130	62	images	image	NOUN
cana-5708	130	63	to	to	ADP
cana-5708	130	64	a	a	DET
cana-5708	130	65	uniform	uniform	ADJ
cana-5708	130	66	size	size	NOUN
cana-5708	130	67	of	of	ADP
cana-5708	130	68	224x224	224x224	NUM
cana-5708	130	69	pixels	pixel	NOUN
cana-5708	130	70	.	.	PUNCT
cana-5708	131	1	intensity	intensity	NOUN
cana-5708	131	2	normalization	normalization	NOUN
cana-5708	131	3	transforming	transform	VERB
cana-5708	131	4	pixel	pixel	ADJ
cana-5708	131	5	values	value	NOUN
cana-5708	131	6	to	to	ADP
cana-5708	131	7	a	a	DET
cana-5708	131	8	scale	scale	NOUN
cana-5708	131	9	between	between	ADP
cana-5708	131	10	0	0	NUM
cana-5708	131	11	and	and	CCONJ
cana-5708	131	12	1	1	NUM
cana-5708	131	13	.	.	X
cana-5708	132	1	rotation	rotation	NOUN
cana-5708	132	2	random	random	ADJ
cana-5708	132	3	rotations	rotation	NOUN
cana-5708	132	4	within	within	ADP
cana-5708	132	5	a	a	DET
cana-5708	132	6	range	range	NOUN
cana-5708	132	7	of	of	ADP
cana-5708	132	8	±20	±20	PROPN
cana-5708	132	9	degrees	degree	NOUN
cana-5708	132	10	.	.	PUNCT
cana-5708	133	1	horizontal	horizontal	ADJ
cana-5708	133	2	flipping	flipping	NOUN
cana-5708	133	3	images	image	NOUN
cana-5708	133	4	flipped	flip	VERB
cana-5708	133	5	horizontally	horizontally	ADV
cana-5708	133	6	with	with	ADP
cana-5708	133	7	a	a	DET
cana-5708	133	8	50	50	NUM
cana-5708	133	9	%	%	NOUN
cana-5708	133	10	probability	probability	NOUN
cana-5708	133	11	.	.	PUNCT
cana-5708	134	1	brightness	brightness	NOUN
cana-5708	134	2	adjustment	adjustment	NOUN
cana-5708	134	3	random	random	ADJ
cana-5708	134	4	brightness	brightness	NOUN
cana-5708	134	5	changes	change	NOUN
cana-5708	134	6	within	within	ADP
cana-5708	134	7	a	a	DET
cana-5708	134	8	20	20	NUM
cana-5708	134	9	%	%	NOUN
cana-5708	134	10	range	range	NOUN
cana-5708	134	11	.	.	PUNCT
cana-5708	135	1	communications	communication	NOUN
cana-5708	135	2	on	on	ADP
cana-5708	135	3	applied	apply	VERB
cana-5708	135	4	nonlinear	nonlinear	ADJ
cana-5708	135	5	analysis	analysis	NOUN
cana-5708	135	6	issn	issn	NOUN
cana-5708	135	7	:	:	PUNCT
cana-5708	135	8	1074	1074	NUM
cana-5708	135	9	-	-	PUNCT
cana-5708	135	10	133x	133x	NUM
cana-5708	135	11	vol	vol	VERB
cana-5708	135	12	32	32	NUM
cana-5708	135	13	no	no	NOUN
cana-5708	135	14	.	.	PUNCT
cana-5708	136	1	10s	10	NOUN
cana-5708	136	2	(	(	PUNCT
cana-5708	136	3	2025	2025	NUM
cana-5708	136	4	)	)	PUNCT
cana-5708	136	5	2674	2674	NUM
cana-5708	136	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5708	136	7	3.3	3.3	NUM
cana-5708	136	8	cnn	cnn	NOUN
cana-5708	136	9	architecture	architecture	NOUN
cana-5708	136	10	the	the	DET
cana-5708	136	11	convolutional	convolutional	ADJ
cana-5708	136	12	neural	neural	ADJ
cana-5708	136	13	network	network	NOUN
cana-5708	136	14	(	(	PUNCT
cana-5708	136	15	cnn	cnn	PROPN
cana-5708	136	16	)	)	PUNCT
cana-5708	136	17	designed	design	VERB
cana-5708	136	18	for	for	ADP
cana-5708	136	19	this	this	DET
cana-5708	136	20	study	study	NOUN
cana-5708	136	21	consists	consist	VERB
cana-5708	136	22	of	of	ADP
cana-5708	136	23	the	the	DET
cana-5708	136	24	following	follow	VERB
cana-5708	136	25	layers	layer	NOUN
cana-5708	136	26	and	and	CCONJ
cana-5708	136	27	parameters	parameter	NOUN
cana-5708	136	28	:	:	PUNCT
cana-5708	136	29	fig	fig	NOUN
cana-5708	136	30	.	.	PUNCT
cana-5708	137	1	iii	iii	NUM
cana-5708	137	2	comparison	comparison	NOUN
cana-5708	137	3	of	of	ADP
cana-5708	137	4	hyperband	hyperband	ADV
cana-5708	137	5	-	-	PUNCT
cana-5708	137	6	optimized	optimize	VERB
cana-5708	137	7	models	model	NOUN
cana-5708	137	8	and	and	CCONJ
cana-5708	137	9	manual	manual	ADJ
cana-5708	137	10	selection	selection	NOUN
cana-5708	137	11	[	[	X
cana-5708	137	12	24	24	NUM
cana-5708	137	13	]	]	PUNCT
cana-5708	137	14	this	this	DET
cana-5708	137	15	figure	figure	NOUN
cana-5708	137	16	iii	iii	NOUN
cana-5708	137	17	,	,	PUNCT
cana-5708	137	18	illustrates	illustrate	VERB
cana-5708	137	19	the	the	DET
cana-5708	137	20	architectures	architecture	NOUN
cana-5708	137	21	of	of	ADP
cana-5708	137	22	three	three	NUM
cana-5708	137	23	models	model	NOUN
cana-5708	137	24	optimized	optimize	VERB
cana-5708	137	25	using	use	VERB
cana-5708	137	26	the	the	DET
cana-5708	137	27	hyperband	hyperband	ADJ
cana-5708	137	28	algorithm	algorithm	NOUN
cana-5708	137	29	(	(	PUNCT
cana-5708	137	30	models	model	NOUN
cana-5708	137	31	1	1	NUM
cana-5708	137	32	,	,	PUNCT
cana-5708	137	33	2	2	NUM
cana-5708	137	34	,	,	PUNCT
cana-5708	137	35	and	and	CCONJ
cana-5708	137	36	3	3	X
cana-5708	137	37	)	)	PUNCT
cana-5708	137	38	and	and	CCONJ
cana-5708	137	39	a	a	DET
cana-5708	137	40	manually	manually	ADV
cana-5708	137	41	selected	select	VERB
cana-5708	137	42	model	model	NOUN
cana-5708	137	43	.	.	PUNCT
cana-5708	138	1	the	the	DET
cana-5708	138	2	architecture	architecture	NOUN
cana-5708	138	3	includes	include	VERB
cana-5708	138	4	a	a	DET
cana-5708	138	5	specific	specific	ADJ
cana-5708	138	6	number	number	NOUN
cana-5708	138	7	of	of	ADP
cana-5708	138	8	convolutional	convolutional	ADJ
cana-5708	138	9	layers	layer	NOUN
cana-5708	138	10	,	,	PUNCT
cana-5708	138	11	with	with	ADP
cana-5708	138	12	designated	designate	VERB
cana-5708	138	13	kernel	kernel	NOUN
cana-5708	138	14	and	and	CCONJ
cana-5708	138	15	filter	filter	NOUN
cana-5708	138	16	sizes	size	NOUN
cana-5708	138	17	.	.	PUNCT
cana-5708	139	1	additionally	additionally	ADV
cana-5708	139	2	,	,	PUNCT
cana-5708	139	3	a	a	DET
cana-5708	139	4	dropout	dropout	NOUN
cana-5708	139	5	rate	rate	NOUN
cana-5708	139	6	is	be	AUX
cana-5708	139	7	applied	apply	VERB
cana-5708	139	8	,	,	PUNCT
cana-5708	139	9	and	and	CCONJ
cana-5708	139	10	the	the	DET
cana-5708	139	11	network	network	NOUN
cana-5708	139	12	concludes	conclude	VERB
cana-5708	139	13	with	with	ADP
cana-5708	139	14	several	several	ADJ
cana-5708	139	15	fully	fully	ADV
cana-5708	139	16	connected	connect	VERB
cana-5708	139	17	(	(	PUNCT
cana-5708	139	18	dense	dense	ADJ
cana-5708	139	19	)	)	PUNCT
cana-5708	139	20	layers	layer	NOUN
cana-5708	139	21	.	.	PUNCT
cana-5708	140	1	i	i	PRON
cana-5708	140	2	)	)	PUNCT
cana-5708	140	3	hyperband	hyperband	NOUN
cana-5708	140	4	models	model	NOUN
cana-5708	140	5	(	(	PUNCT
cana-5708	140	6	1	1	NUM
cana-5708	140	7	,	,	PUNCT
cana-5708	140	8	2	2	NUM
cana-5708	140	9	,	,	PUNCT
cana-5708	140	10	and	and	CCONJ
cana-5708	140	11	3	3	NUM
cana-5708	140	12	):	):	PUNCT
cana-5708	140	13	these	these	DET
cana-5708	140	14	architectures	architecture	NOUN
cana-5708	140	15	were	be	AUX
cana-5708	140	16	derived	derive	VERB
cana-5708	140	17	through	through	ADP
cana-5708	140	18	hyperband	hyperband	ADJ
cana-5708	140	19	optimization	optimization	NOUN
cana-5708	140	20	,	,	PUNCT
cana-5708	140	21	showcasing	showcase	VERB
cana-5708	140	22	differences	difference	NOUN
cana-5708	140	23	in	in	ADP
cana-5708	140	24	layer	layer	NOUN
cana-5708	140	25	configurations	configuration	NOUN
cana-5708	140	26	,	,	PUNCT
cana-5708	140	27	filter	filter	NOUN
cana-5708	140	28	sizes	size	NOUN
cana-5708	140	29	,	,	PUNCT
cana-5708	140	30	kernel	kernel	PROPN
cana-5708	140	31	sizes	size	NOUN
cana-5708	140	32	,	,	PUNCT
cana-5708	140	33	and	and	CCONJ
cana-5708	140	34	dropout	dropout	NOUN
cana-5708	140	35	rates	rate	NOUN
cana-5708	140	36	,	,	PUNCT
cana-5708	140	37	tailored	tailor	VERB
cana-5708	140	38	to	to	PART
cana-5708	140	39	maximize	maximize	VERB
cana-5708	140	40	performance	performance	NOUN
cana-5708	140	41	based	base	VERB
cana-5708	140	42	on	on	ADP
cana-5708	140	43	predefined	predefine	VERB
cana-5708	140	44	hyperparameter	hyperparameter	NOUN
cana-5708	140	45	search	search	NOUN
cana-5708	140	46	spaces	space	NOUN
cana-5708	140	47	.	.	PUNCT
cana-5708	141	1	ii	ii	X
cana-5708	141	2	)	)	PUNCT
cana-5708	141	3	manual	manual	ADJ
cana-5708	141	4	selection	selection	NOUN
cana-5708	141	5	:	:	PUNCT
cana-5708	141	6	the	the	DET
cana-5708	141	7	manually	manually	ADV
cana-5708	141	8	selected	select	VERB
cana-5708	141	9	model	model	NOUN
cana-5708	141	10	represents	represent	VERB
cana-5708	141	11	an	an	DET
cana-5708	141	12	architecture	architecture	NOUN
cana-5708	141	13	chosen	choose	VERB
cana-5708	141	14	without	without	ADP
cana-5708	141	15	automated	automate	VERB
cana-5708	141	16	optimization	optimization	NOUN
cana-5708	141	17	,	,	PUNCT
cana-5708	141	18	relying	rely	VERB
cana-5708	141	19	on	on	ADP
cana-5708	141	20	domain	domain	NOUN
cana-5708	141	21	knowledge	knowledge	NOUN
cana-5708	141	22	and	and	CCONJ
cana-5708	141	23	experimentation	experimentation	NOUN
cana-5708	141	24	.	.	PUNCT
cana-5708	142	1	this	this	DET
cana-5708	142	2	comparison	comparison	NOUN
cana-5708	142	3	highlights	highlight	VERB
cana-5708	142	4	how	how	SCONJ
cana-5708	142	5	hyperband	hyperband	NOUN
cana-5708	142	6	facilitates	facilitate	VERB
cana-5708	142	7	more	more	ADV
cana-5708	142	8	precise	precise	ADJ
cana-5708	142	9	and	and	CCONJ
cana-5708	142	10	effective	effective	ADJ
cana-5708	142	11	hyperparameter	hyperparameter	NOUN
cana-5708	142	12	tuning	tuning	NOUN
cana-5708	142	13	compared	compare	VERB
cana-5708	142	14	to	to	ADP
cana-5708	142	15	manual	manual	ADJ
cana-5708	142	16	selection	selection	NOUN
cana-5708	142	17	methods	method	NOUN
cana-5708	142	18	.	.	PUNCT
cana-5708	143	1	the	the	DET
cana-5708	143	2	cnn	cnn	PROPN
cana-5708	143	3	architecture	architecture	NOUN
cana-5708	143	4	design	design	NOUN
cana-5708	143	5	is	be	AUX
cana-5708	143	6	discussed	discuss	VERB
cana-5708	143	7	in	in	ADP
cana-5708	143	8	table	table	NOUN
cana-5708	143	9	vi	vi	PROPN
cana-5708	143	10	.	.	PUNCT
cana-5708	144	1	table	table	PROPN
cana-5708	144	2	vi	vi	PROPN
cana-5708	144	3	:	:	PUNCT
cana-5708	144	4	cnn	cnn	PROPN
cana-5708	144	5	architecture	architecture	NOUN
cana-5708	144	6	design	design	VERB
cana-5708	144	7	this	this	DET
cana-5708	144	8	table	table	NOUN
cana-5708	144	9	outlines	outline	VERB
cana-5708	144	10	the	the	DET
cana-5708	144	11	cnn	cnn	PROPN
cana-5708	144	12	architecture	architecture	NOUN
cana-5708	144	13	designed	design	VERB
cana-5708	144	14	for	for	ADP
cana-5708	144	15	the	the	DET
cana-5708	144	16	study	study	NOUN
cana-5708	144	17	.	.	PUNCT
cana-5708	145	1	it	it	PRON
cana-5708	145	2	includes	include	VERB
cana-5708	145	3	input	input	NOUN
cana-5708	145	4	dimensions	dimension	NOUN
cana-5708	145	5	,	,	PUNCT
cana-5708	145	6	utilizing	utilize	VERB
cana-5708	145	7	convolutional	convolutional	ADJ
cana-5708	145	8	layers	layer	NOUN
cana-5708	145	9	to	to	PART
cana-5708	145	10	extract	extract	VERB
cana-5708	145	11	features	feature	NOUN
cana-5708	145	12	,	,	PUNCT
cana-5708	145	13	followed	follow	VERB
cana-5708	145	14	by	by	ADP
cana-5708	145	15	dimensionality	dimensionality	NOUN
cana-5708	145	16	reduction	reduction	NOUN
cana-5708	145	17	through	through	ADP
cana-5708	145	18	max	max	PROPN
cana-5708	145	19	pooling	pooling	NOUN
cana-5708	145	20	,	,	PUNCT
cana-5708	145	21	and	and	CCONJ
cana-5708	145	22	culminating	culminate	VERB
cana-5708	145	23	in	in	ADP
cana-5708	145	24	the	the	DET
cana-5708	145	25	final	final	ADJ
cana-5708	145	26	classification	classification	NOUN
cana-5708	145	27	with	with	ADP
cana-5708	145	28	fully	fully	ADV
cana-5708	145	29	connected	connected	ADJ
cana-5708	145	30	layers	layer	NOUN
cana-5708	145	31	.	.	PUNCT
cana-5708	146	1	layer	layer	NOUN
cana-5708	146	2	details	detail	NOUN
cana-5708	146	3	result	result	VERB
cana-5708	146	4	shape	shape	NOUN
cana-5708	146	5	activation	activation	NOUN
cana-5708	146	6	function	function	NOUN
cana-5708	146	7	parameters	parameter	NOUN
cana-5708	146	8	value	value	VERB
cana-5708	146	9	input	input	NOUN
cana-5708	146	10	configuration	configuration	NOUN
cana-5708	146	11	224x224x3	224x224x3	NUM
cana-5708	146	12	communications	communication	NOUN
cana-5708	146	13	on	on	ADP
cana-5708	146	14	applied	apply	VERB
cana-5708	146	15	nonlinear	nonlinear	ADJ
cana-5708	146	16	analysis	analysis	NOUN
cana-5708	146	17	issn	issn	NOUN
cana-5708	146	18	:	:	PUNCT
cana-5708	146	19	1074	1074	NUM
cana-5708	146	20	-	-	PUNCT
cana-5708	146	21	133x	133x	NUM
cana-5708	146	22	vol	vol	VERB
cana-5708	146	23	32	32	NUM
cana-5708	146	24	no	no	NOUN
cana-5708	146	25	.	.	PUNCT
cana-5708	147	1	10s	10	NOUN
cana-5708	147	2	(	(	PUNCT
cana-5708	147	3	2025	2025	NUM
cana-5708	147	4	)	)	PUNCT
cana-5708	147	5	2675	2675	NUM
cana-5708	147	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5708	147	7	layer	layer	NOUN
cana-5708	147	8	details	detail	NOUN
cana-5708	147	9	result	result	VERB
cana-5708	147	10	shape	shape	NOUN
cana-5708	147	11	activation	activation	NOUN
cana-5708	147	12	function	function	NOUN
cana-5708	147	13	parameters	parameter	NOUN
cana-5708	147	14	value	value	NOUN
cana-5708	147	15	convolution	convolution	NOUN
cana-5708	147	16	(	(	PUNCT
cana-5708	147	17	3x3	3x3	NUM
cana-5708	147	18	)	)	PUNCT
cana-5708	147	19	224x224x3	224x224x3	NUM
cana-5708	147	20	2	2	NUM
cana-5708	147	21	relu	relu	NOUN
cana-5708	147	22	896	896	NUM
cana-5708	147	23	max	max	PROPN
cana-5708	147	24	pooling	pooling	NOUN
cana-5708	147	25	(	(	PUNCT
cana-5708	147	26	2x2	2x2	NUM
cana-5708	147	27	)	)	PUNCT
cana-5708	147	28	112x112x3	112x112x3	NUM
cana-5708	147	29	2	2	NUM
cana-5708	147	30	convolution	convolution	NOUN
cana-5708	147	31	(	(	PUNCT
cana-5708	147	32	3x3	3x3	NUM
cana-5708	147	33	)	)	PUNCT
cana-5708	147	34	112x112x6	112x112x6	NUM
cana-5708	147	35	4	4	NUM
cana-5708	147	36	relu	relu	NOUN
cana-5708	147	37	18,496	18,496	NUM
cana-5708	147	38	max	max	PROPN
cana-5708	147	39	pooling	pooling	NOUN
cana-5708	147	40	(	(	PUNCT
cana-5708	147	41	2x2	2x2	NUM
cana-5708	147	42	)	)	PUNCT
cana-5708	147	43	56x56x64	56x56x64	NOUN
cana-5708	147	44	fully	fully	ADV
cana-5708	147	45	connected	connect	VERB
cana-5708	147	46	128	128	NUM
cana-5708	147	47	relu	relu	NOUN
cana-5708	147	48	512k	512k	PROPN
cana-5708	147	49	output	output	NOUN
cana-5708	147	50	layer	layer	NOUN
cana-5708	147	51	2	2	NUM
cana-5708	147	52	(	(	PUNCT
cana-5708	147	53	binary	binary	ADJ
cana-5708	147	54	output	output	PROPN
cana-5708	147	55	)	)	PUNCT
cana-5708	147	56	softmax	softmax	NOUN
cana-5708	147	57	258	258	NUM
cana-5708	147	58	mechanism	mechanism	NOUN
cana-5708	147	59	and	and	CCONJ
cana-5708	147	60	strategy	strategy	NOUN
cana-5708	147	61	hyperband	hyperband	PROPN
cana-5708	147	62	evaluates	evaluate	VERB
cana-5708	147	63	multiple	multiple	ADJ
cana-5708	147	64	configurations	configuration	NOUN
cana-5708	147	65	by	by	ADP
cana-5708	147	66	allocating	allocate	VERB
cana-5708	147	67	resources	resource	NOUN
cana-5708	147	68	based	base	VERB
cana-5708	147	69	on	on	ADP
cana-5708	147	70	successive	successive	ADJ
cana-5708	147	71	halving	halving	NOUN
cana-5708	147	72	,	,	PUNCT
cana-5708	147	73	discarding	discard	VERB
cana-5708	147	74	poor	poor	ADJ
cana-5708	147	75	performers	performer	NOUN
cana-5708	147	76	early	early	ADV
cana-5708	147	77	and	and	CCONJ
cana-5708	147	78	focusing	focus	VERB
cana-5708	147	79	on	on	ADP
cana-5708	147	80	promising	promising	ADJ
cana-5708	147	81	configurations	configuration	NOUN
cana-5708	147	82	.	.	PUNCT
cana-5708	148	1	table	table	NOUN
cana-5708	148	2	vii	vii	PROPN
cana-5708	148	3	covers	cover	VERB
cana-5708	148	4	the	the	DET
cana-5708	148	5	ranges	range	NOUN
cana-5708	148	6	and	and	CCONJ
cana-5708	148	7	optimal	optimal	ADJ
cana-5708	148	8	values	value	NOUN
cana-5708	148	9	of	of	ADP
cana-5708	148	10	hyperparameters	hyperparameter	NOUN
cana-5708	148	11	,	,	PUNCT
cana-5708	148	12	while	while	SCONJ
cana-5708	148	13	table	table	NOUN
cana-5708	148	14	viii	viii	NOUN
cana-5708	148	15	focuses	focus	VERB
cana-5708	148	16	on	on	ADP
cana-5708	148	17	resource	resource	NOUN
cana-5708	148	18	allocation	allocation	NOUN
cana-5708	148	19	in	in	ADP
cana-5708	148	20	hyperband	hyperband	PROPN
cana-5708	148	21	.	.	PUNCT
cana-5708	149	1	table	table	PROPN
cana-5708	149	2	vii	vii	PROPN
cana-5708	149	3	:	:	PUNCT
cana-5708	149	4	hyperparameter	hyperparameter	NOUN
cana-5708	149	5	ranges	range	VERB
cana-5708	149	6	and	and	CCONJ
cana-5708	149	7	optimal	optimal	ADJ
cana-5708	149	8	values	value	NOUN
cana-5708	149	9	hyperparameter	hyperparameter	NOUN
cana-5708	149	10	range	range	VERB
cana-5708	149	11	optimal	optimal	ADJ
cana-5708	149	12	value	value	NOUN
cana-5708	149	13	learning	learn	VERB
cana-5708	149	14	rate	rate	NOUN
cana-5708	150	1	[	[	X
cana-5708	150	2	0.0001	0.0001	NUM
cana-5708	150	3	,	,	PUNCT
cana-5708	150	4	0.01	0.01	NUM
cana-5708	150	5	]	]	SYM
cana-5708	150	6	0.001	0.001	NUM
cana-5708	150	7	batch	batch	NOUN
cana-5708	150	8	size	size	NOUN
cana-5708	150	9	(	(	PUNCT
cana-5708	150	10	allocated	allocate	VERB
cana-5708	150	11	)	)	PUNCT
cana-5708	151	1	[	[	X
cana-5708	151	2	16	16	NUM
cana-5708	151	3	,	,	PUNCT
cana-5708	151	4	32	32	NUM
cana-5708	151	5	,	,	PUNCT
cana-5708	151	6	64	64	NUM
cana-5708	151	7	]	]	SYM
cana-5708	151	8	32	32	NUM
cana-5708	151	9	dropout	dropout	NOUN
cana-5708	151	10	rate	rate	NOUN
cana-5708	151	11	[	[	X
cana-5708	151	12	0.1	0.1	NUM
cana-5708	151	13	,	,	PUNCT
cana-5708	151	14	0.5	0.5	NUM
cana-5708	151	15	]	]	SYM
cana-5708	151	16	0.3	0.3	NUM
cana-5708	151	17	table	table	NOUN
cana-5708	151	18	viii	viii	NOUN
cana-5708	151	19	:	:	PUNCT
cana-5708	151	20	resource	resource	NOUN
cana-5708	151	21	allocation	allocation	NOUN
cana-5708	151	22	in	in	ADP
cana-5708	151	23	hyperband	hyperband	ADJ
cana-5708	151	24	stage	stage	NOUN
cana-5708	151	25	resources	resource	NOUN
cana-5708	151	26	allocated	allocate	VERB
cana-5708	151	27	configurations	configuration	NOUN
cana-5708	151	28	retained	retain	VERB
cana-5708	151	29	initia	initia	NOUN
cana-5708	151	30	l	l	NOUN
cana-5708	151	31	10	10	NUM
cana-5708	151	32	epochs	epoch	NOUN
cana-5708	151	33	100	100	NUM
cana-5708	151	34	communications	communication	NOUN
cana-5708	151	35	on	on	ADP
cana-5708	151	36	applied	apply	VERB
cana-5708	151	37	nonlinear	nonlinear	ADJ
cana-5708	151	38	analysis	analysis	NOUN
cana-5708	151	39	issn	issn	NOUN
cana-5708	151	40	:	:	PUNCT
cana-5708	151	41	1074	1074	NUM
cana-5708	151	42	-	-	PUNCT
cana-5708	151	43	133x	133x	NUM
cana-5708	151	44	vol	vol	VERB
cana-5708	151	45	32	32	NUM
cana-5708	151	46	no	no	NOUN
cana-5708	151	47	.	.	PUNCT
cana-5708	152	1	10s	10	NOUN
cana-5708	152	2	(	(	PUNCT
cana-5708	152	3	2025	2025	NUM
cana-5708	152	4	)	)	PUNCT
cana-5708	152	5	2676	2676	NUM
cana-5708	152	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5708	152	7	stage	stage	NOUN
cana-5708	152	8	resources	resource	NOUN
cana-5708	152	9	allocated	allocate	VERB
cana-5708	152	10	configurations	configuration	NOUN
cana-5708	152	11	retained	retain	VERB
cana-5708	152	12	stage	stage	NOUN
cana-5708	152	13	1	1	NUM
cana-5708	152	14	20	20	NUM
cana-5708	152	15	epochs	epoch	NOUN
cana-5708	152	16	50	50	NUM
cana-5708	152	17	stage	stage	NOUN
cana-5708	152	18	2	2	NUM
cana-5708	152	19	40	40	NUM
cana-5708	152	20	epochs	epoch	NOUN
cana-5708	152	21	20	20	NUM
cana-5708	152	22	stage	stage	NOUN
cana-5708	152	23	3	3	NUM
cana-5708	152	24	80	80	NUM
cana-5708	152	25	epochs	epoch	NOUN
cana-5708	152	26	5	5	NUM
cana-5708	152	27	hyperband	hyperband	NOUN
cana-5708	152	28	optimization	optimization	NOUN
cana-5708	152	29	process	process	NOUN
cana-5708	152	30	the	the	DET
cana-5708	152	31	figure	figure	NOUN
cana-5708	152	32	iv	iv	NUM
cana-5708	152	33	flow	flow	NOUN
cana-5708	152	34	chart	chart	NOUN
cana-5708	152	35	depicts	depict	VERB
cana-5708	152	36	the	the	DET
cana-5708	152	37	procedure	procedure	NOUN
cana-5708	152	38	for	for	ADP
cana-5708	152	39	hyperparameter	hyperparameter	NOUN
cana-5708	152	40	tuning	tuning	NOUN
cana-5708	152	41	applied	apply	VERB
cana-5708	152	42	by	by	ADP
cana-5708	152	43	hyperband	hyperband	PROPN
cana-5708	152	44	.	.	PUNCT
cana-5708	153	1	the	the	DET
cana-5708	153	2	flowchart	flowchart	NOUN
cana-5708	153	3	represents	represent	VERB
cana-5708	153	4	the	the	DET
cana-5708	153	5	sequence	sequence	NOUN
cana-5708	153	6	of	of	ADP
cana-5708	153	7	the	the	DET
cana-5708	153	8	actions	action	NOUN
cana-5708	153	9	performed	perform	VERB
cana-5708	153	10	from	from	ADP
cana-5708	153	11	the	the	DET
cana-5708	153	12	beginning	beginning	NOUN
cana-5708	153	13	through	through	ADP
cana-5708	153	14	early	early	ADJ
cana-5708	153	15	stopping	stopping	NOUN
cana-5708	153	16	of	of	ADP
cana-5708	153	17	the	the	DET
cana-5708	153	18	less	less	ADV
cana-5708	153	19	-	-	PUNCT
cana-5708	153	20	performing	perform	VERB
cana-5708	153	21	configurations	configuration	NOUN
cana-5708	153	22	.	.	PUNCT
cana-5708	154	1	the	the	DET
cana-5708	154	2	flowchart	flowchart	NOUN
cana-5708	154	3	evidently	evidently	ADV
cana-5708	154	4	shows	show	VERB
cana-5708	154	5	how	how	SCONJ
cana-5708	154	6	hyperband	hyperband	NOUN
cana-5708	154	7	reinforces	reinforce	VERB
cana-5708	154	8	the	the	DET
cana-5708	154	9	systematic	systematic	ADJ
cana-5708	154	10	optimization	optimization	NOUN
cana-5708	154	11	of	of	ADP
cana-5708	154	12	the	the	DET
cana-5708	154	13	search	search	NOUN
cana-5708	154	14	space	space	NOUN
cana-5708	154	15	through	through	ADP
cana-5708	154	16	efficient	efficient	ADJ
cana-5708	154	17	elimination	elimination	NOUN
cana-5708	154	18	of	of	ADP
cana-5708	154	19	the	the	DET
cana-5708	154	20	less	less	ADV
cana-5708	154	21	performing	perform	VERB
cana-5708	154	22	configurations	configuration	NOUN
cana-5708	154	23	and	and	CCONJ
cana-5708	154	24	delves	delf	NOUN
cana-5708	154	25	into	into	ADP
cana-5708	154	26	the	the	DET
cana-5708	154	27	resource	resource	NOUN
cana-5708	154	28	allocation	allocation	NOUN
cana-5708	154	29	of	of	ADP
cana-5708	154	30	the	the	DET
cana-5708	154	31	best	well	ADV
cana-5708	154	32	-	-	PUNCT
cana-5708	154	33	performing	perform	VERB
cana-5708	154	34	models	model	NOUN
cana-5708	154	35	.	.	PUNCT
cana-5708	155	1	fig	fig	NOUN
cana-5708	155	2	.	.	PUNCT
cana-5708	156	1	iv	iv	NUM
cana-5708	156	2	hyperband	hyperband	ADJ
cana-5708	156	3	optimization	optimization	NOUN
cana-5708	156	4	process	process	NOUN
cana-5708	156	5	flowchart	flowchart	NOUN
cana-5708	156	6	communications	communication	NOUN
cana-5708	156	7	on	on	ADP
cana-5708	156	8	applied	apply	VERB
cana-5708	156	9	nonlinear	nonlinear	ADJ
cana-5708	156	10	analysis	analysis	NOUN
cana-5708	156	11	issn	issn	NOUN
cana-5708	156	12	:	:	PUNCT
cana-5708	156	13	1074	1074	NUM
cana-5708	156	14	-	-	PUNCT
cana-5708	156	15	133x	133x	NUM
cana-5708	156	16	vol	vol	VERB
cana-5708	156	17	32	32	NUM
cana-5708	156	18	no	no	NOUN
cana-5708	156	19	.	.	PUNCT
cana-5708	157	1	10s	10	NOUN
cana-5708	157	2	(	(	PUNCT
cana-5708	157	3	2025	2025	NUM
cana-5708	157	4	)	)	PUNCT
cana-5708	157	5	2677	2677	NUM
cana-5708	157	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5708	157	7	4	4	X
cana-5708	157	8	.	.	X
cana-5708	157	9	experimental	experimental	ADJ
cana-5708	157	10	setup	setup	NOUN
cana-5708	157	11	this	this	DET
cana-5708	157	12	section	section	NOUN
cana-5708	157	13	gives	give	VERB
cana-5708	157	14	a	a	DET
cana-5708	157	15	general	general	ADJ
cana-5708	157	16	overview	overview	NOUN
cana-5708	157	17	of	of	ADP
cana-5708	157	18	the	the	DET
cana-5708	157	19	hardware	hardware	NOUN
cana-5708	157	20	and	and	CCONJ
cana-5708	157	21	software	software	NOUN
cana-5708	157	22	employed	employ	VERB
cana-5708	157	23	to	to	PART
cana-5708	157	24	train	train	VERB
cana-5708	157	25	and	and	CCONJ
cana-5708	157	26	test	test	VERB
cana-5708	157	27	the	the	DET
cana-5708	157	28	model	model	NOUN
cana-5708	157	29	,	,	PUNCT
cana-5708	157	30	outlines	outline	VERB
cana-5708	157	31	the	the	DET
cana-5708	157	32	training	training	NOUN
cana-5708	157	33	and	and	CCONJ
cana-5708	157	34	validation	validation	NOUN
cana-5708	157	35	process	process	NOUN
cana-5708	157	36	,	,	PUNCT
cana-5708	157	37	and	and	CCONJ
cana-5708	157	38	gives	give	VERB
cana-5708	157	39	a	a	DET
cana-5708	157	40	description	description	NOUN
cana-5708	157	41	of	of	ADP
cana-5708	157	42	the	the	DET
cana-5708	157	43	evaluation	evaluation	NOUN
cana-5708	157	44	measures	measure	NOUN
cana-5708	157	45	employed	employ	VERB
cana-5708	157	46	to	to	PART
cana-5708	157	47	measure	measure	VERB
cana-5708	157	48	model	model	NOUN
cana-5708	157	49	performance	performance	NOUN
cana-5708	157	50	.	.	PUNCT
cana-5708	158	1	4.1	4.1	NUM
cana-5708	158	2	hardware	hardware	NOUN
cana-5708	158	3	and	and	CCONJ
cana-5708	158	4	software	software	NOUN
cana-5708	158	5	configuration	configuration	NOUN
cana-5708	158	6	hardware	hardware	NOUN
cana-5708	158	7	configuration	configuration	NOUN
cana-5708	158	8	:	:	PUNCT
cana-5708	158	9	tests	test	NOUN
cana-5708	158	10	were	be	AUX
cana-5708	158	11	run	run	VERB
cana-5708	158	12	in	in	ADP
cana-5708	158	13	google	google	PROPN
cana-5708	158	14	colab	colab	PROPN
cana-5708	158	15	,	,	PUNCT
cana-5708	158	16	utilizing	utilize	VERB
cana-5708	158	17	nvidia	nvidia	PROPN
cana-5708	158	18	tesla	tesla	PROPN
cana-5708	158	19	t4	t4	PROPN
cana-5708	158	20	gpus	gpus	PROPN
cana-5708	158	21	to	to	PART
cana-5708	158	22	accomplish	accomplish	VERB
cana-5708	158	23	the	the	DET
cana-5708	158	24	accelerated	accelerated	ADJ
cana-5708	158	25	training	training	NOUN
cana-5708	158	26	of	of	ADP
cana-5708	158	27	dl	dl	PROPN
cana-5708	158	28	models	model	NOUN
cana-5708	158	29	.	.	PUNCT
cana-5708	159	1	the	the	DET
cana-5708	159	2	t4	t4	PROPN
cana-5708	159	3	gpus	gpus	PROPN
cana-5708	159	4	support	support	VERB
cana-5708	159	5	16	16	NUM
cana-5708	159	6	gb	gb	NOUN
cana-5708	159	7	gddr6	gddr6	NOUN
cana-5708	159	8	memory	memory	NOUN
cana-5708	159	9	and	and	CCONJ
cana-5708	159	10	highthroughput	highthroughput	VERB
cana-5708	159	11	performance	performance	NOUN
cana-5708	159	12	for	for	ADP
cana-5708	159	13	effective	effective	ADJ
cana-5708	159	14	processing	processing	NOUN
cana-5708	159	15	of	of	ADP
cana-5708	159	16	big	big	ADJ
cana-5708	159	17	data	datum	NOUN
cana-5708	159	18	,	,	PUNCT
cana-5708	159	19	enabling	enable	VERB
cana-5708	159	20	rapid	rapid	ADJ
cana-5708	159	21	training	training	NOUN
cana-5708	159	22	of	of	ADP
cana-5708	159	23	convolutional	convolutional	ADJ
cana-5708	159	24	neural	neural	ADJ
cana-5708	159	25	networks	network	NOUN
cana-5708	159	26	(	(	PUNCT
cana-5708	159	27	cnns	cnns	PROPN
cana-5708	159	28	)	)	PUNCT
cana-5708	159	29	.	.	PUNCT
cana-5708	160	1	software	software	NOUN
cana-5708	160	2	configuration	configuration	NOUN
cana-5708	160	3	:	:	PUNCT
cana-5708	160	4	tensorflow	tensorflow	NOUN
cana-5708	160	5	(	(	PUNCT
cana-5708	160	6	abadi	abadi	PROPN
cana-5708	160	7	et	et	PROPN
cana-5708	160	8	al	al	PROPN
cana-5708	160	9	.	.	PROPN
cana-5708	160	10	,	,	PUNCT
cana-5708	160	11	2016	2016	NUM
cana-5708	160	12	):	):	PUNCT
cana-5708	160	13	an	an	DET
cana-5708	160	14	open	open	ADJ
cana-5708	160	15	-	-	PUNCT
cana-5708	160	16	source	source	NOUN
cana-5708	160	17	stable	stable	ADJ
cana-5708	160	18	platform	platform	NOUN
cana-5708	160	19	utilized	utilize	VERB
cana-5708	160	20	for	for	ADP
cana-5708	160	21	deploying	deploy	VERB
cana-5708	160	22	and	and	CCONJ
cana-5708	160	23	training	train	VERB
cana-5708	160	24	neural	neural	ADJ
cana-5708	160	25	networks	network	NOUN
cana-5708	160	26	.	.	PUNCT
cana-5708	161	1	keras	keras	PROPN
cana-5708	161	2	(	(	PUNCT
cana-5708	161	3	chollet	chollet	PROPN
cana-5708	161	4	,	,	PUNCT
cana-5708	161	5	2015	2015	NUM
cana-5708	161	6	):	):	PUNCT
cana-5708	161	7	being	be	AUX
cana-5708	161	8	a	a	DET
cana-5708	161	9	high	high	ADJ
cana-5708	161	10	-	-	PUNCT
cana-5708	161	11	level	level	NOUN
cana-5708	161	12	api	api	NOUN
cana-5708	161	13	implemented	implement	VERB
cana-5708	161	14	on	on	ADP
cana-5708	161	15	top	top	NOUN
cana-5708	161	16	of	of	ADP
cana-5708	161	17	tensorflow	tensorflow	NOUN
cana-5708	161	18	,	,	PUNCT
cana-5708	161	19	it	it	PRON
cana-5708	161	20	simplifies	simplify	VERB
cana-5708	161	21	defining	define	VERB
cana-5708	161	22	and	and	CCONJ
cana-5708	161	23	training	train	VERB
cana-5708	161	24	convolutional	convolutional	ADJ
cana-5708	161	25	neural	neural	ADJ
cana-5708	161	26	networks	network	NOUN
cana-5708	161	27	(	(	PUNCT
cana-5708	161	28	cnns	cnns	PROPN
cana-5708	161	29	)	)	PUNCT
cana-5708	161	30	.	.	PUNCT
cana-5708	162	1	scikit	scikit	NOUN
cana-5708	162	2	-	-	PUNCT
cana-5708	162	3	learn	learn	VERB
cana-5708	162	4	(	(	PUNCT
cana-5708	162	5	pedregosa	pedregosa	NOUN
cana-5708	162	6	et	et	PROPN
cana-5708	162	7	al	al	PROPN
cana-5708	162	8	.	.	PROPN
cana-5708	162	9	,	,	PUNCT
cana-5708	162	10	2011	2011	NUM
cana-5708	162	11	)	)	PUNCT
cana-5708	162	12	is	be	AUX
cana-5708	162	13	a	a	DET
cana-5708	162	14	python	python	NOUN
cana-5708	162	15	general	general	ADJ
cana-5708	162	16	use	use	NOUN
cana-5708	162	17	library	library	NOUN
cana-5708	162	18	for	for	ADP
cana-5708	162	19	the	the	DET
cana-5708	162	20	implementation	implementation	NOUN
cana-5708	162	21	,	,	PUNCT
cana-5708	162	22	testing	testing	NOUN
cana-5708	162	23	,	,	PUNCT
cana-5708	162	24	and	and	CCONJ
cana-5708	162	25	preprocessing	preprocessing	NOUN
cana-5708	162	26	of	of	ADP
cana-5708	162	27	a	a	DET
cana-5708	162	28	fairly	fairly	ADV
cana-5708	162	29	broad	broad	ADJ
cana-5708	162	30	spectrum	spectrum	NOUN
cana-5708	162	31	of	of	ADP
cana-5708	162	32	machine	machine	NOUN
cana-5708	162	33	learning	learning	NOUN
cana-5708	162	34	models	model	NOUN
cana-5708	162	35	.	.	PUNCT
cana-5708	163	1	matplotlib	matplotlib	PROPN
cana-5708	163	2	(	(	PUNCT
cana-5708	163	3	hunter	hunter	NOUN
cana-5708	163	4	,	,	PUNCT
cana-5708	163	5	2007	2007	NUM
cana-5708	163	6	)	)	PUNCT
cana-5708	163	7	and	and	CCONJ
cana-5708	163	8	seaborn	seaborn	PROPN
cana-5708	163	9	(	(	PUNCT
cana-5708	163	10	waskom	waskom	PROPN
cana-5708	163	11	et	et	PROPN
cana-5708	163	12	al	al	PROPN
cana-5708	163	13	.	.	PROPN
cana-5708	163	14	,	,	PUNCT
cana-5708	163	15	2017	2017	NUM
cana-5708	163	16	)	)	PUNCT
cana-5708	163	17	shone	shine	VERB
cana-5708	163	18	in	in	ADP
cana-5708	163	19	holding	hold	VERB
cana-5708	163	20	the	the	DET
cana-5708	163	21	key	key	NOUN
cana-5708	163	22	for	for	ADP
cana-5708	163	23	all	all	DET
cana-5708	163	24	kinds	kind	NOUN
cana-5708	163	25	of	of	ADP
cana-5708	163	26	training	training	NOUN
cana-5708	163	27	progression	progression	NOUN
cana-5708	163	28	and	and	CCONJ
cana-5708	163	29	performance	performance	NOUN
cana-5708	163	30	metric	metric	ADJ
cana-5708	163	31	plots	plot	NOUN
cana-5708	163	32	.	.	PUNCT
cana-5708	164	1	google	google	PROPN
cana-5708	164	2	colab	colab	PROPN
cana-5708	164	3	is	be	AUX
cana-5708	164	4	an	an	DET
cana-5708	164	5	interoperable	interoperable	ADJ
cana-5708	164	6	,	,	PUNCT
cana-5708	164	7	free	free	ADJ
cana-5708	164	8	cloud	cloud	NOUN
cana-5708	164	9	-	-	PUNCT
cana-5708	164	10	based	base	VERB
cana-5708	164	11	environment	environment	NOUN
cana-5708	164	12	with	with	ADP
cana-5708	164	13	integrated	integrate	VERB
cana-5708	164	14	gpu	gpu	NOUN
cana-5708	164	15	acceleration	acceleration	NOUN
cana-5708	164	16	,	,	PUNCT
cana-5708	164	17	making	make	VERB
cana-5708	164	18	it	it	PRON
cana-5708	164	19	ideal	ideal	ADJ
cana-5708	164	20	for	for	ADP
cana-5708	164	21	conducting	conduct	VERB
cana-5708	164	22	deep	deep	ADJ
cana-5708	164	23	learning	learning	NOUN
cana-5708	164	24	experiments	experiment	NOUN
cana-5708	164	25	unencumbered	unencumbered	ADJ
cana-5708	164	26	by	by	ADP
cana-5708	164	27	local	local	ADJ
cana-5708	164	28	hardware	hardware	NOUN
cana-5708	164	29	constraints	constraint	NOUN
cana-5708	164	30	.	.	PUNCT
cana-5708	165	1	4.2	4.2	NUM
cana-5708	165	2	training	training	NOUN
cana-5708	165	3	and	and	CCONJ
cana-5708	165	4	validation	validation	NOUN
cana-5708	165	5	procedures	procedure	NOUN
cana-5708	165	6	data	datum	NOUN
cana-5708	165	7	split	split	VERB
cana-5708	165	8	ratios	ratio	NOUN
cana-5708	165	9	:	:	PUNCT
cana-5708	165	10	the	the	DET
cana-5708	165	11	data	datum	NOUN
cana-5708	165	12	was	be	AUX
cana-5708	165	13	divided	divide	VERB
cana-5708	165	14	into	into	ADP
cana-5708	165	15	three	three	NUM
cana-5708	165	16	sets	set	NOUN
cana-5708	165	17	:	:	PUNCT
cana-5708	165	18	70	70	NUM
cana-5708	165	19	%	%	NOUN
cana-5708	165	20	for	for	ADP
cana-5708	165	21	training	training	NOUN
cana-5708	165	22	,	,	PUNCT
cana-5708	165	23	15	15	NUM
cana-5708	165	24	%	%	NOUN
cana-5708	165	25	for	for	ADP
cana-5708	165	26	validation	validation	NOUN
cana-5708	165	27	,	,	PUNCT
cana-5708	165	28	and	and	CCONJ
cana-5708	165	29	15	15	NUM
cana-5708	165	30	%	%	NOUN
cana-5708	165	31	for	for	ADP
cana-5708	165	32	testing	testing	NOUN
cana-5708	165	33	,	,	PUNCT
cana-5708	165	34	that	that	PRON
cana-5708	165	35	was	be	AUX
cana-5708	165	36	sufficient	sufficient	ADJ
cana-5708	165	37	data	datum	NOUN
cana-5708	165	38	allocation	allocation	NOUN
cana-5708	165	39	to	to	ADP
cana-5708	165	40	each	each	DET
cana-5708	165	41	phase	phase	NOUN
cana-5708	165	42	for	for	ADP
cana-5708	165	43	proper	proper	ADJ
cana-5708	165	44	training	training	NOUN
cana-5708	165	45	and	and	CCONJ
cana-5708	165	46	accurate	accurate	ADJ
cana-5708	165	47	evaluation	evaluation	NOUN
cana-5708	165	48	of	of	ADP
cana-5708	165	49	the	the	DET
cana-5708	165	50	generalization	generalization	NOUN
cana-5708	165	51	ability	ability	NOUN
cana-5708	165	52	of	of	ADP
cana-5708	165	53	the	the	DET
cana-5708	165	54	model	model	NOUN
cana-5708	165	55	.	.	PUNCT
cana-5708	166	1	cross	cross	ADJ
cana-5708	166	2	-	-	ADJ
cana-5708	166	3	validation	validation	ADJ
cana-5708	166	4	protocols	protocol	NOUN
cana-5708	166	5	:	:	PUNCT
cana-5708	166	6	in	in	ADP
cana-5708	166	7	the	the	DET
cana-5708	166	8	study	study	NOUN
cana-5708	166	9	,	,	PUNCT
cana-5708	166	10	cross	cross	NOUN
cana-5708	166	11	-	-	NOUN
cana-5708	166	12	validation	validation	NOUN
cana-5708	166	13	with	with	ADP
cana-5708	166	14	k	k	NOUN
cana-5708	166	15	-	-	PUNCT
cana-5708	166	16	folds	fold	NOUN
cana-5708	166	17	was	be	AUX
cana-5708	166	18	conducted	conduct	VERB
cana-5708	166	19	for	for	ADP
cana-5708	166	20	model	model	NOUN
cana-5708	166	21	stability	stability	NOUN
cana-5708	166	22	.	.	PUNCT
cana-5708	167	1	the	the	DET
cana-5708	167	2	data	datum	NOUN
cana-5708	167	3	was	be	AUX
cana-5708	167	4	separated	separate	VERB
cana-5708	167	5	into	into	ADP
cana-5708	167	6	k	k	PROPN
cana-5708	167	7	parts	part	NOUN
cana-5708	167	8	.	.	PUNCT
cana-5708	168	1	the	the	DET
cana-5708	168	2	model	model	NOUN
cana-5708	168	3	was	be	AUX
cana-5708	168	4	trained	train	VERB
cana-5708	168	5	k	k	PROPN
cana-5708	168	6	times	time	NOUN
cana-5708	168	7	,	,	PUNCT
cana-5708	168	8	with	with	ADP
cana-5708	168	9	each	each	DET
cana-5708	168	10	portion	portion	NOUN
cana-5708	168	11	used	use	VERB
cana-5708	168	12	for	for	ADP
cana-5708	168	13	validation	validation	NOUN
cana-5708	168	14	and	and	CCONJ
cana-5708	168	15	the	the	DET
cana-5708	168	16	rest	rest	NOUN
cana-5708	168	17	used	use	VERB
cana-5708	168	18	for	for	ADP
cana-5708	168	19	training	training	NOUN
cana-5708	168	20	.	.	PUNCT
cana-5708	169	1	although	although	SCONJ
cana-5708	169	2	the	the	DET
cana-5708	169	3	process	process	NOUN
cana-5708	169	4	is	be	AUX
cana-5708	169	5	computationally	computationally	ADV
cana-5708	169	6	expensive	expensive	ADJ
cana-5708	169	7	,	,	PUNCT
cana-5708	169	8	it	it	PRON
cana-5708	169	9	uses	use	VERB
cana-5708	169	10	all	all	DET
cana-5708	169	11	data	datum	NOUN
cana-5708	169	12	points	point	NOUN
cana-5708	169	13	for	for	ADP
cana-5708	169	14	training	training	NOUN
cana-5708	169	15	and	and	CCONJ
cana-5708	169	16	validation	validation	NOUN
cana-5708	169	17	,	,	PUNCT
cana-5708	169	18	giving	give	VERB
cana-5708	169	19	the	the	DET
cana-5708	169	20	best	good	ADJ
cana-5708	169	21	estimate	estimate	NOUN
cana-5708	169	22	of	of	ADP
cana-5708	169	23	model	model	NOUN
cana-5708	169	24	performance	performance	NOUN
cana-5708	169	25	.	.	PUNCT
cana-5708	170	1	table	table	NOUN
cana-5708	170	2	ix	ix	ADV
cana-5708	170	3	describes	describe	VERB
cana-5708	170	4	the	the	DET
cana-5708	170	5	training	training	NOUN
cana-5708	170	6	data	datum	NOUN
cana-5708	170	7	,	,	PUNCT
cana-5708	170	8	validation	validation	NOUN
cana-5708	170	9	,	,	PUNCT
cana-5708	170	10	and	and	CCONJ
cana-5708	170	11	test	test	NOUN
cana-5708	170	12	splits	split	NOUN
cana-5708	170	13	of	of	ADP
cana-5708	170	14	the	the	DET
cana-5708	170	15	datasets	dataset	NOUN
cana-5708	170	16	used	use	VERB
cana-5708	170	17	in	in	ADP
cana-5708	170	18	this	this	DET
cana-5708	170	19	study	study	NOUN
cana-5708	170	20	.	.	PUNCT
cana-5708	171	1	communications	communication	NOUN
cana-5708	171	2	on	on	ADP
cana-5708	171	3	applied	apply	VERB
cana-5708	171	4	nonlinear	nonlinear	ADJ
cana-5708	171	5	analysis	analysis	NOUN
cana-5708	171	6	issn	issn	NOUN
cana-5708	171	7	:	:	PUNCT
cana-5708	171	8	1074	1074	NUM
cana-5708	171	9	-	-	PUNCT
cana-5708	171	10	133x	133x	NUM
cana-5708	171	11	vol	vol	VERB
cana-5708	171	12	32	32	NUM
cana-5708	171	13	no	no	NOUN
cana-5708	171	14	.	.	PUNCT
cana-5708	172	1	10s	10	NOUN
cana-5708	172	2	(	(	PUNCT
cana-5708	172	3	2025	2025	NUM
cana-5708	172	4	)	)	PUNCT
cana-5708	172	5	2678	2678	NUM
cana-5708	172	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5708	172	7	information	information	NOUN
cana-5708	172	8	capturing	capture	VERB
cana-5708	172	9	a	a	DET
cana-5708	172	10	uniform	uniform	ADJ
cana-5708	172	11	distribution	distribution	NOUN
cana-5708	172	12	of	of	ADP
cana-5708	172	13	70	70	NUM
cana-5708	172	14	-	-	SYM
cana-5708	172	15	15	15	NUM
cana-5708	172	16	-	-	SYM
cana-5708	172	17	15	15	NUM
cana-5708	172	18	shows	show	VERB
cana-5708	172	19	that	that	SCONJ
cana-5708	172	20	the	the	DET
cana-5708	172	21	resources	resource	NOUN
cana-5708	172	22	will	will	AUX
cana-5708	172	23	be	be	AUX
cana-5708	172	24	divided	divide	VERB
cana-5708	172	25	equally	equally	ADV
cana-5708	172	26	between	between	ADP
cana-5708	172	27	modeling	modeling	NOUN
cana-5708	172	28	and	and	CCONJ
cana-5708	172	29	evaluation	evaluation	NOUN
cana-5708	172	30	.	.	PUNCT
cana-5708	173	1	table	table	NOUN
cana-5708	173	2	ix	ix	ADP
cana-5708	173	3	:	:	PUNCT
cana-5708	174	1	training	training	NOUN
cana-5708	174	2	data	datum	NOUN
cana-5708	174	3	,	,	PUNCT
cana-5708	174	4	validation	validation	NOUN
cana-5708	174	5	data	datum	NOUN
cana-5708	174	6	,	,	PUNCT
cana-5708	174	7	and	and	CCONJ
cana-5708	174	8	testing	test	VERB
cana-5708	174	9	data	datum	NOUN
cana-5708	174	10	dataset	dataset	NOUN
cana-5708	174	11	training	training	NOUN
cana-5708	174	12	data	datum	NOUN
cana-5708	174	13	validation	validation	NOUN
cana-5708	174	14	data	datum	NOUN
cana-5708	174	15	testing	testing	NOUN
cana-5708	174	16	data	datum	NOUN
cana-5708	174	17	isic	isic	PROPN
cana-5708	174	18	archive	archive	NOUN
cana-5708	174	19	70	70	NUM
cana-5708	174	20	%	%	NOUN
cana-5708	174	21	15	15	NUM
cana-5708	174	22	%	%	NOUN
cana-5708	174	23	15	15	NUM
cana-5708	174	24	%	%	NOUN
cana-5708	174	25	dermis	dermis	NOUN
cana-5708	174	26	70	70	NUM
cana-5708	174	27	%	%	NOUN
cana-5708	174	28	15	15	NUM
cana-5708	174	29	%	%	NOUN
cana-5708	174	30	15	15	NUM
cana-5708	174	31	%	%	NOUN
cana-5708	174	32	ph2	ph2	NOUN
cana-5708	174	33	70	70	NUM
cana-5708	174	34	%	%	NOUN
cana-5708	174	35	15	15	NUM
cana-5708	174	36	%	%	NOUN
cana-5708	174	37	15	15	NUM
cana-5708	174	38	%	%	NOUN
cana-5708	174	39	4.3	4.3	NUM
cana-5708	174	40	evaluation	evaluation	NOUN
cana-5708	174	41	metrics	metric	NOUN
cana-5708	174	42	we	we	PRON
cana-5708	174	43	evaluated	evaluate	VERB
cana-5708	174	44	the	the	DET
cana-5708	174	45	model	model	NOUN
cana-5708	174	46	's	's	PART
cana-5708	174	47	performance	performance	NOUN
cana-5708	174	48	using	use	VERB
cana-5708	174	49	key	key	ADJ
cana-5708	174	50	performance	performance	NOUN
cana-5708	174	51	metrics	metric	NOUN
cana-5708	174	52	including	include	VERB
cana-5708	174	53	accuracy	accuracy	NOUN
cana-5708	174	54	(	(	PUNCT
cana-5708	174	55	acc	acc	PROPN
cana-5708	174	56	)	)	PUNCT
cana-5708	174	57	,	,	PUNCT
cana-5708	174	58	precision	precision	NOUN
cana-5708	174	59	(	(	PUNCT
cana-5708	174	60	prec	prec	PROPN
cana-5708	174	61	)	)	PUNCT
cana-5708	174	62	,	,	PUNCT
cana-5708	174	63	recall(rec	recall(rec	PROPN
cana-5708	174	64	)	)	PUNCT
cana-5708	174	65	,	,	PUNCT
cana-5708	174	66	f1	f1	NOUN
cana-5708	174	67	-	-	PUNCT
cana-5708	174	68	score	score	NOUN
cana-5708	174	69	and	and	CCONJ
cana-5708	174	70	area	area	NOUN
cana-5708	174	71	under	under	ADP
cana-5708	174	72	the	the	DET
cana-5708	174	73	curve	curve	NOUN
cana-5708	174	74	(	(	PUNCT
cana-5708	174	75	auc	auc	NOUN
cana-5708	174	76	-	-	PUNCT
cana-5708	174	77	roc	roc	NOUN
cana-5708	174	78	)	)	PUNCT
cana-5708	174	79	.	.	PUNCT
cana-5708	175	1	these	these	DET
cana-5708	175	2	metrics	metric	NOUN
cana-5708	175	3	are	be	AUX
cana-5708	175	4	important	important	ADJ
cana-5708	175	5	for	for	ADP
cana-5708	175	6	measuring	measure	VERB
cana-5708	175	7	different	different	ADJ
cana-5708	175	8	aspects	aspect	NOUN
cana-5708	175	9	of	of	ADP
cana-5708	175	10	the	the	DET
cana-5708	175	11	model	model	NOUN
cana-5708	175	12	's	's	PART
cana-5708	175	13	performance	performance	NOUN
cana-5708	175	14	,	,	PUNCT
cana-5708	175	15	especially	especially	ADV
cana-5708	175	16	with	with	ADP
cana-5708	175	17	the	the	DET
cana-5708	175	18	goal	goal	NOUN
cana-5708	175	19	of	of	ADP
cana-5708	175	20	accurately	accurately	ADV
cana-5708	175	21	diagnosing	diagnose	VERB
cana-5708	175	22	skin	skin	NOUN
cana-5708	175	23	lesions	lesion	NOUN
cana-5708	175	24	.	.	PUNCT
cana-5708	176	1	acc	acc	NOUN
cana-5708	176	2	:	:	PUNCT
cana-5708	177	1	accuracy	accuracy	NOUN
cana-5708	177	2	is	be	AUX
cana-5708	177	3	defined	define	VERB
cana-5708	177	4	as	as	ADP
cana-5708	177	5	the	the	DET
cana-5708	177	6	proportion	proportion	NOUN
cana-5708	177	7	of	of	ADP
cana-5708	177	8	accurate	accurate	ADJ
cana-5708	177	9	prediction	prediction	NOUN
cana-5708	177	10	against	against	ADP
cana-5708	177	11	the	the	DET
cana-5708	177	12	overall	overall	ADJ
cana-5708	177	13	predictions	prediction	NOUN
cana-5708	177	14	𝑎𝑐𝑐	𝑎𝑐𝑐	NOUN
cana-5708	177	15	=	=	NOUN
cana-5708	177	16	𝑡𝑟𝑢𝑒	𝑡𝑟𝑢𝑒	PROPN
cana-5708	177	17	𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠	𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠	VERB
cana-5708	178	1	+	+	CCONJ
cana-5708	178	2	𝑡𝑟𝑢𝑒	𝑡𝑟𝑢𝑒	ADJ
cana-5708	178	3	𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑠	𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑠	ADJ
cana-5708	178	4	𝑡𝑟𝑢𝑒	𝑡𝑟𝑢𝑒	NOUN
cana-5708	178	5	𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠	𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠	PROPN
cana-5708	178	6	+	+	CCONJ
cana-5708	178	7	𝑡𝑟𝑢𝑒	𝑡𝑟𝑢𝑒	ADJ
cana-5708	178	8	𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑠	𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑠	ADJ
cana-5708	178	9	+	+	CCONJ
cana-5708	178	10	𝑓𝑎𝑙𝑠𝑒	𝑓𝑎𝑙𝑠𝑒	NOUN
cana-5708	178	11	𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠	𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠	ADJ
cana-5708	178	12	+	+	CCONJ
cana-5708	178	13	𝑓𝑎𝑙𝑠𝑒	𝑓𝑎𝑙𝑠𝑒	NOUN
cana-5708	178	14	𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑠	𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑠	ADJ
cana-5708	178	15	prec	prec	NOUN
cana-5708	178	16	:	:	PUNCT
cana-5708	178	17	precision	precision	NOUN
cana-5708	178	18	is	be	AUX
cana-5708	178	19	defined	define	VERB
cana-5708	178	20	as	as	ADP
cana-5708	178	21	the	the	DET
cana-5708	178	22	proportion	proportion	NOUN
cana-5708	178	23	of	of	ADP
cana-5708	178	24	correct	correct	ADJ
cana-5708	178	25	positive	positive	ADJ
cana-5708	178	26	cases	case	NOUN
cana-5708	178	27	against	against	ADP
cana-5708	178	28	the	the	DET
cana-5708	178	29	all	all	DET
cana-5708	178	30	positive	positive	ADJ
cana-5708	178	31	predictions	prediction	NOUN
cana-5708	178	32	.	.	PUNCT
cana-5708	179	1	𝑝𝑟𝑒𝑐	𝑝𝑟𝑒𝑐	NOUN
cana-5708	179	2	=	=	SYM
cana-5708	179	3	𝑡𝑟𝑢𝑒	𝑡𝑟𝑢𝑒	PROPN
cana-5708	179	4	𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠	𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠	PROPN
cana-5708	179	5	𝑡𝑟𝑢𝑒	𝑡𝑟𝑢𝑒	PROPN
cana-5708	179	6	𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠	𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠	ADJ
cana-5708	179	7	+	+	NUM
cana-5708	179	8	𝑓𝑎𝑙𝑠𝑒	𝑓𝑎𝑙𝑠𝑒	NOUN
cana-5708	179	9	𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠	𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠	ADJ
cana-5708	179	10	rec	rec	NOUN
cana-5708	179	11	:	:	PUNCT
cana-5708	179	12	it	it	PRON
cana-5708	179	13	is	be	AUX
cana-5708	179	14	the	the	DET
cana-5708	179	15	ratio	ratio	NOUN
cana-5708	179	16	of	of	ADP
cana-5708	179	17	positive	positive	ADJ
cana-5708	179	18	predicted	predict	VERB
cana-5708	179	19	cases	case	NOUN
cana-5708	179	20	over	over	ADP
cana-5708	179	21	positive	positive	ADJ
cana-5708	179	22	actual	actual	ADJ
cana-5708	179	23	cases	case	NOUN
cana-5708	179	24	𝑟𝑒𝑐	𝑟𝑒𝑐	NOUN
cana-5708	179	25	=	=	SYM
cana-5708	179	26	𝑡𝑟𝑢𝑒	𝑡𝑟𝑢𝑒	PROPN
cana-5708	179	27	𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠	𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠	PROPN
cana-5708	179	28	𝑡𝑟𝑢𝑒	𝑡𝑟𝑢𝑒	PROPN
cana-5708	179	29	𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠	𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠	ADJ
cana-5708	180	1	+	+	CCONJ
cana-5708	180	2	𝑓𝑎𝑙𝑠𝑒	𝑓𝑎𝑙𝑠𝑒	NOUN
cana-5708	180	3	𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑠	𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑠	ADJ
cana-5708	180	4	f1	f1	NOUN
cana-5708	180	5	-	-	PUNCT
cana-5708	180	6	score	score	NOUN
cana-5708	180	7	:	:	PUNCT
cana-5708	180	8	it	it	PRON
cana-5708	180	9	is	be	AUX
cana-5708	180	10	the	the	DET
cana-5708	180	11	harmonic	harmonic	ADJ
cana-5708	180	12	mean	mean	NOUN
cana-5708	180	13	of	of	ADP
cana-5708	180	14	recall	recall	NOUN
cana-5708	180	15	and	and	CCONJ
cana-5708	180	16	precision	precision	NOUN
cana-5708	180	17	.	.	PUNCT
cana-5708	181	1	communications	communication	NOUN
cana-5708	181	2	on	on	ADP
cana-5708	181	3	applied	apply	VERB
cana-5708	181	4	nonlinear	nonlinear	ADJ
cana-5708	181	5	analysis	analysis	NOUN
cana-5708	181	6	issn	issn	NOUN
cana-5708	181	7	:	:	PUNCT
cana-5708	181	8	1074	1074	NUM
cana-5708	181	9	-	-	PUNCT
cana-5708	181	10	133x	133x	NUM
cana-5708	181	11	vol	vol	VERB
cana-5708	181	12	32	32	NUM
cana-5708	181	13	no	no	NOUN
cana-5708	181	14	.	.	PUNCT
cana-5708	182	1	10s	10	NOUN
cana-5708	182	2	(	(	PUNCT
cana-5708	182	3	2025	2025	NUM
cana-5708	182	4	)	)	PUNCT
cana-5708	182	5	2679	2679	NUM
cana-5708	182	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5708	182	7	𝐹1	𝐹1	NOUN
cana-5708	182	8	=	=	SYM
cana-5708	182	9	2	2	NUM
cana-5708	182	10	∗	∗	NOUN
cana-5708	182	11	𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛	𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛	NOUN
cana-5708	182	12	∗	∗	NOUN
cana-5708	182	13	𝑟𝑒𝑐𝑎𝑙𝑙	𝑟𝑒𝑐𝑎𝑙𝑙	NOUN
cana-5708	182	14	(	(	PUNCT
cana-5708	182	15	𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛	𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛	NOUN
cana-5708	182	16	+	+	SYM
cana-5708	182	17	𝑟𝑒𝑐𝑎𝑙𝑙	𝑟𝑒𝑐𝑎𝑙𝑙	NOUN
cana-5708	182	18	)	)	PUNCT
cana-5708	182	19	auc	auc	NOUN
cana-5708	182	20	-	-	PUNCT
cana-5708	182	21	roc	roc	NOUN
cana-5708	182	22	:	:	PUNCT
cana-5708	182	23	the	the	DET
cana-5708	182	24	receiver	receiver	NOUN
cana-5708	182	25	operating	operate	VERB
cana-5708	182	26	characteristic	characteristic	NOUN
cana-5708	182	27	(	(	PUNCT
cana-5708	182	28	roc	roc	PROPN
cana-5708	182	29	)	)	PUNCT
cana-5708	182	30	curve	curve	NOUN
cana-5708	182	31	was	be	AUX
cana-5708	182	32	also	also	ADV
cana-5708	182	33	used	use	VERB
cana-5708	182	34	to	to	PART
cana-5708	182	35	measure	measure	VERB
cana-5708	182	36	the	the	DET
cana-5708	182	37	model	model	NOUN
cana-5708	182	38	's	's	PART
cana-5708	182	39	ability	ability	NOUN
cana-5708	182	40	to	to	PART
cana-5708	182	41	discriminate	discriminate	VERB
cana-5708	182	42	between	between	ADP
cana-5708	182	43	classes	class	NOUN
cana-5708	182	44	,	,	PUNCT
cana-5708	182	45	with	with	ADP
cana-5708	182	46	the	the	DET
cana-5708	182	47	higher	high	ADJ
cana-5708	182	48	value	value	NOUN
cana-5708	182	49	indicating	indicate	VERB
cana-5708	182	50	better	well	ADJ
cana-5708	182	51	performance	performance	NOUN
cana-5708	182	52	.	.	PUNCT
cana-5708	183	1	5	5	X
cana-5708	183	2	.	.	X
cana-5708	183	3	results	result	VERB
cana-5708	183	4	the	the	DET
cana-5708	183	5	result	result	NOUN
cana-5708	183	6	of	of	ADP
cana-5708	183	7	this	this	DET
cana-5708	183	8	experiment	experiment	NOUN
cana-5708	183	9	underlines	underline	VERB
cana-5708	183	10	the	the	DET
cana-5708	183	11	critical	critical	ADJ
cana-5708	183	12	function	function	NOUN
cana-5708	183	13	of	of	ADP
cana-5708	183	14	hyperband	hyperband	ADJ
cana-5708	183	15	optimization	optimization	NOUN
cana-5708	183	16	in	in	ADP
cana-5708	183	17	hyperparameter	hyperparameter	NOUN
cana-5708	183	18	tuning	tuning	NOUN
cana-5708	183	19	of	of	ADP
cana-5708	183	20	model	model	NOUN
cana-5708	183	21	performance	performance	NOUN
cana-5708	183	22	.	.	PUNCT
cana-5708	184	1	metrics	metric	NOUN
cana-5708	184	2	used	use	VERB
cana-5708	184	3	in	in	ADP
cana-5708	184	4	comparison	comparison	NOUN
cana-5708	184	5	are	be	AUX
cana-5708	184	6	evident	evident	ADJ
cana-5708	184	7	of	of	ADP
cana-5708	184	8	the	the	DET
cana-5708	184	9	method	method	NOUN
cana-5708	184	10	being	be	AUX
cana-5708	184	11	the	the	DET
cana-5708	184	12	most	most	ADV
cana-5708	184	13	effective	effective	ADJ
cana-5708	184	14	with	with	ADP
cana-5708	184	15	maximum	maximum	ADJ
cana-5708	184	16	accuracy	accuracy	NOUN
cana-5708	184	17	with	with	ADP
cana-5708	184	18	more	more	ADV
cana-5708	184	19	stable	stable	ADJ
cana-5708	184	20	learning	learning	NOUN
cana-5708	184	21	curve	curve	NOUN
cana-5708	184	22	variations	variation	NOUN
cana-5708	184	23	and	and	CCONJ
cana-5708	184	24	better	well	ADJ
cana-5708	184	25	convergence	convergence	NOUN
cana-5708	184	26	efficiency	efficiency	NOUN
cana-5708	184	27	5.1	5.1	NUM
cana-5708	184	28	model	model	NOUN
cana-5708	184	29	performance	performance	NOUN
cana-5708	184	30	comparison	comparison	NOUN
cana-5708	184	31	of	of	ADP
cana-5708	184	32	metrics	metric	NOUN
cana-5708	184	33	before	before	ADV
cana-5708	184	34	and	and	CCONJ
cana-5708	184	35	after	after	ADP
cana-5708	184	36	hyperband	hyperband	ADJ
cana-5708	184	37	optimization	optimization	NOUN
cana-5708	184	38	the	the	DET
cana-5708	184	39	comparisons	comparison	NOUN
cana-5708	184	40	done	do	VERB
cana-5708	184	41	with	with	ADP
cana-5708	184	42	hyperband	hyperband	ADJ
cana-5708	184	43	optimization	optimization	NOUN
cana-5708	184	44	showed	show	VERB
cana-5708	184	45	up	up	ADP
cana-5708	184	46	while	while	SCONJ
cana-5708	184	47	comparing	compare	VERB
cana-5708	184	48	the	the	DET
cana-5708	184	49	baseline	baseline	NOUN
cana-5708	184	50	model	model	NOUN
cana-5708	184	51	and	and	CCONJ
cana-5708	184	52	the	the	DET
cana-5708	184	53	optimized	optimize	VERB
cana-5708	184	54	model	model	NOUN
cana-5708	184	55	which	which	PRON
cana-5708	184	56	showed	show	VERB
cana-5708	184	57	itself	itself	PRON
cana-5708	184	58	before	before	ADP
cana-5708	184	59	hyperparameter	hyperparameter	NOUN
cana-5708	184	60	optimization	optimization	NOUN
cana-5708	184	61	.	.	PUNCT
cana-5708	185	1	the	the	DET
cana-5708	185	2	optimized	optimize	VERB
cana-5708	185	3	model	model	NOUN
cana-5708	185	4	showed	show	VERB
cana-5708	185	5	better	well	ADJ
cana-5708	185	6	accuracy	accuracy	NOUN
cana-5708	185	7	for	for	ADP
cana-5708	185	8	training	training	NOUN
cana-5708	185	9	and	and	CCONJ
cana-5708	185	10	validation	validation	NOUN
cana-5708	185	11	loss	loss	NOUN
cana-5708	185	12	vs.	vs.	ADP
cana-5708	185	13	hyperband	hyperband	ADJ
cana-5708	185	14	optimization	optimization	NOUN
cana-5708	185	15	was	be	AUX
cana-5708	185	16	seen	see	VERB
cana-5708	185	17	as	as	ADP
cana-5708	185	18	an	an	DET
cana-5708	185	19	effective	effective	ADJ
cana-5708	185	20	one	one	NOUN
cana-5708	185	21	for	for	ADP
cana-5708	185	22	tuning	tuning	NOUN
cana-5708	185	23	of	of	ADP
cana-5708	185	24	hyperparameters	hyperparameter	NOUN
cana-5708	185	25	.	.	PUNCT
cana-5708	186	1	preand	preand	VERB
cana-5708	186	2	post	post	ADJ
cana-5708	186	3	-	-	ADJ
cana-5708	186	4	tuning	tuning	ADJ
cana-5708	186	5	performance	performance	NOUN
cana-5708	186	6	metrics	metric	NOUN
cana-5708	186	7	are	be	AUX
cana-5708	186	8	presented	present	VERB
cana-5708	186	9	in	in	ADP
cana-5708	186	10	table	table	NOUN
cana-5708	186	11	x	x	NOUN
cana-5708	186	12	,	,	PUNCT
cana-5708	186	13	which	which	PRON
cana-5708	186	14	provides	provide	VERB
cana-5708	186	15	a	a	DET
cana-5708	186	16	comprehensive	comprehensive	ADJ
cana-5708	186	17	comparison	comparison	NOUN
cana-5708	186	18	of	of	ADP
cana-5708	186	19	the	the	DET
cana-5708	186	20	model	model	NOUN
cana-5708	186	21	's	's	PART
cana-5708	186	22	key	key	ADJ
cana-5708	186	23	metrics	metric	NOUN
cana-5708	186	24	.	.	PUNCT
cana-5708	187	1	table	table	NOUN
cana-5708	187	2	x	x	NOUN
cana-5708	187	3	:	:	PUNCT
cana-5708	187	4	performance	performance	NOUN
cana-5708	187	5	metrics	metric	NOUN
cana-5708	187	6	before	before	ADV
cana-5708	187	7	and	and	CCONJ
cana-5708	187	8	after	after	ADP
cana-5708	187	9	tuning	tune	VERB
cana-5708	187	10	metric	metric	ADJ
cana-5708	187	11	baseline	baseline	NOUN
cana-5708	187	12	model	model	NOUN
cana-5708	187	13	(	(	PUNCT
cana-5708	187	14	before	before	ADP
cana-5708	187	15	tuning	tune	VERB
cana-5708	187	16	)	)	PUNCT
cana-5708	187	17	tuned	tune	VERB
cana-5708	187	18	model	model	NOUN
cana-5708	187	19	(	(	PUNCT
cana-5708	187	20	after	after	ADP
cana-5708	187	21	hyperband	hyperband	ADJ
cana-5708	187	22	tuning	tuning	NOUN
cana-5708	187	23	)	)	PUNCT
cana-5708	187	24	improvement	improvement	NOUN
cana-5708	187	25	training	training	NOUN
cana-5708	187	26	accuracy	accuracy	NOUN
cana-5708	187	27	85.3	85.3	NUM
cana-5708	187	28	%	%	NOUN
cana-5708	187	29	92.8	92.8	NUM
cana-5708	187	30	%	%	NOUN
cana-5708	187	31	+7.5	+7.5	NUM
cana-5708	187	32	%	%	NOUN
cana-5708	187	33	validation	validation	NOUN
cana-5708	187	34	accuracy	accuracy	NOUN
cana-5708	187	35	83.5	83.5	NUM
cana-5708	187	36	%	%	NOUN
cana-5708	187	37	90.2	90.2	NUM
cana-5708	187	38	%	%	NOUN
cana-5708	187	39	+6.7	+6.7	ADP
cana-5708	187	40	%	%	NOUN
cana-5708	187	41	training	training	NOUN
cana-5708	187	42	loss	loss	NOUN
cana-5708	187	43	0.45	0.45	NUM
cana-5708	187	44	0.28	0.28	NUM
cana-5708	187	45	-0.17	-0.17	PRON
cana-5708	187	46	validation	validation	NOUN
cana-5708	187	47	loss	loss	NOUN
cana-5708	187	48	0.50	0.50	NUM
cana-5708	187	49	0.30	0.30	NUM
cana-5708	187	50	-0.20	-0.20	NUM
cana-5708	187	51	5.2	5.2	NUM
cana-5708	187	52	effectiveness	effectiveness	NOUN
cana-5708	187	53	of	of	ADP
cana-5708	187	54	hyperband	hyperband	PROPN
cana-5708	187	55	hyperband	hyperband	PROPN
cana-5708	187	56	greatly	greatly	ADV
cana-5708	187	57	enhanced	enhance	VERB
cana-5708	187	58	accuracy	accuracy	NOUN
cana-5708	187	59	,	,	PUNCT
cana-5708	187	60	computation	computation	NOUN
cana-5708	187	61	time	time	NOUN
cana-5708	187	62	,	,	PUNCT
cana-5708	187	63	and	and	CCONJ
cana-5708	187	64	generalization	generalization	NOUN
cana-5708	187	65	by	by	ADP
cana-5708	187	66	performing	perform	VERB
cana-5708	187	67	exhaustive	exhaustive	ADJ
cana-5708	187	68	search	search	NOUN
cana-5708	187	69	and	and	CCONJ
cana-5708	187	70	hyperparameter	hyperparameter	NOUN
cana-5708	187	71	tuning	tune	VERB
cana-5708	187	72	as	as	SCONJ
cana-5708	187	73	presented	present	VERB
cana-5708	187	74	in	in	ADP
cana-5708	187	75	figure	figure	NOUN
cana-5708	187	76	v	v	ADP
cana-5708	187	77	..	..	PUNCT
cana-5708	187	78	communications	communication	NOUN
cana-5708	187	79	on	on	ADP
cana-5708	187	80	applied	apply	VERB
cana-5708	187	81	nonlinear	nonlinear	ADJ
cana-5708	187	82	analysis	analysis	NOUN
cana-5708	187	83	issn	issn	NOUN
cana-5708	187	84	:	:	PUNCT
cana-5708	187	85	1074	1074	NUM
cana-5708	187	86	-	-	PUNCT
cana-5708	187	87	133x	133x	NUM
cana-5708	187	88	vol	vol	VERB
cana-5708	187	89	32	32	NUM
cana-5708	187	90	no	no	NOUN
cana-5708	187	91	.	.	PUNCT
cana-5708	188	1	10s	10	NOUN
cana-5708	188	2	(	(	PUNCT
cana-5708	188	3	2025	2025	NUM
cana-5708	188	4	)	)	PUNCT
cana-5708	188	5	2680	2680	NUM
cana-5708	188	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5708	188	7	fig	fig	NOUN
cana-5708	188	8	.	.	PUNCT
cana-5708	189	1	v	v	X
cana-5708	189	2	k	k	ADJ
cana-5708	189	3	-	-	ADJ
cana-5708	189	4	fold	fold	ADJ
cana-5708	189	5	cross	cross	ADJ
cana-5708	189	6	-	-	ADJ
cana-5708	189	7	validation	validation	ADJ
cana-5708	189	8	accuracy	accuracy	NOUN
cana-5708	189	9	per	per	ADP
cana-5708	189	10	fold	fold	VERB
cana-5708	189	11	the	the	DET
cana-5708	189	12	accuracy	accuracy	NOUN
cana-5708	189	13	scores	score	NOUN
cana-5708	189	14	for	for	ADP
cana-5708	189	15	each	each	DET
cana-5708	189	16	fold	fold	NOUN
cana-5708	189	17	are	be	AUX
cana-5708	189	18	depicted	depict	VERB
cana-5708	189	19	,	,	PUNCT
cana-5708	189	20	showing	show	VERB
cana-5708	189	21	a	a	DET
cana-5708	189	22	range	range	NOUN
cana-5708	189	23	between	between	ADP
cana-5708	189	24	71.33	71.33	NUM
cana-5708	189	25	%	%	NOUN
cana-5708	189	26	and	and	CCONJ
cana-5708	189	27	76.91	76.91	NUM
cana-5708	189	28	%	%	NOUN
cana-5708	189	29	whereas	whereas	SCONJ
cana-5708	189	30	the	the	DET
cana-5708	189	31	mean	mean	ADJ
cana-5708	189	32	accuracy	accuracy	NOUN
cana-5708	189	33	of	of	ADP
cana-5708	189	34	75.65	75.65	NUM
cana-5708	189	35	%	%	NOUN
cana-5708	189	36	is	be	AUX
cana-5708	189	37	marked	mark	VERB
cana-5708	189	38	with	with	ADP
cana-5708	189	39	a	a	DET
cana-5708	189	40	dashed	dash	VERB
cana-5708	189	41	red	red	ADJ
cana-5708	189	42	line	line	NOUN
cana-5708	189	43	,	,	PUNCT
cana-5708	189	44	indicating	indicate	VERB
cana-5708	189	45	the	the	DET
cana-5708	189	46	model	model	NOUN
cana-5708	189	47	's	's	PART
cana-5708	189	48	overall	overall	ADJ
cana-5708	189	49	performance	performance	NOUN
cana-5708	189	50	on	on	ADP
cana-5708	189	51	all	all	DET
cana-5708	189	52	the	the	DET
cana-5708	189	53	folds	fold	NOUN
cana-5708	189	54	.	.	PUNCT
cana-5708	190	1	the	the	DET
cana-5708	190	2	shaded	shaded	ADJ
cana-5708	190	3	area	area	NOUN
cana-5708	190	4	around	around	ADP
cana-5708	190	5	the	the	DET
cana-5708	190	6	mean	mean	ADJ
cana-5708	190	7	accuracy	accuracy	NOUN
cana-5708	190	8	indicates	indicate	VERB
cana-5708	190	9	the	the	DET
cana-5708	190	10	standard	standard	ADJ
cana-5708	190	11	deviation	deviation	NOUN
cana-5708	190	12	(	(	PUNCT
cana-5708	190	13	±0.56	±0.56	NOUN
cana-5708	190	14	%	%	NOUN
cana-5708	190	15	)	)	PUNCT
cana-5708	190	16	,	,	PUNCT
cana-5708	190	17	which	which	PRON
cana-5708	190	18	is	be	AUX
cana-5708	190	19	the	the	DET
cana-5708	190	20	most	most	ADV
cana-5708	190	21	crucial	crucial	ADJ
cana-5708	190	22	sign	sign	NOUN
cana-5708	190	23	of	of	ADP
cana-5708	190	24	how	how	SCONJ
cana-5708	190	25	stable	stable	ADJ
cana-5708	190	26	and	and	CCONJ
cana-5708	190	27	robust	robust	ADJ
cana-5708	190	28	the	the	DET
cana-5708	190	29	model	model	NOUN
cana-5708	190	30	was	be	AUX
cana-5708	190	31	throughout	throughout	ADP
cana-5708	190	32	cross	cross	NOUN
cana-5708	190	33	-	-	NOUN
cana-5708	190	34	validation	validation	NOUN
cana-5708	190	35	.	.	PUNCT
cana-5708	191	1	the	the	DET
cana-5708	191	2	following	follow	VERB
cana-5708	191	3	observations	observation	NOUN
cana-5708	191	4	were	be	AUX
cana-5708	191	5	presented	present	VERB
cana-5708	191	6	because	because	SCONJ
cana-5708	191	7	of	of	ADP
cana-5708	191	8	performance	performance	NOUN
cana-5708	191	9	visualizations	visualization	NOUN
cana-5708	191	10	and	and	CCONJ
cana-5708	191	11	major	major	ADJ
cana-5708	191	12	takeaway	takeaway	NOUN
cana-5708	191	13	of	of	ADP
cana-5708	191	14	experimental	experimental	ADJ
cana-5708	191	15	outcomes	outcome	NOUN
cana-5708	191	16	,	,	PUNCT
cana-5708	191	17	indicating	indicate	VERB
cana-5708	191	18	the	the	DET
cana-5708	191	19	effect	effect	NOUN
cana-5708	191	20	of	of	ADP
cana-5708	191	21	hyperband	hyperband	ADJ
cana-5708	191	22	optimisation	optimisation	NOUN
cana-5708	191	23	on	on	ADP
cana-5708	191	24	stability	stability	NOUN
cana-5708	191	25	,	,	PUNCT
cana-5708	191	26	loss	loss	NOUN
cana-5708	191	27	,	,	PUNCT
cana-5708	191	28	and	and	CCONJ
cana-5708	191	29	accuracy	accuracy	NOUN
cana-5708	191	30	:	:	PUNCT
cana-5708	191	31	1	1	X
cana-5708	191	32	.	.	NUM
cana-5708	191	33	improved	improved	ADJ
cana-5708	191	34	accuracy	accuracy	NOUN
cana-5708	191	35	the	the	DET
cana-5708	191	36	model	model	NOUN
cana-5708	191	37	showed	show	VERB
cana-5708	191	38	a	a	DET
cana-5708	191	39	significant	significant	ADJ
cana-5708	191	40	increase	increase	NOUN
cana-5708	191	41	in	in	ADP
cana-5708	191	42	training	training	NOUN
cana-5708	191	43	as	as	ADV
cana-5708	191	44	well	well	ADV
cana-5708	191	45	as	as	ADP
cana-5708	191	46	validation	validation	NOUN
cana-5708	191	47	accuracy	accuracy	NOUN
cana-5708	191	48	after	after	SCONJ
cana-5708	191	49	hyperparameter	hyperparameter	NOUN
cana-5708	191	50	tuning	tune	VERB
cana-5708	191	51	with	with	ADP
cana-5708	191	52	hyperband	hyperband	PROPN
cana-5708	191	53	.	.	PUNCT
cana-5708	192	1	in	in	ADP
cana-5708	192	2	particular	particular	ADJ
cana-5708	192	3	,	,	PUNCT
cana-5708	192	4	training	training	NOUN
cana-5708	192	5	accuracy	accuracy	NOUN
cana-5708	192	6	was	be	AUX
cana-5708	192	7	increased	increase	VERB
cana-5708	192	8	by	by	ADP
cana-5708	192	9	7.5	7.5	NUM
cana-5708	192	10	%	%	NOUN
cana-5708	192	11	,	,	PUNCT
cana-5708	192	12	and	and	CCONJ
cana-5708	192	13	validation	validation	NOUN
cana-5708	192	14	accuracy	accuracy	NOUN
cana-5708	192	15	by	by	ADP
cana-5708	192	16	6.7	6.7	NUM
cana-5708	192	17	%	%	NOUN
cana-5708	192	18	,	,	PUNCT
cana-5708	192	19	which	which	PRON
cana-5708	192	20	indicates	indicate	VERB
cana-5708	192	21	the	the	DET
cana-5708	192	22	better	well	ADJ
cana-5708	192	23	model	model	NOUN
cana-5708	192	24	to	to	PART
cana-5708	192	25	learn	learn	VERB
cana-5708	192	26	appropriate	appropriate	ADJ
cana-5708	192	27	features	feature	NOUN
cana-5708	192	28	from	from	ADP
cana-5708	192	29	the	the	DET
cana-5708	192	30	data	datum	NOUN
cana-5708	192	31	.	.	PUNCT
cana-5708	193	1	training	training	NOUN
cana-5708	193	2	accuracy	accuracy	NOUN
cana-5708	193	3	:	:	PUNCT
cana-5708	193	4	the	the	DET
cana-5708	193	5	training	training	NOUN
cana-5708	193	6	accuracy	accuracy	NOUN
cana-5708	193	7	improved	improve	VERB
cana-5708	193	8	from	from	ADP
cana-5708	193	9	85.3	85.3	NUM
cana-5708	193	10	%	%	NOUN
cana-5708	193	11	to	to	ADP
cana-5708	193	12	92.8	92.8	NUM
cana-5708	193	13	%	%	NOUN
cana-5708	193	14	,	,	PUNCT
cana-5708	193	15	converting	convert	VERB
cana-5708	193	16	the	the	DET
cana-5708	193	17	model	model	NOUN
cana-5708	193	18	into	into	ADP
cana-5708	193	19	a	a	DET
cana-5708	193	20	vivid	vivid	ADJ
cana-5708	193	21	means	mean	NOUN
cana-5708	193	22	of	of	ADP
cana-5708	193	23	recognizing	recognize	VERB
cana-5708	193	24	complex	complex	ADJ
cana-5708	193	25	patterns	pattern	NOUN
cana-5708	193	26	from	from	ADP
cana-5708	193	27	training	train	VERB
cana-5708	193	28	data	datum	NOUN
cana-5708	193	29	.	.	PUNCT
cana-5708	194	1	the	the	DET
cana-5708	194	2	installation	installation	NOUN
cana-5708	194	3	of	of	ADP
cana-5708	194	4	hyperparameter	hyperparameter	NOUN
cana-5708	194	5	tuning	tune	VERB
cana-5708	194	6	such	such	ADJ
cana-5708	194	7	as	as	ADP
cana-5708	194	8	learning	learn	VERB
cana-5708	194	9	rate	rate	NOUN
cana-5708	194	10	and	and	CCONJ
cana-5708	194	11	batch	batch	NOUN
cana-5708	194	12	size	size	NOUN
cana-5708	194	13	permits	permit	VERB
cana-5708	194	14	efficient	efficient	ADJ
cana-5708	194	15	and	and	CCONJ
cana-5708	194	16	uniform	uniform	ADJ
cana-5708	194	17	model	model	NOUN
cana-5708	194	18	training	training	NOUN
cana-5708	194	19	[	[	X
cana-5708	194	20	4	4	NUM
cana-5708	194	21	]	]	PUNCT
cana-5708	194	22	.	.	PUNCT
cana-5708	195	1	validation	validation	NOUN
cana-5708	195	2	accuracy	accuracy	NOUN
cana-5708	195	3	:	:	PUNCT
cana-5708	195	4	the	the	DET
cana-5708	195	5	validation	validation	NOUN
cana-5708	195	6	accuracy	accuracy	NOUN
cana-5708	195	7	was	be	AUX
cana-5708	195	8	increased	increase	VERB
cana-5708	195	9	from	from	ADP
cana-5708	195	10	83.5	83.5	NUM
cana-5708	195	11	%	%	NOUN
cana-5708	195	12	to	to	ADP
cana-5708	195	13	90.2	90.2	NUM
cana-5708	195	14	%	%	NOUN
cana-5708	195	15	,	,	PUNCT
cana-5708	195	16	which	which	PRON
cana-5708	195	17	indicates	indicate	VERB
cana-5708	195	18	an	an	DET
cana-5708	195	19	enhanced	enhanced	ADJ
cana-5708	195	20	ability	ability	NOUN
cana-5708	195	21	to	to	PART
cana-5708	195	22	generalize	generalize	VERB
cana-5708	195	23	.	.	PUNCT
cana-5708	196	1	this	this	DET
cana-5708	196	2	specifies	specifie	NOUN
cana-5708	196	3	that	that	PRON
cana-5708	196	4	hyperband	hyperband	ADJ
cana-5708	196	5	optimization	optimization	NOUN
cana-5708	196	6	avoided	avoid	VERB
cana-5708	196	7	overfitting	overfitting	NOUN
cana-5708	196	8	and	and	CCONJ
cana-5708	196	9	optimized	optimize	VERB
cana-5708	196	10	the	the	DET
cana-5708	196	11	execution	execution	NOUN
cana-5708	196	12	of	of	ADP
cana-5708	196	13	the	the	DET
cana-5708	196	14	model	model	NOUN
cana-5708	196	15	toward	toward	ADP
cana-5708	196	16	unseen	unseen	ADJ
cana-5708	196	17	new	new	ADJ
cana-5708	196	18	data	datum	NOUN
cana-5708	196	19	.	.	PUNCT
cana-5708	197	1	this	this	PRON
cana-5708	197	2	is	be	AUX
cana-5708	197	3	a	a	DET
cana-5708	197	4	metric	metric	NOUN
cana-5708	197	5	that	that	PRON
cana-5708	197	6	plays	play	VERB
cana-5708	197	7	a	a	DET
cana-5708	197	8	huge	huge	ADJ
cana-5708	197	9	role	role	NOUN
cana-5708	197	10	in	in	ADP
cana-5708	197	11	determining	determine	VERB
cana-5708	197	12	the	the	DET
cana-5708	197	13	faith	faith	NOUN
cana-5708	197	14	of	of	ADP
cana-5708	197	15	the	the	DET
cana-5708	197	16	model	model	NOUN
cana-5708	197	17	for	for	ADP
cana-5708	197	18	real	real	ADJ
cana-5708	197	19	-	-	PUNCT
cana-5708	197	20	life	life	NOUN
cana-5708	197	21	deployment	deployment	NOUN
cana-5708	197	22	,	,	PUNCT
cana-5708	197	23	such	such	ADJ
cana-5708	197	24	as	as	ADP
cana-5708	197	25	analyzing	analyze	VERB
cana-5708	197	26	novel	novel	ADJ
cana-5708	197	27	skin	skin	NOUN
cana-5708	197	28	lesion	lesion	NOUN
cana-5708	197	29	images	image	NOUN
cana-5708	197	30	[	[	X
cana-5708	197	31	4	4	NUM
cana-5708	197	32	]	]	PUNCT
cana-5708	197	33	.	.	PUNCT
cana-5708	198	1	2	2	X
cana-5708	198	2	.	.	NUM
cana-5708	198	3	reduced	reduce	VERB
cana-5708	198	4	loss	loss	NOUN
cana-5708	198	5	:	:	PUNCT
cana-5708	198	6	the	the	DET
cana-5708	198	7	optimization	optimization	NOUN
cana-5708	198	8	process	process	NOUN
cana-5708	198	9	has	have	AUX
cana-5708	198	10	considerably	considerably	ADV
cana-5708	198	11	decreased	decrease	VERB
cana-5708	198	12	the	the	DET
cana-5708	198	13	training	training	NOUN
cana-5708	198	14	and	and	CCONJ
cana-5708	198	15	validation	validation	NOUN
cana-5708	198	16	loss	loss	NOUN
cana-5708	198	17	.	.	PUNCT
cana-5708	199	1	this	this	PRON
cana-5708	199	2	indicates	indicate	VERB
cana-5708	199	3	a	a	DET
cana-5708	199	4	much	much	ADV
cana-5708	199	5	wider	wide	ADJ
cana-5708	199	6	adjustment	adjustment	NOUN
cana-5708	199	7	on	on	ADP
cana-5708	199	8	the	the	DET
cana-5708	199	9	part	part	NOUN
cana-5708	199	10	of	of	ADP
cana-5708	199	11	learning	learn	VERB
cana-5708	199	12	efficiency	efficiency	NOUN
cana-5708	199	13	.	.	PUNCT
cana-5708	200	1	training	training	NOUN
cana-5708	200	2	loss	loss	NOUN
cana-5708	200	3	:	:	PUNCT
cana-5708	200	4	training	training	NOUN
cana-5708	200	5	loss	loss	NOUN
cana-5708	200	6	decreased	decrease	VERB
cana-5708	200	7	from	from	ADP
cana-5708	200	8	0.45	0.45	NUM
cana-5708	200	9	to	to	ADP
cana-5708	200	10	0.28	0.28	NUM
cana-5708	200	11	,	,	PUNCT
cana-5708	200	12	indicating	indicate	VERB
cana-5708	200	13	some	some	DET
cana-5708	200	14	efficiency	efficiency	NOUN
cana-5708	200	15	in	in	ADP
cana-5708	200	16	the	the	DET
cana-5708	200	17	learning	learning	NOUN
cana-5708	200	18	process	process	NOUN
cana-5708	200	19	-	-	PUNCT
cana-5708	200	20	a	a	DET
cana-5708	200	21	proper	proper	ADJ
cana-5708	200	22	learning	learning	NOUN
cana-5708	200	23	process	process	NOUN
cana-5708	200	24	indicated	indicate	VERB
cana-5708	200	25	errors	error	NOUN
cana-5708	200	26	well	well	ADV
cana-5708	200	27	while	while	SCONJ
cana-5708	200	28	training	training	NOUN
cana-5708	200	29	.	.	PUNCT
cana-5708	201	1	these	these	PRON
cana-5708	201	2	show	show	VERB
cana-5708	201	3	the	the	DET
cana-5708	201	4	communications	communication	NOUN
cana-5708	201	5	on	on	ADP
cana-5708	201	6	applied	apply	VERB
cana-5708	201	7	nonlinear	nonlinear	ADJ
cana-5708	201	8	analysis	analysis	NOUN
cana-5708	201	9	issn	issn	NOUN
cana-5708	201	10	:	:	PUNCT
cana-5708	201	11	1074	1074	NUM
cana-5708	201	12	-	-	PUNCT
cana-5708	201	13	133x	133x	NUM
cana-5708	201	14	vol	vol	VERB
cana-5708	201	15	32	32	NUM
cana-5708	201	16	no	no	NOUN
cana-5708	201	17	.	.	PUNCT
cana-5708	202	1	10s	10	NOUN
cana-5708	202	2	(	(	PUNCT
cana-5708	202	3	2025	2025	NUM
cana-5708	202	4	)	)	PUNCT
cana-5708	202	5	2681	2681	NUM
cana-5708	202	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5708	202	7	model	model	NOUN
cana-5708	202	8	makes	make	VERB
cana-5708	202	9	predictions	prediction	NOUN
cana-5708	202	10	closer	close	ADJ
cana-5708	202	11	to	to	ADP
cana-5708	202	12	the	the	DET
cana-5708	202	13	actual	actual	ADJ
cana-5708	202	14	labels	label	NOUN
cana-5708	202	15	,	,	PUNCT
cana-5708	202	16	hence	hence	ADV
cana-5708	202	17	improving	improve	VERB
cana-5708	202	18	the	the	DET
cana-5708	202	19	performance	performance	NOUN
cana-5708	202	20	of	of	ADP
cana-5708	202	21	the	the	DET
cana-5708	202	22	model	model	NOUN
cana-5708	202	23	in	in	ADP
cana-5708	202	24	general	general	ADJ
cana-5708	202	25	[	[	X
cana-5708	202	26	4	4	NUM
cana-5708	202	27	]	]	PUNCT
cana-5708	202	28	.	.	PUNCT
cana-5708	203	1	validation	validation	NOUN
cana-5708	203	2	loss	loss	NOUN
cana-5708	203	3	:	:	PUNCT
cana-5708	203	4	the	the	DET
cana-5708	203	5	validation	validation	NOUN
cana-5708	203	6	loss	loss	NOUN
cana-5708	203	7	was	be	AUX
cana-5708	203	8	scaled	scale	VERB
cana-5708	203	9	back	back	ADV
cana-5708	203	10	from	from	ADP
cana-5708	203	11	0.50	0.50	NUM
cana-5708	203	12	to	to	ADP
cana-5708	203	13	0.30	0.30	NUM
cana-5708	203	14	,	,	PUNCT
cana-5708	203	15	this	this	PRON
cana-5708	203	16	is	be	AUX
cana-5708	203	17	a	a	DET
cana-5708	203	18	representation	representation	NOUN
cana-5708	203	19	of	of	ADP
cana-5708	203	20	the	the	DET
cana-5708	203	21	ability	ability	NOUN
cana-5708	203	22	of	of	ADP
cana-5708	203	23	the	the	DET
cana-5708	203	24	model	model	NOUN
cana-5708	203	25	to	to	PART
cana-5708	203	26	generalize	generalize	VERB
cana-5708	203	27	by	by	ADP
cana-5708	203	28	reducing	reduce	VERB
cana-5708	203	29	the	the	DET
cana-5708	203	30	discrepancies	discrepancy	NOUN
cana-5708	203	31	between	between	ADP
cana-5708	203	32	predicted	predict	VERB
cana-5708	203	33	and	and	CCONJ
cana-5708	203	34	actual	actual	ADJ
cana-5708	203	35	values	value	NOUN
cana-5708	203	36	.	.	PUNCT
cana-5708	204	1	the	the	DET
cana-5708	204	2	drop	drop	NOUN
cana-5708	204	3	also	also	ADV
cana-5708	204	4	denotes	denote	VERB
cana-5708	204	5	the	the	DET
cana-5708	204	6	contribution	contribution	NOUN
cana-5708	204	7	of	of	ADP
cana-5708	204	8	hyperband	hyperband	ADJ
cana-5708	204	9	optimization	optimization	NOUN
cana-5708	204	10	to	to	ADP
cana-5708	204	11	getting	get	VERB
cana-5708	204	12	to	to	ADP
cana-5708	204	13	optimal	optimal	ADJ
cana-5708	204	14	model	model	NOUN
cana-5708	204	15	efficiency	efficiency	NOUN
cana-5708	204	16	[	[	X
cana-5708	204	17	4	4	NUM
cana-5708	204	18	]	]	PUNCT
cana-5708	204	19	.	.	PUNCT
cana-5708	205	1	6	6	X
cana-5708	205	2	.	.	X
cana-5708	205	3	conclusion	conclusion	NOUN
cana-5708	205	4	and	and	CCONJ
cana-5708	205	5	future	future	ADJ
cana-5708	205	6	work	work	NOUN
cana-5708	205	7	this	this	DET
cana-5708	205	8	study	study	NOUN
cana-5708	205	9	validates	validate	VERB
cana-5708	205	10	the	the	DET
cana-5708	205	11	effectiveness	effectiveness	NOUN
cana-5708	205	12	of	of	ADP
cana-5708	205	13	hyperband	hyperband	NOUN
cana-5708	205	14	to	to	PART
cana-5708	205	15	enhance	enhance	VERB
cana-5708	205	16	ml	ml	NOUN
cana-5708	205	17	models	model	NOUN
cana-5708	205	18	for	for	ADP
cana-5708	205	19	clinical	clinical	ADJ
cana-5708	205	20	applications	application	NOUN
cana-5708	205	21	such	such	ADJ
cana-5708	205	22	as	as	ADP
cana-5708	205	23	skin	skin	NOUN
cana-5708	205	24	cancer	cancer	NOUN
cana-5708	205	25	detection	detection	NOUN
cana-5708	205	26	.	.	PUNCT
cana-5708	206	1	the	the	DET
cana-5708	206	2	flexibility	flexibility	NOUN
cana-5708	206	3	to	to	PART
cana-5708	206	4	allocate	allocate	VERB
cana-5708	206	5	resources	resource	NOUN
cana-5708	206	6	with	with	ADP
cana-5708	206	7	hyperband	hyperband	NOUN
cana-5708	206	8	and	and	CCONJ
cana-5708	206	9	the	the	DET
cana-5708	206	10	improved	improved	ADJ
cana-5708	206	11	exploration	exploration	NOUN
cana-5708	206	12	of	of	ADP
cana-5708	206	13	the	the	DET
cana-5708	206	14	hyperparameter	hyperparameter	NOUN
cana-5708	206	15	search	search	NOUN
cana-5708	206	16	space	space	NOUN
cana-5708	206	17	allowed	allow	VERB
cana-5708	206	18	for	for	ADP
cana-5708	206	19	improved	improved	ADJ
cana-5708	206	20	model	model	NOUN
cana-5708	206	21	accuracy	accuracy	NOUN
cana-5708	206	22	,	,	PUNCT
cana-5708	206	23	computational	computational	ADJ
cana-5708	206	24	efficiency	efficiency	NOUN
cana-5708	206	25	,	,	PUNCT
cana-5708	206	26	and	and	CCONJ
cana-5708	206	27	generalizability	generalizability	NOUN
cana-5708	206	28	.	.	PUNCT
cana-5708	207	1	the	the	DET
cana-5708	207	2	research	research	NOUN
cana-5708	207	3	gives	give	VERB
cana-5708	207	4	a	a	DET
cana-5708	207	5	notable	notable	ADJ
cana-5708	207	6	increase	increase	NOUN
cana-5708	207	7	in	in	ADP
cana-5708	207	8	training	training	NOUN
cana-5708	207	9	and	and	CCONJ
cana-5708	207	10	validation	validation	NOUN
cana-5708	207	11	accuracy	accuracy	NOUN
cana-5708	207	12	,	,	PUNCT
cana-5708	207	13	with	with	ADP
cana-5708	207	14	increases	increase	NOUN
cana-5708	207	15	of	of	ADP
cana-5708	207	16	7.5	7.5	NUM
cana-5708	207	17	%	%	NOUN
cana-5708	207	18	and	and	CCONJ
cana-5708	207	19	6.7	6.7	NUM
cana-5708	207	20	%	%	NOUN
cana-5708	207	21	,	,	PUNCT
cana-5708	207	22	respectively	respectively	ADV
cana-5708	207	23	with	with	ADP
cana-5708	207	24	the	the	DET
cana-5708	207	25	sharp	sharp	ADJ
cana-5708	207	26	drop	drop	NOUN
cana-5708	207	27	in	in	ADP
cana-5708	207	28	loss	loss	NOUN
cana-5708	207	29	values	value	NOUN
cana-5708	207	30	for	for	ADP
cana-5708	207	31	both	both	DET
cana-5708	207	32	sets	set	NOUN
cana-5708	207	33	,	,	PUNCT
cana-5708	207	34	reflecting	reflect	VERB
cana-5708	207	35	better	well	ADJ
cana-5708	207	36	model	model	NOUN
cana-5708	207	37	performance	performance	NOUN
cana-5708	207	38	and	and	CCONJ
cana-5708	207	39	better	well	ADJ
cana-5708	207	40	learning	learning	NOUN
cana-5708	207	41	.	.	PUNCT
cana-5708	208	1	the	the	DET
cana-5708	208	2	model	model	NOUN
cana-5708	208	3	resulted	result	VERB
cana-5708	208	4	in	in	ADP
cana-5708	208	5	more	more	ADV
cana-5708	208	6	stable	stable	ADJ
cana-5708	208	7	and	and	CCONJ
cana-5708	208	8	smoother	smooth	ADJ
cana-5708	208	9	training	training	NOUN
cana-5708	208	10	curves	curve	NOUN
cana-5708	208	11	,	,	PUNCT
cana-5708	208	12	enabling	enable	VERB
cana-5708	208	13	this	this	DET
cana-5708	208	14	model	model	NOUN
cana-5708	208	15	to	to	PART
cana-5708	208	16	better	well	ADV
cana-5708	208	17	generalize	generalize	VERB
cana-5708	208	18	to	to	ADP
cana-5708	208	19	unfamiliar	unfamiliar	ADJ
cana-5708	208	20	data	datum	NOUN
cana-5708	208	21	.	.	PUNCT
cana-5708	209	1	further	far	ADV
cana-5708	209	2	,	,	PUNCT
cana-5708	209	3	we	we	PRON
cana-5708	209	4	see	see	VERB
cana-5708	209	5	an	an	DET
cana-5708	209	6	improved	improved	ADJ
cana-5708	209	7	computational	computational	ADJ
cana-5708	209	8	efficiency	efficiency	NOUN
cana-5708	209	9	,	,	PUNCT
cana-5708	209	10	which	which	PRON
cana-5708	209	11	is	be	AUX
cana-5708	209	12	faster	fast	ADJ
cana-5708	209	13	than	than	ADP
cana-5708	209	14	human	human	ADJ
cana-5708	209	15	tuning	tuning	NOUN
cana-5708	209	16	and	and	CCONJ
cana-5708	209	17	random	random	ADJ
cana-5708	209	18	search	search	NOUN
cana-5708	209	19	for	for	ADP
cana-5708	209	20	finding	find	VERB
cana-5708	209	21	the	the	DET
cana-5708	209	22	optimal	optimal	ADJ
cana-5708	209	23	hyperparameters	hyperparameter	NOUN
cana-5708	209	24	.	.	PUNCT
cana-5708	210	1	the	the	DET
cana-5708	210	2	paper	paper	NOUN
cana-5708	210	3	illustrates	illustrate	VERB
cana-5708	210	4	the	the	DET
cana-5708	210	5	potential	potential	ADJ
cana-5708	210	6	benefit	benefit	NOUN
cana-5708	210	7	of	of	ADP
cana-5708	210	8	applying	apply	VERB
cana-5708	210	9	automated	automate	VERB
cana-5708	210	10	hyperparameter	hyperparameter	NOUN
cana-5708	210	11	tuning	tuning	NOUN
cana-5708	210	12	methods	method	NOUN
cana-5708	210	13	in	in	ADP
cana-5708	210	14	enhancing	enhance	VERB
cana-5708	210	15	model	model	NOUN
cana-5708	210	16	performance	performance	NOUN
cana-5708	210	17	and	and	CCONJ
cana-5708	210	18	making	make	VERB
cana-5708	210	19	machine	machine	NOUN
cana-5708	210	20	learning	learning	NOUN
cana-5708	210	21	methods	method	NOUN
cana-5708	210	22	more	more	ADV
cana-5708	210	23	efficient	efficient	ADJ
cana-5708	210	24	and	and	CCONJ
cana-5708	210	25	effective	effective	ADJ
cana-5708	210	26	in	in	ADP
cana-5708	210	27	the	the	DET
cana-5708	210	28	healthcare	healthcare	NOUN
cana-5708	210	29	industry	industry	NOUN
cana-5708	210	30	.	.	PUNCT
cana-5708	211	1	future	future	ADJ
cana-5708	211	2	research	research	NOUN
cana-5708	211	3	in	in	ADP
cana-5708	211	4	enhancing	enhance	VERB
cana-5708	211	5	hyperband	hyperband	ADJ
cana-5708	211	6	optimization	optimization	NOUN
cana-5708	211	7	is	be	AUX
cana-5708	211	8	proposed	propose	VERB
cana-5708	211	9	that	that	SCONJ
cana-5708	211	10	more	more	ADV
cana-5708	211	11	advanced	advanced	ADJ
cana-5708	211	12	methods	method	NOUN
cana-5708	211	13	,	,	PUNCT
cana-5708	211	14	such	such	ADJ
cana-5708	211	15	as	as	ADP
cana-5708	211	16	bayesian	bayesian	NOUN
cana-5708	211	17	optimization	optimization	NOUN
cana-5708	211	18	or	or	CCONJ
cana-5708	211	19	meta	meta	NOUN
cana-5708	211	20	-	-	PUNCT
cana-5708	211	21	learning	learning	NOUN
cana-5708	211	22	,	,	PUNCT
cana-5708	211	23	that	that	PRON
cana-5708	211	24	incorporate	incorporate	VERB
cana-5708	211	25	probabilistic	probabilistic	ADJ
cana-5708	211	26	models	model	NOUN
cana-5708	211	27	may	may	AUX
cana-5708	211	28	be	be	AUX
cana-5708	211	29	better	well	ADV
cana-5708	211	30	positioned	position	VERB
cana-5708	211	31	to	to	PART
cana-5708	211	32	search	search	VERB
cana-5708	211	33	for	for	ADP
cana-5708	211	34	hyperparameters	hyperparameter	NOUN
cana-5708	211	35	(	(	PUNCT
cana-5708	211	36	snoek	snoek	NOUN
cana-5708	211	37	et	et	PROPN
cana-5708	211	38	al	al	PROPN
cana-5708	211	39	.	.	PROPN
cana-5708	211	40	,	,	PUNCT
cana-5708	211	41	2012	2012	NUM
cana-5708	211	42	;	;	PUNCT
cana-5708	212	1	liu	liu	PROPN
cana-5708	212	2	et	et	PROPN
cana-5708	212	3	al	al	PROPN
cana-5708	212	4	.	.	PROPN
cana-5708	212	5	,	,	PUNCT
cana-5708	212	6	2019	2019	NUM
cana-5708	212	7	)	)	PUNCT
cana-5708	212	8	.	.	PUNCT
cana-5708	213	1	these	these	DET
cana-5708	213	2	advanced	advanced	ADJ
cana-5708	213	3	methods	method	NOUN
cana-5708	213	4	may	may	AUX
cana-5708	213	5	lead	lead	VERB
cana-5708	213	6	to	to	ADP
cana-5708	213	7	improved	improved	ADJ
cana-5708	213	8	performance	performance	NOUN
cana-5708	213	9	with	with	ADP
cana-5708	213	10	hard	hard	ADJ
cana-5708	213	11	problems	problem	NOUN
cana-5708	213	12	that	that	PRON
cana-5708	213	13	benefit	benefit	VERB
cana-5708	213	14	from	from	ADP
cana-5708	213	15	incorporating	incorporate	VERB
cana-5708	213	16	domain	domain	NOUN
cana-5708	213	17	knowledge	knowledge	NOUN
cana-5708	213	18	to	to	PART
cana-5708	213	19	improve	improve	VERB
cana-5708	213	20	search	search	NOUN
cana-5708	213	21	steps	step	NOUN
cana-5708	213	22	and	and	CCONJ
cana-5708	213	23	thereby	thereby	ADV
cana-5708	213	24	improve	improve	VERB
cana-5708	213	25	performance	performance	NOUN
cana-5708	213	26	.	.	PUNCT
cana-5708	214	1	another	another	DET
cana-5708	214	2	exciting	exciting	ADJ
cana-5708	214	3	direction	direction	NOUN
cana-5708	214	4	would	would	AUX
cana-5708	214	5	be	be	AUX
cana-5708	214	6	to	to	PART
cana-5708	214	7	develop	develop	VERB
cana-5708	214	8	a	a	DET
cana-5708	214	9	hybrid	hybrid	ADJ
cana-5708	214	10	model	model	NOUN
cana-5708	214	11	of	of	ADP
cana-5708	214	12	hyperband	hyperband	ADJ
cana-5708	214	13	and	and	CCONJ
cana-5708	214	14	genetic	genetic	ADJ
cana-5708	214	15	algorithms	algorithm	NOUN
cana-5708	214	16	that	that	PRON
cana-5708	214	17	would	would	AUX
cana-5708	214	18	produce	produce	VERB
cana-5708	214	19	an	an	DET
cana-5708	214	20	optimally	optimally	ADV
cana-5708	214	21	efficient	efficient	ADJ
cana-5708	214	22	and	and	CCONJ
cana-5708	214	23	accurate	accurate	ADJ
cana-5708	214	24	solution	solution	NOUN
cana-5708	214	25	for	for	ADP
cana-5708	214	26	large	large	ADJ
cana-5708	214	27	-	-	PUNCT
cana-5708	214	28	scale	scale	NOUN
cana-5708	214	29	scenarios	scenario	NOUN
cana-5708	214	30	,	,	PUNCT
cana-5708	214	31	and	and	CCONJ
cana-5708	214	32	especially	especially	ADV
cana-5708	214	33	in	in	ADP
cana-5708	214	34	noisy	noisy	ADJ
cana-5708	214	35	or	or	CCONJ
cana-5708	214	36	high	high	ADV
cana-5708	214	37	-	-	PUNCT
cana-5708	214	38	dimensional	dimensional	ADJ
cana-5708	214	39	contexts	context	NOUN
cana-5708	214	40	(	(	PUNCT
cana-5708	214	41	wang	wang	PROPN
cana-5708	214	42	et	et	PROPN
cana-5708	214	43	al	al	PROPN
cana-5708	214	44	.	.	PROPN
cana-5708	214	45	,	,	PUNCT
cana-5708	214	46	2019	2019	NUM
cana-5708	214	47	;	;	PUNCT
cana-5708	214	48	mendez	mendez	PROPN
cana-5708	214	49	et	et	PROPN
cana-5708	214	50	al	al	PROPN
cana-5708	214	51	.	.	PROPN
cana-5708	214	52	,	,	PUNCT
cana-5708	214	53	2019	2019	NUM
cana-5708	214	54	)	)	PUNCT
cana-5708	214	55	.	.	PUNCT
cana-5708	215	1	furthermore	furthermore	ADV
cana-5708	215	2	,	,	PUNCT
cana-5708	215	3	an	an	DET
cana-5708	215	4	application	application	NOUN
cana-5708	215	5	of	of	ADP
cana-5708	215	6	hyperband	hyperband	NOUN
cana-5708	215	7	to	to	ADP
cana-5708	215	8	specific	specific	ADJ
cana-5708	215	9	datasets	dataset	NOUN
cana-5708	215	10	(	(	PUNCT
cana-5708	215	11	e.g.	e.g.	ADV
cana-5708	215	12	radiology	radiology	NOUN
cana-5708	215	13	,	,	PUNCT
cana-5708	215	14	pathology	pathology	NOUN
cana-5708	215	15	,	,	PUNCT
cana-5708	215	16	or	or	CCONJ
cana-5708	215	17	ophthalmology	ophthalmology	NOUN
cana-5708	215	18	)	)	PUNCT
cana-5708	215	19	would	would	AUX
cana-5708	215	20	add	add	VERB
cana-5708	215	21	to	to	ADP
cana-5708	215	22	knowledge	knowledge	NOUN
cana-5708	215	23	on	on	ADP
cana-5708	215	24	the	the	DET
cana-5708	215	25	versatility	versatility	NOUN
cana-5708	215	26	of	of	ADP
cana-5708	215	27	hyperband	hyperband	NOUN
cana-5708	215	28	in	in	ADP
cana-5708	215	29	being	be	AUX
cana-5708	215	30	generalized	generalize	VERB
cana-5708	215	31	to	to	ADP
cana-5708	215	32	other	other	ADJ
cana-5708	215	33	medical	medical	ADJ
cana-5708	215	34	imaging	imaging	NOUN
cana-5708	215	35	subdomains	subdomain	NOUN
cana-5708	215	36	,	,	PUNCT
cana-5708	215	37	which	which	PRON
cana-5708	215	38	is	be	AUX
cana-5708	215	39	vital	vital	ADJ
cana-5708	215	40	for	for	ADP
cana-5708	215	41	better	well	ADJ
cana-5708	215	42	dissemination	dissemination	NOUN
cana-5708	215	43	and	and	CCONJ
cana-5708	215	44	uptake	uptake	NOUN
cana-5708	215	45	of	of	ADP
cana-5708	215	46	hyperband	hyperband	NOUN
cana-5708	215	47	in	in	ADP
cana-5708	215	48	the	the	DET
cana-5708	215	49	medical	medical	ADJ
cana-5708	215	50	field	field	NOUN
cana-5708	215	51	(	(	PUNCT
cana-5708	215	52	tschandl	tschandl	NOUN
cana-5708	215	53	et	et	PROPN
cana-5708	215	54	al	al	PROPN
cana-5708	215	55	.	.	PROPN
cana-5708	215	56	,	,	PUNCT
cana-5708	215	57	2018	2018	NUM
cana-5708	215	58	;	;	PUNCT
cana-5708	215	59	li	li	PROPN
cana-5708	215	60	et	et	PROPN
cana-5708	215	61	al	al	PROPN
cana-5708	215	62	.	.	PROPN
cana-5708	215	63	,	,	PUNCT
cana-5708	215	64	2017	2017	NUM
cana-5708	215	65	)	)	PUNCT
cana-5708	215	66	.	.	PUNCT
cana-5708	216	1	finally	finally	ADV
cana-5708	216	2	,	,	PUNCT
cana-5708	216	3	we	we	PRON
cana-5708	216	4	also	also	ADV
cana-5708	216	5	propose	propose	VERB
cana-5708	216	6	to	to	PART
cana-5708	216	7	incorporate	incorporate	VERB
cana-5708	216	8	transfer	transfer	NOUN
cana-5708	216	9	learning	learning	NOUN
cana-5708	216	10	with	with	ADP
cana-5708	216	11	hyperband	hyperband	NOUN
cana-5708	216	12	in	in	SCONJ
cana-5708	216	13	order	order	NOUN
cana-5708	216	14	to	to	PART
cana-5708	216	15	improve	improve	VERB
cana-5708	216	16	hyperband	hyperband	PROPN
cana-5708	216	17	's	's	PART
cana-5708	216	18	performance	performance	NOUN
cana-5708	216	19	by	by	ADP
cana-5708	216	20	enhancing	enhance	VERB
cana-5708	216	21	pre	pre	ADJ
cana-5708	216	22	-	-	ADJ
cana-5708	216	23	trained	train	VERB
cana-5708	216	24	models	model	NOUN
cana-5708	216	25	on	on	ADP
cana-5708	216	26	large	large	ADJ
cana-5708	216	27	image	image	NOUN
cana-5708	216	28	corpora	corpora	NOUN
cana-5708	216	29	for	for	ADP
cana-5708	216	30	specific	specific	ADJ
cana-5708	216	31	medical	medical	ADJ
cana-5708	216	32	tasks	task	NOUN
cana-5708	216	33	(	(	PUNCT
cana-5708	216	34	such	such	ADJ
cana-5708	216	35	as	as	ADP
cana-5708	216	36	skin	skin	NOUN
cana-5708	216	37	cancer	cancer	NOUN
cana-5708	216	38	)	)	PUNCT
cana-5708	216	39	and	and	CCONJ
cana-5708	216	40	thus	thus	ADV
cana-5708	216	41	help	help	VERB
cana-5708	216	42	to	to	PART
cana-5708	216	43	overcome	overcome	VERB
cana-5708	216	44	the	the	DET
cana-5708	216	45	shortage	shortage	NOUN
cana-5708	216	46	of	of	ADP
cana-5708	216	47	a	a	DET
cana-5708	216	48	limited	limited	ADJ
cana-5708	216	49	size	size	NOUN
cana-5708	216	50	of	of	ADP
cana-5708	216	51	annotated	annotate	VERB
cana-5708	216	52	medical	medical	ADJ
cana-5708	216	53	data	datum	NOUN
cana-5708	216	54	(	(	PUNCT
cana-5708	216	55	he	he	PRON
cana-5708	216	56	et	et	PROPN
cana-5708	216	57	al	al	PROPN
cana-5708	216	58	.	.	PROPN
cana-5708	216	59	,	,	PUNCT
cana-5708	216	60	2016	2016	NUM
cana-5708	216	61	;	;	PUNCT
cana-5708	216	62	yosinski	yosinski	ADV
cana-5708	216	63	et	et	PROPN
cana-5708	216	64	al	al	PROPN
cana-5708	216	65	.	.	PROPN
cana-5708	216	66	,	,	PUNCT
cana-5708	216	67	2014	2014	NUM
cana-5708	216	68	)	)	PUNCT
cana-5708	216	69	.	.	PUNCT
cana-5708	217	1	7	7	X
cana-5708	217	2	.	.	X
cana-5708	217	3	declarations	declaration	NOUN
cana-5708	217	4	competing	compete	VERB
cana-5708	217	5	interest	interest	NOUN
cana-5708	217	6	on	on	ADP
cana-5708	217	7	behalf	behalf	NOUN
cana-5708	217	8	of	of	ADP
cana-5708	217	9	all	all	DET
cana-5708	217	10	authors	author	NOUN
cana-5708	217	11	,	,	PUNCT
cana-5708	217	12	the	the	DET
cana-5708	217	13	corresponding	corresponding	ADJ
cana-5708	217	14	author	author	NOUN
cana-5708	217	15	states	state	VERB
cana-5708	217	16	that	that	SCONJ
cana-5708	217	17	there	there	PRON
cana-5708	217	18	is	be	VERB
cana-5708	217	19	no	no	DET
cana-5708	217	20	conflict	conflict	NOUN
cana-5708	217	21	of	of	ADP
cana-5708	217	22	interest	interest	NOUN
cana-5708	217	23	.	.	PUNCT
cana-5708	218	1	funding	funding	NOUN
cana-5708	218	2	information	information	NOUN
cana-5708	218	3	this	this	DET
cana-5708	218	4	research	research	NOUN
cana-5708	218	5	received	receive	VERB
cana-5708	218	6	no	no	DET
cana-5708	218	7	external	external	ADJ
cana-5708	218	8	funding	funding	NOUN
cana-5708	218	9	.	.	PUNCT
cana-5708	219	1	communications	communication	NOUN
cana-5708	219	2	on	on	ADP
cana-5708	219	3	applied	apply	VERB
cana-5708	219	4	nonlinear	nonlinear	ADJ
cana-5708	219	5	analysis	analysis	NOUN
cana-5708	219	6	issn	issn	NOUN
cana-5708	219	7	:	:	PUNCT
cana-5708	219	8	1074	1074	NUM
cana-5708	219	9	-	-	PUNCT
cana-5708	219	10	133x	133x	NUM
cana-5708	219	11	vol	vol	VERB
cana-5708	219	12	32	32	NUM
cana-5708	219	13	no	no	NOUN
cana-5708	219	14	.	.	PUNCT
cana-5708	220	1	10s	10	NOUN
cana-5708	220	2	(	(	PUNCT
cana-5708	220	3	2025	2025	NUM
cana-5708	220	4	)	)	PUNCT
cana-5708	220	5	2682	2682	NUM
cana-5708	220	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5708	220	7	author	author	NOUN
cana-5708	220	8	contributions	contribution	VERB
cana-5708	220	9	sakshi	sakshi	PROPN
cana-5708	220	10	gupta	gupta	PROPN
cana-5708	220	11	:	:	PUNCT
cana-5708	220	12	conceptualization	conceptualization	NOUN
cana-5708	220	13	,	,	PUNCT
cana-5708	220	14	methodology	methodology	NOUN
cana-5708	220	15	,	,	PUNCT
cana-5708	220	16	writing	write	VERB
cana-5708	220	17	–	–	PUNCT
cana-5708	220	18	original	original	ADJ
cana-5708	220	19	draft	draft	NOUN
cana-5708	220	20	preparation	preparation	NOUN
cana-5708	220	21	.	.	PUNCT
cana-5708	221	1	sonam	sonam	PROPN
cana-5708	221	2	juneja	juneja	PROPN
cana-5708	221	3	:	:	PUNCT
cana-5708	221	4	data	datum	NOUN
cana-5708	221	5	curation	curation	NOUN
cana-5708	221	6	,	,	PUNCT
cana-5708	221	7	review	review	NOUN
cana-5708	221	8	and	and	CCONJ
cana-5708	221	9	editing	editing	NOUN
cana-5708	221	10	,	,	PUNCT
cana-5708	221	11	supervision	supervision	NOUN
cana-5708	221	12	.	.	PUNCT
cana-5708	222	1	data	datum	NOUN
cana-5708	222	2	availability	availability	NOUN
cana-5708	222	3	statement	statement	NOUN
cana-5708	222	4	the	the	DET
cana-5708	222	5	datasets	dataset	NOUN
cana-5708	222	6	generated	generate	VERB
cana-5708	222	7	and	and	CCONJ
cana-5708	222	8	analyzed	analyze	VERB
cana-5708	222	9	during	during	ADP
cana-5708	222	10	the	the	DET
cana-5708	222	11	current	current	ADJ
cana-5708	222	12	study	study	NOUN
cana-5708	222	13	are	be	AUX
cana-5708	222	14	available	available	ADJ
cana-5708	222	15	from	from	ADP
cana-5708	222	16	the	the	DET
cana-5708	222	17	corresponding	corresponding	ADJ
cana-5708	222	18	author	author	NOUN
cana-5708	222	19	on	on	ADP
cana-5708	222	20	reasonable	reasonable	ADJ
cana-5708	222	21	request	request	NOUN
cana-5708	222	22	.	.	PUNCT
cana-5708	223	1	research	research	NOUN
cana-5708	223	2	involving	involve	VERB
cana-5708	223	3	human	human	ADJ
cana-5708	223	4	and/or	and/or	CCONJ
cana-5708	223	5	animals	animal	NOUN
cana-5708	223	6	not	not	PART
cana-5708	223	7	applicable	applicable	ADJ
cana-5708	223	8	.	.	PUNCT
cana-5708	224	1	informed	informed	ADJ
cana-5708	224	2	consent	consent	NOUN
cana-5708	224	3	not	not	PART
cana-5708	224	4	applicable	applicable	ADJ
cana-5708	224	5	.	.	PUNCT
cana-5708	225	1	references	reference	NOUN
cana-5708	225	2	1	1	NUM
cana-5708	225	3	.	.	PUNCT
cana-5708	226	1	esteva	esteva	PROPN
cana-5708	226	2	,	,	PUNCT
cana-5708	226	3	a.	a.	PROPN
cana-5708	226	4	,	,	PUNCT
cana-5708	226	5	kuprel	kuprel	PROPN
cana-5708	226	6	,	,	PUNCT
cana-5708	226	7	b.	b.	PROPN
cana-5708	226	8	,	,	PUNCT
cana-5708	226	9	novoa	novoa	NOUN
cana-5708	226	10	,	,	PUNCT
cana-5708	226	11	r.	r.	PROPN
cana-5708	226	12	a.	a.	PROPN
cana-5708	226	13	,	,	PUNCT
cana-5708	226	14	et	et	PROPN
cana-5708	226	15	al	al	PROPN
cana-5708	226	16	.	.	PUNCT
cana-5708	226	17	(	(	PUNCT
cana-5708	226	18	2017	2017	NUM
cana-5708	226	19	)	)	PUNCT
cana-5708	226	20	.	.	PUNCT
cana-5708	227	1	dermatologist	dermatologist	NOUN
cana-5708	227	2	-	-	PUNCT
cana-5708	227	3	level	level	NOUN
cana-5708	227	4	classification	classification	NOUN
cana-5708	227	5	of	of	ADP
cana-5708	227	6	skin	skin	NOUN
cana-5708	227	7	cancer	cancer	NOUN
cana-5708	227	8	with	with	ADP
cana-5708	227	9	deep	deep	ADJ
cana-5708	227	10	neural	neural	ADJ
cana-5708	227	11	networks	network	NOUN
cana-5708	227	12	.	.	PUNCT
cana-5708	228	1	nature	nature	NOUN
cana-5708	228	2	,	,	PUNCT
cana-5708	228	3	542(7639	542(7639	NUM
cana-5708	228	4	)	)	PUNCT
cana-5708	228	5	,	,	PUNCT
cana-5708	228	6	115	115	NUM
cana-5708	228	7	-	-	SYM
cana-5708	228	8	118	118	NUM
cana-5708	228	9	.	.	PUNCT
cana-5708	229	1	https://doi.org/10.1038/nature21056	https://doi.org/10.1038/nature21056	PROPN
cana-5708	229	2	2	2	NUM
cana-5708	229	3	.	.	PUNCT
cana-5708	230	1	liu	liu	PROPN
cana-5708	230	2	,	,	PUNCT
cana-5708	230	3	x.	x.	PROPN
cana-5708	230	4	,	,	PUNCT
cana-5708	230	5	faes	faes	PROPN
cana-5708	230	6	,	,	PUNCT
cana-5708	230	7	l.	l.	PROPN
cana-5708	230	8	,	,	PUNCT
cana-5708	230	9	kale	kale	PROPN
cana-5708	230	10	,	,	PUNCT
cana-5708	230	11	a.	a.	NOUN
cana-5708	230	12	u.	u.	PROPN
cana-5708	230	13	,	,	PUNCT
cana-5708	230	14	et	et	PROPN
cana-5708	230	15	al	al	PROPN
cana-5708	230	16	.	.	PROPN
cana-5708	230	17	(	(	PUNCT
cana-5708	230	18	2019	2019	NUM
cana-5708	230	19	)	)	PUNCT
cana-5708	230	20	.	.	PUNCT
cana-5708	231	1	deep	deep	ADJ
cana-5708	231	2	learning	learning	NOUN
cana-5708	231	3	for	for	ADP
cana-5708	231	4	detecting	detect	VERB
cana-5708	231	5	retinal	retinal	ADJ
cana-5708	231	6	diseases	disease	NOUN
cana-5708	231	7	by	by	ADP
cana-5708	231	8	interpreting	interpret	VERB
cana-5708	231	9	ocular	ocular	ADJ
cana-5708	231	10	images	image	NOUN
cana-5708	231	11	:	:	PUNCT
cana-5708	231	12	a	a	DET
cana-5708	231	13	comprehensive	comprehensive	ADJ
cana-5708	231	14	review	review	NOUN
cana-5708	231	15	.	.	PUNCT
cana-5708	232	1	nature	nature	PROPN
cana-5708	232	2	biomedical	biomedical	ADJ
cana-5708	232	3	engineering	engineering	NOUN
cana-5708	232	4	,	,	PUNCT
cana-5708	232	5	3(8	3(8	NUM
cana-5708	232	6	)	)	PUNCT
cana-5708	232	7	,	,	PUNCT
cana-5708	232	8	742	742	NUM
cana-5708	232	9	-	-	SYM
cana-5708	232	10	758	758	NUM
cana-5708	232	11	.	.	PUNCT
cana-5708	233	1	https://doi.org/10.1038/s41551-019-0367-2	https://doi.org/10.1038/s41551-019-0367-2	NUM
cana-5708	233	2	3	3	X
cana-5708	233	3	.	.	X
cana-5708	233	4	rawat	rawat	PROPN
cana-5708	233	5	,	,	PUNCT
cana-5708	233	6	w.	w.	PROPN
cana-5708	233	7	,	,	PUNCT
cana-5708	233	8	&	&	CCONJ
cana-5708	233	9	wang	wang	PROPN
cana-5708	233	10	,	,	PUNCT
cana-5708	233	11	z.	z.	PROPN
cana-5708	233	12	(	(	PUNCT
cana-5708	233	13	2017	2017	NUM
cana-5708	233	14	)	)	PUNCT
cana-5708	233	15	.	.	PUNCT
cana-5708	234	1	deep	deep	ADJ
cana-5708	234	2	convolutional	convolutional	ADJ
cana-5708	234	3	neural	neural	ADJ
cana-5708	234	4	networks	network	NOUN
cana-5708	234	5	for	for	ADP
cana-5708	234	6	image	image	NOUN
cana-5708	234	7	classification	classification	NOUN
cana-5708	234	8	:	:	PUNCT
cana-5708	234	9	a	a	DET
cana-5708	234	10	comprehensive	comprehensive	ADJ
cana-5708	234	11	review	review	NOUN
cana-5708	234	12	.	.	PUNCT
cana-5708	235	1	neural	neural	ADJ
cana-5708	235	2	computation	computation	NOUN
cana-5708	235	3	,	,	PUNCT
cana-5708	235	4	29(9	29(9	NUM
cana-5708	235	5	)	)	PUNCT
cana-5708	235	6	,	,	PUNCT
cana-5708	235	7	2352	2352	NUM
cana-5708	235	8	-	-	SYM
cana-5708	235	9	2449	2449	NUM
cana-5708	235	10	.	.	PUNCT
cana-5708	236	1	https://doi.org/10.1162/neco_a_00990	https://doi.org/10.1162/neco_a_00990	PROPN
cana-5708	236	2	4	4	NUM
cana-5708	236	3	.	.	PUNCT
cana-5708	237	1	li	li	PROPN
cana-5708	237	2	,	,	PUNCT
cana-5708	237	3	l.	l.	PROPN
cana-5708	237	4	,	,	PUNCT
cana-5708	237	5	jamieson	jamieson	PROPN
cana-5708	237	6	,	,	PUNCT
cana-5708	237	7	k.	k.	PROPN
cana-5708	237	8	,	,	PUNCT
cana-5708	237	9	desalvo	desalvo	PROPN
cana-5708	237	10	,	,	PUNCT
cana-5708	237	11	g.	g.	PROPN
cana-5708	237	12	,	,	PUNCT
cana-5708	237	13	rostamizadeh	rostamizadeh	PROPN
cana-5708	237	14	,	,	PUNCT
cana-5708	237	15	a.	a.	PROPN
cana-5708	237	16	,	,	PUNCT
cana-5708	237	17	&	&	CCONJ
cana-5708	237	18	talwalkar	talwalkar	PROPN
cana-5708	237	19	,	,	PUNCT
cana-5708	237	20	a.	a.	PROPN
cana-5708	237	21	(	(	PUNCT
cana-5708	237	22	2017	2017	NUM
cana-5708	237	23	)	)	PUNCT
cana-5708	237	24	.	.	PUNCT
cana-5708	238	1	hyperband	hyperband	ADV
cana-5708	238	2	:	:	PUNCT
cana-5708	238	3	a	a	DET
cana-5708	238	4	novel	novel	ADJ
cana-5708	238	5	bandit	bandit	NOUN
cana-5708	238	6	-	-	PUNCT
cana-5708	238	7	based	base	VERB
cana-5708	238	8	approach	approach	NOUN
cana-5708	238	9	to	to	ADP
cana-5708	238	10	hyperparameter	hyperparameter	NOUN
cana-5708	238	11	optimization	optimization	NOUN
cana-5708	238	12	.	.	PUNCT
cana-5708	239	1	journal	journal	NOUN
cana-5708	239	2	of	of	ADP
cana-5708	239	3	machine	machine	NOUN
cana-5708	239	4	learning	learn	VERB
cana-5708	239	5	research	research	NOUN
cana-5708	239	6	,	,	PUNCT
cana-5708	239	7	18(185	18(185	NOUN
cana-5708	239	8	)	)	PUNCT
cana-5708	239	9	,	,	PUNCT
cana-5708	239	10	1	1	NUM
cana-5708	239	11	-	-	SYM
cana-5708	239	12	52	52	NUM
cana-5708	239	13	.	.	PUNCT
cana-5708	240	1	https://www.jmlr.org/papers/volume18/16-558/16558.pdf	https://www.jmlr.org/papers/volume18/16-558/16558.pdf	PROPN
cana-5708	240	2	5	5	NUM
cana-5708	240	3	.	.	PUNCT
cana-5708	240	4	simonyan	simonyan	PROPN
cana-5708	240	5	,	,	PUNCT
cana-5708	240	6	k.	k.	PROPN
cana-5708	240	7	,	,	PUNCT
cana-5708	240	8	&	&	CCONJ
cana-5708	240	9	zisserman	zisserman	PROPN
cana-5708	240	10	,	,	PUNCT
cana-5708	240	11	a.	a.	NOUN
cana-5708	240	12	(	(	PUNCT
cana-5708	240	13	2015	2015	NUM
cana-5708	240	14	)	)	PUNCT
cana-5708	240	15	.	.	PUNCT
cana-5708	241	1	very	very	ADV
cana-5708	241	2	deep	deep	ADJ
cana-5708	241	3	convolutional	convolutional	ADJ
cana-5708	241	4	networks	network	NOUN
cana-5708	241	5	for	for	ADP
cana-5708	241	6	large	large	ADJ
cana-5708	241	7	-	-	PUNCT
cana-5708	241	8	scale	scale	NOUN
cana-5708	241	9	image	image	NOUN
cana-5708	241	10	recognition	recognition	NOUN
cana-5708	241	11	.	.	PUNCT
cana-5708	242	1	international	international	ADJ
cana-5708	242	2	conference	conference	NOUN
cana-5708	242	3	on	on	ADP
cana-5708	242	4	learning	learn	VERB
cana-5708	242	5	representations	representation	NOUN
cana-5708	242	6	(	(	PUNCT
cana-5708	242	7	iclr	iclr	NOUN
cana-5708	242	8	)	)	PUNCT
cana-5708	242	9	.	.	PUNCT
cana-5708	243	1	https://arxiv.org/abs/1409.1556	https://arxiv.org/abs/1409.1556	NOUN
cana-5708	243	2	6	6	NUM
cana-5708	243	3	.	.	PUNCT
cana-5708	244	1	szegedy	szegedy	PROPN
cana-5708	244	2	,	,	PUNCT
cana-5708	244	3	c.	c.	NOUN
cana-5708	244	4	,	,	PUNCT
cana-5708	244	5	ioffe	ioffe	PROPN
cana-5708	244	6	,	,	PUNCT
cana-5708	244	7	s.	s.	PROPN
cana-5708	244	8	,	,	PUNCT
cana-5708	244	9	vanhoucke	vanhoucke	PROPN
cana-5708	244	10	,	,	PUNCT
cana-5708	244	11	v.	v.	ADV
cana-5708	244	12	,	,	PUNCT
cana-5708	244	13	&	&	CCONJ
cana-5708	244	14	alemi	alemi	PROPN
cana-5708	244	15	,	,	PUNCT
cana-5708	244	16	a.	a.	NOUN
cana-5708	244	17	a.	a.	NOUN
cana-5708	244	18	(	(	PUNCT
cana-5708	244	19	2017	2017	NUM
cana-5708	244	20	)	)	PUNCT
cana-5708	244	21	.	.	PUNCT
cana-5708	245	1	inception	inception	NOUN
cana-5708	245	2	-	-	PUNCT
cana-5708	245	3	v4	v4	NOUN
cana-5708	245	4	,	,	PUNCT
cana-5708	245	5	inceptionresnet	inceptionresnet	NOUN
cana-5708	245	6	and	and	CCONJ
cana-5708	245	7	the	the	DET
cana-5708	245	8	impact	impact	NOUN
cana-5708	245	9	of	of	ADP
cana-5708	245	10	residual	residual	ADJ
cana-5708	245	11	connections	connection	NOUN
cana-5708	245	12	on	on	ADP
cana-5708	245	13	learning	learn	VERB
cana-5708	245	14	.	.	PUNCT
cana-5708	246	1	aaai	aaai	PROPN
cana-5708	246	2	conference	conference	PROPN
cana-5708	246	3	on	on	ADP
cana-5708	246	4	artificial	artificial	ADJ
cana-5708	246	5	intelligence	intelligence	NOUN
cana-5708	246	6	.	.	PUNCT
cana-5708	247	1	https://arxiv.org/abs/1602.07261	https://arxiv.org/abs/1602.07261	PROPN
cana-5708	247	2	7	7	NUM
cana-5708	247	3	.	.	PUNCT
cana-5708	248	1	he	he	PRON
cana-5708	248	2	,	,	PUNCT
cana-5708	248	3	k.	k.	PROPN
cana-5708	248	4	,	,	PUNCT
cana-5708	248	5	zhang	zhang	PROPN
cana-5708	248	6	,	,	PUNCT
cana-5708	248	7	x.	x.	PROPN
cana-5708	248	8	,	,	PUNCT
cana-5708	248	9	ren	ren	PROPN
cana-5708	248	10	,	,	PUNCT
cana-5708	248	11	s.	s.	PROPN
cana-5708	248	12	,	,	PUNCT
cana-5708	248	13	&	&	CCONJ
cana-5708	248	14	sun	sun	PROPN
cana-5708	248	15	,	,	PUNCT
cana-5708	248	16	j.	j.	PROPN
cana-5708	248	17	(	(	PUNCT
cana-5708	248	18	2016	2016	NUM
cana-5708	248	19	)	)	PUNCT
cana-5708	248	20	.	.	PUNCT
cana-5708	249	1	deep	deep	ADJ
cana-5708	249	2	residual	residual	ADJ
cana-5708	249	3	learning	learning	NOUN
cana-5708	249	4	for	for	ADP
cana-5708	249	5	image	image	NOUN
cana-5708	249	6	recognition	recognition	NOUN
cana-5708	249	7	.	.	PUNCT
cana-5708	250	1	proceedings	proceeding	NOUN
cana-5708	250	2	of	of	ADP
cana-5708	250	3	the	the	DET
cana-5708	250	4	ieee	ieee	NOUN
cana-5708	250	5	conference	conference	NOUN
cana-5708	250	6	on	on	ADP
cana-5708	250	7	computer	computer	NOUN
cana-5708	250	8	vision	vision	NOUN
cana-5708	250	9	and	and	CCONJ
cana-5708	250	10	pattern	pattern	NOUN
cana-5708	250	11	recognition	recognition	NOUN
cana-5708	250	12	(	(	PUNCT
cana-5708	250	13	cvpr	cvpr	NOUN
cana-5708	250	14	)	)	PUNCT
cana-5708	250	15	.	.	PUNCT
cana-5708	251	1	https://doi.org/10.1109/cvpr.2016.90	https://doi.org/10.1109/cvpr.2016.90	ADV
cana-5708	251	2	8	8	NUM
cana-5708	251	3	.	.	PUNCT
cana-5708	252	1	howard	howard	PROPN
cana-5708	252	2	,	,	PUNCT
cana-5708	252	3	j.	j.	PROPN
cana-5708	252	4	,	,	PUNCT
cana-5708	252	5	&	&	CCONJ
cana-5708	252	6	gugger	gugger	PROPN
cana-5708	252	7	,	,	PUNCT
cana-5708	252	8	s.	s.	PROPN
cana-5708	252	9	(	(	PUNCT
cana-5708	252	10	2020	2020	NUM
cana-5708	252	11	)	)	PUNCT
cana-5708	252	12	.	.	PUNCT
cana-5708	253	1	fastai	fastai	NOUN
cana-5708	253	2	:	:	PUNCT
cana-5708	253	3	a	a	DET
cana-5708	253	4	layered	layered	ADJ
cana-5708	253	5	api	api	NOUN
cana-5708	253	6	for	for	ADP
cana-5708	253	7	deep	deep	ADJ
cana-5708	253	8	learning	learning	NOUN
cana-5708	253	9	.	.	PUNCT
cana-5708	254	1	information	information	NOUN
cana-5708	254	2	,	,	PUNCT
cana-5708	254	3	11(2	11(2	NOUN
cana-5708	254	4	)	)	PUNCT
cana-5708	254	5	,	,	PUNCT
cana-5708	254	6	108	108	NUM
cana-5708	254	7	.	.	PUNCT
cana-5708	255	1	https://doi.org/10.3390/info11020108	https://doi.org/10.3390/info11020108	PROPN
cana-5708	255	2	https://doi.org/10.1038/nature21056	https://doi.org/10.1038/nature21056	PROPN
cana-5708	255	3	https://doi.org/10.1038/s41551-019-0367-2	https://doi.org/10.1038/s41551-019-0367-2	NUM
cana-5708	255	4	https://doi.org/10.1162/neco_a_00990	https://doi.org/10.1162/neco_a_00990	PROPN
cana-5708	255	5	https://www.jmlr.org/papers/volume18/16-558/16-558.pdf	https://www.jmlr.org/papers/volume18/16-558/16-558.pdf	PROPN
cana-5708	255	6	https://www.jmlr.org/papers/volume18/16-558/16-558.pdf	https://www.jmlr.org/papers/volume18/16-558/16-558.pdf	PROPN
cana-5708	255	7	https://arxiv.org/abs/1409.1556	https://arxiv.org/abs/1409.1556	PROPN
cana-5708	255	8	https://arxiv.org/abs/1602.07261	https://arxiv.org/abs/1602.07261	PROPN
cana-5708	255	9	https://doi.org/10.1109/cvpr.2016.90	https://doi.org/10.1109/cvpr.2016.90	PROPN
cana-5708	255	10	https://doi.org/10.3390/info11020108	https://doi.org/10.3390/info11020108	NOUN
cana-5708	255	11	communications	communication	NOUN
cana-5708	255	12	on	on	ADP
cana-5708	255	13	applied	apply	VERB
cana-5708	255	14	nonlinear	nonlinear	ADJ
cana-5708	255	15	analysis	analysis	NOUN
cana-5708	255	16	issn	issn	NOUN
cana-5708	255	17	:	:	PUNCT
cana-5708	255	18	1074	1074	NUM
cana-5708	255	19	-	-	PUNCT
cana-5708	255	20	133x	133x	NUM
cana-5708	255	21	vol	vol	VERB
cana-5708	255	22	32	32	NUM
cana-5708	255	23	no	no	NOUN
cana-5708	255	24	.	.	PUNCT
cana-5708	256	1	10s	10	NOUN
cana-5708	256	2	(	(	PUNCT
cana-5708	256	3	2025	2025	NUM
cana-5708	256	4	)	)	PUNCT
cana-5708	256	5	2683	2683	NUM
cana-5708	256	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5708	256	7	9	9	NUM
cana-5708	256	8	.	.	PUNCT
cana-5708	257	1	redmon	redmon	PROPN
cana-5708	257	2	,	,	PUNCT
cana-5708	257	3	j.	j.	PROPN
cana-5708	257	4	,	,	PUNCT
cana-5708	257	5	&	&	CCONJ
cana-5708	257	6	farhadi	farhadi	PROPN
cana-5708	257	7	,	,	PUNCT
cana-5708	257	8	a.	a.	NOUN
cana-5708	257	9	(	(	PUNCT
cana-5708	257	10	2018	2018	NUM
cana-5708	257	11	)	)	PUNCT
cana-5708	257	12	.	.	PUNCT
cana-5708	258	1	yolov3	yolov3	PROPN
cana-5708	258	2	:	:	PUNCT
cana-5708	259	1	an	an	DET
cana-5708	259	2	incremental	incremental	ADJ
cana-5708	259	3	improvement	improvement	NOUN
cana-5708	259	4	.	.	PUNCT
cana-5708	260	1	https://arxiv.org/abs/1804.02767	https://arxiv.org/abs/1804.02767	PROPN
cana-5708	260	2	10	10	NUM
cana-5708	260	3	.	.	PUNCT
cana-5708	261	1	kingma	kingma	PROPN
cana-5708	261	2	,	,	PUNCT
cana-5708	261	3	d.	d.	PROPN
cana-5708	261	4	p.	p.	PROPN
cana-5708	261	5	,	,	PUNCT
cana-5708	261	6	&	&	CCONJ
cana-5708	261	7	ba	ba	PROPN
cana-5708	261	8	,	,	PUNCT
cana-5708	261	9	j.	j.	PROPN
cana-5708	261	10	(	(	PUNCT
cana-5708	261	11	2015	2015	NUM
cana-5708	261	12	)	)	PUNCT
cana-5708	261	13	.	.	PUNCT
cana-5708	262	1	adam	adam	PROPN
cana-5708	262	2	:	:	PUNCT
cana-5708	262	3	a	a	DET
cana-5708	262	4	method	method	NOUN
cana-5708	262	5	for	for	ADP
cana-5708	262	6	stochastic	stochastic	ADJ
cana-5708	262	7	optimization	optimization	NOUN
cana-5708	262	8	.	.	PUNCT
cana-5708	263	1	international	international	ADJ
cana-5708	263	2	conference	conference	NOUN
cana-5708	263	3	on	on	ADP
cana-5708	263	4	learning	learn	VERB
cana-5708	263	5	representations	representation	NOUN
cana-5708	263	6	(	(	PUNCT
cana-5708	263	7	iclr	iclr	NOUN
cana-5708	263	8	)	)	PUNCT
cana-5708	263	9	.	.	PUNCT
cana-5708	264	1	https://arxiv.org/abs/1412.6980	https://arxiv.org/abs/1412.6980	PROPN
cana-5708	264	2	11	11	NUM
cana-5708	264	3	.	.	PUNCT
cana-5708	265	1	tschandl	tschandl	NOUN
cana-5708	265	2	,	,	PUNCT
cana-5708	265	3	p.	p.	PROPN
cana-5708	265	4	,	,	PUNCT
cana-5708	265	5	rosendahl	rosendahl	PROPN
cana-5708	265	6	,	,	PUNCT
cana-5708	265	7	c.	c.	PROPN
cana-5708	265	8	,	,	PUNCT
cana-5708	265	9	&	&	CCONJ
cana-5708	265	10	kittler	kittler	PROPN
cana-5708	265	11	,	,	PUNCT
cana-5708	265	12	h.	h.	PROPN
cana-5708	265	13	(	(	PUNCT
cana-5708	265	14	2018	2018	NUM
cana-5708	265	15	)	)	PUNCT
cana-5708	265	16	.	.	PUNCT
cana-5708	266	1	the	the	DET
cana-5708	266	2	ham10000	ham10000	PROPN
cana-5708	266	3	dataset	dataset	PROPN
cana-5708	266	4	,	,	PUNCT
cana-5708	266	5	a	a	DET
cana-5708	266	6	large	large	ADJ
cana-5708	266	7	collection	collection	NOUN
cana-5708	266	8	of	of	ADP
cana-5708	266	9	multi	multi	ADJ
cana-5708	266	10	-	-	ADJ
cana-5708	266	11	source	source	NOUN
cana-5708	266	12	dermatoscopic	dermatoscopic	NOUN
cana-5708	266	13	images	image	NOUN
cana-5708	266	14	of	of	ADP
cana-5708	266	15	common	common	ADJ
cana-5708	266	16	pigmented	pigmented	ADJ
cana-5708	266	17	skin	skin	NOUN
cana-5708	266	18	lesions	lesion	NOUN
cana-5708	266	19	.	.	PUNCT
cana-5708	267	1	scientific	scientific	ADJ
cana-5708	267	2	data	datum	NOUN
cana-5708	267	3	,	,	PUNCT
cana-5708	267	4	5	5	NUM
cana-5708	267	5	,	,	PUNCT
cana-5708	267	6	180161	180161	NUM
cana-5708	267	7	.	.	PUNCT
cana-5708	268	1	https://doi.org/10.1038/sdata.2018.161	https://doi.org/10.1038/sdata.2018.161	ADJ
cana-5708	268	2	12	12	NUM
cana-5708	268	3	.	.	PUNCT
cana-5708	269	1	international	international	ADJ
cana-5708	269	2	skin	skin	NOUN
cana-5708	269	3	imaging	imaging	NOUN
cana-5708	269	4	collaboration	collaboration	NOUN
cana-5708	269	5	(	(	PUNCT
cana-5708	269	6	isic	isic	NOUN
cana-5708	269	7	)	)	PUNCT
cana-5708	269	8	.	.	PUNCT
cana-5708	270	1	the	the	DET
cana-5708	270	2	isic	isic	PROPN
cana-5708	270	3	2020	2020	NUM
cana-5708	270	4	challenge	challenge	NOUN
cana-5708	270	5	dataset	dataset	NOUN
cana-5708	270	6	.	.	PUNCT
cana-5708	271	1	https://challenge.isic-archive.com	https://challenge.isic-archive.com	X
cana-5708	271	2	13	13	NUM
cana-5708	271	3	.	.	PUNCT
cana-5708	271	4	kaggle	kaggle	PROPN
cana-5708	271	5	.	.	PUNCT
cana-5708	272	1	(	(	PUNCT
cana-5708	272	2	2020	2020	NUM
cana-5708	272	3	)	)	PUNCT
cana-5708	272	4	.	.	PUNCT
cana-5708	273	1	skin	skin	NOUN
cana-5708	273	2	cancer	cancer	NOUN
cana-5708	273	3	mnist	mnist	NOUN
cana-5708	273	4	:	:	PUNCT
cana-5708	273	5	ham10000	ham10000	PROPN
cana-5708	273	6	dataset	dataset	PROPN
cana-5708	273	7	.	.	PUNCT
cana-5708	274	1	https://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000	https://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000	PROPN
cana-5708	274	2	14	14	NUM
cana-5708	274	3	.	.	PUNCT
cana-5708	274	4	codella	codella	PROPN
cana-5708	274	5	,	,	PUNCT
cana-5708	274	6	n.	n.	PROPN
cana-5708	274	7	c.	c.	PROPN
cana-5708	274	8	f.	f.	PROPN
cana-5708	274	9	,	,	PUNCT
cana-5708	274	10	rotemberg	rotemberg	PROPN
cana-5708	274	11	,	,	PUNCT
cana-5708	274	12	v.	v.	ADV
cana-5708	274	13	,	,	PUNCT
cana-5708	274	14	tschandl	tschandl	NOUN
cana-5708	274	15	,	,	PUNCT
cana-5708	274	16	p.	p.	PROPN
cana-5708	274	17	,	,	PUNCT
cana-5708	274	18	et	et	PROPN
cana-5708	274	19	al	al	PROPN
cana-5708	274	20	.	.	PROPN
cana-5708	274	21	(	(	PUNCT
cana-5708	274	22	2019	2019	NUM
cana-5708	274	23	)	)	PUNCT
cana-5708	274	24	.	.	PUNCT
cana-5708	275	1	skin	skin	NOUN
cana-5708	275	2	lesion	lesion	NOUN
cana-5708	275	3	analysis	analysis	NOUN
cana-5708	275	4	toward	toward	ADP
cana-5708	275	5	melanoma	melanoma	NOUN
cana-5708	275	6	detection	detection	NOUN
cana-5708	275	7	:	:	PUNCT
cana-5708	275	8	a	a	DET
cana-5708	275	9	challenge	challenge	NOUN
cana-5708	275	10	at	at	ADP
cana-5708	275	11	the	the	DET
cana-5708	275	12	2017	2017	NUM
cana-5708	275	13	isbi	isbi	NOUN
cana-5708	275	14	international	international	ADJ
cana-5708	275	15	symposium	symposium	NOUN
cana-5708	275	16	on	on	ADP
cana-5708	275	17	biomedical	biomedical	ADJ
cana-5708	275	18	imaging	imaging	NOUN
cana-5708	275	19	.	.	PUNCT
cana-5708	276	1	ieee	ieee	NOUN
cana-5708	276	2	transactions	transaction	NOUN
cana-5708	276	3	on	on	ADP
cana-5708	276	4	medical	medical	ADJ
cana-5708	276	5	imaging	imaging	NOUN
cana-5708	276	6	,	,	PUNCT
cana-5708	276	7	38(8	38(8	NUM
cana-5708	276	8	)	)	PUNCT
cana-5708	276	9	,	,	PUNCT
cana-5708	276	10	1935	1935	NUM
cana-5708	276	11	-	-	SYM
cana-5708	276	12	1945	1945	NUM
cana-5708	276	13	.	.	PUNCT
cana-5708	277	1	https://doi.org/10.1109/tmi.2019.2905817	https://doi.org/10.1109/tmi.2019.2905817	PROPN
cana-5708	277	2	15	15	NUM
cana-5708	277	3	.	.	PUNCT
cana-5708	278	1	goodfellow	goodfellow	PROPN
cana-5708	278	2	,	,	PUNCT
cana-5708	278	3	i.	i.	PROPN
cana-5708	278	4	,	,	PUNCT
cana-5708	278	5	bengio	bengio	PROPN
cana-5708	278	6	,	,	PUNCT
cana-5708	278	7	y.	y.	PROPN
cana-5708	278	8	,	,	PUNCT
cana-5708	278	9	&	&	CCONJ
cana-5708	278	10	courville	courville	PROPN
cana-5708	278	11	,	,	PUNCT
cana-5708	278	12	a.	a.	NOUN
cana-5708	278	13	(	(	PUNCT
cana-5708	278	14	2016	2016	NUM
cana-5708	278	15	)	)	PUNCT
cana-5708	278	16	.	.	PUNCT
cana-5708	279	1	deep	deep	ADJ
cana-5708	279	2	learning	learning	NOUN
cana-5708	279	3	.	.	PUNCT
cana-5708	280	1	mit	mit	PROPN
cana-5708	280	2	press	press	NOUN
cana-5708	280	3	.	.	PUNCT
cana-5708	281	1	http://www.deeplearningbook.org	http://www.deeplearningbook.org	X
cana-5708	281	2	16	16	NUM
cana-5708	281	3	.	.	PUNCT
cana-5708	282	1	chollet	chollet	PROPN
cana-5708	282	2	,	,	PUNCT
cana-5708	282	3	f.	f.	PROPN
cana-5708	282	4	(	(	PUNCT
cana-5708	282	5	2018	2018	NUM
cana-5708	282	6	)	)	PUNCT
cana-5708	282	7	.	.	PUNCT
cana-5708	283	1	deep	deep	ADJ
cana-5708	283	2	learning	learn	VERB
cana-5708	283	3	with	with	ADP
cana-5708	283	4	python	python	PROPN
cana-5708	283	5	.	.	PUNCT
cana-5708	284	1	manning	man	VERB
cana-5708	284	2	publications	publication	NOUN
cana-5708	284	3	.	.	PUNCT
cana-5708	285	1	https://www.manning.com/books/deep-learning-with-python	https://www.manning.com/books/deep-learning-with-python	PROPN
cana-5708	285	2	17	17	NUM
cana-5708	285	3	.	.	PUNCT
cana-5708	286	1	géron	géron	PROPN
cana-5708	286	2	,	,	PUNCT
cana-5708	286	3	a.	a.	NOUN
cana-5708	286	4	(	(	PUNCT
cana-5708	286	5	2019	2019	NUM
cana-5708	286	6	)	)	PUNCT
cana-5708	286	7	.	.	PUNCT
cana-5708	287	1	hands	hand	NOUN
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cana-5708	287	8	-	-	PUNCT
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cana-5708	287	13	and	and	CCONJ
cana-5708	287	14	tensorflow	tensorflow	NOUN
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cana-5708	288	1	o'reilly	o'reilly	PROPN
cana-5708	288	2	media	medium	NOUN
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cana-5708	289	2	.	.	X
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cana-5708	289	9	)	)	PUNCT
cana-5708	289	10	.	.	PUNCT
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cana-5708	290	6	.	.	PUNCT
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cana-5708	291	2	.	.	PUNCT
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cana-5708	292	2	.	.	X
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cana-5708	293	8	,	,	PUNCT
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cana-5708	293	17	2016	2016	NUM
cana-5708	293	18	)	)	PUNCT
cana-5708	293	19	.	.	PUNCT
cana-5708	294	1	tensorflow	tensorflow	NOUN
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cana-5708	294	7	-	-	PUNCT
cana-5708	294	8	scale	scale	NOUN
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cana-5708	295	4	on	on	ADP
cana-5708	295	5	operating	operating	NOUN
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cana-5708	295	10	(	(	PUNCT
cana-5708	295	11	osdi	osdi	NOUN
cana-5708	295	12	16	16	NUM
cana-5708	295	13	)	)	PUNCT
cana-5708	295	14	.	.	PUNCT
cana-5708	296	1	20	20	NUM
cana-5708	296	2	.	.	X
cana-5708	297	1	chollet	chollet	PROPN
cana-5708	297	2	,	,	PUNCT
cana-5708	297	3	f.	f.	PROPN
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cana-5708	297	5	et	et	PROPN
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cana-5708	298	1	(	(	PUNCT
cana-5708	298	2	2015	2015	NUM
cana-5708	298	3	)	)	PUNCT
cana-5708	298	4	.	.	PUNCT
cana-5708	299	1	keras	keras	PROPN
cana-5708	299	2	:	:	PUNCT
cana-5708	299	3	the	the	DET
cana-5708	299	4	python	python	NOUN
cana-5708	299	5	deep	deep	ADJ
cana-5708	299	6	learning	learn	VERB
cana-5708	299	7	library	library	NOUN
cana-5708	299	8	.	.	PUNCT
cana-5708	300	1	https://keras.io	https://keras.io	PROPN
cana-5708	300	2	21	21	NUM
cana-5708	300	3	.	.	PUNCT
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cana-5708	301	4	,	,	PUNCT
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cana-5708	301	8	,	,	PUNCT
cana-5708	301	9	massa	massa	PROPN
cana-5708	301	10	,	,	PUNCT
cana-5708	301	11	f.	f.	PROPN
cana-5708	301	12	,	,	PUNCT
cana-5708	301	13	et	et	PROPN
cana-5708	301	14	al	al	PROPN
cana-5708	301	15	.	.	PROPN
cana-5708	302	1	(	(	PUNCT
cana-5708	302	2	2019	2019	NUM
cana-5708	302	3	)	)	PUNCT
cana-5708	302	4	.	.	PUNCT
cana-5708	303	1	pytorch	pytorch	NOUN
cana-5708	303	2	:	:	PUNCT
cana-5708	303	3	an	an	DET
cana-5708	303	4	imperative	imperative	ADJ
cana-5708	303	5	style	style	NOUN
cana-5708	303	6	,	,	PUNCT
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cana-5708	303	8	deep	deep	ADJ
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cana-5708	304	6	systems	system	NOUN
cana-5708	304	7	(	(	PUNCT
cana-5708	304	8	neurips	neurip	NOUN
cana-5708	304	9	)	)	PUNCT
cana-5708	304	10	.	.	PUNCT
cana-5708	305	1	22	22	NUM
cana-5708	305	2	.	.	PUNCT
cana-5708	305	3	litjens	litjen	NOUN
cana-5708	305	4	,	,	PUNCT
cana-5708	305	5	g.	g.	PROPN
cana-5708	305	6	,	,	PUNCT
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cana-5708	305	8	,	,	PUNCT
cana-5708	305	9	t.	t.	PROPN
cana-5708	305	10	,	,	PUNCT
cana-5708	305	11	bejnordi	bejnordi	PROPN
cana-5708	305	12	,	,	PUNCT
cana-5708	305	13	b.	b.	PROPN
cana-5708	305	14	e.	e.	PROPN
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cana-5708	305	19	(	(	PUNCT
cana-5708	305	20	2017	2017	NUM
cana-5708	305	21	)	)	PUNCT
cana-5708	305	22	.	.	PUNCT
cana-5708	306	1	a	a	DET
cana-5708	306	2	survey	survey	NOUN
cana-5708	306	3	on	on	ADP
cana-5708	306	4	deep	deep	ADJ
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cana-5708	306	6	in	in	ADP
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cana-5708	306	8	image	image	NOUN
cana-5708	306	9	analysis	analysis	NOUN
cana-5708	306	10	.	.	PUNCT
cana-5708	307	1	medical	medical	ADJ
cana-5708	307	2	image	image	NOUN
cana-5708	307	3	analysis	analysis	NOUN
cana-5708	307	4	,	,	PUNCT
cana-5708	307	5	42	42	NUM
cana-5708	307	6	,	,	PUNCT
cana-5708	307	7	60	60	NUM
cana-5708	307	8	-	-	SYM
cana-5708	307	9	88	88	NUM
cana-5708	307	10	.	.	PUNCT
cana-5708	308	1	https://doi.org/10.1016/j.media.2017.07.005	https://doi.org/10.1016/j.media.2017.07.005	PROPN
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cana-5708	308	3	.	.	PUNCT
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cana-5708	309	4	,	,	PUNCT
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cana-5708	309	6	,	,	PUNCT
cana-5708	309	7	y.	y.	PROPN
cana-5708	309	8	,	,	PUNCT
cana-5708	309	9	jiang	jiang	PROPN
cana-5708	309	10	,	,	PUNCT
cana-5708	309	11	j.	j.	PROPN
cana-5708	309	12	,	,	PUNCT
cana-5708	309	13	gao	gao	PROPN
cana-5708	309	14	,	,	PUNCT
cana-5708	309	15	j.	j.	PROPN
cana-5708	309	16	,	,	PUNCT
cana-5708	309	17	zhang	zhang	PROPN
cana-5708	309	18	,	,	PUNCT
cana-5708	309	19	c.	c.	PROPN
cana-5708	309	20	,	,	PUNCT
cana-5708	309	21	&	&	CCONJ
cana-5708	309	22	cui	cui	PROPN
cana-5708	309	23	,	,	PUNCT
cana-5708	309	24	b.	b.	PROPN
cana-5708	309	25	(	(	PUNCT
cana-5708	309	26	2020	2020	NUM
cana-5708	309	27	)	)	PUNCT
cana-5708	309	28	.	.	PUNCT
cana-5708	310	1	mfes	mfes	PROPN
cana-5708	310	2	-	-	PUNCT
cana-5708	310	3	hb	hb	PROPN
cana-5708	310	4	:	:	PUNCT
cana-5708	310	5	efficient	efficient	ADJ
cana-5708	310	6	hyperband	hyperband	NOUN
cana-5708	310	7	with	with	ADP
cana-5708	310	8	multi	multi	ADJ
cana-5708	310	9	-	-	ADJ
cana-5708	310	10	fidelity	fidelity	ADJ
cana-5708	310	11	quality	quality	NOUN
cana-5708	310	12	measurements	measurement	NOUN
cana-5708	310	13	.	.	PUNCT
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cana-5708	311	2	from	from	ADP
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cana-5708	311	4	24	24	NUM
cana-5708	311	5	.	.	PUNCT
cana-5708	312	1	brandt	brandt	PROPN
cana-5708	312	2	,	,	PUNCT
cana-5708	312	3	j.	j.	PROPN
cana-5708	312	4	,	,	PUNCT
cana-5708	312	5	wever	wever	PROPN
cana-5708	312	6	,	,	PUNCT
cana-5708	312	7	m.	m.	NOUN
cana-5708	312	8	,	,	PUNCT
cana-5708	312	9	iliadis	iliadis	PROPN
cana-5708	312	10	,	,	PUNCT
cana-5708	312	11	d.	d.	PROPN
cana-5708	312	12	,	,	PUNCT
cana-5708	312	13	bengs	beng	NOUN
cana-5708	312	14	,	,	PUNCT
cana-5708	312	15	v.	v.	ADV
cana-5708	312	16	,	,	PUNCT
cana-5708	312	17	&	&	CCONJ
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cana-5708	312	19	,	,	PUNCT
cana-5708	312	20	e.	e.	PROPN
cana-5708	312	21	(	(	PUNCT
cana-5708	312	22	2023	2023	NUM
cana-5708	312	23	)	)	PUNCT
cana-5708	312	24	.	.	PUNCT
cana-5708	313	1	iterative	iterative	PROPN
cana-5708	313	2	deepening	deepen	VERB
cana-5708	313	3	hyperband	hyperband	PROPN
cana-5708	313	4	.	.	PUNCT
cana-5708	314	1	retrieved	retrieve	VERB
cana-5708	314	2	from	from	ADP
cana-5708	314	3	https://arxiv.org/abs/2302.00511	https://arxiv.org/abs/2302.00511	NOUN
cana-5708	314	4	.	.	PUNCT
cana-5708	315	1	https://arxiv.org/abs/1804.02767	https://arxiv.org/abs/1804.02767	PROPN
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cana-5708	315	4	https://challenge.isic-archive.com/	https://challenge.isic-archive.com/	ADJ
cana-5708	315	5	https://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000	https://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000	PROPN
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cana-5708	315	7	http://www.deeplearningbook.org/	http://www.deeplearningbook.org/	NOUN
cana-5708	315	8	https://www.manning.com/books/deep-learning-with-python	https://www.manning.com/books/deep-learning-with-python	VERB
cana-5708	315	9	https://keras.io/	https://keras.io/	PROPN
cana-5708	315	10	https://doi.org/10.1016/j.media.2017.07.005	https://doi.org/10.1016/j.media.2017.07.005	PROPN
cana-5708	315	11	https://arxiv.org/abs/2012.03011	https://arxiv.org/abs/2012.03011	ADJ
cana-5708	315	12	https://arxiv.org/abs/2302.00511	https://arxiv.org/abs/2302.00511	NOUN