id	sid	tid	token	lemma	pos
cana-2739	1	1	communications	communication	NOUN
cana-2739	1	2	on	on	ADP
cana-2739	1	3	applied	apply	VERB
cana-2739	1	4	nonlinear	nonlinear	ADJ
cana-2739	1	5	analysis	analysis	NOUN
cana-2739	1	6	issn	issn	NOUN
cana-2739	1	7	:	:	PUNCT
cana-2739	1	8	1074	1074	NUM
cana-2739	1	9	-	-	PUNCT
cana-2739	1	10	133x	133x	NUM
cana-2739	1	11	vol	vol	NOUN
cana-2739	1	12	32	32	NUM
cana-2739	1	13	no	no	NOUN
cana-2739	1	14	.	.	PUNCT
cana-2739	2	1	4s	4s	NUM
cana-2739	2	2	(	(	PUNCT
cana-2739	2	3	2025	2025	NUM
cana-2739	2	4	)	)	PUNCT
cana-2739	2	5	59	59	NUM
cana-2739	2	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2739	2	7	hybrid	hybrid	ADJ
cana-2739	2	8	textural	textural	ADJ
cana-2739	2	9	feature	feature	NOUN
cana-2739	2	10	descriptors	descriptor	NOUN
cana-2739	2	11	for	for	ADP
cana-2739	2	12	ovarian	ovarian	ADJ
cana-2739	2	13	cyst	cyst	NOUN
cana-2739	2	14	classification	classification	NOUN
cana-2739	2	15	using	use	VERB
cana-2739	2	16	machine	machine	NOUN
cana-2739	2	17	learning	learn	VERB
cana-2739	2	18	aditi	aditi	PROPN
cana-2739	2	19	gupta1	gupta1	PROPN
cana-2739	2	20	,	,	PUNCT
cana-2739	2	21	sudeep	sudeep	PROPN
cana-2739	2	22	varshney2	varshney2	PROPN
cana-2739	2	23	,	,	PUNCT
cana-2739	2	24	hoor	hoor	PROPN
cana-2739	2	25	fatima3	fatima3	NOUN
cana-2739	3	1	1department	1department	NUM
cana-2739	3	2	of	of	ADP
cana-2739	3	3	computer	computer	NOUN
cana-2739	3	4	science	science	NOUN
cana-2739	3	5	and	and	CCONJ
cana-2739	3	6	engineering	engineering	NOUN
cana-2739	3	7	,	,	PUNCT
cana-2739	3	8	sharda	sharda	PROPN
cana-2739	3	9	university	university	PROPN
cana-2739	3	10	,	,	PUNCT
cana-2739	3	11	greater	great	ADJ
cana-2739	3	12	noida	noida	PROPN
cana-2739	3	13	,	,	PUNCT
cana-2739	3	14	india	india	PROPN
cana-2739	3	15	.	.	PUNCT
cana-2739	4	1	gpaditi77@gmail.com	gpaditi77@gmail.com	X
cana-2739	4	2	*	*	PUNCT
cana-2739	4	3	corresponding	correspond	VERB
cana-2739	4	4	author	author	NOUN
cana-2739	4	5	2department	2department	NUM
cana-2739	4	6	of	of	ADP
cana-2739	4	7	computer	computer	NOUN
cana-2739	4	8	science	science	NOUN
cana-2739	4	9	and	and	CCONJ
cana-2739	4	10	engineering	engineering	NOUN
cana-2739	4	11	,	,	PUNCT
cana-2739	4	12	sharda	sharda	PROPN
cana-2739	4	13	university	university	PROPN
cana-2739	4	14	,	,	PUNCT
cana-2739	4	15	greater	great	ADJ
cana-2739	4	16	noida	noida	PROPN
cana-2739	4	17	,	,	PUNCT
cana-2739	4	18	india	india	PROPN
cana-2739	4	19	.	.	PUNCT
cana-2739	5	1	sudeep149@gmail.com	sudeep149@gmail.com	X
cana-2739	6	1	3school	3school	NUM
cana-2739	6	2	of	of	ADP
cana-2739	6	3	computer	computer	NOUN
cana-2739	6	4	science	science	NOUN
cana-2739	6	5	engineering	engineering	NOUN
cana-2739	6	6	and	and	CCONJ
cana-2739	6	7	technology	technology	NOUN
cana-2739	6	8	,	,	PUNCT
cana-2739	6	9	bennett	bennett	PROPN
cana-2739	6	10	university	university	PROPN
cana-2739	6	11	,	,	PUNCT
cana-2739	6	12	greater	great	ADJ
cana-2739	6	13	noida	noida	PROPN
cana-2739	6	14	,	,	PUNCT
cana-2739	6	15	india	india	PROPN
cana-2739	6	16	.	.	PUNCT
cana-2739	7	1	hoor.iitd@gmail.com	hoor.iitd@gmail.com	PROPN
cana-2739	7	2	article	article	NOUN
cana-2739	7	3	history	history	NOUN
cana-2739	7	4	:	:	PUNCT
cana-2739	7	5	received	receive	VERB
cana-2739	7	6	:	:	PUNCT
cana-2739	7	7	13	13	NUM
cana-2739	7	8	-	-	SYM
cana-2739	7	9	09	09	NUM
cana-2739	7	10	-	-	PUNCT
cana-2739	7	11	2024	2024	NUM
cana-2739	7	12	revised	revise	VERB
cana-2739	7	13	:	:	PUNCT
cana-2739	7	14	17	17	NUM
cana-2739	7	15	-	-	SYM
cana-2739	7	16	11	11	NUM
cana-2739	7	17	-	-	PUNCT
cana-2739	7	18	2024	2024	NUM
cana-2739	7	19	accepted	accept	VERB
cana-2739	7	20	:	:	PUNCT
cana-2739	7	21	27	27	NUM
cana-2739	7	22	-	-	SYM
cana-2739	7	23	11	11	NUM
cana-2739	7	24	-	-	PUNCT
cana-2739	7	25	2024	2024	NUM
cana-2739	7	26	abstract	abstract	NOUN
cana-2739	7	27	:	:	PUNCT
cana-2739	7	28	a	a	DET
cana-2739	7	29	computer	computer	NOUN
cana-2739	7	30	aided	aid	VERB
cana-2739	7	31	diagnosis	diagnosis	NOUN
cana-2739	7	32	system	system	NOUN
cana-2739	7	33	for	for	ADP
cana-2739	7	34	classification	classification	NOUN
cana-2739	7	35	of	of	ADP
cana-2739	7	36	ovarian	ovarian	ADJ
cana-2739	7	37	cyst	cyst	NOUN
cana-2739	7	38	is	be	AUX
cana-2739	7	39	a	a	DET
cana-2739	7	40	crucial	crucial	ADJ
cana-2739	7	41	step	step	NOUN
cana-2739	7	42	towards	towards	ADP
cana-2739	7	43	treatment	treatment	NOUN
cana-2739	7	44	of	of	ADP
cana-2739	7	45	women	woman	NOUN
cana-2739	7	46	suffering	suffer	VERB
cana-2739	7	47	from	from	ADP
cana-2739	7	48	cysts	cyst	NOUN
cana-2739	7	49	in	in	ADP
cana-2739	7	50	ovaries	ovary	NOUN
cana-2739	7	51	.	.	PUNCT
cana-2739	8	1	ultrasound	ultrasound	NOUN
cana-2739	8	2	or	or	CCONJ
cana-2739	8	3	sonography	sonography	NOUN
cana-2739	8	4	is	be	AUX
cana-2739	8	5	an	an	DET
cana-2739	8	6	efficacious	efficacious	ADJ
cana-2739	8	7	technique	technique	NOUN
cana-2739	8	8	that	that	PRON
cana-2739	8	9	assists	assist	VERB
cana-2739	8	10	healthcare	healthcare	NOUN
cana-2739	8	11	providers	provider	NOUN
cana-2739	8	12	in	in	ADP
cana-2739	8	13	diagnosis	diagnosis	NOUN
cana-2739	8	14	and	and	CCONJ
cana-2739	8	15	therapeutics	therapeutic	NOUN
cana-2739	8	16	for	for	ADP
cana-2739	8	17	cysts	cyst	NOUN
cana-2739	8	18	in	in	ADP
cana-2739	8	19	ovaries	ovary	NOUN
cana-2739	8	20	.	.	PUNCT
cana-2739	9	1	in	in	ADP
cana-2739	9	2	this	this	DET
cana-2739	9	3	research	research	NOUN
cana-2739	9	4	,	,	PUNCT
cana-2739	9	5	an	an	DET
cana-2739	9	6	automated	automate	VERB
cana-2739	9	7	ovarian	ovarian	ADJ
cana-2739	9	8	cyst	cyst	NOUN
cana-2739	9	9	diagnosis	diagnosis	NOUN
cana-2739	9	10	system	system	NOUN
cana-2739	9	11	based	base	VERB
cana-2739	9	12	on	on	ADP
cana-2739	9	13	textural	textural	ADJ
cana-2739	9	14	features	feature	NOUN
cana-2739	9	15	from	from	ADP
cana-2739	9	16	ultrasound	ultrasound	ADJ
cana-2739	9	17	images	image	NOUN
cana-2739	9	18	is	be	AUX
cana-2739	9	19	designed	design	VERB
cana-2739	9	20	.	.	PUNCT
cana-2739	10	1	local	local	ADJ
cana-2739	10	2	binary	binary	ADJ
cana-2739	10	3	pattern	pattern	NOUN
cana-2739	10	4	and	and	CCONJ
cana-2739	10	5	fractal	fractal	ADJ
cana-2739	10	6	dimension	dimension	NOUN
cana-2739	10	7	textual	textual	ADJ
cana-2739	10	8	features	feature	NOUN
cana-2739	10	9	are	be	AUX
cana-2739	10	10	combined	combine	VERB
cana-2739	10	11	to	to	PART
cana-2739	10	12	deduce	deduce	VERB
cana-2739	10	13	features	feature	NOUN
cana-2739	10	14	related	relate	VERB
cana-2739	10	15	to	to	ADP
cana-2739	10	16	texture	texture	NOUN
cana-2739	10	17	from	from	ADP
cana-2739	10	18	ultrasound	ultrasound	NOUN
cana-2739	10	19	images	image	NOUN
cana-2739	10	20	and	and	CCONJ
cana-2739	10	21	these	these	DET
cana-2739	10	22	textural	textural	ADJ
cana-2739	10	23	features	feature	NOUN
cana-2739	10	24	are	be	AUX
cana-2739	10	25	given	give	VERB
cana-2739	10	26	as	as	ADP
cana-2739	10	27	input	input	NOUN
cana-2739	10	28	to	to	ADP
cana-2739	10	29	machine	machine	NOUN
cana-2739	10	30	learning	learn	VERB
cana-2739	10	31	classifiers	classifier	NOUN
cana-2739	10	32	like	like	VERB
cana-2739	10	33	,	,	PUNCT
cana-2739	10	34	decision	decision	NOUN
cana-2739	10	35	tree	tree	NOUN
cana-2739	10	36	,	,	PUNCT
cana-2739	10	37	logistic	logistic	ADJ
cana-2739	10	38	regression	regression	NOUN
cana-2739	10	39	and	and	CCONJ
cana-2739	10	40	support	support	VERB
cana-2739	10	41	vector	vector	NOUN
cana-2739	10	42	machine	machine	NOUN
cana-2739	10	43	.	.	PUNCT
cana-2739	11	1	this	this	DET
cana-2739	11	2	novel	novel	ADJ
cana-2739	11	3	method	method	NOUN
cana-2739	11	4	assists	assist	VERB
cana-2739	11	5	healthcare	healthcare	NOUN
cana-2739	11	6	providers	provider	NOUN
cana-2739	11	7	to	to	PART
cana-2739	11	8	distinguish	distinguish	VERB
cana-2739	11	9	cystic	cystic	ADJ
cana-2739	11	10	ovaries	ovary	NOUN
cana-2739	11	11	from	from	ADP
cana-2739	11	12	normal	normal	ADJ
cana-2739	11	13	ovaries	ovary	NOUN
cana-2739	11	14	in	in	ADP
cana-2739	11	15	ultrasound	ultrasound	ADJ
cana-2739	11	16	images	image	NOUN
cana-2739	11	17	.	.	PUNCT
cana-2739	12	1	evaluation	evaluation	NOUN
cana-2739	12	2	metrics	metric	NOUN
cana-2739	12	3	namely	namely	ADV
cana-2739	12	4	,	,	PUNCT
cana-2739	12	5	precision	precision	NOUN
cana-2739	12	6	,	,	PUNCT
cana-2739	12	7	accuracy	accuracy	NOUN
cana-2739	12	8	,	,	PUNCT
cana-2739	12	9	recall	recall	NOUN
cana-2739	12	10	,	,	PUNCT
cana-2739	12	11	f1	f1	NOUN
cana-2739	12	12	-	-	PUNCT
cana-2739	12	13	score	score	NOUN
cana-2739	12	14	,	,	PUNCT
cana-2739	12	15	specificity	specificity	NOUN
cana-2739	12	16	and	and	CCONJ
cana-2739	12	17	receiver	receiver	ADJ
cana-2739	12	18	operating	operating	NOUN
cana-2739	12	19	characteristics	characteristic	NOUN
cana-2739	12	20	–	–	PUNCT
cana-2739	12	21	area	area	NOUN
cana-2739	12	22	under	under	ADP
cana-2739	12	23	curve	curve	NOUN
cana-2739	12	24	are	be	AUX
cana-2739	12	25	used	use	VERB
cana-2739	12	26	to	to	PART
cana-2739	12	27	validate	validate	VERB
cana-2739	12	28	the	the	DET
cana-2739	12	29	efficacy	efficacy	NOUN
cana-2739	12	30	of	of	ADP
cana-2739	12	31	the	the	DET
cana-2739	12	32	classifiers	classifier	NOUN
cana-2739	12	33	.	.	PUNCT
cana-2739	13	1	decision	decision	NOUN
cana-2739	13	2	tree	tree	NOUN
cana-2739	13	3	,	,	PUNCT
cana-2739	13	4	logistic	logistic	ADJ
cana-2739	13	5	regression	regression	NOUN
cana-2739	13	6	and	and	CCONJ
cana-2739	13	7	support	support	VERB
cana-2739	13	8	vector	vector	NOUN
cana-2739	13	9	machine	machine	NOUN
cana-2739	13	10	classifiers	classifier	NOUN
cana-2739	13	11	furnished	furnish	VERB
cana-2739	13	12	an	an	DET
cana-2739	13	13	accuracy	accuracy	NOUN
cana-2739	13	14	of	of	ADP
cana-2739	13	15	83.42	83.42	NUM
cana-2739	13	16	%	%	NOUN
cana-2739	13	17	,	,	PUNCT
cana-2739	13	18	89.12	89.12	NUM
cana-2739	13	19	%	%	NOUN
cana-2739	13	20	and	and	CCONJ
cana-2739	13	21	91.19	91.19	NUM
cana-2739	13	22	%	%	NOUN
cana-2739	13	23	,	,	PUNCT
cana-2739	13	24	respectively	respectively	ADV
cana-2739	13	25	on	on	ADP
cana-2739	13	26	a	a	DET
cana-2739	13	27	dataset	dataset	NOUN
cana-2739	13	28	of	of	ADP
cana-2739	13	29	1203	1203	NUM
cana-2739	13	30	ultrasound	ultrasound	NOUN
cana-2739	13	31	images	image	NOUN
cana-2739	13	32	procured	procure	VERB
cana-2739	13	33	from	from	ADP
cana-2739	13	34	a	a	DET
cana-2739	13	35	medical	medical	ADJ
cana-2739	13	36	diagnostic	diagnostic	ADJ
cana-2739	13	37	centre	centre	NOUN
cana-2739	13	38	in	in	ADP
cana-2739	13	39	india	india	PROPN
cana-2739	13	40	.	.	PUNCT
cana-2739	14	1	support	support	NOUN
cana-2739	14	2	vector	vector	NOUN
cana-2739	14	3	machine	machine	NOUN
cana-2739	14	4	has	have	AUX
cana-2739	14	5	proved	prove	VERB
cana-2739	14	6	to	to	PART
cana-2739	14	7	be	be	AUX
cana-2739	14	8	a	a	DET
cana-2739	14	9	better	well	ADJ
cana-2739	14	10	classifier	classifier	NOUN
cana-2739	14	11	in	in	ADP
cana-2739	14	12	relation	relation	NOUN
cana-2739	14	13	to	to	ADP
cana-2739	14	14	accuracy	accuracy	NOUN
cana-2739	14	15	(	(	PUNCT
cana-2739	14	16	91.19	91.19	NUM
cana-2739	14	17	%	%	NOUN
cana-2739	14	18	)	)	PUNCT
cana-2739	14	19	,	,	PUNCT
cana-2739	14	20	precision	precision	NOUN
cana-2739	14	21	(	(	PUNCT
cana-2739	14	22	97.98	97.98	NUM
cana-2739	14	23	%	%	NOUN
cana-2739	14	24	)	)	PUNCT
cana-2739	14	25	,	,	PUNCT
cana-2739	14	26	f1	f1	NOUN
cana-2739	14	27	-	-	PUNCT
cana-2739	14	28	score	score	NOUN
cana-2739	14	29	(	(	PUNCT
cana-2739	14	30	94.49	94.49	NUM
cana-2739	14	31	%	%	NOUN
cana-2739	14	32	)	)	PUNCT
cana-2739	14	33	,	,	PUNCT
cana-2739	14	34	specificity	specificity	NOUN
cana-2739	14	35	(	(	PUNCT
cana-2739	14	36	90.90	90.90	NUM
cana-2739	14	37	%	%	NOUN
cana-2739	14	38	)	)	PUNCT
cana-2739	14	39	and	and	CCONJ
cana-2739	14	40	area	area	NOUN
cana-2739	14	41	under	under	ADP
cana-2739	14	42	curve	curve	NOUN
cana-2739	14	43	(	(	PUNCT
cana-2739	14	44	0.93	0.93	NUM
cana-2739	14	45	)	)	PUNCT
cana-2739	14	46	compared	compare	VERB
cana-2739	14	47	to	to	ADP
cana-2739	14	48	decision	decision	NOUN
cana-2739	14	49	tree	tree	NOUN
cana-2739	14	50	and	and	CCONJ
cana-2739	14	51	logistic	logistic	ADJ
cana-2739	14	52	regression	regression	NOUN
cana-2739	14	53	.	.	PUNCT
cana-2739	15	1	this	this	DET
cana-2739	15	2	computer	computer	NOUN
cana-2739	15	3	aided	aid	VERB
cana-2739	15	4	diagnosis	diagnosis	NOUN
cana-2739	15	5	system	system	NOUN
cana-2739	15	6	helps	help	VERB
cana-2739	15	7	healthcare	healthcare	NOUN
cana-2739	15	8	providers	provider	NOUN
cana-2739	15	9	in	in	ADP
cana-2739	15	10	making	make	VERB
cana-2739	15	11	efficient	efficient	ADJ
cana-2739	15	12	and	and	CCONJ
cana-2739	15	13	accurate	accurate	ADJ
cana-2739	15	14	decisions	decision	NOUN
cana-2739	15	15	towards	towards	ADP
cana-2739	15	16	treatment	treatment	NOUN
cana-2739	15	17	plan	plan	NOUN
cana-2739	15	18	of	of	ADP
cana-2739	15	19	patients	patient	NOUN
cana-2739	15	20	.	.	PUNCT
cana-2739	16	1	keywords	keyword	NOUN
cana-2739	16	2	:	:	PUNCT
cana-2739	16	3	ovarian	ovarian	ADJ
cana-2739	16	4	cyst	cyst	NOUN
cana-2739	16	5	,	,	PUNCT
cana-2739	16	6	machine	machine	NOUN
cana-2739	16	7	learning	learning	NOUN
cana-2739	16	8	,	,	PUNCT
cana-2739	16	9	decision	decision	NOUN
cana-2739	16	10	tree	tree	NOUN
cana-2739	16	11	,	,	PUNCT
cana-2739	16	12	logistic	logistic	ADJ
cana-2739	16	13	regression	regression	NOUN
cana-2739	16	14	,	,	PUNCT
cana-2739	16	15	support	support	NOUN
cana-2739	16	16	vector	vector	NOUN
cana-2739	16	17	machine	machine	NOUN
cana-2739	16	18	.	.	PUNCT
cana-2739	17	1	1	1	X
cana-2739	17	2	.	.	X
cana-2739	17	3	introduction	introduction	NOUN
cana-2739	17	4	ovarian	ovarian	ADJ
cana-2739	17	5	cysts	cyst	NOUN
cana-2739	17	6	are	be	AUX
cana-2739	17	7	minute	minute	ADJ
cana-2739	17	8	saccules	saccule	NOUN
cana-2739	17	9	found	find	VERB
cana-2739	17	10	inside	inside	ADP
cana-2739	17	11	or	or	CCONJ
cana-2739	17	12	around	around	ADP
cana-2739	17	13	the	the	DET
cana-2739	17	14	two	two	NUM
cana-2739	17	15	ovaries	ovary	NOUN
cana-2739	17	16	of	of	ADP
cana-2739	17	17	female	female	ADJ
cana-2739	17	18	reproductive	reproductive	ADJ
cana-2739	17	19	system	system	NOUN
cana-2739	17	20	.	.	PUNCT
cana-2739	18	1	they	they	PRON
cana-2739	18	2	can	can	AUX
cana-2739	18	3	befall	befall	VERB
cana-2739	18	4	at	at	ADP
cana-2739	18	5	any	any	DET
cana-2739	18	6	age	age	NOUN
cana-2739	18	7	,	,	PUNCT
cana-2739	18	8	but	but	CCONJ
cana-2739	18	9	are	be	AUX
cana-2739	18	10	most	most	ADV
cana-2739	18	11	commonly	commonly	ADV
cana-2739	18	12	found	find	VERB
cana-2739	18	13	in	in	ADP
cana-2739	18	14	women	woman	NOUN
cana-2739	18	15	of	of	ADP
cana-2739	18	16	reproductive	reproductive	ADJ
cana-2739	18	17	age	age	NOUN
cana-2739	18	18	.	.	PUNCT
cana-2739	19	1	these	these	DET
cana-2739	19	2	cysts	cyst	NOUN
cana-2739	19	3	differ	differ	VERB
cana-2739	19	4	in	in	ADP
cana-2739	19	5	size	size	NOUN
cana-2739	19	6	from	from	ADP
cana-2739	19	7	few	few	ADJ
cana-2739	19	8	millimetres	millimetre	NOUN
cana-2739	19	9	to	to	ADP
cana-2739	19	10	centimetres	centimetre	NOUN
cana-2739	19	11	(	(	PUNCT
cana-2739	19	12	greater	great	ADJ
cana-2739	19	13	than	than	ADP
cana-2739	19	14	10	10	NUM
cana-2739	19	15	centimetres	centimetre	NOUN
cana-2739	19	16	)	)	PUNCT
cana-2739	19	17	(	(	PUNCT
cana-2739	19	18	parekh	parekh	PROPN
cana-2739	19	19	&	&	CCONJ
cana-2739	19	20	shah	shah	PROPN
cana-2739	19	21	,	,	PUNCT
cana-2739	19	22	2017	2017	NUM
cana-2739	19	23	)	)	PUNCT
cana-2739	19	24	.	.	PUNCT
cana-2739	20	1	most	most	ADJ
cana-2739	20	2	of	of	ADP
cana-2739	20	3	the	the	DET
cana-2739	20	4	cysts	cyst	NOUN
cana-2739	20	5	in	in	ADP
cana-2739	20	6	ovaries	ovary	NOUN
cana-2739	20	7	are	be	AUX
cana-2739	20	8	non	non	ADJ
cana-2739	20	9	-	-	ADJ
cana-2739	20	10	cancerous	cancerous	ADJ
cana-2739	20	11	,	,	PUNCT
cana-2739	20	12	but	but	CCONJ
cana-2739	20	13	few	few	ADJ
cana-2739	20	14	of	of	ADP
cana-2739	20	15	them	they	PRON
cana-2739	20	16	may	may	AUX
cana-2739	20	17	result	result	VERB
cana-2739	20	18	in	in	ADP
cana-2739	20	19	soreness	soreness	NOUN
cana-2739	20	20	,	,	PUNCT
cana-2739	20	21	pelvic	pelvic	ADJ
cana-2739	20	22	ache	ache	NOUN
cana-2739	20	23	,	,	PUNCT
cana-2739	20	24	bloating	bloat	VERB
cana-2739	20	25	or	or	CCONJ
cana-2739	20	26	other	other	ADJ
cana-2739	20	27	symptoms	symptom	NOUN
cana-2739	20	28	that	that	PRON
cana-2739	20	29	demand	demand	VERB
cana-2739	20	30	medical	medical	ADJ
cana-2739	20	31	treatment	treatment	NOUN
cana-2739	20	32	.	.	PUNCT
cana-2739	21	1	healthcare	healthcare	NOUN
cana-2739	21	2	providers	provider	NOUN
cana-2739	21	3	diagnose	diagnose	VERB
cana-2739	21	4	cysts	cyst	NOUN
cana-2739	21	5	in	in	ADP
cana-2739	21	6	ovaries	ovary	NOUN
cana-2739	21	7	through	through	ADP
cana-2739	21	8	a	a	DET
cana-2739	21	9	series	series	NOUN
cana-2739	21	10	of	of	ADP
cana-2739	21	11	procedures	procedure	NOUN
cana-2739	21	12	like	like	ADP
cana-2739	21	13	physical	physical	ADJ
cana-2739	21	14	examination	examination	NOUN
cana-2739	21	15	,	,	PUNCT
cana-2739	21	16	imaging	imaging	NOUN
cana-2739	21	17	tests	test	NOUN
cana-2739	21	18	like	like	ADP
cana-2739	21	19	sonography	sonography	NOUN
cana-2739	21	20	or	or	CCONJ
cana-2739	21	21	ultrasound	ultrasound	NOUN
cana-2739	21	22	,	,	PUNCT
cana-2739	21	23	mri	mri	NOUN
cana-2739	21	24	and	and	CCONJ
cana-2739	21	25	pathology	pathology	NOUN
cana-2739	21	26	tests	test	VERB
cana-2739	21	27	to	to	PART
cana-2739	21	28	check	check	VERB
cana-2739	21	29	various	various	ADJ
cana-2739	21	30	hormone	hormone	NOUN
cana-2739	21	31	levels	level	NOUN
cana-2739	21	32	.	.	PUNCT
cana-2739	22	1	ultrasound	ultrasound	NOUN
cana-2739	22	2	is	be	AUX
cana-2739	22	3	the	the	DET
cana-2739	22	4	most	most	ADV
cana-2739	22	5	valuable	valuable	ADJ
cana-2739	22	6	and	and	CCONJ
cana-2739	22	7	commonly	commonly	ADV
cana-2739	22	8	used	use	VERB
cana-2739	22	9	imaging	imaging	NOUN
cana-2739	22	10	test	test	NOUN
cana-2739	22	11	for	for	ADP
cana-2739	22	12	detection	detection	NOUN
cana-2739	22	13	of	of	ADP
cana-2739	22	14	ovarian	ovarian	ADJ
cana-2739	22	15	cysts	cyst	NOUN
cana-2739	22	16	.	.	PUNCT
cana-2739	23	1	it	it	PRON
cana-2739	23	2	works	work	VERB
cana-2739	23	3	by	by	ADP
cana-2739	23	4	producing	produce	VERB
cana-2739	23	5	images	image	NOUN
cana-2739	23	6	of	of	ADP
cana-2739	23	7	the	the	DET
cana-2739	23	8	organs	organ	NOUN
cana-2739	23	9	,	,	PUNCT
cana-2739	23	10	tissues	tissue	NOUN
cana-2739	23	11	and	and	CCONJ
cana-2739	23	12	blood	blood	NOUN
cana-2739	23	13	flow	flow	NOUN
cana-2739	23	14	of	of	ADP
cana-2739	23	15	a	a	DET
cana-2739	23	16	human	human	ADJ
cana-2739	23	17	body	body	NOUN
cana-2739	23	18	with	with	ADP
cana-2739	23	19	the	the	DET
cana-2739	23	20	help	help	NOUN
cana-2739	23	21	of	of	ADP
cana-2739	23	22	mailto:gpaditi77@gmail.com	mailto:gpaditi77@gmail.com	PROPN
cana-2739	23	23	mailto:sudeep149@gmail.com	mailto:sudeep149@gmail.com	PROPN
cana-2739	23	24	mailto:hoor.iitd@gmail.com	mailto:hoor.iitd@gmail.com	X
cana-2739	24	1	communications	communication	NOUN
cana-2739	24	2	on	on	ADP
cana-2739	24	3	applied	apply	VERB
cana-2739	24	4	nonlinear	nonlinear	ADJ
cana-2739	24	5	analysis	analysis	NOUN
cana-2739	24	6	issn	issn	NOUN
cana-2739	24	7	:	:	PUNCT
cana-2739	24	8	1074	1074	NUM
cana-2739	24	9	-	-	PUNCT
cana-2739	24	10	133x	133x	NUM
cana-2739	24	11	vol	vol	NOUN
cana-2739	24	12	32	32	NUM
cana-2739	24	13	no	no	NOUN
cana-2739	24	14	.	.	PUNCT
cana-2739	25	1	4s	4s	NUM
cana-2739	25	2	(	(	PUNCT
cana-2739	25	3	2025	2025	NUM
cana-2739	25	4	)	)	PUNCT
cana-2739	25	5	60	60	NUM
cana-2739	25	6	https://internationalpubls.com	https://internationalpubls.com	NUM
cana-2739	25	7	high	high	ADJ
cana-2739	25	8	frequency	frequency	NOUN
cana-2739	25	9	sound	sound	NOUN
cana-2739	25	10	waves	wave	NOUN
cana-2739	25	11	(	(	PUNCT
cana-2739	25	12	gupta	gupta	PROPN
cana-2739	25	13	&	&	CCONJ
cana-2739	25	14	fatima	fatima	PROPN
cana-2739	25	15	,	,	PUNCT
cana-2739	25	16	2023	2023	NUM
cana-2739	25	17	)	)	PUNCT
cana-2739	25	18	.	.	PUNCT
cana-2739	26	1	transvaginal	transvaginal	NOUN
cana-2739	26	2	or	or	CCONJ
cana-2739	26	3	abdominal	abdominal	ADJ
cana-2739	26	4	ultrasound	ultrasound	NOUN
cana-2739	26	5	scans	scan	NOUN
cana-2739	26	6	give	give	VERB
cana-2739	26	7	lucid	lucid	ADJ
cana-2739	26	8	view	view	NOUN
cana-2739	26	9	of	of	ADP
cana-2739	26	10	ovarian	ovarian	ADJ
cana-2739	26	11	cysts	cyst	NOUN
cana-2739	26	12	with	with	ADP
cana-2739	26	13	the	the	DET
cana-2739	26	14	details	detail	NOUN
cana-2739	26	15	like	like	VERB
cana-2739	26	16	,	,	PUNCT
cana-2739	26	17	shape	shape	NOUN
cana-2739	26	18	,	,	PUNCT
cana-2739	26	19	dimensions	dimension	NOUN
cana-2739	26	20	,	,	PUNCT
cana-2739	26	21	texture	texture	NOUN
cana-2739	26	22	,	,	PUNCT
cana-2739	26	23	location	location	NOUN
cana-2739	26	24	,	,	PUNCT
cana-2739	26	25	etc	etc	X
cana-2739	26	26	.	.	X
cana-2739	26	27	and	and	CCONJ
cana-2739	26	28	are	be	AUX
cana-2739	26	29	employed	employ	VERB
cana-2739	26	30	to	to	PART
cana-2739	26	31	diagnose	diagnose	VERB
cana-2739	26	32	ovarian	ovarian	ADJ
cana-2739	26	33	cysts	cyst	NOUN
cana-2739	26	34	.	.	PUNCT
cana-2739	27	1	but	but	CCONJ
cana-2739	27	2	still	still	ADV
cana-2739	27	3	,	,	PUNCT
cana-2739	27	4	identification	identification	NOUN
cana-2739	27	5	of	of	ADP
cana-2739	27	6	ovarian	ovarian	ADJ
cana-2739	27	7	cysts	cyst	NOUN
cana-2739	27	8	is	be	AUX
cana-2739	27	9	a	a	DET
cana-2739	27	10	complicated	complicated	ADJ
cana-2739	27	11	task	task	NOUN
cana-2739	27	12	as	as	SCONJ
cana-2739	27	13	they	they	PRON
cana-2739	27	14	may	may	AUX
cana-2739	27	15	appear	appear	VERB
cana-2739	27	16	similar	similar	ADJ
cana-2739	27	17	to	to	ADP
cana-2739	27	18	the	the	DET
cana-2739	27	19	non	non	ADJ
cana-2739	27	20	-	-	ADJ
cana-2739	27	21	cystic	cystic	ADJ
cana-2739	27	22	or	or	CCONJ
cana-2739	27	23	follicular	follicular	ADJ
cana-2739	27	24	regions	region	NOUN
cana-2739	27	25	in	in	ADP
cana-2739	27	26	the	the	DET
cana-2739	27	27	ovary	ovary	NOUN
cana-2739	27	28	.	.	PUNCT
cana-2739	28	1	inter	inter	ADJ
cana-2739	28	2	-	-	ADJ
cana-2739	28	3	follicular	follicular	ADJ
cana-2739	28	4	regions	region	NOUN
cana-2739	28	5	also	also	ADV
cana-2739	28	6	look	look	VERB
cana-2739	28	7	alike	alike	ADV
cana-2739	28	8	to	to	ADP
cana-2739	28	9	the	the	DET
cana-2739	28	10	background	background	NOUN
cana-2739	28	11	in	in	ADP
cana-2739	28	12	the	the	DET
cana-2739	28	13	ultrasound	ultrasound	NOUN
cana-2739	28	14	images	image	NOUN
cana-2739	28	15	.	.	PUNCT
cana-2739	29	1	henceforth	henceforth	ADV
cana-2739	29	2	,	,	PUNCT
cana-2739	29	3	the	the	DET
cana-2739	29	4	interpretation	interpretation	NOUN
cana-2739	29	5	of	of	ADP
cana-2739	29	6	ultrasound	ultrasound	PROPN
cana-2739	29	7	scans	scan	NOUN
cana-2739	29	8	entirely	entirely	ADV
cana-2739	29	9	depends	depend	VERB
cana-2739	29	10	on	on	ADP
cana-2739	29	11	the	the	DET
cana-2739	29	12	expertise	expertise	NOUN
cana-2739	29	13	of	of	ADP
cana-2739	29	14	medical	medical	ADJ
cana-2739	29	15	specialist	specialist	NOUN
cana-2739	29	16	performing	perform	VERB
cana-2739	29	17	the	the	DET
cana-2739	29	18	scans	scan	NOUN
cana-2739	29	19	.	.	PUNCT
cana-2739	30	1	an	an	DET
cana-2739	30	2	incorrect	incorrect	ADJ
cana-2739	30	3	diagnosis	diagnosis	NOUN
cana-2739	30	4	may	may	AUX
cana-2739	30	5	result	result	VERB
cana-2739	30	6	in	in	ADP
cana-2739	30	7	an	an	DET
cana-2739	30	8	erroneous	erroneous	ADJ
cana-2739	30	9	treatment	treatment	NOUN
cana-2739	30	10	plan	plan	NOUN
cana-2739	30	11	which	which	PRON
cana-2739	30	12	may	may	AUX
cana-2739	30	13	have	have	VERB
cana-2739	30	14	adverse	adverse	ADJ
cana-2739	30	15	effect	effect	NOUN
cana-2739	30	16	on	on	ADP
cana-2739	30	17	patient	patient	NOUN
cana-2739	30	18	’s	’s	PART
cana-2739	30	19	health	health	NOUN
cana-2739	30	20	.	.	PUNCT
cana-2739	31	1	to	to	PART
cana-2739	31	2	avoid	avoid	VERB
cana-2739	31	3	any	any	DET
cana-2739	31	4	such	such	ADJ
cana-2739	31	5	circumstance	circumstance	NOUN
cana-2739	31	6	,	,	PUNCT
cana-2739	31	7	computer	computer	NOUN
cana-2739	31	8	aided	aid	VERB
cana-2739	31	9	diagnosis	diagnosis	NOUN
cana-2739	31	10	systems	system	NOUN
cana-2739	31	11	can	can	AUX
cana-2739	31	12	act	act	VERB
cana-2739	31	13	as	as	ADP
cana-2739	31	14	a	a	DET
cana-2739	31	15	supportive	supportive	ADJ
cana-2739	31	16	decision	decision	NOUN
cana-2739	31	17	tool	tool	NOUN
cana-2739	31	18	to	to	ADP
cana-2739	31	19	healthcare	healthcare	NOUN
cana-2739	31	20	providers	provider	NOUN
cana-2739	31	21	in	in	ADP
cana-2739	31	22	the	the	DET
cana-2739	31	23	recognition	recognition	NOUN
cana-2739	31	24	of	of	ADP
cana-2739	31	25	ovarian	ovarian	ADJ
cana-2739	31	26	cysts	cyst	NOUN
cana-2739	31	27	with	with	ADP
cana-2739	31	28	better	well	ADJ
cana-2739	31	29	accuracy	accuracy	NOUN
cana-2739	31	30	and	and	CCONJ
cana-2739	31	31	efficient	efficient	ADJ
cana-2739	31	32	results	result	NOUN
cana-2739	31	33	(	(	PUNCT
cana-2739	31	34	alzubi	alzubi	NOUN
cana-2739	31	35	et	et	PROPN
cana-2739	31	36	al	al	PROPN
cana-2739	31	37	.	.	PROPN
cana-2739	31	38	,	,	PUNCT
cana-2739	31	39	2023	2023	NUM
cana-2739	31	40	)	)	PUNCT
cana-2739	31	41	.	.	PUNCT
cana-2739	32	1	the	the	DET
cana-2739	32	2	research	research	NOUN
cana-2739	32	3	proposes	propose	VERB
cana-2739	32	4	a	a	DET
cana-2739	32	5	novel	novel	ADJ
cana-2739	32	6	method	method	NOUN
cana-2739	32	7	to	to	PART
cana-2739	32	8	segregate	segregate	VERB
cana-2739	32	9	ovarian	ovarian	ADJ
cana-2739	32	10	ultrasound	ultrasound	NOUN
cana-2739	32	11	images	image	NOUN
cana-2739	32	12	into	into	ADP
cana-2739	32	13	two	two	NUM
cana-2739	32	14	classes	class	NOUN
cana-2739	32	15	–	–	PUNCT
cana-2739	32	16	cystic	cystic	ADJ
cana-2739	32	17	and	and	CCONJ
cana-2739	32	18	non	non	ADJ
cana-2739	32	19	-	-	ADJ
cana-2739	32	20	cystic	cystic	ADJ
cana-2739	32	21	or	or	CCONJ
cana-2739	32	22	normal	normal	ADJ
cana-2739	32	23	images	image	NOUN
cana-2739	32	24	.	.	PUNCT
cana-2739	33	1	it	it	PRON
cana-2739	33	2	combines	combine	VERB
cana-2739	33	3	two	two	NUM
cana-2739	33	4	different	different	ADJ
cana-2739	33	5	texture	texture	NOUN
cana-2739	33	6	features	feature	NOUN
cana-2739	33	7	of	of	ADP
cana-2739	33	8	ultrasound	ultrasound	ADJ
cana-2739	33	9	images	image	NOUN
cana-2739	33	10	as	as	ADP
cana-2739	33	11	the	the	DET
cana-2739	33	12	basis	basis	NOUN
cana-2739	33	13	for	for	ADP
cana-2739	33	14	the	the	DET
cana-2739	33	15	classification	classification	NOUN
cana-2739	33	16	.	.	PUNCT
cana-2739	34	1	once	once	SCONJ
cana-2739	34	2	the	the	DET
cana-2739	34	3	texture	texture	NOUN
cana-2739	34	4	features	feature	NOUN
cana-2739	34	5	are	be	AUX
cana-2739	34	6	extracted	extract	VERB
cana-2739	34	7	,	,	PUNCT
cana-2739	34	8	machine	machine	NOUN
cana-2739	34	9	learning	learning	NOUN
cana-2739	34	10	algorithms	algorithm	NOUN
cana-2739	34	11	like	like	ADP
cana-2739	34	12	decision	decision	NOUN
cana-2739	34	13	tree	tree	NOUN
cana-2739	34	14	(	(	PUNCT
cana-2739	34	15	edwards	edwards	PROPN
cana-2739	34	16	.	.	PROPN
cana-2739	34	17	,	,	PUNCT
cana-2739	34	18	1987	1987	NUM
cana-2739	34	19	)	)	PUNCT
cana-2739	34	20	,	,	PUNCT
cana-2739	34	21	logistic	logistic	ADJ
cana-2739	34	22	regression	regression	NOUN
cana-2739	34	23	(	(	PUNCT
cana-2739	34	24	cramer	cramer	PROPN
cana-2739	34	25	,	,	PUNCT
cana-2739	34	26	2003	2003	NUM
cana-2739	34	27	)	)	PUNCT
cana-2739	34	28	and	and	CCONJ
cana-2739	34	29	support	support	VERB
cana-2739	34	30	vector	vector	NOUN
cana-2739	34	31	machine	machine	NOUN
cana-2739	34	32	(	(	PUNCT
cana-2739	34	33	svm	svm	PROPN
cana-2739	34	34	)	)	PUNCT
cana-2739	34	35	(	(	PUNCT
cana-2739	34	36	c.	c.	PROPN
cana-2739	34	37	c.	c.	PROPN
cana-2739	34	38	chang	chang	PROPN
cana-2739	34	39	&	&	CCONJ
cana-2739	34	40	lin	lin	PROPN
cana-2739	34	41	,	,	PUNCT
cana-2739	34	42	2011	2011	NUM
cana-2739	34	43	)	)	PUNCT
cana-2739	34	44	are	be	AUX
cana-2739	34	45	applied	apply	VERB
cana-2739	34	46	for	for	ADP
cana-2739	34	47	classification	classification	NOUN
cana-2739	34	48	.	.	PUNCT
cana-2739	35	1	our	our	PRON
cana-2739	35	2	contributions	contribution	NOUN
cana-2739	35	3	in	in	ADP
cana-2739	35	4	this	this	DET
cana-2739	35	5	research	research	NOUN
cana-2739	35	6	are	be	AUX
cana-2739	35	7	–	–	PUNCT
cana-2739	35	8	•	•	ADV
cana-2739	35	9	after	after	ADP
cana-2739	35	10	performing	perform	VERB
cana-2739	35	11	a	a	DET
cana-2739	35	12	comprehensive	comprehensive	ADJ
cana-2739	35	13	analysis	analysis	NOUN
cana-2739	35	14	of	of	ADP
cana-2739	35	15	research	research	NOUN
cana-2739	35	16	articles	article	NOUN
cana-2739	35	17	related	relate	VERB
cana-2739	35	18	to	to	ADP
cana-2739	35	19	ovarian	ovarian	ADJ
cana-2739	35	20	cyst	cyst	NOUN
cana-2739	35	21	classification	classification	NOUN
cana-2739	35	22	,	,	PUNCT
cana-2739	35	23	it	it	PRON
cana-2739	35	24	is	be	AUX
cana-2739	35	25	found	find	VERB
cana-2739	35	26	that	that	SCONJ
cana-2739	35	27	most	most	ADJ
cana-2739	35	28	of	of	ADP
cana-2739	35	29	the	the	DET
cana-2739	35	30	researches	research	NOUN
cana-2739	35	31	worked	work	VERB
cana-2739	35	32	on	on	ADP
cana-2739	35	33	a	a	DET
cana-2739	35	34	smaller	small	ADJ
cana-2739	35	35	number	number	NOUN
cana-2739	35	36	of	of	ADP
cana-2739	35	37	ultrasound	ultrasound	NOUN
cana-2739	35	38	images	image	NOUN
cana-2739	35	39	taken	take	VERB
cana-2739	35	40	mostly	mostly	ADV
cana-2739	35	41	from	from	ADP
cana-2739	35	42	various	various	ADJ
cana-2739	35	43	internet	internet	NOUN
cana-2739	35	44	resources	resource	NOUN
cana-2739	35	45	.	.	PUNCT
cana-2739	36	1	this	this	DET
cana-2739	36	2	research	research	NOUN
cana-2739	36	3	caters	cater	VERB
cana-2739	36	4	an	an	DET
cana-2739	36	5	ultrasound	ultrasound	NOUN
cana-2739	36	6	dataset	dataset	NOUN
cana-2739	36	7	from	from	ADP
cana-2739	36	8	a	a	DET
cana-2739	36	9	medical	medical	ADJ
cana-2739	36	10	diagnostic	diagnostic	ADJ
cana-2739	36	11	centre	centre	NOUN
cana-2739	36	12	with	with	ADP
cana-2739	36	13	sufficient	sufficient	ADJ
cana-2739	36	14	number	number	NOUN
cana-2739	36	15	of	of	ADP
cana-2739	36	16	images	image	NOUN
cana-2739	36	17	.	.	PUNCT
cana-2739	37	1	•	•	NOUN
cana-2739	37	2	the	the	DET
cana-2739	37	3	research	research	NOUN
cana-2739	37	4	is	be	AUX
cana-2739	37	5	based	base	VERB
cana-2739	37	6	on	on	ADP
cana-2739	37	7	the	the	DET
cana-2739	37	8	textural	textural	ADJ
cana-2739	37	9	features	feature	NOUN
cana-2739	37	10	of	of	ADP
cana-2739	37	11	ultrasound	ultrasound	NOUN
cana-2739	37	12	images	image	NOUN
cana-2739	37	13	which	which	PRON
cana-2739	37	14	is	be	AUX
cana-2739	37	15	an	an	DET
cana-2739	37	16	essential	essential	ADJ
cana-2739	37	17	parameter	parameter	NOUN
cana-2739	37	18	to	to	PART
cana-2739	37	19	differentiate	differentiate	VERB
cana-2739	37	20	cystic	cystic	ADJ
cana-2739	37	21	regions	region	NOUN
cana-2739	37	22	from	from	ADP
cana-2739	37	23	ultrasound	ultrasound	NOUN
cana-2739	37	24	images	image	NOUN
cana-2739	37	25	.	.	PUNCT
cana-2739	38	1	•	•	NOUN
cana-2739	38	2	the	the	DET
cana-2739	38	3	research	research	NOUN
cana-2739	38	4	proposes	propose	VERB
cana-2739	38	5	a	a	DET
cana-2739	38	6	computer	computer	NOUN
cana-2739	38	7	aided	aid	VERB
cana-2739	38	8	diagnosis	diagnosis	NOUN
cana-2739	38	9	system	system	NOUN
cana-2739	38	10	of	of	ADP
cana-2739	38	11	cystic	cystic	ADJ
cana-2739	38	12	images	image	NOUN
cana-2739	38	13	from	from	ADP
cana-2739	38	14	non	non	ADJ
cana-2739	38	15	-	-	ADJ
cana-2739	38	16	cystic	cystic	ADJ
cana-2739	38	17	images	image	NOUN
cana-2739	38	18	.	.	PUNCT
cana-2739	39	1	2	2	X
cana-2739	39	2	.	.	X
cana-2739	39	3	literature	literature	NOUN
cana-2739	39	4	review	review	VERB
cana-2739	39	5	many	many	ADJ
cana-2739	39	6	earlier	early	ADJ
cana-2739	39	7	research	research	NOUN
cana-2739	39	8	works	work	NOUN
cana-2739	39	9	have	have	AUX
cana-2739	39	10	been	be	AUX
cana-2739	39	11	accomplished	accomplish	VERB
cana-2739	39	12	for	for	ADP
cana-2739	39	13	ovarian	ovarian	ADJ
cana-2739	39	14	cyst	cyst	NOUN
cana-2739	39	15	classification	classification	NOUN
cana-2739	39	16	.	.	PUNCT
cana-2739	40	1	v.	v.	ADP
cana-2739	40	2	kiruthika	kiruthika	PROPN
cana-2739	40	3	et	et	PROPN
cana-2739	40	4	al	al	PROPN
cana-2739	40	5	.	.	PROPN
cana-2739	40	6	developed	develop	VERB
cana-2739	40	7	a	a	DET
cana-2739	40	8	system	system	NOUN
cana-2739	40	9	to	to	PART
cana-2739	40	10	classify	classify	VERB
cana-2739	40	11	normal	normal	ADJ
cana-2739	40	12	,	,	PUNCT
cana-2739	40	13	cystic	cystic	ADJ
cana-2739	40	14	and	and	CCONJ
cana-2739	40	15	polycystic	polycystic	ADJ
cana-2739	40	16	ovaries	ovary	NOUN
cana-2739	40	17	using	use	VERB
cana-2739	40	18	105	105	NUM
cana-2739	40	19	ultrasound	ultrasound	NOUN
cana-2739	40	20	images	image	NOUN
cana-2739	40	21	taken	take	VERB
cana-2739	40	22	from	from	ADP
cana-2739	40	23	chettinad	chettinad	PROPN
cana-2739	40	24	hospital	hospital	PROPN
cana-2739	40	25	and	and	CCONJ
cana-2739	40	26	research	research	PROPN
cana-2739	40	27	institute	institute	PROPN
cana-2739	40	28	,	,	PUNCT
cana-2739	40	29	tamil	tamil	PROPN
cana-2739	40	30	nadu	nadu	PROPN
cana-2739	40	31	,	,	PUNCT
cana-2739	40	32	india	india	PROPN
cana-2739	40	33	(	(	PUNCT
cana-2739	40	34	kiruthika	kiruthika	X
cana-2739	40	35	et	et	PROPN
cana-2739	40	36	al	al	PROPN
cana-2739	40	37	.	.	PROPN
cana-2739	40	38	,	,	PUNCT
cana-2739	40	39	2023	2023	NUM
cana-2739	40	40	)	)	PUNCT
cana-2739	40	41	.	.	PUNCT
cana-2739	41	1	intensity	intensity	NOUN
cana-2739	41	2	,	,	PUNCT
cana-2739	41	3	auto	auto	NOUN
cana-2739	41	4	-	-	PUNCT
cana-2739	41	5	correlation	correlation	NOUN
cana-2739	41	6	,	,	PUNCT
cana-2739	41	7	sum	sum	NOUN
cana-2739	41	8	average	average	NOUN
cana-2739	41	9	,	,	PUNCT
cana-2739	41	10	sum	sum	NOUN
cana-2739	41	11	variance	variance	NOUN
cana-2739	41	12	along	along	ADP
cana-2739	41	13	with	with	ADP
cana-2739	41	14	demographic	demographic	ADJ
cana-2739	41	15	and	and	CCONJ
cana-2739	41	16	diagnostic	diagnostic	ADJ
cana-2739	41	17	data	datum	NOUN
cana-2739	41	18	(	(	PUNCT
cana-2739	41	19	size	size	NOUN
cana-2739	41	20	of	of	ADP
cana-2739	41	21	follicle	follicle	NOUN
cana-2739	41	22	,	,	PUNCT
cana-2739	41	23	number	number	NOUN
cana-2739	41	24	of	of	ADP
cana-2739	41	25	follicles	follicle	NOUN
cana-2739	41	26	,	,	PUNCT
cana-2739	41	27	prolactin	prolactin	NOUN
cana-2739	41	28	etc	etc	X
cana-2739	41	29	.	.	X
cana-2739	41	30	)	)	PUNCT
cana-2739	41	31	are	be	AUX
cana-2739	41	32	used	use	VERB
cana-2739	41	33	as	as	ADP
cana-2739	41	34	features	feature	NOUN
cana-2739	41	35	for	for	ADP
cana-2739	41	36	classification	classification	NOUN
cana-2739	41	37	.	.	PUNCT
cana-2739	42	1	the	the	DET
cana-2739	42	2	different	different	ADJ
cana-2739	42	3	classifiers	classifier	NOUN
cana-2739	42	4	used	use	VERB
cana-2739	42	5	in	in	ADP
cana-2739	42	6	the	the	DET
cana-2739	42	7	system	system	NOUN
cana-2739	42	8	are	be	AUX
cana-2739	42	9	svm	svm	ADJ
cana-2739	42	10	,	,	PUNCT
cana-2739	42	11	artificial	artificial	ADJ
cana-2739	42	12	neural	neural	ADJ
cana-2739	42	13	network	network	NOUN
cana-2739	42	14	and	and	CCONJ
cana-2739	42	15	linear	linear	ADJ
cana-2739	42	16	discriminant	discriminant	ADJ
cana-2739	42	17	analysis	analysis	NOUN
cana-2739	42	18	and	and	CCONJ
cana-2739	42	19	svm	svm	PROPN
cana-2739	42	20	turned	turn	VERB
cana-2739	42	21	out	out	ADP
cana-2739	42	22	to	to	PART
cana-2739	42	23	be	be	AUX
cana-2739	42	24	the	the	DET
cana-2739	42	25	best	good	ADJ
cana-2739	42	26	classifier	classifier	NOUN
cana-2739	42	27	with	with	ADP
cana-2739	42	28	an	an	DET
cana-2739	42	29	accuracy	accuracy	NOUN
cana-2739	42	30	of	of	ADP
cana-2739	42	31	98	98	NUM
cana-2739	42	32	%	%	NOUN
cana-2739	42	33	.	.	PUNCT
cana-2739	43	1	c.	c.	PROPN
cana-2739	43	2	gopalakrishnan	gopalakrishnan	PROPN
cana-2739	43	3	and	and	CCONJ
cana-2739	43	4	m.	m.	NOUN
cana-2739	43	5	iyapparaja	iyapparaja	PROPN
cana-2739	43	6	designed	design	VERB
cana-2739	43	7	pcos	pcos	PROPN
cana-2739	43	8	detection	detection	NOUN
cana-2739	43	9	and	and	CCONJ
cana-2739	43	10	classification	classification	NOUN
cana-2739	43	11	model	model	NOUN
cana-2739	43	12	(	(	PUNCT
cana-2739	43	13	gopalakrishnan	gopalakrishnan	PROPN
cana-2739	43	14	&	&	CCONJ
cana-2739	43	15	iyapparaja	iyapparaja	NOUN
cana-2739	43	16	,	,	PUNCT
cana-2739	43	17	2021	2021	NUM
cana-2739	43	18	)	)	PUNCT
cana-2739	43	19	.	.	PUNCT
cana-2739	44	1	90	90	NUM
cana-2739	44	2	ultrasound	ultrasound	NOUN
cana-2739	44	3	images	image	NOUN
cana-2739	44	4	are	be	AUX
cana-2739	44	5	obtained	obtain	VERB
cana-2739	44	6	from	from	ADP
cana-2739	44	7	nandhini	nandhini	PROPN
cana-2739	44	8	sri	sri	ADJ
cana-2739	44	9	diagnostic	diagnostic	ADJ
cana-2739	44	10	center	center	NOUN
cana-2739	44	11	,	,	PUNCT
cana-2739	44	12	tamil	tamil	PROPN
cana-2739	44	13	nadu	nadu	PROPN
cana-2739	44	14	,	,	PUNCT
cana-2739	44	15	india	india	PROPN
cana-2739	44	16	in	in	ADP
cana-2739	44	17	which	which	PRON
cana-2739	44	18	30	30	NUM
cana-2739	44	19	images	image	NOUN
cana-2739	44	20	are	be	AUX
cana-2739	44	21	of	of	ADP
cana-2739	44	22	normal	normal	ADJ
cana-2739	44	23	ovaries	ovary	NOUN
cana-2739	44	24	,	,	PUNCT
cana-2739	44	25	25	25	NUM
cana-2739	44	26	images	image	NOUN
cana-2739	44	27	are	be	AUX
cana-2739	44	28	cystic	cystic	ADJ
cana-2739	44	29	ovaries	ovary	NOUN
cana-2739	44	30	and	and	CCONJ
cana-2739	44	31	35	35	NUM
cana-2739	44	32	images	image	NOUN
cana-2739	44	33	are	be	AUX
cana-2739	44	34	of	of	ADP
cana-2739	44	35	polycystic	polycystic	ADJ
cana-2739	44	36	ovary	ovary	ADJ
cana-2739	44	37	syndrome	syndrome	NOUN
cana-2739	44	38	.	.	PUNCT
cana-2739	45	1	features	feature	NOUN
cana-2739	45	2	like	like	ADP
cana-2739	45	3	,	,	PUNCT
cana-2739	45	4	area	area	NOUN
cana-2739	45	5	,	,	PUNCT
cana-2739	45	6	perimeter	perimeter	NOUN
cana-2739	45	7	,	,	PUNCT
cana-2739	45	8	eccentricity	eccentricity	NOUN
cana-2739	45	9	,	,	PUNCT
cana-2739	45	10	major	major	ADJ
cana-2739	45	11	axis	axis	NOUN
cana-2739	45	12	and	and	CCONJ
cana-2739	45	13	minor	minor	ADJ
cana-2739	45	14	axis	axis	NOUN
cana-2739	45	15	are	be	AUX
cana-2739	45	16	given	give	VERB
cana-2739	45	17	as	as	ADP
cana-2739	45	18	input	input	NOUN
cana-2739	45	19	to	to	ADP
cana-2739	45	20	svm	svm	VERB
cana-2739	45	21	,	,	PUNCT
cana-2739	45	22	random	random	ADJ
cana-2739	45	23	forest	forest	NOUN
cana-2739	45	24	,	,	PUNCT
cana-2739	45	25	lda	lda	PROPN
cana-2739	45	26	and	and	CCONJ
cana-2739	45	27	naïve	naïve	ADJ
cana-2739	45	28	bayes	bayes	PROPN
cana-2739	45	29	machine	machine	NOUN
cana-2739	45	30	learning	learn	VERB
cana-2739	45	31	algorithms	algorithm	NOUN
cana-2739	45	32	which	which	PRON
cana-2739	45	33	produced	produce	VERB
cana-2739	45	34	accuracies	accuracy	NOUN
cana-2739	45	35	of	of	ADP
cana-2739	45	36	93.82	93.82	NUM
cana-2739	45	37	%	%	NOUN
cana-2739	45	38	,	,	PUNCT
cana-2739	45	39	89.7	89.7	NUM
cana-2739	45	40	%	%	NOUN
cana-2739	45	41	,	,	PUNCT
cana-2739	45	42	91.05	91.05	NUM
cana-2739	45	43	%	%	NOUN
cana-2739	45	44	and	and	CCONJ
cana-2739	45	45	88.26	88.26	NUM
cana-2739	45	46	%	%	NOUN
cana-2739	45	47	,	,	PUNCT
cana-2739	45	48	respectively	respectively	ADV
cana-2739	45	49	.	.	PUNCT
cana-2739	46	1	r.	r.	PROPN
cana-2739	46	2	benazir	benazir	PROPN
cana-2739	46	3	begam	begam	PROPN
cana-2739	46	4	along	along	ADP
cana-2739	46	5	with	with	ADP
cana-2739	46	6	other	other	ADJ
cana-2739	46	7	researchers	researcher	NOUN
cana-2739	46	8	proposed	propose	VERB
cana-2739	46	9	a	a	DET
cana-2739	46	10	classification	classification	NOUN
cana-2739	46	11	system	system	NOUN
cana-2739	46	12	using	use	VERB
cana-2739	46	13	convolutional	convolutional	ADJ
cana-2739	46	14	neural	neural	ADJ
cana-2739	46	15	networks	network	NOUN
cana-2739	46	16	and	and	CCONJ
cana-2739	46	17	differentiated	differentiate	VERB
cana-2739	46	18	three	three	NUM
cana-2739	46	19	different	different	ADJ
cana-2739	46	20	types	type	NOUN
cana-2739	46	21	of	of	ADP
cana-2739	46	22	ovarian	ovarian	ADJ
cana-2739	46	23	cysts	cyst	NOUN
cana-2739	46	24	dermoid	dermoid	ADJ
cana-2739	46	25	,	,	PUNCT
cana-2739	46	26	haemorrhage	haemorrhage	NOUN
cana-2739	46	27	and	and	CCONJ
cana-2739	46	28	communications	communication	NOUN
cana-2739	46	29	on	on	ADP
cana-2739	46	30	applied	apply	VERB
cana-2739	46	31	nonlinear	nonlinear	ADJ
cana-2739	46	32	analysis	analysis	NOUN
cana-2739	46	33	issn	issn	NOUN
cana-2739	46	34	:	:	PUNCT
cana-2739	46	35	1074	1074	NUM
cana-2739	46	36	-	-	PUNCT
cana-2739	46	37	133x	133x	NUM
cana-2739	46	38	vol	vol	NOUN
cana-2739	46	39	32	32	NUM
cana-2739	46	40	no	no	NOUN
cana-2739	46	41	.	.	PUNCT
cana-2739	47	1	4s	4s	NUM
cana-2739	47	2	(	(	PUNCT
cana-2739	47	3	2025	2025	NUM
cana-2739	47	4	)	)	PUNCT
cana-2739	47	5	61	61	NUM
cana-2739	47	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2739	47	7	endometrioma	endometrioma	NOUN
cana-2739	47	8	(	(	PUNCT
cana-2739	47	9	benazir	benazir	PROPN
cana-2739	47	10	begam	begam	PROPN
cana-2739	47	11	et	et	PROPN
cana-2739	47	12	al	al	PROPN
cana-2739	47	13	.	.	PROPN
cana-2739	47	14	,	,	PUNCT
cana-2739	47	15	2022	2022	NUM
cana-2739	47	16	)	)	PUNCT
cana-2739	47	17	.	.	PUNCT
cana-2739	48	1	they	they	PRON
cana-2739	48	2	used	use	VERB
cana-2739	48	3	80	80	NUM
cana-2739	48	4	ultrasound	ultrasound	NOUN
cana-2739	48	5	images	image	NOUN
cana-2739	48	6	,	,	PUNCT
cana-2739	48	7	out	out	ADP
cana-2739	48	8	of	of	ADP
cana-2739	48	9	which	which	PRON
cana-2739	48	10	20	20	NUM
cana-2739	48	11	images	image	NOUN
cana-2739	48	12	were	be	AUX
cana-2739	48	13	of	of	ADP
cana-2739	48	14	normal	normal	ADJ
cana-2739	48	15	ovaries	ovary	NOUN
cana-2739	48	16	and	and	CCONJ
cana-2739	48	17	60	60	NUM
cana-2739	48	18	images	image	NOUN
cana-2739	48	19	were	be	AUX
cana-2739	48	20	of	of	ADP
cana-2739	48	21	ovarian	ovarian	ADJ
cana-2739	48	22	cysts	cyst	NOUN
cana-2739	48	23	.	.	PUNCT
cana-2739	49	1	the	the	DET
cana-2739	49	2	accuracy	accuracy	NOUN
cana-2739	49	3	achieved	achieve	VERB
cana-2739	49	4	using	use	VERB
cana-2739	49	5	the	the	DET
cana-2739	49	6	classification	classification	NOUN
cana-2739	49	7	system	system	NOUN
cana-2739	49	8	is	be	AUX
cana-2739	49	9	94	94	NUM
cana-2739	49	10	%	%	NOUN
cana-2739	49	11	.	.	PUNCT
cana-2739	50	1	a	a	DET
cana-2739	50	2	pconet	pconet	NOUN
cana-2739	50	3	model	model	NOUN
cana-2739	50	4	is	be	AUX
cana-2739	50	5	fabricated	fabricate	VERB
cana-2739	50	6	with	with	ADP
cana-2739	50	7	fined	fine	VERB
cana-2739	50	8	tuned	tune	VERB
cana-2739	50	9	inception	inception	ADJ
cana-2739	50	10	v3	v3	PROPN
cana-2739	50	11	network	network	NOUN
cana-2739	50	12	to	to	PART
cana-2739	50	13	identify	identify	VERB
cana-2739	50	14	polycystic	polycystic	ADJ
cana-2739	50	15	ovaries	ovary	NOUN
cana-2739	50	16	from	from	ADP
cana-2739	50	17	normal	normal	ADJ
cana-2739	50	18	ovaries	ovary	NOUN
cana-2739	50	19	(	(	PUNCT
cana-2739	50	20	salman	salman	PROPN
cana-2739	50	21	hosain	hosain	PROPN
cana-2739	50	22	et	et	PROPN
cana-2739	50	23	al	al	PROPN
cana-2739	50	24	.	.	PROPN
cana-2739	50	25	,	,	PUNCT
cana-2739	50	26	2023	2023	NUM
cana-2739	50	27	)	)	PUNCT
cana-2739	50	28	.	.	PUNCT
cana-2739	51	1	originally	originally	ADV
cana-2739	51	2	,	,	PUNCT
cana-2739	51	3	inception	inception	PROPN
cana-2739	51	4	v3	v3	PROPN
cana-2739	51	5	model	model	NOUN
cana-2739	51	6	comprises	comprise	NOUN
cana-2739	51	7	of	of	ADP
cana-2739	51	8	42	42	NUM
cana-2739	51	9	layers	layer	NOUN
cana-2739	51	10	;	;	PUNCT
cana-2739	51	11	it	it	PRON
cana-2739	51	12	is	be	AUX
cana-2739	51	13	fine	fine	ADV
cana-2739	51	14	-	-	PUNCT
cana-2739	51	15	tuned	tune	VERB
cana-2739	51	16	by	by	ADP
cana-2739	51	17	adding	add	VERB
cana-2739	51	18	2	2	NUM
cana-2739	51	19	dense	dense	ADJ
cana-2739	51	20	layers	layer	NOUN
cana-2739	51	21	and	and	CCONJ
cana-2739	51	22	removing	remove	VERB
cana-2739	51	23	top	top	ADJ
cana-2739	51	24	layers	layer	NOUN
cana-2739	51	25	of	of	ADP
cana-2739	51	26	the	the	DET
cana-2739	51	27	model	model	NOUN
cana-2739	51	28	.	.	PUNCT
cana-2739	52	1	the	the	DET
cana-2739	52	2	number	number	NOUN
cana-2739	52	3	of	of	ADP
cana-2739	52	4	neurons	neuron	NOUN
cana-2739	52	5	in	in	ADP
cana-2739	52	6	the	the	DET
cana-2739	52	7	dense	dense	ADJ
cana-2739	52	8	layer	layer	NOUN
cana-2739	52	9	near	near	ADP
cana-2739	52	10	the	the	DET
cana-2739	52	11	output	output	NOUN
cana-2739	52	12	layer	layer	NOUN
cana-2739	52	13	is	be	AUX
cana-2739	52	14	512	512	NUM
cana-2739	52	15	.	.	PUNCT
cana-2739	53	1	the	the	DET
cana-2739	53	2	proposed	propose	VERB
cana-2739	53	3	model	model	NOUN
cana-2739	53	4	outperforms	outperform	VERB
cana-2739	53	5	the	the	DET
cana-2739	53	6	original	original	ADJ
cana-2739	53	7	model	model	NOUN
cana-2739	53	8	with	with	ADP
cana-2739	53	9	an	an	DET
cana-2739	53	10	accuracy	accuracy	NOUN
cana-2739	53	11	of	of	ADP
cana-2739	53	12	98.12	98.12	NUM
cana-2739	53	13	%	%	NOUN
cana-2739	53	14	over	over	ADP
cana-2739	53	15	96.56	96.56	NUM
cana-2739	53	16	%	%	NOUN
cana-2739	53	17	.	.	PUNCT
cana-2739	54	1	svm	svm	PROPN
cana-2739	54	2	,	,	PUNCT
cana-2739	54	3	knn	knn	PROPN
cana-2739	54	4	and	and	CCONJ
cana-2739	54	5	logistic	logistic	ADJ
cana-2739	54	6	regression	regression	NOUN
cana-2739	54	7	are	be	AUX
cana-2739	54	8	the	the	DET
cana-2739	54	9	classifiers	classifier	NOUN
cana-2739	54	10	employed	employ	VERB
cana-2739	54	11	by	by	ADP
cana-2739	54	12	j.	j.	PROPN
cana-2739	54	13	madhumitha	madhumitha	PROPN
cana-2739	54	14	et	et	PROPN
cana-2739	54	15	al	al	PROPN
cana-2739	54	16	.	.	PROPN
cana-2739	55	1	to	to	PART
cana-2739	55	2	separate	separate	VERB
cana-2739	55	3	polycystic	polycystic	ADJ
cana-2739	55	4	ovaries	ovary	NOUN
cana-2739	55	5	from	from	ADP
cana-2739	55	6	normal	normal	ADJ
cana-2739	55	7	ovaries	ovary	NOUN
cana-2739	55	8	in	in	ADP
cana-2739	55	9	ultrasound	ultrasound	ADJ
cana-2739	55	10	images	image	NOUN
cana-2739	55	11	(	(	PUNCT
cana-2739	55	12	madhumitha	madhumitha	NOUN
cana-2739	55	13	et	et	PROPN
cana-2739	55	14	al	al	PROPN
cana-2739	55	15	.	.	PROPN
cana-2739	55	16	,	,	PUNCT
cana-2739	55	17	2021	2021	NUM
cana-2739	55	18	)	)	PUNCT
cana-2739	55	19	.	.	PUNCT
cana-2739	56	1	a	a	DET
cana-2739	56	2	hybrid	hybrid	ADJ
cana-2739	56	3	system	system	NOUN
cana-2739	56	4	is	be	AUX
cana-2739	56	5	generated	generate	VERB
cana-2739	56	6	using	use	VERB
cana-2739	56	7	all	all	DET
cana-2739	56	8	the	the	DET
cana-2739	56	9	three	three	NUM
cana-2739	56	10	machine	machine	NOUN
cana-2739	56	11	learning	learn	VERB
cana-2739	56	12	algorithms	algorithm	NOUN
cana-2739	56	13	with	with	ADP
cana-2739	56	14	the	the	DET
cana-2739	56	15	dataset	dataset	NOUN
cana-2739	56	16	taken	take	VERB
cana-2739	56	17	from	from	ADP
cana-2739	56	18	https://radiologykey.com	https://radiologykey.com	X
cana-2739	56	19	and	and	CCONJ
cana-2739	56	20	produced	produce	VERB
cana-2739	56	21	an	an	DET
cana-2739	56	22	accuracy	accuracy	NOUN
cana-2739	56	23	of	of	ADP
cana-2739	56	24	98	98	NUM
cana-2739	56	25	%	%	NOUN
cana-2739	56	26	.	.	PUNCT
cana-2739	57	1	the	the	DET
cana-2739	57	2	factors	factor	NOUN
cana-2739	57	3	used	use	VERB
cana-2739	57	4	as	as	ADP
cana-2739	57	5	the	the	DET
cana-2739	57	6	basis	basis	NOUN
cana-2739	57	7	of	of	ADP
cana-2739	57	8	machine	machine	NOUN
cana-2739	57	9	learning	learn	VERB
cana-2739	57	10	algorithms	algorithm	NOUN
cana-2739	57	11	in	in	ADP
cana-2739	57	12	this	this	DET
cana-2739	57	13	research	research	NOUN
cana-2739	57	14	are	be	AUX
cana-2739	57	15	area	area	NOUN
cana-2739	57	16	,	,	PUNCT
cana-2739	57	17	freakishness	freakishness	NOUN
cana-2739	57	18	and	and	CCONJ
cana-2739	57	19	denseness	denseness	NOUN
cana-2739	57	20	of	of	ADP
cana-2739	57	21	images	image	NOUN
cana-2739	57	22	.	.	PUNCT
cana-2739	58	1	anisah	anisah	PROPN
cana-2739	58	2	nabilah	nabilah	ADV
cana-2739	58	3	along	along	ADP
cana-2739	58	4	with	with	ADP
cana-2739	58	5	other	other	ADJ
cana-2739	58	6	researchers	researcher	NOUN
cana-2739	58	7	worked	work	VERB
cana-2739	58	8	on	on	ADP
cana-2739	58	9	a	a	DET
cana-2739	58	10	decision	decision	NOUN
cana-2739	58	11	tree	tree	NOUN
cana-2739	58	12	model	model	NOUN
cana-2739	58	13	to	to	PART
cana-2739	58	14	segregate	segregate	VERB
cana-2739	58	15	functional	functional	ADJ
cana-2739	58	16	cysts	cyst	NOUN
cana-2739	58	17	from	from	ADP
cana-2739	58	18	pathological	pathological	ADJ
cana-2739	58	19	cysts	cyst	NOUN
cana-2739	58	20	(	(	PUNCT
cana-2739	58	21	nabilah	nabilah	PROPN
cana-2739	58	22	et	et	PROPN
cana-2739	58	23	al	al	PROPN
cana-2739	58	24	.	.	PROPN
cana-2739	58	25	,	,	PUNCT
cana-2739	58	26	2020	2020	NUM
cana-2739	58	27	)	)	PUNCT
cana-2739	58	28	.	.	PUNCT
cana-2739	59	1	46	46	NUM
cana-2739	59	2	ultrasound	ultrasound	NOUN
cana-2739	59	3	images	image	NOUN
cana-2739	59	4	from	from	ADP
cana-2739	59	5	mother	mother	NOUN
cana-2739	59	6	child	child	PROPN
cana-2739	59	7	hospital	hospital	NOUN
cana-2739	59	8	“	"	PUNCT
cana-2739	59	9	putri	putri	NOUN
cana-2739	59	10	”	"	PUNCT
cana-2739	59	11	,	,	PUNCT
cana-2739	59	12	indonesia	indonesia	PROPN
cana-2739	59	13	are	be	AUX
cana-2739	59	14	used	use	VERB
cana-2739	59	15	in	in	ADP
cana-2739	59	16	this	this	DET
cana-2739	59	17	research	research	NOUN
cana-2739	59	18	and	and	CCONJ
cana-2739	59	19	an	an	DET
cana-2739	59	20	accuracy	accuracy	NOUN
cana-2739	59	21	of	of	ADP
cana-2739	59	22	97.8	97.8	NUM
cana-2739	59	23	%	%	NOUN
cana-2739	59	24	is	be	AUX
cana-2739	59	25	accomplished	accomplish	VERB
cana-2739	59	26	.	.	PUNCT
cana-2739	60	1	the	the	DET
cana-2739	60	2	selected	select	VERB
cana-2739	60	3	features	feature	NOUN
cana-2739	60	4	given	give	VERB
cana-2739	60	5	as	as	ADP
cana-2739	60	6	input	input	NOUN
cana-2739	60	7	to	to	ADP
cana-2739	60	8	the	the	DET
cana-2739	60	9	decision	decision	NOUN
cana-2739	60	10	tree	tree	NOUN
cana-2739	60	11	are	be	AUX
cana-2739	60	12	length	length	NOUN
cana-2739	60	13	and	and	CCONJ
cana-2739	60	14	width	width	NOUN
cana-2739	60	15	of	of	ADP
cana-2739	60	16	cyst	cyst	NOUN
cana-2739	60	17	along	along	ADP
cana-2739	60	18	with	with	ADP
cana-2739	60	19	papillary	papillary	ADJ
cana-2739	60	20	size	size	NOUN
cana-2739	60	21	.	.	PUNCT
cana-2739	61	1	several	several	ADJ
cana-2739	61	2	researchers	researcher	NOUN
cana-2739	61	3	have	have	AUX
cana-2739	61	4	worked	work	VERB
cana-2739	61	5	on	on	ADP
cana-2739	61	6	textural	textural	ADJ
cana-2739	61	7	features	feature	NOUN
cana-2739	61	8	of	of	ADP
cana-2739	61	9	ultrasound	ultrasound	NOUN
cana-2739	61	10	images	image	NOUN
cana-2739	61	11	and	and	CCONJ
cana-2739	61	12	used	use	VERB
cana-2739	61	13	as	as	ADP
cana-2739	61	14	a	a	DET
cana-2739	61	15	classification	classification	NOUN
cana-2739	61	16	criterion	criterion	NOUN
cana-2739	61	17	of	of	ADP
cana-2739	61	18	medical	medical	ADJ
cana-2739	61	19	images	image	NOUN
cana-2739	61	20	.	.	PUNCT
cana-2739	62	1	maha	maha	PROPN
cana-2739	62	2	abdulameer	abdulameer	PROPN
cana-2739	62	3	kadhim	kadhim	PROPN
cana-2739	62	4	analysed	analyse	VERB
cana-2739	62	5	and	and	CCONJ
cana-2739	62	6	processed	process	VERB
cana-2739	62	7	80	80	NUM
cana-2739	62	8	medical	medical	ADJ
cana-2739	62	9	images	image	NOUN
cana-2739	62	10	with	with	ADP
cana-2739	62	11	the	the	DET
cana-2739	62	12	help	help	NOUN
cana-2739	62	13	of	of	ADP
cana-2739	62	14	grey	grey	ADJ
cana-2739	62	15	-	-	PUNCT
cana-2739	62	16	level	level	NOUN
cana-2739	62	17	co	co	NOUN
cana-2739	62	18	-	-	NOUN
cana-2739	62	19	occurrence	occurrence	ADJ
cana-2739	62	20	matrices	matrix	NOUN
cana-2739	62	21	(	(	PUNCT
cana-2739	62	22	glcm	glcm	PROPN
cana-2739	62	23	)	)	PUNCT
cana-2739	62	24	,	,	PUNCT
cana-2739	62	25	fractal	fractal	ADJ
cana-2739	62	26	geometry	geometry	NOUN
cana-2739	62	27	and	and	CCONJ
cana-2739	62	28	lacunarity	lacunarity	NOUN
cana-2739	62	29	(	(	PUNCT
cana-2739	62	30	kadhim	kadhim	ADJ
cana-2739	62	31	,	,	PUNCT
cana-2739	62	32	2022	2022	NUM
cana-2739	62	33	)	)	PUNCT
cana-2739	62	34	.	.	PUNCT
cana-2739	63	1	this	this	DET
cana-2739	63	2	research	research	NOUN
cana-2739	63	3	is	be	AUX
cana-2739	63	4	performed	perform	VERB
cana-2739	63	5	using	use	VERB
cana-2739	63	6	matlab	matlab	PROPN
cana-2739	63	7	and	and	CCONJ
cana-2739	63	8	microsoft	microsoft	PROPN
cana-2739	63	9	visual	visual	PROPN
cana-2739	63	10	c++	c++	NOUN
cana-2739	63	11	;	;	PUNCT
cana-2739	63	12	and	and	CCONJ
cana-2739	63	13	svm	svm	VERB
cana-2739	63	14	as	as	ADP
cana-2739	63	15	a	a	DET
cana-2739	63	16	classifier	classifier	NOUN
cana-2739	63	17	with	with	ADP
cana-2739	63	18	95	95	NUM
cana-2739	63	19	%	%	NOUN
cana-2739	63	20	classification	classification	NOUN
cana-2739	63	21	rate	rate	NOUN
cana-2739	63	22	.	.	PUNCT
cana-2739	64	1	hurst	hurst	PROPN
cana-2739	64	2	components	component	NOUN
cana-2739	64	3	are	be	AUX
cana-2739	64	4	used	use	VERB
cana-2739	64	5	as	as	ADP
cana-2739	64	6	a	a	DET
cana-2739	64	7	classification	classification	NOUN
cana-2739	64	8	feature	feature	NOUN
cana-2739	64	9	in	in	ADP
cana-2739	64	10	logistic	logistic	ADJ
cana-2739	64	11	regression	regression	NOUN
cana-2739	64	12	,	,	PUNCT
cana-2739	64	13	decision	decision	NOUN
cana-2739	64	14	tree	tree	NOUN
cana-2739	64	15	,	,	PUNCT
cana-2739	64	16	svm	svm	VERB
cana-2739	64	17	and	and	CCONJ
cana-2739	64	18	knn	knn	VERB
cana-2739	64	19	by	by	ADP
cana-2739	64	20	yen	yen	PROPN
cana-2739	64	21	-	-	PUNCT
cana-2739	64	22	ching	che	VERB
cana-2739	64	23	chang	chang	PROPN
cana-2739	64	24	and	and	CCONJ
cana-2739	64	25	jin	jin	PROPN
cana-2739	64	26	-	-	PUNCT
cana-2739	64	27	tsong	tsong	ADJ
cana-2739	64	28	jeng	jeng	NOUN
cana-2739	64	29	on	on	ADP
cana-2739	64	30	2d	2d	NUM
cana-2739	64	31	fractional	fractional	ADJ
cana-2739	64	32	brownian	brownian	ADJ
cana-2739	64	33	motion	motion	NOUN
cana-2739	64	34	images	image	NOUN
cana-2739	64	35	(	(	PUNCT
cana-2739	64	36	y.	y.	PROPN
cana-2739	64	37	c.	c.	PROPN
cana-2739	64	38	chang	chang	PROPN
cana-2739	64	39	&	&	CCONJ
cana-2739	64	40	jeng	jeng	PROPN
cana-2739	64	41	,	,	PUNCT
cana-2739	64	42	2023	2023	NUM
cana-2739	64	43	)	)	PUNCT
cana-2739	64	44	.	.	PUNCT
cana-2739	65	1	deep	deep	ADJ
cana-2739	65	2	learning	learning	NOUN
cana-2739	65	3	models	model	NOUN
cana-2739	65	4	like	like	ADP
cana-2739	65	5	,	,	PUNCT
cana-2739	65	6	alexnet	alexnet	NOUN
cana-2739	65	7	,	,	PUNCT
cana-2739	65	8	googlenet	googlenet	NOUN
cana-2739	65	9	and	and	CCONJ
cana-2739	65	10	xception	xception	NOUN
cana-2739	65	11	are	be	AUX
cana-2739	65	12	used	use	VERB
cana-2739	65	13	that	that	PRON
cana-2739	65	14	turn	turn	VERB
cana-2739	65	15	out	out	ADP
cana-2739	65	16	to	to	PART
cana-2739	65	17	perform	perform	VERB
cana-2739	65	18	better	well	ADJ
cana-2739	65	19	than	than	ADP
cana-2739	65	20	machine	machine	NOUN
cana-2739	65	21	learning	learn	VERB
cana-2739	65	22	algorithms	algorithm	NOUN
cana-2739	65	23	.	.	PUNCT
cana-2739	66	1	they	they	PRON
cana-2739	66	2	used	use	VERB
cana-2739	66	3	chest	chest	NOUN
cana-2739	66	4	x	x	PART
cana-2739	66	5	ray	ray	NOUN
cana-2739	66	6	images	image	NOUN
cana-2739	66	7	from	from	ADP
cana-2739	66	8	kaggle	kaggle	VERB
cana-2739	66	9	with	with	ADP
cana-2739	66	10	127	127	NUM
cana-2739	66	11	x	x	SYM
cana-2739	66	12	384	384	NUM
cana-2739	66	13	as	as	ADP
cana-2739	66	14	input	input	NOUN
cana-2739	66	15	size	size	NOUN
cana-2739	66	16	.	.	PUNCT
cana-2739	67	1	it	it	PRON
cana-2739	67	2	was	be	AUX
cana-2739	67	3	found	find	VERB
cana-2739	67	4	that	that	SCONJ
cana-2739	67	5	their	their	PRON
cana-2739	67	6	proposed	propose	VERB
cana-2739	67	7	model	model	NOUN
cana-2739	67	8	and	and	CCONJ
cana-2739	67	9	google	google	PROPN
cana-2739	67	10	net	net	NOUN
cana-2739	67	11	performed	perform	VERB
cana-2739	67	12	well	well	ADV
cana-2739	67	13	on	on	ADP
cana-2739	67	14	the	the	DET
cana-2739	67	15	basis	basis	NOUN
cana-2739	67	16	of	of	ADP
cana-2739	67	17	maximum	maximum	ADJ
cana-2739	67	18	likelihood	likelihood	NOUN
cana-2739	67	19	estimator	estimator	NOUN
cana-2739	67	20	,	,	PUNCT
cana-2739	67	21	mean	mean	VERB
cana-2739	67	22	absolute	absolute	ADJ
cana-2739	67	23	error	error	NOUN
cana-2739	67	24	and	and	CCONJ
cana-2739	67	25	mean	mean	ADJ
cana-2739	67	26	square	square	ADJ
cana-2739	67	27	error	error	NOUN
cana-2739	67	28	.	.	PUNCT
cana-2739	68	1	the	the	DET
cana-2739	68	2	classification	classification	NOUN
cana-2739	68	3	rate	rate	NOUN
cana-2739	68	4	which	which	PRON
cana-2739	68	5	comes	come	VERB
cana-2739	68	6	out	out	ADP
cana-2739	68	7	to	to	PART
cana-2739	68	8	be	be	AUX
cana-2739	68	9	the	the	DET
cana-2739	68	10	best	good	ADJ
cana-2739	68	11	for	for	ADP
cana-2739	68	12	machine	machine	NOUN
cana-2739	68	13	learning	learning	NOUN
cana-2739	68	14	algorithms	algorithm	NOUN
cana-2739	68	15	is	be	AUX
cana-2739	68	16	81.4	81.4	NUM
cana-2739	68	17	%	%	NOUN
cana-2739	68	18	for	for	ADP
cana-2739	68	19	11	11	NUM
cana-2739	68	20	classes	class	NOUN
cana-2739	68	21	and	and	CCONJ
cana-2739	68	22	53.2	53.2	NUM
cana-2739	68	23	%	%	NOUN
cana-2739	68	24	for	for	ADP
cana-2739	68	25	21	21	NUM
cana-2739	68	26	classes	class	NOUN
cana-2739	68	27	.	.	PUNCT
cana-2739	69	1	in	in	ADP
cana-2739	69	2	case	case	NOUN
cana-2739	69	3	of	of	ADP
cana-2739	69	4	deep	deep	ADJ
cana-2739	69	5	learning	learning	NOUN
cana-2739	69	6	models	model	NOUN
cana-2739	69	7	,	,	PUNCT
cana-2739	69	8	the	the	DET
cana-2739	69	9	classification	classification	NOUN
cana-2739	69	10	rates	rate	NOUN
cana-2739	69	11	are	be	AUX
cana-2739	69	12	96	96	NUM
cana-2739	69	13	%	%	NOUN
cana-2739	69	14	and	and	CCONJ
cana-2739	69	15	93.7	93.7	NUM
cana-2739	69	16	%	%	NOUN
cana-2739	69	17	for	for	ADP
cana-2739	69	18	11	11	NUM
cana-2739	69	19	and	and	CCONJ
cana-2739	69	20	21	21	NUM
cana-2739	69	21	classes	class	NOUN
cana-2739	69	22	respectively	respectively	ADV
cana-2739	69	23	.	.	PUNCT
cana-2739	70	1	dmytro	dmytro	PROPN
cana-2739	70	2	m.	m.	PROPN
cana-2739	70	3	bayzitov	bayzitov	PROPN
cana-2739	70	4	along	along	ADP
cana-2739	70	5	with	with	ADP
cana-2739	70	6	co	co	NOUN
cana-2739	70	7	-	-	NOUN
cana-2739	70	8	researchers	researcher	NOUN
cana-2739	70	9	worked	work	VERB
cana-2739	70	10	on	on	ADP
cana-2739	70	11	digital	digital	ADJ
cana-2739	70	12	image	image	NOUN
cana-2739	70	13	classification	classification	NOUN
cana-2739	70	14	of	of	ADP
cana-2739	70	15	appendicitis	appendicitis	NOUN
cana-2739	70	16	and	and	CCONJ
cana-2739	70	17	ovarian	ovarian	ADJ
cana-2739	70	18	cysts	cyst	NOUN
cana-2739	70	19	with	with	ADP
cana-2739	70	20	haar	haar	PROPN
cana-2739	70	21	features	feature	NOUN
cana-2739	70	22	cascade	cascade	NOUN
cana-2739	70	23	and	and	CCONJ
cana-2739	70	24	adaboost	adaboost	ADJ
cana-2739	70	25	classifiers	classifier	NOUN
cana-2739	70	26	(	(	PUNCT
cana-2739	70	27	bayzitov	bayzitov	X
cana-2739	70	28	et	et	PROPN
cana-2739	70	29	al	al	PROPN
cana-2739	70	30	.	.	PROPN
cana-2739	70	31	,	,	PUNCT
cana-2739	70	32	2023	2023	NUM
cana-2739	70	33	)	)	PUNCT
cana-2739	70	34	.	.	PUNCT
cana-2739	71	1	features	feature	NOUN
cana-2739	71	2	are	be	AUX
cana-2739	71	3	selected	select	VERB
cana-2739	71	4	from	from	ADP
cana-2739	71	5	images	image	NOUN
cana-2739	71	6	or	or	CCONJ
cana-2739	71	7	frames	frame	NOUN
cana-2739	71	8	of	of	ADP
cana-2739	71	9	laparoscopic	laparoscopic	NOUN
cana-2739	71	10	diagnostics	diagnostic	NOUN
cana-2739	71	11	using	use	VERB
cana-2739	71	12	modified	modify	VERB
cana-2739	71	13	color	color	NOUN
cana-2739	71	14	local	local	ADJ
cana-2739	71	15	binary	binary	NOUN
cana-2739	71	16	pattern	pattern	NOUN
cana-2739	71	17	(	(	PUNCT
cana-2739	71	18	lbp	lbp	PROPN
cana-2739	71	19	)	)	PUNCT
cana-2739	71	20	.	.	PUNCT
cana-2739	72	1	for	for	ADP
cana-2739	72	2	training	training	NOUN
cana-2739	72	3	,	,	PUNCT
cana-2739	72	4	95	95	NUM
cana-2739	72	5	and	and	CCONJ
cana-2739	72	6	78	78	NUM
cana-2739	72	7	video	video	NOUN
cana-2739	72	8	images	image	NOUN
cana-2739	72	9	are	be	AUX
cana-2739	72	10	used	use	VERB
cana-2739	72	11	for	for	ADP
cana-2739	72	12	appendicitis	appendicitis	NOUN
cana-2739	72	13	and	and	CCONJ
cana-2739	72	14	ovarian	ovarian	ADJ
cana-2739	72	15	cysts	cyst	NOUN
cana-2739	72	16	,	,	PUNCT
cana-2739	72	17	respectively	respectively	ADV
cana-2739	72	18	.	.	PUNCT
cana-2739	73	1	439	439	NUM
cana-2739	73	2	and	and	CCONJ
cana-2739	73	3	182	182	NUM
cana-2739	73	4	frames	frame	NOUN
cana-2739	73	5	are	be	AUX
cana-2739	73	6	utilized	utilize	VERB
cana-2739	73	7	for	for	ADP
cana-2739	73	8	testing	testing	NOUN
cana-2739	73	9	of	of	ADP
cana-2739	73	10	appendicitis	appendicitis	NOUN
cana-2739	73	11	and	and	CCONJ
cana-2739	73	12	ovarian	ovarian	ADJ
cana-2739	73	13	cysts	cyst	NOUN
cana-2739	73	14	.	.	PUNCT
cana-2739	74	1	adaboost	adaboost	ADJ
cana-2739	74	2	classifier	classifier	NOUN
cana-2739	74	3	achieved	achieve	VERB
cana-2739	74	4	a	a	DET
cana-2739	74	5	better	well	ADJ
cana-2739	74	6	accuracy	accuracy	NOUN
cana-2739	74	7	of	of	ADP
cana-2739	74	8	73.6	73.6	NUM
cana-2739	74	9	%	%	NOUN
cana-2739	74	10	for	for	ADP
cana-2739	74	11	appendicitis	appendicitis	NOUN
cana-2739	74	12	and	and	CCONJ
cana-2739	74	13	85.4	85.4	NUM
cana-2739	74	14	%	%	NOUN
cana-2739	74	15	for	for	ADP
cana-2739	74	16	ovarian	ovarian	ADJ
cana-2739	74	17	cysts	cyst	NOUN
cana-2739	74	18	.	.	PUNCT
cana-2739	75	1	mengwan	mengwan	PROPN
cana-2739	75	2	wei	wei	PROPN
cana-2739	75	3	and	and	CCONJ
cana-2739	75	4	other	other	ADJ
cana-2739	75	5	researchers	researcher	NOUN
cana-2739	75	6	combined	combine	VERB
cana-2739	75	7	texture	texture	NOUN
cana-2739	75	8	and	and	CCONJ
cana-2739	75	9	morphological	morphological	ADJ
cana-2739	75	10	features	feature	NOUN
cana-2739	75	11	of	of	ADP
cana-2739	75	12	448	448	NUM
cana-2739	75	13	ultrasound	ultrasound	NOUN
cana-2739	75	14	images	image	NOUN
cana-2739	75	15	to	to	PART
cana-2739	75	16	differentiate	differentiate	VERB
cana-2739	75	17	non	non	ADJ
cana-2739	75	18	-	-	ADJ
cana-2739	75	19	cancerous	cancerous	ADJ
cana-2739	75	20	and	and	CCONJ
cana-2739	75	21	malignant	malignant	ADJ
cana-2739	75	22	breast	breast	NOUN
cana-2739	75	23	tumour	tumour	NOUN
cana-2739	75	24	(	(	PUNCT
cana-2739	75	25	wei	wei	PROPN
cana-2739	75	26	et	et	PROPN
cana-2739	75	27	al	al	PROPN
cana-2739	75	28	.	.	PROPN
cana-2739	75	29	,	,	PUNCT
cana-2739	75	30	2020	2020	NUM
cana-2739	75	31	)	)	PUNCT
cana-2739	75	32	.	.	PUNCT
cana-2739	76	1	they	they	PRON
cana-2739	76	2	used	use	VERB
cana-2739	76	3	local	local	ADJ
cana-2739	76	4	communications	communication	NOUN
cana-2739	76	5	on	on	ADP
cana-2739	76	6	applied	apply	VERB
cana-2739	76	7	nonlinear	nonlinear	ADJ
cana-2739	76	8	analysis	analysis	NOUN
cana-2739	76	9	issn	issn	NOUN
cana-2739	76	10	:	:	PUNCT
cana-2739	76	11	1074	1074	NUM
cana-2739	76	12	-	-	PUNCT
cana-2739	76	13	133x	133x	NUM
cana-2739	76	14	vol	vol	NOUN
cana-2739	76	15	32	32	NUM
cana-2739	76	16	no	no	NOUN
cana-2739	76	17	.	.	PUNCT
cana-2739	77	1	4s	4s	NUM
cana-2739	77	2	(	(	PUNCT
cana-2739	77	3	2025	2025	NUM
cana-2739	77	4	)	)	PUNCT
cana-2739	77	5	62	62	NUM
cana-2739	77	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2739	77	7	binary	binary	NOUN
cana-2739	77	8	patterns	pattern	NOUN
cana-2739	77	9	(	(	PUNCT
cana-2739	77	10	lbp	lbp	PROPN
cana-2739	77	11	)	)	PUNCT
cana-2739	77	12	(	(	PUNCT
cana-2739	77	13	ojala	ojala	X
cana-2739	77	14	et	et	PROPN
cana-2739	77	15	al	al	PROPN
cana-2739	77	16	.	.	PROPN
cana-2739	77	17	,	,	PUNCT
cana-2739	77	18	1996	1996	NUM
cana-2739	77	19	)	)	PUNCT
cana-2739	77	20	,	,	PUNCT
cana-2739	77	21	grey	grey	ADJ
cana-2739	77	22	-	-	PUNCT
cana-2739	77	23	level	level	NOUN
cana-2739	77	24	co	co	NOUN
cana-2739	77	25	-	-	NOUN
cana-2739	77	26	occurrence	occurrence	ADJ
cana-2739	77	27	matrices	matrix	NOUN
cana-2739	77	28	(	(	PUNCT
cana-2739	77	29	glcm	glcm	PROPN
cana-2739	77	30	)	)	PUNCT
cana-2739	77	31	(	(	PUNCT
cana-2739	77	32	haralick	haralick	NOUN
cana-2739	77	33	et	et	PROPN
cana-2739	77	34	al	al	PROPN
cana-2739	77	35	.	.	PROPN
cana-2739	77	36	,	,	PUNCT
cana-2739	77	37	1973	1973	NUM
cana-2739	77	38	)	)	PUNCT
cana-2739	77	39	and	and	CCONJ
cana-2739	77	40	histogram	histogram	NOUN
cana-2739	77	41	of	of	ADP
cana-2739	77	42	oriented	orient	VERB
cana-2739	77	43	gradients	gradient	NOUN
cana-2739	77	44	(	(	PUNCT
cana-2739	77	45	hog	hog	PROPN
cana-2739	77	46	)	)	PUNCT
cana-2739	77	47	(	(	PUNCT
cana-2739	77	48	dalal	dalal	PROPN
cana-2739	77	49	&	&	CCONJ
cana-2739	77	50	triggs	triggs	PROPN
cana-2739	77	51	,	,	PUNCT
cana-2739	77	52	2005	2005	NUM
cana-2739	77	53	)	)	PUNCT
cana-2739	77	54	under	under	ADP
cana-2739	77	55	texture	texture	NOUN
cana-2739	77	56	features	feature	NOUN
cana-2739	77	57	.	.	PUNCT
cana-2739	78	1	noncancerous	noncancerous	ADJ
cana-2739	78	2	tumours	tumour	NOUN
cana-2739	78	3	differ	differ	VERB
cana-2739	78	4	in	in	ADP
cana-2739	78	5	morphology	morphology	NOUN
cana-2739	78	6	with	with	ADP
cana-2739	78	7	the	the	DET
cana-2739	78	8	cancerous	cancerous	ADJ
cana-2739	78	9	tumours	tumour	NOUN
cana-2739	78	10	like	like	ADP
cana-2739	78	11	compactness	compactness	NOUN
cana-2739	78	12	,	,	PUNCT
cana-2739	78	13	elliptical	elliptical	ADJ
cana-2739	78	14	compactness	compactness	NOUN
cana-2739	78	15	and	and	CCONJ
cana-2739	78	16	radial	radial	ADJ
cana-2739	78	17	distance	distance	NOUN
cana-2739	78	18	spectrum	spectrum	NOUN
cana-2739	78	19	which	which	PRON
cana-2739	78	20	are	be	AUX
cana-2739	78	21	considered	consider	VERB
cana-2739	78	22	in	in	ADP
cana-2739	78	23	this	this	DET
cana-2739	78	24	research	research	NOUN
cana-2739	78	25	.	.	PUNCT
cana-2739	79	1	their	their	PRON
cana-2739	79	2	research	research	NOUN
cana-2739	79	3	combined	combine	VERB
cana-2739	79	4	multiple	multiple	ADJ
cana-2739	79	5	classifiers	classifier	NOUN
cana-2739	79	6	like	like	VERB
cana-2739	79	7	,	,	PUNCT
cana-2739	79	8	svm	svm	ADJ
cana-2739	79	9	and	and	CCONJ
cana-2739	79	10	naïve	naïve	ADJ
cana-2739	79	11	bayes	bayes	NOUN
cana-2739	79	12	and	and	CCONJ
cana-2739	79	13	achieved	achieve	VERB
cana-2739	79	14	an	an	DET
cana-2739	79	15	accuracy	accuracy	NOUN
cana-2739	79	16	of	of	ADP
cana-2739	79	17	91.11	91.11	NUM
cana-2739	79	18	%	%	NOUN
cana-2739	79	19	with	with	ADP
cana-2739	79	20	sensitivity	sensitivity	NOUN
cana-2739	79	21	as	as	ADP
cana-2739	79	22	94.34	94.34	NUM
cana-2739	79	23	%	%	NOUN
cana-2739	79	24	and	and	CCONJ
cana-2739	79	25	specificity	specificity	NOUN
cana-2739	79	26	as	as	ADP
cana-2739	79	27	86.49	86.49	NUM
cana-2739	79	28	%	%	NOUN
cana-2739	79	29	.	.	PUNCT
cana-2739	80	1	hybrid	hybrid	ADJ
cana-2739	80	2	convolutional	convolutional	ADJ
cana-2739	80	3	neural	neural	ADJ
cana-2739	80	4	networks	network	NOUN
cana-2739	80	5	(	(	PUNCT
cana-2739	80	6	combination	combination	NOUN
cana-2739	80	7	of	of	ADP
cana-2739	80	8	convolutional	convolutional	ADJ
cana-2739	80	9	neural	neural	ADJ
cana-2739	80	10	networks	network	NOUN
cana-2739	80	11	and	and	CCONJ
cana-2739	80	12	lbp	lbp	PROPN
cana-2739	80	13	)	)	PUNCT
cana-2739	80	14	are	be	AUX
cana-2739	80	15	used	use	VERB
cana-2739	80	16	for	for	ADP
cana-2739	80	17	feature	feature	NOUN
cana-2739	80	18	extraction	extraction	NOUN
cana-2739	80	19	in	in	ADP
cana-2739	80	20	classification	classification	NOUN
cana-2739	80	21	of	of	ADP
cana-2739	80	22	medical	medical	ADJ
cana-2739	80	23	images	image	NOUN
cana-2739	80	24	(	(	PUNCT
cana-2739	80	25	rocha	rocha	PROPN
cana-2739	80	26	et	et	PROPN
cana-2739	80	27	al	al	PROPN
cana-2739	80	28	.	.	PROPN
cana-2739	80	29	,	,	PUNCT
cana-2739	80	30	2023	2023	NUM
cana-2739	80	31	)	)	PUNCT
cana-2739	80	32	.	.	PUNCT
cana-2739	81	1	svm	svm	PROPN
cana-2739	81	2	,	,	PUNCT
cana-2739	81	3	random	random	ADJ
cana-2739	81	4	forest	forest	NOUN
cana-2739	81	5	,	,	PUNCT
cana-2739	81	6	knn	knn	PROPN
cana-2739	81	7	,	,	PUNCT
cana-2739	81	8	linear	linear	VERB
cana-2739	81	9	discriminant	discriminant	ADJ
cana-2739	81	10	analysis	analysis	NOUN
cana-2739	81	11	and	and	CCONJ
cana-2739	81	12	fully	fully	ADV
cana-2739	81	13	connected	connected	ADJ
cana-2739	81	14	layer	layer	NOUN
cana-2739	81	15	are	be	AUX
cana-2739	81	16	employed	employ	VERB
cana-2739	81	17	as	as	ADP
cana-2739	81	18	classifiers	classifier	NOUN
cana-2739	81	19	and	and	CCONJ
cana-2739	81	20	their	their	PRON
cana-2739	81	21	results	result	NOUN
cana-2739	81	22	are	be	AUX
cana-2739	81	23	combined	combine	VERB
cana-2739	81	24	through	through	ADP
cana-2739	81	25	ensemble	ensemble	ADJ
cana-2739	81	26	method	method	NOUN
cana-2739	81	27	.	.	PUNCT
cana-2739	82	1	the	the	DET
cana-2739	82	2	dataset	dataset	NOUN
cana-2739	82	3	comprises	comprise	NOUN
cana-2739	82	4	of	of	ADP
cana-2739	82	5	jaw	jaw	NOUN
cana-2739	82	6	oral	oral	ADJ
cana-2739	82	7	cysts	cyst	NOUN
cana-2739	82	8	–	–	PUNCT
cana-2739	82	9	radicular	radicular	ADJ
cana-2739	82	10	cysts	cyst	NOUN
cana-2739	82	11	and	and	CCONJ
cana-2739	82	12	odontogenic	odontogenic	ADJ
cana-2739	82	13	keratocysts	keratocyst	NOUN
cana-2739	82	14	.	.	PUNCT
cana-2739	83	1	it	it	PRON
cana-2739	83	2	produced	produce	VERB
cana-2739	83	3	83.4	83.4	NUM
cana-2739	83	4	%	%	NOUN
cana-2739	83	5	and	and	CCONJ
cana-2739	83	6	85.9	85.9	NUM
cana-2739	83	7	%	%	NOUN
cana-2739	83	8	as	as	ADP
cana-2739	83	9	an	an	DET
cana-2739	83	10	average	average	ADJ
cana-2739	83	11	accuracy	accuracy	NOUN
cana-2739	83	12	in	in	ADP
cana-2739	83	13	different	different	ADJ
cana-2739	83	14	combinations	combination	NOUN
cana-2739	83	15	of	of	ADP
cana-2739	83	16	oral	oral	ADJ
cana-2739	83	17	cysts	cyst	NOUN
cana-2739	83	18	classification	classification	NOUN
cana-2739	83	19	.	.	PUNCT
cana-2739	84	1	xing	xing	PROPN
cana-2739	84	2	-	-	PUNCT
cana-2739	84	3	xing	xing	PROPN
cana-2739	84	4	bao	bao	PROPN
cana-2739	84	5	et	et	PROPN
cana-2739	84	6	al	al	PROPN
cana-2739	84	7	.	.	PROPN
cana-2739	84	8	aims	aim	VERB
cana-2739	84	9	to	to	PART
cana-2739	84	10	locate	locate	VERB
cana-2739	84	11	necrotic	necrotic	ADJ
cana-2739	84	12	regions	region	NOUN
cana-2739	84	13	in	in	ADP
cana-2739	84	14	the	the	DET
cana-2739	84	15	chronic	chronic	ADJ
cana-2739	84	16	spinal	spinal	NOUN
cana-2739	84	17	cord	cord	NOUN
cana-2739	84	18	injury	injury	NOUN
cana-2739	84	19	using	use	VERB
cana-2739	84	20	mri	mri	NOUN
cana-2739	84	21	images	image	NOUN
cana-2739	84	22	of	of	ADP
cana-2739	84	23	wistar	wistar	PROPN
cana-2739	84	24	rats	rat	NOUN
cana-2739	84	25	(	(	PUNCT
cana-2739	84	26	bao	bao	PROPN
cana-2739	84	27	et	et	PROPN
cana-2739	84	28	al	al	PROPN
cana-2739	84	29	.	.	PROPN
cana-2739	84	30	,	,	PUNCT
cana-2739	84	31	2023	2023	NUM
cana-2739	84	32	)	)	PUNCT
cana-2739	84	33	.	.	PUNCT
cana-2739	85	1	the	the	DET
cana-2739	85	2	features	feature	NOUN
cana-2739	85	3	are	be	AUX
cana-2739	85	4	extracted	extract	VERB
cana-2739	85	5	using	use	VERB
cana-2739	85	6	lbp	lbp	PROPN
cana-2739	85	7	,	,	PUNCT
cana-2739	85	8	gabor	gabor	PROPN
cana-2739	85	9	texture	texture	NOUN
cana-2739	85	10	features	feature	NOUN
cana-2739	85	11	,	,	PUNCT
cana-2739	85	12	glcm	glcm	NOUN
cana-2739	85	13	,	,	PUNCT
cana-2739	85	14	statistical	statistical	ADJ
cana-2739	85	15	features	feature	NOUN
cana-2739	85	16	related	relate	VERB
cana-2739	85	17	to	to	ADP
cana-2739	85	18	intensity	intensity	NOUN
cana-2739	85	19	and	and	CCONJ
cana-2739	85	20	superpixel	superpixel	ADJ
cana-2739	85	21	areas	area	NOUN
cana-2739	85	22	.	.	PUNCT
cana-2739	86	1	svm	svm	VERB
cana-2739	86	2	and	and	CCONJ
cana-2739	86	3	random	random	ADJ
cana-2739	86	4	forest	forest	NOUN
cana-2739	86	5	are	be	AUX
cana-2739	86	6	used	use	VERB
cana-2739	86	7	as	as	ADP
cana-2739	86	8	a	a	DET
cana-2739	86	9	classification	classification	NOUN
cana-2739	86	10	model	model	NOUN
cana-2739	86	11	in	in	ADP
cana-2739	86	12	which	which	PRON
cana-2739	86	13	svm	svm	PROPN
cana-2739	86	14	showed	show	VERB
cana-2739	86	15	higher	high	ADJ
cana-2739	86	16	accuracy	accuracy	NOUN
cana-2739	86	17	.	.	PUNCT
cana-2739	87	1	many	many	ADJ
cana-2739	87	2	textural	textural	ADJ
cana-2739	87	3	feature	feature	NOUN
cana-2739	87	4	methods	method	NOUN
cana-2739	87	5	like	like	ADP
cana-2739	87	6	lbp	lbp	PROPN
cana-2739	87	7	,	,	PUNCT
cana-2739	87	8	local	local	ADJ
cana-2739	87	9	directional	directional	ADJ
cana-2739	87	10	pattern	pattern	NOUN
cana-2739	87	11	(	(	PUNCT
cana-2739	87	12	ldp	ldp	PROPN
cana-2739	87	13	)	)	PUNCT
cana-2739	87	14	and	and	CCONJ
cana-2739	87	15	local	local	ADJ
cana-2739	87	16	optimal	optimal	ADJ
cana-2739	87	17	oriented	orient	VERB
cana-2739	87	18	pattern	pattern	NOUN
cana-2739	87	19	(	(	PUNCT
cana-2739	87	20	loop	loop	NOUN
cana-2739	87	21	)	)	PUNCT
cana-2739	87	22	are	be	AUX
cana-2739	87	23	used	use	VERB
cana-2739	87	24	in	in	ADP
cana-2739	87	25	classification	classification	NOUN
cana-2739	87	26	of	of	ADP
cana-2739	87	27	ovarian	ovarian	ADJ
cana-2739	87	28	cysts	cyst	NOUN
cana-2739	87	29	using	use	VERB
cana-2739	87	30	knn	knn	PROPN
cana-2739	87	31	,	,	PUNCT
cana-2739	87	32	svm	svm	ADJ
cana-2739	87	33	,	,	PUNCT
cana-2739	87	34	naïve	naïve	ADJ
cana-2739	87	35	bayes	bayes	NOUN
cana-2739	87	36	,	,	PUNCT
cana-2739	87	37	decision	decision	NOUN
cana-2739	87	38	tree	tree	NOUN
cana-2739	87	39	and	and	CCONJ
cana-2739	87	40	ensemble	ensemble	ADJ
cana-2739	87	41	classifiers	classifier	NOUN
cana-2739	87	42	(	(	PUNCT
cana-2739	87	43	sheela	sheela	PROPN
cana-2739	87	44	et	et	PROPN
cana-2739	87	45	al	al	PROPN
cana-2739	87	46	.	.	PROPN
cana-2739	87	47	,	,	PUNCT
cana-2739	87	48	2020	2020	NUM
cana-2739	87	49	)	)	PUNCT
cana-2739	87	50	.	.	PUNCT
cana-2739	88	1	the	the	DET
cana-2739	88	2	dataset	dataset	NOUN
cana-2739	88	3	used	use	VERB
cana-2739	88	4	in	in	ADP
cana-2739	88	5	the	the	DET
cana-2739	88	6	research	research	NOUN
cana-2739	88	7	consists	consist	VERB
cana-2739	88	8	of	of	ADP
cana-2739	88	9	100	100	NUM
cana-2739	88	10	ultrasound	ultrasound	NOUN
cana-2739	88	11	images	image	NOUN
cana-2739	88	12	,	,	PUNCT
cana-2739	88	13	out	out	ADP
cana-2739	88	14	of	of	ADP
cana-2739	88	15	which	which	PRON
cana-2739	88	16	50	50	NUM
cana-2739	88	17	images	image	NOUN
cana-2739	88	18	are	be	AUX
cana-2739	88	19	of	of	ADP
cana-2739	88	20	normal	normal	ADJ
cana-2739	88	21	ovaries	ovary	NOUN
cana-2739	88	22	and	and	CCONJ
cana-2739	88	23	remaining	remain	VERB
cana-2739	88	24	50	50	NUM
cana-2739	88	25	are	be	AUX
cana-2739	88	26	of	of	ADP
cana-2739	88	27	cystic	cystic	ADJ
cana-2739	88	28	ovaries	ovary	NOUN
cana-2739	88	29	.	.	PUNCT
cana-2739	89	1	the	the	DET
cana-2739	89	2	comparative	comparative	ADJ
cana-2739	89	3	analysis	analysis	NOUN
cana-2739	89	4	of	of	ADP
cana-2739	89	5	the	the	DET
cana-2739	89	6	three	three	NUM
cana-2739	89	7	texture	texture	NOUN
cana-2739	89	8	features	feature	NOUN
cana-2739	89	9	proves	prove	VERB
cana-2739	89	10	that	that	SCONJ
cana-2739	89	11	lbp	lbp	PROPN
cana-2739	89	12	is	be	AUX
cana-2739	89	13	the	the	DET
cana-2739	89	14	most	most	ADV
cana-2739	89	15	appropriate	appropriate	ADJ
cana-2739	89	16	texture	texture	ADJ
cana-2739	89	17	feature	feature	NOUN
cana-2739	89	18	for	for	ADP
cana-2739	89	19	ovarian	ovarian	ADJ
cana-2739	89	20	cyst	cyst	NOUN
cana-2739	89	21	classification	classification	NOUN
cana-2739	89	22	.	.	PUNCT
cana-2739	90	1	3	3	X
cana-2739	90	2	.	.	X
cana-2739	90	3	materials	material	NOUN
cana-2739	90	4	and	and	CCONJ
cana-2739	90	5	methods	method	NOUN
cana-2739	90	6	3.1	3.1	NUM
cana-2739	90	7	data	datum	NOUN
cana-2739	90	8	acquisition	acquisition	NOUN
cana-2739	90	9	the	the	DET
cana-2739	90	10	dataset	dataset	NOUN
cana-2739	90	11	used	use	VERB
cana-2739	90	12	for	for	ADP
cana-2739	90	13	experimentation	experimentation	NOUN
cana-2739	90	14	comprises	comprise	NOUN
cana-2739	90	15	of	of	ADP
cana-2739	90	16	transvaginal	transvaginal	ADJ
cana-2739	90	17	and	and	CCONJ
cana-2739	90	18	abdominal	abdominal	ADJ
cana-2739	90	19	ultrasound	ultrasound	ADJ
cana-2739	90	20	scan	scan	NOUN
cana-2739	90	21	images	image	NOUN
cana-2739	90	22	that	that	PRON
cana-2739	90	23	are	be	AUX
cana-2739	90	24	collected	collect	VERB
cana-2739	90	25	from	from	ADP
cana-2739	90	26	the	the	DET
cana-2739	90	27	“	"	PUNCT
cana-2739	90	28	skop	skop	NOUN
cana-2739	90	29	”	"	PUNCT
cana-2739	90	30	medical	medical	ADJ
cana-2739	90	31	diagnostic	diagnostic	ADJ
cana-2739	90	32	centre	centre	NOUN
cana-2739	90	33	located	locate	VERB
cana-2739	90	34	in	in	ADP
cana-2739	90	35	agra	agra	PROPN
cana-2739	90	36	,	,	PUNCT
cana-2739	90	37	india	india	PROPN
cana-2739	90	38	.	.	PUNCT
cana-2739	91	1	the	the	DET
cana-2739	91	2	dataset	dataset	NOUN
cana-2739	91	3	is	be	AUX
cana-2739	91	4	captured	capture	VERB
cana-2739	91	5	on	on	ADP
cana-2739	91	6	a	a	DET
cana-2739	91	7	toshiba	toshiba	PROPN
cana-2739	91	8	xario	xario	PROPN
cana-2739	91	9	100	100	NUM
cana-2739	91	10	ultrasound	ultrasound	NOUN
cana-2739	91	11	machine	machine	NOUN
cana-2739	91	12	with	with	ADP
cana-2739	91	13	probe	probe	NOUN
cana-2739	91	14	frequency	frequency	NOUN
cana-2739	91	15	of	of	ADP
cana-2739	91	16	11mhz	11mhz	NOUN
cana-2739	91	17	.	.	PUNCT
cana-2739	92	1	the	the	DET
cana-2739	92	2	parameters	parameter	NOUN
cana-2739	92	3	of	of	ADP
cana-2739	92	4	ultrasound	ultrasound	NOUN
cana-2739	92	5	machine	machine	NOUN
cana-2739	92	6	are	be	AUX
cana-2739	92	7	adjusted	adjust	VERB
cana-2739	92	8	by	by	ADP
cana-2739	92	9	the	the	DET
cana-2739	92	10	radiologist	radiologist	NOUN
cana-2739	92	11	.	.	PUNCT
cana-2739	93	1	the	the	DET
cana-2739	93	2	cases	case	NOUN
cana-2739	93	3	dealing	deal	VERB
cana-2739	93	4	with	with	ADP
cana-2739	93	5	previous	previous	ADJ
cana-2739	93	6	ovarian	ovarian	ADJ
cana-2739	93	7	surgeries	surgery	NOUN
cana-2739	93	8	,	,	PUNCT
cana-2739	93	9	pregnancy	pregnancy	NOUN
cana-2739	93	10	and	and	CCONJ
cana-2739	93	11	inferior	inferior	ADJ
cana-2739	93	12	quality	quality	NOUN
cana-2739	93	13	of	of	ADP
cana-2739	93	14	images	image	NOUN
cana-2739	93	15	are	be	AUX
cana-2739	93	16	discarded	discard	VERB
cana-2739	93	17	.	.	PUNCT
cana-2739	94	1	eventually	eventually	ADV
cana-2739	94	2	1203	1203	NUM
cana-2739	94	3	ultrasound	ultrasound	NOUN
cana-2739	94	4	images	image	NOUN
cana-2739	94	5	of	of	ADP
cana-2739	94	6	ovaries	ovary	NOUN
cana-2739	94	7	are	be	AUX
cana-2739	94	8	used	use	VERB
cana-2739	94	9	in	in	ADP
cana-2739	94	10	the	the	DET
cana-2739	94	11	research	research	NOUN
cana-2739	94	12	.	.	PUNCT
cana-2739	95	1	the	the	DET
cana-2739	95	2	dataset	dataset	NOUN
cana-2739	95	3	has	have	AUX
cana-2739	95	4	been	be	AUX
cana-2739	95	5	previously	previously	ADV
cana-2739	95	6	anonymized	anonymize	VERB
cana-2739	95	7	to	to	PART
cana-2739	95	8	ensure	ensure	VERB
cana-2739	95	9	the	the	DET
cana-2739	95	10	privacy	privacy	NOUN
cana-2739	95	11	and	and	CCONJ
cana-2739	95	12	confidentiality	confidentiality	NOUN
cana-2739	95	13	of	of	ADP
cana-2739	95	14	the	the	DET
cana-2739	95	15	individuals	individual	NOUN
cana-2739	95	16	depicted	depict	VERB
cana-2739	95	17	,	,	PUNCT
cana-2739	95	18	and	and	CCONJ
cana-2739	95	19	no	no	DET
cana-2739	95	20	identifiable	identifiable	ADJ
cana-2739	95	21	information	information	NOUN
cana-2739	95	22	is	be	AUX
cana-2739	95	23	associated	associate	VERB
cana-2739	95	24	with	with	ADP
cana-2739	95	25	the	the	DET
cana-2739	95	26	images	image	NOUN
cana-2739	95	27	.	.	PUNCT
cana-2739	96	1	3.2	3.2	NUM
cana-2739	96	2	methodology	methodology	NOUN
cana-2739	96	3	the	the	DET
cana-2739	96	4	method	method	NOUN
cana-2739	96	5	of	of	ADP
cana-2739	96	6	ovarian	ovarian	ADJ
cana-2739	96	7	cyst	cyst	NOUN
cana-2739	96	8	classification	classification	NOUN
cana-2739	96	9	comprises	comprise	NOUN
cana-2739	96	10	of	of	ADP
cana-2739	96	11	number	number	NOUN
cana-2739	96	12	of	of	ADP
cana-2739	96	13	steps	step	NOUN
cana-2739	96	14	which	which	PRON
cana-2739	96	15	starts	start	VERB
cana-2739	96	16	with	with	ADP
cana-2739	96	17	preprocessing	preprocessing	NOUN
cana-2739	96	18	of	of	ADP
cana-2739	96	19	input	input	NOUN
cana-2739	96	20	ultrasound	ultrasound	NOUN
cana-2739	96	21	images	image	NOUN
cana-2739	96	22	and	and	CCONJ
cana-2739	96	23	finally	finally	ADV
cana-2739	96	24	ends	end	VERB
cana-2739	96	25	in	in	ADP
cana-2739	96	26	evaluation	evaluation	NOUN
cana-2739	96	27	of	of	ADP
cana-2739	96	28	the	the	DET
cana-2739	96	29	machine	machine	NOUN
cana-2739	96	30	learning	learn	VERB
cana-2739	96	31	classifiers	classifier	NOUN
cana-2739	96	32	.	.	PUNCT
cana-2739	97	1	the	the	DET
cana-2739	97	2	overall	overall	ADJ
cana-2739	97	3	methodology	methodology	NOUN
cana-2739	97	4	is	be	AUX
cana-2739	97	5	shown	show	VERB
cana-2739	97	6	in	in	ADP
cana-2739	97	7	figure	figure	NOUN
cana-2739	97	8	1	1	NUM
cana-2739	97	9	.	.	X
cana-2739	97	10	3.2.1	3.2.1	NUM
cana-2739	97	11	image	image	NOUN
cana-2739	97	12	preprocessing	preprocesse	VERB
cana-2739	97	13	image	image	NOUN
cana-2739	97	14	preprocessing	preprocessing	NOUN
cana-2739	97	15	is	be	AUX
cana-2739	97	16	the	the	DET
cana-2739	97	17	foremost	foremost	ADJ
cana-2739	97	18	step	step	NOUN
cana-2739	97	19	performed	perform	VERB
cana-2739	97	20	on	on	ADP
cana-2739	97	21	the	the	DET
cana-2739	97	22	ultrasound	ultrasound	NOUN
cana-2739	97	23	images	image	NOUN
cana-2739	97	24	to	to	PART
cana-2739	97	25	enhance	enhance	VERB
cana-2739	97	26	the	the	DET
cana-2739	97	27	quality	quality	NOUN
cana-2739	97	28	and	and	CCONJ
cana-2739	97	29	crucial	crucial	ADJ
cana-2739	97	30	features	feature	NOUN
cana-2739	97	31	stored	store	VERB
cana-2739	97	32	in	in	ADP
cana-2739	97	33	them	they	PRON
cana-2739	97	34	.	.	PUNCT
cana-2739	98	1	firstly	firstly	ADV
cana-2739	98	2	,	,	PUNCT
cana-2739	98	3	the	the	DET
cana-2739	98	4	ultrasound	ultrasound	NOUN
cana-2739	98	5	images	image	NOUN
cana-2739	98	6	are	be	AUX
cana-2739	98	7	converted	convert	VERB
cana-2739	98	8	from	from	ADP
cana-2739	98	9	rgb	rgb	PROPN
cana-2739	98	10	scale	scale	NOUN
cana-2739	98	11	to	to	ADP
cana-2739	98	12	grey	grey	ADJ
cana-2739	98	13	scale	scale	NOUN
cana-2739	98	14	images	image	NOUN
cana-2739	98	15	.	.	PUNCT
cana-2739	99	1	then	then	ADV
cana-2739	99	2	,	,	PUNCT
cana-2739	99	3	region	region	NOUN
cana-2739	99	4	of	of	ADP
cana-2739	99	5	interest	interest	NOUN
cana-2739	99	6	(	(	PUNCT
cana-2739	99	7	roi	roi	NOUN
cana-2739	99	8	)	)	PUNCT
cana-2739	99	9	is	be	AUX
cana-2739	99	10	drawn	draw	VERB
cana-2739	99	11	out	out	ADP
cana-2739	99	12	from	from	ADP
cana-2739	99	13	the	the	DET
cana-2739	99	14	grey	grey	ADJ
cana-2739	99	15	scale	scale	NOUN
cana-2739	99	16	ultrasound	ultrasound	NOUN
cana-2739	99	17	images	image	NOUN
cana-2739	99	18	communications	communication	NOUN
cana-2739	99	19	on	on	ADP
cana-2739	99	20	applied	apply	VERB
cana-2739	99	21	nonlinear	nonlinear	ADJ
cana-2739	99	22	analysis	analysis	NOUN
cana-2739	99	23	issn	issn	NOUN
cana-2739	99	24	:	:	PUNCT
cana-2739	99	25	1074	1074	NUM
cana-2739	99	26	-	-	PUNCT
cana-2739	99	27	133x	133x	NUM
cana-2739	99	28	vol	vol	NOUN
cana-2739	99	29	32	32	NUM
cana-2739	99	30	no	no	NOUN
cana-2739	99	31	.	.	PUNCT
cana-2739	100	1	4s	4s	NUM
cana-2739	100	2	(	(	PUNCT
cana-2739	100	3	2025	2025	NUM
cana-2739	100	4	)	)	PUNCT
cana-2739	100	5	63	63	NUM
cana-2739	100	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2739	100	7	by	by	ADP
cana-2739	100	8	cropping	crop	VERB
cana-2739	100	9	the	the	DET
cana-2739	100	10	image	image	NOUN
cana-2739	100	11	to	to	ADP
cana-2739	100	12	400	400	NUM
cana-2739	100	13			NOUN
cana-2739	100	14	300	300	NUM
cana-2739	100	15	-	-	PUNCT
cana-2739	100	16	pixel	pixel	NOUN
cana-2739	100	17	size	size	NOUN
cana-2739	100	18	to	to	PART
cana-2739	100	19	simplify	simplify	VERB
cana-2739	100	20	the	the	DET
cana-2739	100	21	classification	classification	NOUN
cana-2739	100	22	method	method	NOUN
cana-2739	100	23	.	.	PUNCT
cana-2739	101	1	for	for	ADP
cana-2739	101	2	further	further	ADJ
cana-2739	101	3	processing	processing	NOUN
cana-2739	101	4	,	,	PUNCT
cana-2739	101	5	the	the	DET
cana-2739	101	6	quality	quality	NOUN
cana-2739	101	7	of	of	ADP
cana-2739	101	8	the	the	DET
cana-2739	101	9	ultrasound	ultrasound	NOUN
cana-2739	101	10	images	image	NOUN
cana-2739	101	11	needs	need	VERB
cana-2739	101	12	figure	figure	NOUN
cana-2739	101	13	1	1	NUM
cana-2739	101	14	methodology	methodology	NOUN
cana-2739	101	15	.	.	PUNCT
cana-2739	102	1	to	to	PART
cana-2739	102	2	be	be	AUX
cana-2739	102	3	improved	improve	VERB
cana-2739	102	4	by	by	ADP
cana-2739	102	5	removing	remove	VERB
cana-2739	102	6	speckle	speckle	NOUN
cana-2739	102	7	noise	noise	NOUN
cana-2739	102	8	from	from	ADP
cana-2739	102	9	them	they	PRON
cana-2739	102	10	(	(	PUNCT
cana-2739	102	11	pradeep	pradeep	PROPN
cana-2739	102	12	&	&	CCONJ
cana-2739	102	13	nirmaladevi	nirmaladevi	PROPN
cana-2739	102	14	,	,	PUNCT
cana-2739	102	15	2021	2021	NUM
cana-2739	102	16	)	)	PUNCT
cana-2739	102	17	.	.	PUNCT
cana-2739	103	1	speckle	speckle	NOUN
cana-2739	103	2	noise	noise	NOUN
cana-2739	103	3	is	be	AUX
cana-2739	103	4	automatically	automatically	ADV
cana-2739	103	5	added	add	VERB
cana-2739	103	6	in	in	ADP
cana-2739	103	7	the	the	DET
cana-2739	103	8	process	process	NOUN
cana-2739	103	9	of	of	ADP
cana-2739	103	10	acquisition	acquisition	NOUN
cana-2739	103	11	of	of	ADP
cana-2739	103	12	the	the	DET
cana-2739	103	13	image	image	NOUN
cana-2739	103	14	;	;	PUNCT
cana-2739	103	15	for	for	ADP
cana-2739	103	16	instance	instance	NOUN
cana-2739	103	17	,	,	PUNCT
cana-2739	103	18	it	it	PRON
cana-2739	103	19	is	be	AUX
cana-2739	103	20	produced	produce	VERB
cana-2739	103	21	owing	owe	VERB
cana-2739	103	22	to	to	ADP
cana-2739	103	23	the	the	DET
cana-2739	103	24	fact	fact	NOUN
cana-2739	103	25	that	that	SCONJ
cana-2739	103	26	head	head	NOUN
cana-2739	103	27	of	of	ADP
cana-2739	103	28	the	the	DET
cana-2739	103	29	ultrasound	ultrasound	NOUN
cana-2739	103	30	machine	machine	NOUN
cana-2739	103	31	is	be	AUX
cana-2739	103	32	not	not	PART
cana-2739	103	33	moisture	moisture	NOUN
cana-2739	103	34	-	-	PUNCT
cana-2739	103	35	laden	laden	ADJ
cana-2739	103	36	.	.	PUNCT
cana-2739	104	1	the	the	DET
cana-2739	104	2	task	task	NOUN
cana-2739	104	3	of	of	ADP
cana-2739	104	4	removing	remove	VERB
cana-2739	104	5	speckle	speckle	NOUN
cana-2739	104	6	noise	noise	NOUN
cana-2739	104	7	is	be	AUX
cana-2739	104	8	performed	perform	VERB
cana-2739	104	9	by	by	ADP
cana-2739	104	10	fast	fast	ADJ
cana-2739	105	1	no	no	DET
cana-2739	105	2	local	local	ADJ
cana-2739	105	3	-	-	PUNCT
cana-2739	105	4	means	means	NOUN
cana-2739	105	5	(	(	PUNCT
cana-2739	105	6	nl	nl	NOUN
cana-2739	105	7	)	)	PUNCT
cana-2739	105	8	filter	filter	NOUN
cana-2739	105	9	(	(	PUNCT
cana-2739	105	10	pang	pang	NOUN
cana-2739	105	11	et	et	PROPN
cana-2739	105	12	al	al	PROPN
cana-2739	105	13	.	.	PROPN
cana-2739	105	14	,	,	PUNCT
cana-2739	105	15	2009	2009	NUM
cana-2739	105	16	)	)	PUNCT
cana-2739	105	17	.	.	PUNCT
cana-2739	106	1	the	the	DET
cana-2739	106	2	fast	fast	ADJ
cana-2739	106	3	nl	nl	PROPN
cana-2739	106	4	filter	filter	NOUN
cana-2739	106	5	algorithm	algorithm	NOUN
cana-2739	106	6	works	work	VERB
cana-2739	106	7	on	on	ADP
cana-2739	106	8	the	the	DET
cana-2739	106	9	similarity	similarity	NOUN
cana-2739	106	10	principle	principle	NOUN
cana-2739	106	11	of	of	ADP
cana-2739	106	12	spatially	spatially	ADV
cana-2739	106	13	sampled	sample	VERB
cana-2739	106	14	pixels	pixel	NOUN
cana-2739	106	15	using	use	VERB
cana-2739	106	16	equation	equation	NOUN
cana-2739	106	17	(	(	PUNCT
cana-2739	106	18	1	1	NUM
cana-2739	106	19	)	)	PUNCT
cana-2739	106	20	.	.	PUNCT
cana-2739	107	1	it	it	PRON
cana-2739	107	2	produces	produce	VERB
cana-2739	107	3	better	well	ADJ
cana-2739	107	4	quality	quality	NOUN
cana-2739	107	5	image	image	NOUN
cana-2739	107	6	in	in	ADP
cana-2739	107	7	less	less	ADJ
cana-2739	107	8	processing	processing	NOUN
cana-2739	107	9	time	time	NOUN
cana-2739	107	10	.	.	PUNCT
cana-2739	108	1	communications	communication	NOUN
cana-2739	108	2	on	on	ADP
cana-2739	108	3	applied	apply	VERB
cana-2739	108	4	nonlinear	nonlinear	ADJ
cana-2739	108	5	analysis	analysis	NOUN
cana-2739	108	6	issn	issn	NOUN
cana-2739	108	7	:	:	PUNCT
cana-2739	108	8	1074	1074	NUM
cana-2739	108	9	-	-	PUNCT
cana-2739	108	10	133x	133x	NUM
cana-2739	108	11	vol	vol	NOUN
cana-2739	108	12	32	32	NUM
cana-2739	108	13	no	no	NOUN
cana-2739	108	14	.	.	PUNCT
cana-2739	109	1	4s	4s	NUM
cana-2739	109	2	(	(	PUNCT
cana-2739	109	3	2025	2025	NUM
cana-2739	109	4	)	)	PUNCT
cana-2739	109	5	64	64	NUM
cana-2739	109	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2739	109	7	𝑖𝑓	𝑖𝑓	ADP
cana-2739	110	1	|𝑣(𝑖	|𝑣(𝑖	NOUN
cana-2739	110	2	,	,	PUNCT
cana-2739	110	3	𝑗	𝑗	NOUN
cana-2739	110	4	)	)	PUNCT
cana-2739	110	5	−	−	ADP
cana-2739	110	6	𝑣(𝑘	𝑣(𝑘	PROPN
cana-2739	110	7	,	,	PUNCT
cana-2739	110	8	𝑙)|	𝑙)|	NOUN
cana-2739	110	9	<	<	X
cana-2739	110	10	𝑇ℎ𝑢	𝑇ℎ𝑢	PROPN
cana-2739	110	11	+	+	CCONJ
cana-2739	110	12	2√2σ𝑛	2√2σ𝑛	NUM
cana-2739	110	13	𝑎𝑛𝑑	𝑎𝑛𝑑	NOUN
cana-2739	110	14	(	(	PUNCT
cana-2739	110	15	(	(	PUNCT
cana-2739	110	16	|𝑣(𝑖	|𝑣(𝑖	NOUN
cana-2739	110	17	+	+	CCONJ
cana-2739	110	18	1	1	NUM
cana-2739	110	19	,	,	PUNCT
cana-2739	110	20	𝑗	𝑗	NOUN
cana-2739	110	21	)	)	PUNCT
cana-2739	110	22	−	−	PROPN
cana-2739	111	1	𝑣(𝑘	𝑣(𝑘	NOUN
cana-2739	111	2	+	+	CCONJ
cana-2739	111	3	1	1	NUM
cana-2739	111	4	,	,	PUNCT
cana-2739	111	5	𝑙)|	𝑙)|	VERB
cana-2739	111	6	<	<	X
cana-2739	111	7	𝑇ℎ𝑢	𝑇ℎ𝑢	PROPN
cana-2739	111	8	+	+	CCONJ
cana-2739	111	9	2√2σ𝑛	2√2σ𝑛	NUM
cana-2739	111	10	)	)	PUNCT
cana-2739	111	11	𝑜𝑟	𝑜𝑟	NOUN
cana-2739	111	12	(	(	PUNCT
cana-2739	111	13	|𝑣(𝑖	|𝑣(𝑖	NOUN
cana-2739	111	14	−	−	PROPN
cana-2739	111	15	1	1	NUM
cana-2739	111	16	,	,	PUNCT
cana-2739	111	17	𝑗	𝑗	NOUN
cana-2739	111	18	)	)	PUNCT
cana-2739	112	1	−	−	PROPN
cana-2739	113	1	𝑣(𝑘	𝑣(𝑘	ADP
cana-2739	113	2	−	−	NOUN
cana-2739	113	3	1	1	NUM
cana-2739	113	4	,	,	PUNCT
cana-2739	113	5	𝑙)|	𝑙)|	VERB
cana-2739	113	6	<	<	X
cana-2739	113	7	𝑇ℎ𝑢	𝑇ℎ𝑢	PROPN
cana-2739	114	1	+	+	CCONJ
cana-2739	114	2	2√2σ𝑛	2√2σ𝑛	NUM
cana-2739	114	3	𝑜𝑟	𝑜𝑟	NOUN
cana-2739	114	4	(	(	PUNCT
cana-2739	114	5	|𝑣(𝑖	|𝑣(𝑖	NOUN
cana-2739	114	6	,	,	PUNCT
cana-2739	114	7	𝑗	𝑗	NOUN
cana-2739	114	8	−	−	PROPN
cana-2739	114	9	1	1	NUM
cana-2739	114	10	)	)	PUNCT
cana-2739	114	11	−	−	ADP
cana-2739	115	1	𝑣(𝑘	𝑣(𝑘	PROPN
cana-2739	115	2	,	,	PUNCT
cana-2739	115	3	𝑙	𝑙	PROPN
cana-2739	115	4	−	−	PROPN
cana-2739	115	5	1)|	1)|	NUM
cana-2739	115	6	<	<	X
cana-2739	115	7	𝑇ℎ𝑢	𝑇ℎ𝑢	PROPN
cana-2739	115	8	+	+	CCONJ
cana-2739	115	9	2√2σ𝑛	2√2σ𝑛	NUM
cana-2739	115	10	𝑜𝑟	𝑜𝑟	NOUN
cana-2739	115	11	(	(	PUNCT
cana-2739	115	12	|𝑣(𝑖	|𝑣(𝑖	NOUN
cana-2739	115	13	,	,	PUNCT
cana-2739	115	14	𝑗	𝑗	NOUN
cana-2739	115	15	+	+	ADJ
cana-2739	115	16	1	1	NUM
cana-2739	115	17	)	)	PUNCT
cana-2739	115	18	−	−	ADP
cana-2739	115	19	𝑣(𝑘	𝑣(𝑘	PROPN
cana-2739	115	20	,	,	PUNCT
cana-2739	115	21	𝑙	𝑙	PROPN
cana-2739	115	22	+	+	NUM
cana-2739	115	23	1)|	1)|	NUM
cana-2739	115	24	<	<	X
cana-2739	115	25	𝑇ℎ𝑢	𝑇ℎ𝑢	PROPN
cana-2739	115	26	+	+	CCONJ
cana-2739	115	27	2√2σ𝑛	2√2σ𝑛	NUM
cana-2739	115	28	𝑁𝑖,𝑗𝑎𝑛𝑑	𝑁𝑖,𝑗𝑎𝑛𝑑	ADJ
cana-2739	115	29	𝑁𝑘,𝑙𝑎𝑟𝑒	𝑁𝑘,𝑙𝑎𝑟𝑒	PROPN
cana-2739	115	30	𝑠𝑖𝑚𝑖𝑙𝑎𝑟	𝑠𝑖𝑚𝑖𝑙𝑎𝑟	PROPN
cana-2739	115	31	𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑢𝑟𝑠	𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑢𝑟𝑠	ADJ
cana-2739	115	32	,	,	PUNCT
cana-2739	115	33	𝑒𝑙𝑠𝑒	𝑒𝑙𝑠𝑒	ADJ
cana-2739	115	34	𝑁𝑖,𝑗𝑎𝑛𝑑	𝑁𝑖,𝑗𝑎𝑛𝑑	PROPN
cana-2739	115	35	𝑁𝑘,𝑙𝑎𝑟𝑒	𝑁𝑘,𝑙𝑎𝑟𝑒	PROPN
cana-2739	115	36	𝑢𝑛𝑟𝑒𝑙𝑎𝑡𝑒𝑑	𝑢𝑛𝑟𝑒𝑙𝑎𝑡𝑒𝑑	NOUN
cana-2739	115	37	(	(	PUNCT
cana-2739	115	38	1	1	NUM
cana-2739	115	39	)	)	PUNCT
cana-2739	115	40	in	in	ADP
cana-2739	115	41	equation	equation	NOUN
cana-2739	115	42	(	(	PUNCT
cana-2739	115	43	1	1	NUM
cana-2739	115	44	)	)	PUNCT
cana-2739	115	45	,	,	PUNCT
cana-2739	115	46	v(i	v(i	NUM
cana-2739	115	47	,	,	PUNCT
cana-2739	115	48	j	j	NOUN
cana-2739	115	49	)	)	PUNCT
cana-2739	115	50	and	and	CCONJ
cana-2739	115	51	v(k	v(k	PROPN
cana-2739	115	52	,	,	PUNCT
cana-2739	115	53	l	l	NOUN
cana-2739	115	54	)	)	PUNCT
cana-2739	115	55	represents	represent	VERB
cana-2739	115	56	the	the	DET
cana-2739	115	57	pixel	pixel	PROPN
cana-2739	115	58	values	value	NOUN
cana-2739	115	59	at	at	ADP
cana-2739	115	60	the	the	DET
cana-2739	115	61	locations	location	NOUN
cana-2739	115	62	(	(	PUNCT
cana-2739	115	63	i	i	PROPN
cana-2739	115	64	,	,	PUNCT
cana-2739	115	65	j	j	PROPN
cana-2739	115	66	)	)	PUNCT
cana-2739	115	67	and	and	CCONJ
cana-2739	115	68	(	(	PUNCT
cana-2739	115	69	k	k	X
cana-2739	115	70	,	,	PUNCT
cana-2739	115	71	l	l	NOUN
cana-2739	115	72	)	)	PUNCT
cana-2739	115	73	;	;	PUNCT
cana-2739	115	74	σn	σn	PROPN
cana-2739	115	75	refers	refer	VERB
cana-2739	115	76	to	to	ADP
cana-2739	115	77	the	the	DET
cana-2739	115	78	variance	variance	NOUN
cana-2739	115	79	related	relate	VERB
cana-2739	115	80	to	to	ADP
cana-2739	115	81	noise	noise	NOUN
cana-2739	115	82	,	,	PUNCT
cana-2739	115	83	n(i	n(i	PROPN
cana-2739	115	84	,	,	PUNCT
cana-2739	115	85	j	j	PROPN
cana-2739	115	86	)	)	PUNCT
cana-2739	115	87	;	;	PUNCT
cana-2739	115	88	thu	thu	PROPN
cana-2739	115	89	is	be	AUX
cana-2739	115	90	the	the	DET
cana-2739	115	91	threshold	threshold	NOUN
cana-2739	115	92	for	for	ADP
cana-2739	115	93	the	the	DET
cana-2739	115	94	absolute	absolute	ADJ
cana-2739	115	95	difference	difference	NOUN
cana-2739	115	96	.	.	PUNCT
cana-2739	116	1	figure	figure	NOUN
cana-2739	116	2	2	2	NUM
cana-2739	116	3	shows	show	VERB
cana-2739	116	4	an	an	DET
cana-2739	116	5	ultrasound	ultrasound	ADJ
cana-2739	116	6	image	image	NOUN
cana-2739	116	7	having	have	VERB
cana-2739	116	8	speckle	speckle	NOUN
cana-2739	116	9	noise	noise	NOUN
cana-2739	116	10	as	as	ADP
cana-2739	116	11	left	left	ADJ
cana-2739	116	12	-	-	PUNCT
cana-2739	116	13	hand	hand	NOUN
cana-2739	116	14	side	side	NOUN
cana-2739	116	15	image	image	NOUN
cana-2739	116	16	and	and	CCONJ
cana-2739	116	17	speckle	speckle	NOUN
cana-2739	116	18	noise	noise	NOUN
cana-2739	116	19	free	free	ADJ
cana-2739	116	20	ultrasound	ultrasound	NOUN
cana-2739	116	21	image	image	NOUN
cana-2739	116	22	produced	produce	VERB
cana-2739	116	23	by	by	ADP
cana-2739	116	24	fast	fast	ADJ
cana-2739	116	25	nl	nl	NOUN
cana-2739	116	26	filter	filter	NOUN
cana-2739	116	27	as	as	ADP
cana-2739	116	28	right	right	ADJ
cana-2739	116	29	-	-	PUNCT
cana-2739	116	30	hand	hand	NOUN
cana-2739	116	31	side	side	NOUN
cana-2739	116	32	image	image	NOUN
cana-2739	116	33	.	.	PUNCT
cana-2739	117	1	figure	figure	NOUN
cana-2739	117	2	2	2	NUM
cana-2739	117	3	ultrasound	ultrasound	NOUN
cana-2739	117	4	with	with	ADP
cana-2739	117	5	speckle	speckle	NOUN
cana-2739	117	6	noise	noise	NOUN
cana-2739	117	7	(	(	PUNCT
cana-2739	117	8	left	left	ADV
cana-2739	117	9	)	)	PUNCT
cana-2739	117	10	,	,	PUNCT
cana-2739	117	11	ultrasound	ultrasound	VERB
cana-2739	117	12	with	with	ADP
cana-2739	117	13	no	no	DET
cana-2739	117	14	speckle	speckle	NOUN
cana-2739	117	15	noise	noise	NOUN
cana-2739	117	16	(	(	PUNCT
cana-2739	117	17	right	right	ADJ
cana-2739	117	18	)	)	PUNCT
cana-2739	117	19	.	.	PUNCT
cana-2739	118	1	3.2.2	3.2.2	NUM
cana-2739	118	2	feature	feature	NOUN
cana-2739	118	3	extraction	extraction	NOUN
cana-2739	118	4	in	in	ADP
cana-2739	118	5	the	the	DET
cana-2739	118	6	classification	classification	NOUN
cana-2739	118	7	of	of	ADP
cana-2739	118	8	medical	medical	ADJ
cana-2739	118	9	images	image	NOUN
cana-2739	118	10	,	,	PUNCT
cana-2739	118	11	unsheathing	unsheathing	NOUN
cana-2739	118	12	of	of	ADP
cana-2739	118	13	distinguishing	distinguish	VERB
cana-2739	118	14	features	feature	NOUN
cana-2739	118	15	from	from	ADP
cana-2739	118	16	the	the	DET
cana-2739	118	17	images	image	NOUN
cana-2739	118	18	is	be	AUX
cana-2739	118	19	an	an	DET
cana-2739	118	20	essential	essential	ADJ
cana-2739	118	21	and	and	CCONJ
cana-2739	118	22	principal	principal	ADJ
cana-2739	118	23	task	task	NOUN
cana-2739	118	24	.	.	PUNCT
cana-2739	119	1	these	these	DET
cana-2739	119	2	features	feature	NOUN
cana-2739	119	3	are	be	AUX
cana-2739	119	4	given	give	VERB
cana-2739	119	5	as	as	ADP
cana-2739	119	6	input	input	NOUN
cana-2739	119	7	to	to	ADP
cana-2739	119	8	the	the	DET
cana-2739	119	9	machine	machine	NOUN
cana-2739	119	10	learning	learn	VERB
cana-2739	119	11	algorithms	algorithm	NOUN
cana-2739	119	12	for	for	ADP
cana-2739	119	13	training	training	NOUN
cana-2739	119	14	purposes	purpose	NOUN
cana-2739	119	15	.	.	PUNCT
cana-2739	120	1	thus	thus	ADV
cana-2739	120	2	,	,	PUNCT
cana-2739	120	3	the	the	DET
cana-2739	120	4	goal	goal	NOUN
cana-2739	120	5	of	of	ADP
cana-2739	120	6	the	the	DET
cana-2739	120	7	feature	feature	NOUN
cana-2739	120	8	extraction	extraction	NOUN
cana-2739	120	9	is	be	AUX
cana-2739	120	10	to	to	PART
cana-2739	120	11	find	find	VERB
cana-2739	120	12	out	out	ADP
cana-2739	120	13	significant	significant	ADJ
cana-2739	120	14	features	feature	NOUN
cana-2739	120	15	that	that	PRON
cana-2739	120	16	can	can	AUX
cana-2739	120	17	assist	assist	VERB
cana-2739	120	18	in	in	ADP
cana-2739	120	19	efficient	efficient	ADJ
cana-2739	120	20	diagnosis	diagnosis	NOUN
cana-2739	120	21	and	and	CCONJ
cana-2739	120	22	prognosis	prognosis	NOUN
cana-2739	120	23	of	of	ADP
cana-2739	120	24	diseases	disease	NOUN
cana-2739	120	25	.	.	PUNCT
cana-2739	121	1	features	feature	NOUN
cana-2739	121	2	based	base	VERB
cana-2739	121	3	on	on	ADP
cana-2739	121	4	textures	texture	NOUN
cana-2739	121	5	in	in	ADP
cana-2739	121	6	the	the	DET
cana-2739	121	7	ultrasound	ultrasound	NOUN
cana-2739	121	8	images	image	NOUN
cana-2739	121	9	play	play	VERB
cana-2739	121	10	an	an	DET
cana-2739	121	11	important	important	ADJ
cana-2739	121	12	role	role	NOUN
cana-2739	121	13	in	in	ADP
cana-2739	121	14	medical	medical	ADJ
cana-2739	121	15	image	image	NOUN
cana-2739	121	16	classification	classification	NOUN
cana-2739	121	17	as	as	SCONJ
cana-2739	121	18	there	there	PRON
cana-2739	121	19	are	be	VERB
cana-2739	121	20	remarkable	remarkable	ADJ
cana-2739	121	21	differences	difference	NOUN
cana-2739	121	22	in	in	ADP
cana-2739	121	23	echoes	echo	NOUN
cana-2739	121	24	of	of	ADP
cana-2739	121	25	a	a	DET
cana-2739	121	26	cyst	cyst	NOUN
cana-2739	121	27	and	and	CCONJ
cana-2739	121	28	a	a	DET
cana-2739	121	29	normal	normal	ADJ
cana-2739	121	30	ultrasound	ultrasound	NOUN
cana-2739	121	31	image	image	NOUN
cana-2739	121	32	(	(	PUNCT
cana-2739	121	33	humeau	humeau	PROPN
cana-2739	121	34	-	-	PUNCT
cana-2739	121	35	heurtier	heurtier	NOUN
cana-2739	121	36	,	,	PUNCT
cana-2739	121	37	2019	2019	NUM
cana-2739	121	38	)	)	PUNCT
cana-2739	121	39	.	.	PUNCT
cana-2739	122	1	the	the	DET
cana-2739	122	2	textural	textural	ADJ
cana-2739	122	3	features	feature	NOUN
cana-2739	122	4	draw	draw	VERB
cana-2739	122	5	out	out	ADP
cana-2739	122	6	the	the	DET
cana-2739	122	7	surface	surface	NOUN
cana-2739	122	8	characteristic	characteristic	ADJ
cana-2739	122	9	along	along	ADP
cana-2739	122	10	with	with	ADP
cana-2739	122	11	dimension	dimension	NOUN
cana-2739	122	12	,	,	PUNCT
cana-2739	122	13	density	density	NOUN
cana-2739	122	14	,	,	PUNCT
cana-2739	122	15	etc	etc	X
cana-2739	122	16	.	.	X
cana-2739	122	17	from	from	ADP
cana-2739	122	18	the	the	DET
cana-2739	122	19	image	image	NOUN
cana-2739	122	20	.	.	PUNCT
cana-2739	123	1	in	in	ADP
cana-2739	123	2	this	this	DET
cana-2739	123	3	research	research	NOUN
cana-2739	123	4	,	,	PUNCT
cana-2739	123	5	fractal	fractal	ADJ
cana-2739	123	6	dimension	dimension	NOUN
cana-2739	123	7	texture	texture	NOUN
cana-2739	123	8	analysis	analysis	NOUN
cana-2739	123	9	(	(	PUNCT
cana-2739	123	10	fdta	fdta	PROPN
cana-2739	123	11	)	)	PUNCT
cana-2739	123	12	(	(	PUNCT
cana-2739	123	13	pentland	pentland	PROPN
cana-2739	123	14	,	,	PUNCT
cana-2739	123	15	1984	1984	NUM
cana-2739	123	16	)	)	PUNCT
cana-2739	123	17	and	and	CCONJ
cana-2739	123	18	local	local	ADJ
cana-2739	123	19	binary	binary	ADJ
cana-2739	123	20	pattern	pattern	NOUN
cana-2739	123	21	(	(	PUNCT
cana-2739	123	22	lbp	lbp	PROPN
cana-2739	123	23	)	)	PUNCT
cana-2739	123	24	(	(	PUNCT
cana-2739	123	25	ojala	ojala	X
cana-2739	123	26	et	et	PROPN
cana-2739	123	27	al	al	PROPN
cana-2739	123	28	.	.	PROPN
cana-2739	123	29	,	,	PUNCT
cana-2739	123	30	1996	1996	NUM
cana-2739	123	31	)	)	PUNCT
cana-2739	123	32	features	feature	NOUN
cana-2739	123	33	are	be	AUX
cana-2739	123	34	extracted	extract	VERB
cana-2739	123	35	from	from	ADP
cana-2739	123	36	the	the	DET
cana-2739	123	37	ultrasound	ultrasound	NOUN
cana-2739	123	38	images	image	NOUN
cana-2739	123	39	.	.	PUNCT
cana-2739	124	1	fractal	fractal	ADJ
cana-2739	124	2	dimension	dimension	NOUN
cana-2739	124	3	texture	texture	NOUN
cana-2739	124	4	analysis	analysis	NOUN
cana-2739	124	5	is	be	AUX
cana-2739	124	6	a	a	DET
cana-2739	124	7	procedure	procedure	NOUN
cana-2739	124	8	to	to	PART
cana-2739	124	9	measure	measure	VERB
cana-2739	124	10	the	the	DET
cana-2739	124	11	complexity	complexity	NOUN
cana-2739	124	12	of	of	ADP
cana-2739	124	13	textures	texture	NOUN
cana-2739	124	14	in	in	ADP
cana-2739	124	15	images	image	NOUN
cana-2739	124	16	.	.	PUNCT
cana-2739	125	1	it	it	PRON
cana-2739	125	2	has	have	VERB
cana-2739	125	3	its	its	PRON
cana-2739	125	4	roots	root	NOUN
cana-2739	125	5	from	from	ADP
cana-2739	125	6	fractals	fractal	NOUN
cana-2739	125	7	which	which	PRON
cana-2739	125	8	mathematically	mathematically	ADV
cana-2739	125	9	represents	represent	VERB
cana-2739	125	10	intricate	intricate	ADJ
cana-2739	125	11	and	and	CCONJ
cana-2739	125	12	self	self	NOUN
cana-2739	125	13	-	-	PUNCT
cana-2739	125	14	similar	similar	ADJ
cana-2739	125	15	patterns	pattern	NOUN
cana-2739	125	16	.	.	PUNCT
cana-2739	126	1	fractal	fractal	ADJ
cana-2739	126	2	dimension	dimension	NOUN
cana-2739	126	3	is	be	AUX
cana-2739	126	4	computed	compute	VERB
cana-2739	126	5	by	by	ADP
cana-2739	126	6	inspecting	inspect	VERB
cana-2739	126	7	the	the	DET
cana-2739	126	8	details	detail	NOUN
cana-2739	126	9	of	of	ADP
cana-2739	126	10	texture	texture	NOUN
cana-2739	126	11	with	with	ADP
cana-2739	126	12	the	the	DET
cana-2739	126	13	changes	change	NOUN
cana-2739	126	14	in	in	ADP
cana-2739	126	15	scale	scale	NOUN
cana-2739	126	16	of	of	ADP
cana-2739	126	17	observation	observation	NOUN
cana-2739	126	18	.	.	PUNCT
cana-2739	127	1	a	a	DET
cana-2739	127	2	complex	complex	ADJ
cana-2739	127	3	and	and	CCONJ
cana-2739	127	4	rough	rough	ADJ
cana-2739	127	5	texture	texture	NOUN
cana-2739	127	6	gives	give	VERB
cana-2739	127	7	a	a	DET
cana-2739	127	8	higher	high	ADJ
cana-2739	127	9	fractal	fractal	ADJ
cana-2739	127	10	dimension	dimension	NOUN
cana-2739	127	11	in	in	ADP
cana-2739	127	12	comparison	comparison	NOUN
cana-2739	127	13	with	with	ADP
cana-2739	127	14	a	a	DET
cana-2739	127	15	smooth	smooth	ADJ
cana-2739	127	16	texture	texture	NOUN
cana-2739	127	17	.	.	PUNCT
cana-2739	128	1	fractal	fractal	ADJ
cana-2739	128	2	dimension	dimension	NOUN
cana-2739	128	3	is	be	AUX
cana-2739	128	4	calculated	calculate	VERB
cana-2739	128	5	using	use	VERB
cana-2739	128	6	fractional	fractional	ADJ
cana-2739	128	7	brownian	brownian	ADJ
cana-2739	128	8	motion	motion	NOUN
cana-2739	128	9	(	(	PUNCT
cana-2739	128	10	fbm	fbm	NOUN
cana-2739	128	11	)	)	PUNCT
cana-2739	128	12	model	model	NOUN
cana-2739	128	13	(	(	PUNCT
cana-2739	128	14	decreusefond	decreusefond	NOUN
cana-2739	128	15	&	&	CCONJ
cana-2739	128	16	üstünel	üstünel	NOUN
cana-2739	128	17	,	,	PUNCT
cana-2739	128	18	1998	1998	NUM
cana-2739	128	19	)	)	PUNCT
cana-2739	128	20	.	.	PUNCT
cana-2739	129	1	the	the	DET
cana-2739	129	2	fbm	fbm	NOUN
cana-2739	129	3	model	model	NOUN
cana-2739	129	4	computes	compute	VERB
cana-2739	129	5	the	the	DET
cana-2739	129	6	smoothness	smoothness	NOUN
cana-2739	129	7	or	or	CCONJ
cana-2739	129	8	roughness	roughness	NOUN
cana-2739	129	9	of	of	ADP
cana-2739	129	10	textures	texture	NOUN
cana-2739	129	11	in	in	ADP
cana-2739	129	12	images	image	NOUN
cana-2739	129	13	.	.	PUNCT
cana-2739	130	1	hurst	hurst	PROPN
cana-2739	130	2	parameter	parameter	PROPN
cana-2739	130	3	(	(	PUNCT
cana-2739	130	4	h	h	NOUN
cana-2739	130	5	)	)	PUNCT
cana-2739	130	6	is	be	AUX
cana-2739	130	7	a	a	DET
cana-2739	130	8	key	key	ADJ
cana-2739	130	9	factor	factor	NOUN
cana-2739	130	10	in	in	ADP
cana-2739	130	11	fbm	fbm	NOUN
cana-2739	130	12	and	and	CCONJ
cana-2739	130	13	is	be	AUX
cana-2739	130	14	utilized	utilize	VERB
cana-2739	130	15	to	to	PART
cana-2739	130	16	identify	identify	VERB
cana-2739	130	17	the	the	DET
cana-2739	130	18	self	self	NOUN
cana-2739	130	19	-	-	PUNCT
cana-2739	130	20	similarity	similarity	NOUN
cana-2739	130	21	characteristic	characteristic	NOUN
cana-2739	130	22	of	of	ADP
cana-2739	130	23	fbm	fbm	NOUN
cana-2739	130	24	communications	communication	NOUN
cana-2739	130	25	on	on	ADP
cana-2739	130	26	applied	apply	VERB
cana-2739	130	27	nonlinear	nonlinear	ADJ
cana-2739	130	28	analysis	analysis	NOUN
cana-2739	130	29	issn	issn	NOUN
cana-2739	130	30	:	:	PUNCT
cana-2739	130	31	1074	1074	NUM
cana-2739	130	32	-	-	PUNCT
cana-2739	130	33	133x	133x	NUM
cana-2739	130	34	vol	vol	NOUN
cana-2739	130	35	32	32	NUM
cana-2739	130	36	no	no	NOUN
cana-2739	130	37	.	.	PUNCT
cana-2739	131	1	4s	4s	NUM
cana-2739	131	2	(	(	PUNCT
cana-2739	131	3	2025	2025	NUM
cana-2739	131	4	)	)	PUNCT
cana-2739	131	5	65	65	NUM
cana-2739	131	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2739	131	7	(	(	PUNCT
cana-2739	131	8	hurst	hurst	PROPN
cana-2739	131	9	,	,	PUNCT
cana-2739	131	10	1951	1951	NUM
cana-2739	131	11	)	)	PUNCT
cana-2739	131	12	.	.	PUNCT
cana-2739	132	1	it	it	PRON
cana-2739	132	2	is	be	AUX
cana-2739	132	3	an	an	DET
cana-2739	132	4	important	important	ADJ
cana-2739	132	5	parameter	parameter	NOUN
cana-2739	132	6	in	in	ADP
cana-2739	132	7	identifying	identify	VERB
cana-2739	132	8	similar	similar	ADJ
cana-2739	132	9	or	or	CCONJ
cana-2739	132	10	dissimilar	dissimilar	ADJ
cana-2739	132	11	textures	texture	NOUN
cana-2739	132	12	in	in	ADP
cana-2739	132	13	the	the	DET
cana-2739	132	14	image	image	NOUN
cana-2739	132	15	under	under	ADP
cana-2739	132	16	study	study	NOUN
cana-2739	132	17	(	(	PUNCT
cana-2739	132	18	yakubu	yakubu	PROPN
cana-2739	132	19	et	et	PROPN
cana-2739	132	20	al	al	PROPN
cana-2739	132	21	.	.	PROPN
cana-2739	132	22	,	,	PUNCT
cana-2739	132	23	2019	2019	NUM
cana-2739	132	24	)	)	PUNCT
cana-2739	132	25	.	.	PUNCT
cana-2739	133	1	ojala	ojala	PROPN
cana-2739	133	2	et	et	PROPN
cana-2739	133	3	al	al	PROPN
cana-2739	133	4	.	.	PROPN
cana-2739	133	5	first	first	PROPN
cana-2739	133	6	presented	present	VERB
cana-2739	133	7	local	local	ADJ
cana-2739	133	8	binary	binary	NOUN
cana-2739	133	9	pattern	pattern	NOUN
cana-2739	133	10	as	as	ADP
cana-2739	133	11	an	an	DET
cana-2739	133	12	efficient	efficient	ADJ
cana-2739	133	13	texture	texture	NOUN
cana-2739	133	14	descriptor	descriptor	NOUN
cana-2739	133	15	(	(	PUNCT
cana-2739	133	16	ojala	ojala	X
cana-2739	133	17	et	et	PROPN
cana-2739	133	18	al	al	PROPN
cana-2739	133	19	.	.	PROPN
cana-2739	133	20	,	,	PUNCT
cana-2739	133	21	1996	1996	NUM
cana-2739	133	22	)	)	PUNCT
cana-2739	133	23	.	.	PUNCT
cana-2739	134	1	lbp	lbp	PROPN
cana-2739	134	2	is	be	AUX
cana-2739	134	3	a	a	DET
cana-2739	134	4	statistical	statistical	ADJ
cana-2739	134	5	textural	textural	ADJ
cana-2739	134	6	descriptor	descriptor	NOUN
cana-2739	134	7	with	with	ADP
cana-2739	134	8	an	an	DET
cana-2739	134	9	advantage	advantage	NOUN
cana-2739	134	10	of	of	ADP
cana-2739	134	11	unvarying	unvarye	VERB
cana-2739	134	12	grey	grey	NOUN
cana-2739	134	13	and	and	CCONJ
cana-2739	134	14	rotation	rotation	NOUN
cana-2739	134	15	values	value	NOUN
cana-2739	134	16	.	.	PUNCT
cana-2739	135	1	it	it	PRON
cana-2739	135	2	considers	consider	VERB
cana-2739	135	3	a	a	DET
cana-2739	135	4	circular	circular	ADJ
cana-2739	135	5	neighbourhood	neighbourhood	NOUN
cana-2739	135	6	around	around	ADP
cana-2739	135	7	a	a	DET
cana-2739	135	8	pixel	pixel	NOUN
cana-2739	135	9	(	(	PUNCT
cana-2739	135	10	either	either	CCONJ
cana-2739	135	11	clockwise	clockwise	NOUN
cana-2739	135	12	or	or	CCONJ
cana-2739	135	13	anti	anti	ADJ
cana-2739	135	14	-	-	NOUN
cana-2739	135	15	clockwise	clockwise	NOUN
cana-2739	135	16	)	)	PUNCT
cana-2739	135	17	,	,	PUNCT
cana-2739	135	18	comprising	comprising	NOUN
cana-2739	135	19	of	of	ADP
cana-2739	135	20	p	p	X
cana-2739	135	21	equidistant	equidistant	ADJ
cana-2739	135	22	points	point	NOUN
cana-2739	135	23	with	with	ADP
cana-2739	135	24	radius	radius	PROPN
cana-2739	135	25	r.	r.	PROPN
cana-2739	135	26	the	the	DET
cana-2739	135	27	p	p	PROPN
cana-2739	135	28	points	point	NOUN
cana-2739	135	29	are	be	AUX
cana-2739	135	30	changed	change	VERB
cana-2739	135	31	to	to	ADP
cana-2739	135	32	a	a	DET
cana-2739	135	33	stream	stream	NOUN
cana-2739	135	34	of	of	ADP
cana-2739	135	35	binary	binary	ADJ
cana-2739	135	36	values	value	NOUN
cana-2739	135	37	,	,	PUNCT
cana-2739	135	38	depending	depend	VERB
cana-2739	135	39	upon	upon	SCONJ
cana-2739	135	40	the	the	DET
cana-2739	135	41	grey	grey	ADJ
cana-2739	135	42	level	level	NOUN
cana-2739	135	43	of	of	ADP
cana-2739	135	44	pixel	pixel	NOUN
cana-2739	135	45	is	be	AUX
cana-2739	135	46	less	less	ADJ
cana-2739	135	47	or	or	CCONJ
cana-2739	135	48	greater	great	ADJ
cana-2739	135	49	than	than	ADP
cana-2739	135	50	the	the	DET
cana-2739	135	51	grey	grey	ADJ
cana-2739	135	52	level	level	NOUN
cana-2739	135	53	of	of	ADP
cana-2739	135	54	pixel	pixel	NOUN
cana-2739	135	55	in	in	ADP
cana-2739	135	56	the	the	DET
cana-2739	135	57	center	center	NOUN
cana-2739	135	58	.	.	PUNCT
cana-2739	136	1	the	the	DET
cana-2739	136	2	lbp	lbp	PROPN
cana-2739	136	3	codes	code	NOUN
cana-2739	136	4	are	be	AUX
cana-2739	136	5	calculated	calculate	VERB
cana-2739	136	6	using	use	VERB
cana-2739	136	7	equation	equation	NOUN
cana-2739	136	8	(	(	PUNCT
cana-2739	136	9	2	2	NUM
cana-2739	136	10	)	)	PUNCT
cana-2739	136	11	.	.	PUNCT
cana-2739	137	1	𝐿𝐵𝑃(𝑥	𝐿𝐵𝑃(𝑥	PROPN
cana-2739	137	2	,	,	PUNCT
cana-2739	137	3	𝑦	𝑦	NOUN
cana-2739	137	4	)	)	PUNCT
cana-2739	137	5	=	=	PUNCT
cana-2739	137	6	∑	∑	PUNCT
cana-2739	137	7	𝑠(𝑖𝑝	𝑠(𝑖𝑝	NUM
cana-2739	137	8	−	−	PROPN
cana-2739	137	9	𝑖𝑐)2	𝑖𝑐)2	PROPN
cana-2739	137	10	𝑝	𝑝	PROPN
cana-2739	137	11	𝑃−1	𝑃−1	ADJ
cana-2739	137	12	𝑝=0	𝑝=0	X
cana-2739	137	13	(	(	PUNCT
cana-2739	137	14	2	2	NUM
cana-2739	137	15	)	)	PUNCT
cana-2739	137	16	where	where	SCONJ
cana-2739	137	17	p	p	NOUN
cana-2739	137	18	denotes	denote	VERB
cana-2739	137	19	the	the	DET
cana-2739	137	20	adjacent	adjacent	ADJ
cana-2739	137	21	pixel	pixel	PROPN
cana-2739	137	22	number	number	NOUN
cana-2739	137	23	,	,	PUNCT
cana-2739	137	24	ic	ic	PROPN
cana-2739	137	25	and	and	CCONJ
cana-2739	137	26	ip	ip	NOUN
cana-2739	137	27	are	be	AUX
cana-2739	137	28	the	the	DET
cana-2739	137	29	grey	grey	ADJ
cana-2739	137	30	levels	level	NOUN
cana-2739	137	31	of	of	ADP
cana-2739	137	32	center	center	ADJ
cana-2739	137	33	pixel	pixel	NOUN
cana-2739	137	34	(	(	PUNCT
cana-2739	137	35	x	x	X
cana-2739	137	36	,	,	PUNCT
cana-2739	137	37	y	y	PROPN
cana-2739	137	38	)	)	PUNCT
cana-2739	137	39	and	and	CCONJ
cana-2739	137	40	adjacent	adjacent	ADJ
cana-2739	137	41	pixel	pixel	PROPN
cana-2739	137	42	.	.	PUNCT
cana-2739	138	1	in	in	ADP
cana-2739	138	2	equation	equation	NOUN
cana-2739	138	3	(	(	PUNCT
cana-2739	138	4	2	2	NUM
cana-2739	138	5	)	)	PUNCT
cana-2739	138	6	,	,	PUNCT
cana-2739	138	7	s(x	s(x	PROPN
cana-2739	138	8	)	)	PUNCT
cana-2739	138	9	is	be	AUX
cana-2739	138	10	a	a	DET
cana-2739	138	11	function	function	NOUN
cana-2739	138	12	defined	define	VERB
cana-2739	138	13	as	as	ADP
cana-2739	138	14	𝑠(𝑥	𝑠(𝑥	NUM
cana-2739	138	15	)	)	PUNCT
cana-2739	139	1	=	=	PRON
cana-2739	139	2	{	{	PUNCT
cana-2739	139	3	1	1	NUM
cana-2739	139	4	,	,	PUNCT
cana-2739	139	5	𝑖𝑓	𝑖𝑓	ADP
cana-2739	139	6	𝑥	𝑥	PRON
cana-2739	139	7	≥	≥	NUM
cana-2739	139	8	0	0	NUM
cana-2739	139	9	0	0	NUM
cana-2739	139	10	,	,	PUNCT
cana-2739	139	11	𝑖𝑓	𝑖𝑓	ADP
cana-2739	139	12	𝑥	𝑥	NOUN
cana-2739	139	13	<	<	X
cana-2739	139	14	0	0	PUNCT
cana-2739	139	15	(	(	PUNCT
cana-2739	139	16	3	3	NUM
cana-2739	139	17	)	)	PUNCT
cana-2739	139	18	since	since	SCONJ
cana-2739	139	19	lbp	lbp	NOUN
cana-2739	139	20	merges	merge	VERB
cana-2739	139	21	both	both	CCONJ
cana-2739	139	22	statistical	statistical	ADJ
cana-2739	139	23	and	and	CCONJ
cana-2739	139	24	structural	structural	ADJ
cana-2739	139	25	approaches	approach	NOUN
cana-2739	139	26	,	,	PUNCT
cana-2739	139	27	it	it	PRON
cana-2739	139	28	performs	perform	VERB
cana-2739	139	29	better	well	ADV
cana-2739	139	30	in	in	ADP
cana-2739	139	31	texture	texture	ADJ
cana-2739	139	32	analysis	analysis	NOUN
cana-2739	139	33	.	.	PUNCT
cana-2739	140	1	it	it	PRON
cana-2739	140	2	is	be	AUX
cana-2739	140	3	easy	easy	ADJ
cana-2739	140	4	to	to	PART
cana-2739	140	5	implement	implement	VERB
cana-2739	140	6	and	and	CCONJ
cana-2739	140	7	has	have	VERB
cana-2739	140	8	less	less	ADV
cana-2739	140	9	computational	computational	ADJ
cana-2739	140	10	cost	cost	NOUN
cana-2739	140	11	.	.	PUNCT
cana-2739	141	1	the	the	DET
cana-2739	141	2	research	research	NOUN
cana-2739	141	3	presents	present	VERB
cana-2739	141	4	a	a	DET
cana-2739	141	5	novel	novel	ADJ
cana-2739	141	6	technique	technique	NOUN
cana-2739	141	7	for	for	ADP
cana-2739	141	8	feature	feature	NOUN
cana-2739	141	9	extraction	extraction	NOUN
cana-2739	141	10	from	from	ADP
cana-2739	141	11	ovarian	ovarian	ADJ
cana-2739	141	12	ultrasound	ultrasound	NOUN
cana-2739	141	13	images	image	NOUN
cana-2739	141	14	.	.	PUNCT
cana-2739	142	1	it	it	PRON
cana-2739	142	2	combines	combine	VERB
cana-2739	142	3	fractal	fractal	ADJ
cana-2739	142	4	dimension	dimension	NOUN
cana-2739	142	5	texture	texture	NOUN
cana-2739	142	6	analysis	analysis	NOUN
cana-2739	142	7	with	with	ADP
cana-2739	142	8	local	local	ADJ
cana-2739	142	9	binary	binary	ADJ
cana-2739	142	10	patterns	pattern	NOUN
cana-2739	142	11	and	and	CCONJ
cana-2739	142	12	provides	provide	VERB
cana-2739	142	13	an	an	DET
cana-2739	142	14	efficient	efficient	ADJ
cana-2739	142	15	and	and	CCONJ
cana-2739	142	16	more	more	ADV
cana-2739	142	17	robust	robust	ADJ
cana-2739	142	18	computer	computer	NOUN
cana-2739	142	19	aided	aid	VERB
cana-2739	142	20	diagnosis	diagnosis	NOUN
cana-2739	142	21	system	system	NOUN
cana-2739	142	22	for	for	ADP
cana-2739	142	23	ovarian	ovarian	ADJ
cana-2739	142	24	cyst	cyst	NOUN
cana-2739	142	25	classification	classification	NOUN
cana-2739	142	26	.	.	PUNCT
cana-2739	143	1	3.2.3	3.2.3	NUM
cana-2739	143	2	classification	classification	NOUN
cana-2739	143	3	the	the	DET
cana-2739	143	4	textural	textural	ADJ
cana-2739	143	5	features	feature	NOUN
cana-2739	143	6	extracted	extract	VERB
cana-2739	143	7	from	from	ADP
cana-2739	143	8	the	the	DET
cana-2739	143	9	ultrasound	ultrasound	NOUN
cana-2739	143	10	images	image	NOUN
cana-2739	143	11	are	be	AUX
cana-2739	143	12	given	give	VERB
cana-2739	143	13	as	as	ADP
cana-2739	143	14	input	input	NOUN
cana-2739	143	15	to	to	ADP
cana-2739	143	16	the	the	DET
cana-2739	143	17	machine	machine	NOUN
cana-2739	143	18	learning	learn	VERB
cana-2739	143	19	classifiers	classifier	NOUN
cana-2739	143	20	for	for	ADP
cana-2739	143	21	training	training	NOUN
cana-2739	143	22	and	and	CCONJ
cana-2739	143	23	evaluation	evaluation	NOUN
cana-2739	143	24	purposes	purpose	NOUN
cana-2739	143	25	.	.	PUNCT
cana-2739	144	1	in	in	ADP
cana-2739	144	2	the	the	DET
cana-2739	144	3	research	research	NOUN
cana-2739	144	4	,	,	PUNCT
cana-2739	144	5	decision	decision	NOUN
cana-2739	144	6	tree	tree	NOUN
cana-2739	144	7	,	,	PUNCT
cana-2739	144	8	logistics	logistic	NOUN
cana-2739	144	9	regression	regression	NOUN
cana-2739	144	10	and	and	CCONJ
cana-2739	144	11	svm	svm	PROPN
cana-2739	144	12	are	be	AUX
cana-2739	144	13	used	use	VERB
cana-2739	144	14	as	as	ADP
cana-2739	144	15	classifiers	classifier	NOUN
cana-2739	144	16	.	.	PUNCT
cana-2739	145	1	decision	decision	NOUN
cana-2739	145	2	tree	tree	NOUN
cana-2739	145	3	is	be	AUX
cana-2739	145	4	a	a	DET
cana-2739	145	5	hierarchical	hierarchical	ADJ
cana-2739	145	6	classification	classification	NOUN
cana-2739	145	7	tree	tree	NOUN
cana-2739	145	8	model	model	NOUN
cana-2739	145	9	in	in	ADP
cana-2739	145	10	machine	machine	NOUN
cana-2739	145	11	learning	learning	NOUN
cana-2739	145	12	(	(	PUNCT
cana-2739	145	13	edwards	edwards	PROPN
cana-2739	145	14	.	.	PROPN
cana-2739	145	15	,	,	PUNCT
cana-2739	145	16	1987	1987	NUM
cana-2739	145	17	)	)	PUNCT
cana-2739	145	18	.	.	PUNCT
cana-2739	146	1	it	it	PRON
cana-2739	146	2	is	be	AUX
cana-2739	146	3	constructed	construct	VERB
cana-2739	146	4	on	on	ADP
cana-2739	146	5	the	the	DET
cana-2739	146	6	input	input	NOUN
cana-2739	146	7	training	training	NOUN
cana-2739	146	8	dataset	dataset	NOUN
cana-2739	146	9	in	in	ADP
cana-2739	146	10	which	which	PRON
cana-2739	146	11	internal	internal	ADJ
cana-2739	146	12	node	node	NOUN
cana-2739	146	13	performs	perform	VERB
cana-2739	146	14	a	a	DET
cana-2739	146	15	test	test	NOUN
cana-2739	146	16	on	on	ADP
cana-2739	146	17	a	a	DET
cana-2739	146	18	particular	particular	ADJ
cana-2739	146	19	feature	feature	NOUN
cana-2739	146	20	;	;	PUNCT
cana-2739	146	21	the	the	DET
cana-2739	146	22	branch	branch	NOUN
cana-2739	146	23	rising	rise	VERB
cana-2739	146	24	from	from	ADP
cana-2739	146	25	the	the	DET
cana-2739	146	26	internal	internal	ADJ
cana-2739	146	27	node	node	NOUN
cana-2739	146	28	represents	represent	VERB
cana-2739	146	29	the	the	DET
cana-2739	146	30	result	result	NOUN
cana-2739	146	31	of	of	ADP
cana-2739	146	32	test	test	NOUN
cana-2739	146	33	and	and	CCONJ
cana-2739	146	34	every	every	DET
cana-2739	146	35	leaf	leaf	NOUN
cana-2739	146	36	node	node	NOUN
cana-2739	146	37	specifies	specifie	NOUN
cana-2739	146	38	to	to	PART
cana-2739	146	39	which	which	PRON
cana-2739	146	40	class	class	NOUN
cana-2739	146	41	label	label	NOUN
cana-2739	146	42	or	or	CCONJ
cana-2739	146	43	category	category	NOUN
cana-2739	146	44	it	it	PRON
cana-2739	146	45	belongs	belong	VERB
cana-2739	146	46	.	.	PUNCT
cana-2739	147	1	decision	decision	NOUN
cana-2739	147	2	tree	tree	NOUN
cana-2739	147	3	targets	target	NOUN
cana-2739	147	4	to	to	PART
cana-2739	147	5	provide	provide	VERB
cana-2739	147	6	an	an	DET
cana-2739	147	7	optimal	optimal	ADJ
cana-2739	147	8	tree	tree	NOUN
cana-2739	147	9	for	for	ADP
cana-2739	147	10	classification	classification	NOUN
cana-2739	147	11	.	.	PUNCT
cana-2739	148	1	the	the	DET
cana-2739	148	2	final	final	ADJ
cana-2739	148	3	result	result	NOUN
cana-2739	148	4	of	of	ADP
cana-2739	148	5	classification	classification	NOUN
cana-2739	148	6	is	be	AUX
cana-2739	148	7	depicted	depict	VERB
cana-2739	148	8	by	by	ADP
cana-2739	148	9	the	the	DET
cana-2739	148	10	leaf	leaf	NOUN
cana-2739	148	11	nodes	node	NOUN
cana-2739	148	12	.	.	PUNCT
cana-2739	149	1	logistic	logistic	ADJ
cana-2739	149	2	regression	regression	NOUN
cana-2739	149	3	is	be	AUX
cana-2739	149	4	a	a	DET
cana-2739	149	5	machine	machine	NOUN
cana-2739	149	6	learning	learn	VERB
cana-2739	149	7	algorithm	algorithm	NOUN
cana-2739	149	8	that	that	PRON
cana-2739	149	9	models	model	VERB
cana-2739	149	10	the	the	DET
cana-2739	149	11	probability	probability	NOUN
cana-2739	149	12	of	of	ADP
cana-2739	149	13	occurring	occur	VERB
cana-2739	149	14	event	event	NOUN
cana-2739	149	15	by	by	ADP
cana-2739	149	16	converting	convert	VERB
cana-2739	149	17	log	log	NOUN
cana-2739	149	18	-	-	PUNCT
cana-2739	149	19	odds	odd	NOUN
cana-2739	149	20	to	to	ADP
cana-2739	149	21	probability	probability	NOUN
cana-2739	149	22	(	(	PUNCT
cana-2739	149	23	cramer	cramer	PROPN
cana-2739	149	24	,	,	PUNCT
cana-2739	149	25	2003	2003	NUM
cana-2739	149	26	)	)	PUNCT
cana-2739	149	27	.	.	PUNCT
cana-2739	150	1	the	the	DET
cana-2739	150	2	logistic	logistic	ADJ
cana-2739	150	3	function	function	NOUN
cana-2739	150	4	used	use	VERB
cana-2739	150	5	is	be	AUX
cana-2739	150	6	defined	define	VERB
cana-2739	150	7	as	as	ADP
cana-2739	150	8	:	:	PUNCT
cana-2739	150	9	𝑓(𝑝	𝑓(𝑝	PROPN
cana-2739	150	10	)	)	PUNCT
cana-2739	150	11	=	=	SYM
cana-2739	150	12	1	1	NUM
cana-2739	150	13	1	1	NUM
cana-2739	150	14	+	+	NUM
cana-2739	150	15	𝑒−𝑝	𝑒−𝑝	NOUN
cana-2739	150	16	(	(	PUNCT
cana-2739	150	17	4	4	NUM
cana-2739	150	18	)	)	PUNCT
cana-2739	150	19	where	where	SCONJ
cana-2739	150	20	p	p	PROPN
cana-2739	150	21	=	=	PROPN
cana-2739	150	22	w0	w0	PROPN
cana-2739	150	23	+	+	CCONJ
cana-2739	150	24	w1x1	w1x1	PROPN
cana-2739	150	25	+	+	NUM
cana-2739	150	26	w2x2	w2x2	NOUN
cana-2739	150	27	+	+	X
cana-2739	150	28	...	...	PUNCT
cana-2739	151	1	+	+	CCONJ
cana-2739	151	2	wnxn	wnxn	NOUN
cana-2739	151	3	is	be	AUX
cana-2739	151	4	the	the	DET
cana-2739	151	5	linear	linear	ADJ
cana-2739	151	6	combination	combination	NOUN
cana-2739	151	7	of	of	ADP
cana-2739	151	8	weights	weight	NOUN
cana-2739	151	9	to	to	ADP
cana-2739	151	10	their	their	PRON
cana-2739	151	11	corresponding	corresponding	ADJ
cana-2739	151	12	input	input	NOUN
cana-2739	151	13	features	feature	NOUN
cana-2739	151	14	and	and	CCONJ
cana-2739	151	15	w0	w0	PROPN
cana-2739	151	16	is	be	AUX
cana-2739	151	17	the	the	DET
cana-2739	151	18	bias	bias	NOUN
cana-2739	151	19	term	term	NOUN
cana-2739	151	20	.	.	PUNCT
cana-2739	152	1	the	the	DET
cana-2739	152	2	likelihood	likelihood	NOUN
cana-2739	152	3	function	function	NOUN
cana-2739	152	4	used	use	VERB
cana-2739	152	5	in	in	ADP
cana-2739	152	6	logistic	logistic	ADJ
cana-2739	152	7	regression	regression	NOUN
cana-2739	152	8	is	be	AUX
cana-2739	152	9	represented	represent	VERB
cana-2739	152	10	as	as	ADP
cana-2739	152	11	:	:	PUNCT
cana-2739	152	12	communications	communication	NOUN
cana-2739	152	13	on	on	ADP
cana-2739	152	14	applied	apply	VERB
cana-2739	152	15	nonlinear	nonlinear	ADJ
cana-2739	152	16	analysis	analysis	NOUN
cana-2739	152	17	issn	issn	NOUN
cana-2739	152	18	:	:	PUNCT
cana-2739	152	19	1074	1074	NUM
cana-2739	152	20	-	-	PUNCT
cana-2739	152	21	133x	133x	NUM
cana-2739	152	22	vol	vol	NOUN
cana-2739	152	23	32	32	NUM
cana-2739	152	24	no	no	NOUN
cana-2739	152	25	.	.	PUNCT
cana-2739	153	1	4s	4s	NUM
cana-2739	153	2	(	(	PUNCT
cana-2739	153	3	2025	2025	NUM
cana-2739	153	4	)	)	PUNCT
cana-2739	153	5	66	66	NUM
cana-2739	153	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2739	153	7	𝐿(𝑤	𝐿(𝑤	NOUN
cana-2739	153	8	)	)	PUNCT
cana-2739	153	9	=	=	SYM
cana-2739	153	10	∏	∏	PROPN
cana-2739	153	11	𝑓(𝑧𝑘)𝑦𝑘	𝑓(𝑧𝑘)𝑦𝑘	NOUN
cana-2739	153	12	𝑛	𝑛	PRON
cana-2739	153	13	𝑘=1	𝑘=1	X
cana-2739	153	14	(	(	PUNCT
cana-2739	153	15	1	1	NUM
cana-2739	153	16	−	−	PROPN
cana-2739	153	17	𝑓(𝑧𝑘))1−𝑦𝑘	𝑓(𝑧𝑘))1−𝑦𝑘	PROPN
cana-2739	153	18	(	(	PUNCT
cana-2739	153	19	5	5	NUM
cana-2739	153	20	)	)	PUNCT
cana-2739	153	21	where	where	SCONJ
cana-2739	153	22	n	n	PRON
cana-2739	153	23	is	be	AUX
cana-2739	153	24	the	the	DET
cana-2739	153	25	number	number	NOUN
cana-2739	153	26	of	of	ADP
cana-2739	153	27	training	training	NOUN
cana-2739	153	28	dataset	dataset	NOUN
cana-2739	153	29	,	,	PUNCT
cana-2739	153	30	yk	yk	PROPN
cana-2739	153	31	is	be	AUX
cana-2739	153	32	the	the	DET
cana-2739	153	33	actual	actual	ADJ
cana-2739	153	34	resultant	resultant	NOUN
cana-2739	153	35	for	for	ADP
cana-2739	153	36	the	the	DET
cana-2739	153	37	k	k	PROPN
cana-2739	153	38	-	-	PUNCT
cana-2739	153	39	th	th	VERB
cana-2739	153	40	data	datum	NOUN
cana-2739	153	41	,	,	PUNCT
cana-2739	153	42	and	and	CCONJ
cana-2739	153	43	zk	zk	PROPN
cana-2739	153	44	is	be	AUX
cana-2739	153	45	the	the	DET
cana-2739	153	46	linear	linear	ADJ
cana-2739	153	47	combination	combination	NOUN
cana-2739	153	48	of	of	ADP
cana-2739	153	49	weights	weight	NOUN
cana-2739	153	50	to	to	ADP
cana-2739	153	51	their	their	PRON
cana-2739	153	52	corresponding	corresponding	ADJ
cana-2739	153	53	input	input	NOUN
cana-2739	153	54	features	feature	NOUN
cana-2739	153	55	for	for	ADP
cana-2739	153	56	the	the	DET
cana-2739	153	57	k	k	PROPN
cana-2739	153	58	-	-	PUNCT
cana-2739	153	59	th	th	VERB
cana-2739	153	60	data	data	NOUN
cana-2739	153	61	.	.	PUNCT
cana-2739	154	1	the	the	DET
cana-2739	154	2	key	key	ADJ
cana-2739	154	3	point	point	NOUN
cana-2739	154	4	of	of	ADP
cana-2739	154	5	logistic	logistic	ADJ
cana-2739	154	6	regression	regression	NOUN
cana-2739	154	7	is	be	AUX
cana-2739	154	8	that	that	SCONJ
cana-2739	154	9	it	it	PRON
cana-2739	154	10	is	be	AUX
cana-2739	154	11	very	very	ADV
cana-2739	154	12	simple	simple	ADJ
cana-2739	154	13	to	to	PART
cana-2739	154	14	implement	implement	VERB
cana-2739	154	15	and	and	CCONJ
cana-2739	154	16	robust	robust	ADJ
cana-2739	154	17	to	to	ADP
cana-2739	154	18	multicollinearity	multicollinearity	NOUN
cana-2739	154	19	.	.	PUNCT
cana-2739	155	1	svm	svm	PROPN
cana-2739	155	2	which	which	PRON
cana-2739	155	3	is	be	AUX
cana-2739	155	4	based	base	VERB
cana-2739	155	5	on	on	ADP
cana-2739	155	6	supervised	supervised	ADJ
cana-2739	155	7	learning	learning	NOUN
cana-2739	155	8	,	,	PUNCT
cana-2739	155	9	is	be	AUX
cana-2739	155	10	one	one	NUM
cana-2739	155	11	of	of	ADP
cana-2739	155	12	the	the	DET
cana-2739	155	13	efficient	efficient	ADJ
cana-2739	155	14	predictive	predictive	ADJ
cana-2739	155	15	models	model	NOUN
cana-2739	155	16	(	(	PUNCT
cana-2739	155	17	c.	c.	PROPN
cana-2739	155	18	c.	c.	PROPN
cana-2739	155	19	chang	chang	PROPN
cana-2739	155	20	&	&	CCONJ
cana-2739	155	21	lin	lin	PROPN
cana-2739	155	22	,	,	PUNCT
cana-2739	155	23	2011	2011	NUM
cana-2739	155	24	)	)	PUNCT
cana-2739	155	25	.	.	PUNCT
cana-2739	156	1	in	in	ADP
cana-2739	156	2	case	case	NOUN
cana-2739	156	3	of	of	ADP
cana-2739	156	4	linear	linear	PROPN
cana-2739	156	5	classification	classification	NOUN
cana-2739	156	6	,	,	PUNCT
cana-2739	156	7	it	it	PRON
cana-2739	156	8	tries	try	VERB
cana-2739	156	9	to	to	PART
cana-2739	156	10	maximize	maximize	VERB
cana-2739	156	11	the	the	DET
cana-2739	156	12	separation	separation	NOUN
cana-2739	156	13	(	(	PUNCT
cana-2739	156	14	margin	margin	NOUN
cana-2739	156	15	)	)	PUNCT
cana-2739	156	16	between	between	ADP
cana-2739	156	17	two	two	NUM
cana-2739	156	18	classes	class	NOUN
cana-2739	156	19	and	and	CCONJ
cana-2739	156	20	establishes	establish	VERB
cana-2739	156	21	a	a	DET
cana-2739	156	22	hyperplane	hyperplane	NOUN
cana-2739	156	23	classifying	classify	VERB
cana-2739	156	24	the	the	DET
cana-2739	156	25	n	n	PROPN
cana-2739	156	26	data	data	NOUN
cana-2739	156	27	points	point	NOUN
cana-2739	156	28	(	(	PUNCT
cana-2739	156	29	xi	xi	PROPN
cana-2739	156	30	,	,	PUNCT
cana-2739	156	31	yi	yi	NOUN
cana-2739	156	32	where	where	SCONJ
cana-2739	156	33	xi	xi	PROPN
cana-2739	156	34	is	be	AUX
cana-2739	156	35	a	a	DET
cana-2739	156	36	p	p	ADJ
cana-2739	156	37	-	-	PUNCT
cana-2739	156	38	dimensional	dimensional	ADJ
cana-2739	156	39	vector	vector	NOUN
cana-2739	156	40	such	such	ADJ
cana-2739	156	41	that	that	PRON
cana-2739	156	42	xi	xi	PROPN
cana-2739	156	43	∈	∈	PROPN
cana-2739	157	1	ℝp	ℝp	PROPN
cana-2739	157	2	,	,	PUNCT
cana-2739	157	3	yi	yi	PROPN
cana-2739	157	4	∈	∈	PROPN
cana-2739	157	5	{	{	PUNCT
cana-2739	157	6	-1	-1	ADJ
cana-2739	157	7	,	,	PUNCT
cana-2739	157	8	1	1	X
cana-2739	157	9	}	}	PUNCT
cana-2739	157	10	and	and	CCONJ
cana-2739	157	11	i	i	PRON
cana-2739	157	12	ranges	range	VERB
cana-2739	157	13	from	from	ADP
cana-2739	157	14	1	1	NUM
cana-2739	157	15	to	to	ADP
cana-2739	157	16	n	n	CCONJ
cana-2739	157	17	)	)	PUNCT
cana-2739	157	18	into	into	ADP
cana-2739	157	19	two	two	NUM
cana-2739	157	20	classes	class	NOUN
cana-2739	157	21	.	.	PUNCT
cana-2739	158	1	support	support	NOUN
cana-2739	158	2	vector	vector	NOUN
cana-2739	158	3	term	term	NOUN
cana-2739	158	4	is	be	AUX
cana-2739	158	5	coined	coin	VERB
cana-2739	158	6	to	to	ADP
cana-2739	158	7	specific	specific	ADJ
cana-2739	158	8	data	datum	NOUN
cana-2739	158	9	points	point	NOUN
cana-2739	158	10	that	that	PRON
cana-2739	158	11	establish	establish	VERB
cana-2739	158	12	the	the	DET
cana-2739	158	13	maximum	maximum	ADJ
cana-2739	158	14	margin	margin	NOUN
cana-2739	158	15	of	of	ADP
cana-2739	158	16	the	the	DET
cana-2739	158	17	hyperplane	hyperplane	NOUN
cana-2739	158	18	to	to	ADP
cana-2739	158	19	the	the	DET
cana-2739	158	20	data	data	NOUN
cana-2739	158	21	points	point	NOUN
cana-2739	158	22	.	.	PUNCT
cana-2739	159	1	new	new	ADJ
cana-2739	159	2	data	datum	NOUN
cana-2739	159	3	points	point	NOUN
cana-2739	159	4	are	be	AUX
cana-2739	159	5	mapped	map	VERB
cana-2739	159	6	into	into	ADP
cana-2739	159	7	the	the	DET
cana-2739	159	8	class	class	NOUN
cana-2739	159	9	based	base	VERB
cana-2739	159	10	on	on	ADP
cana-2739	159	11	which	which	DET
cana-2739	159	12	side	side	NOUN
cana-2739	159	13	of	of	ADP
cana-2739	159	14	the	the	DET
cana-2739	159	15	separation	separation	NOUN
cana-2739	159	16	they	they	PRON
cana-2739	159	17	fall	fall	VERB
cana-2739	159	18	.	.	PUNCT
cana-2739	160	1	consider	consider	VERB
cana-2739	160	2	a	a	DET
cana-2739	160	3	hyperplane	hyperplane	NOUN
cana-2739	160	4	is	be	AUX
cana-2739	160	5	defined	define	VERB
cana-2739	160	6	through	through	ADP
cana-2739	160	7	w	w	PROPN
cana-2739	160	8	and	and	CCONJ
cana-2739	160	9	b	b	PROPN
cana-2739	160	10	is	be	AUX
cana-2739	160	11	the	the	DET
cana-2739	160	12	set	set	NOUN
cana-2739	160	13	of	of	ADP
cana-2739	160	14	points	point	NOUN
cana-2739	160	15	such	such	ADJ
cana-2739	160	16	that	that	SCONJ
cana-2739	160	17	ⱨ	ⱨ	PROPN
cana-2739	160	18	=	=	PRON
cana-2739	160	19	{	{	PUNCT
cana-2739	160	20	x|wtx	x|wtx	PROPN
cana-2739	160	21	+	+	CCONJ
cana-2739	160	22	b=0	b=0	PROPN
cana-2739	160	23	}	}	PUNCT
cana-2739	160	24	.	.	PUNCT
cana-2739	161	1	the	the	DET
cana-2739	161	2	goal	goal	NOUN
cana-2739	161	3	is	be	AUX
cana-2739	161	4	to	to	PART
cana-2739	161	5	maximize	maximize	VERB
cana-2739	161	6	margin	margin	NOUN
cana-2739	161	7	defined	define	VERB
cana-2739	161	8	as	as	ADP
cana-2739	161	9	max	max	PROPN
cana-2739	161	10	𝑤,𝑏	𝑤,𝑏	PROPN
cana-2739	161	11	1	1	NUM
cana-2739	161	12	‖𝑤‖₂	‖𝑤‖₂	NOUN
cana-2739	161	13	min	min	NOUN
cana-2739	161	14	𝑥ᵢ∈	𝑥ᵢ∈	PROPN
cana-2739	161	15	𝐷	𝐷	PROPN
cana-2739	161	16	|𝑤ᵀ𝑥ᵢ	|𝑤ᵀ𝑥ᵢ	NOUN
cana-2739	161	17	+	+	PUNCT
cana-2739	161	18	𝑏|	𝑏|	NOUN
cana-2739	161	19	𝑠𝑢𝑐ℎ	𝑠𝑢𝑐ℎ	NOUN
cana-2739	161	20	𝑡ℎ𝑎𝑡	𝑡ℎ𝑎𝑡	PROPN
cana-2739	161	21	𝑖	𝑖	SYM
cana-2739	161	22	𝑦ᵢ(𝑤ᵀ𝑥ᵢ	𝑦ᵢ(𝑤ᵀ𝑥ᵢ	PROPN
cana-2739	161	23	+	+	CCONJ
cana-2739	161	24	𝑏	𝑏	NOUN
cana-2739	161	25	)	)	PUNCT
cana-2739	161	26	≥	≥	NOUN
cana-2739	161	27	0	0	NUM
cana-2739	161	28	(	(	PUNCT
cana-2739	161	29	6	6	NUM
cana-2739	161	30	)	)	PUNCT
cana-2739	161	31	in	in	ADP
cana-2739	161	32	case	case	NOUN
cana-2739	161	33	of	of	ADP
cana-2739	161	34	non	non	ADJ
cana-2739	161	35	-	-	ADJ
cana-2739	161	36	linear	linear	ADJ
cana-2739	161	37	dataset	dataset	NOUN
cana-2739	161	38	,	,	PUNCT
cana-2739	161	39	svm	svm	PROPN
cana-2739	161	40	performs	perform	VERB
cana-2739	161	41	classification	classification	NOUN
cana-2739	161	42	using	use	VERB
cana-2739	161	43	the	the	DET
cana-2739	161	44	concept	concept	NOUN
cana-2739	161	45	of	of	ADP
cana-2739	161	46	kernel	kernel	PROPN
cana-2739	161	47	functions	function	NOUN
cana-2739	161	48	.	.	PUNCT
cana-2739	162	1	the	the	DET
cana-2739	162	2	kernel	kernel	PROPN
cana-2739	162	3	functions	function	NOUN
cana-2739	162	4	transform	transform	VERB
cana-2739	162	5	the	the	DET
cana-2739	162	6	non	non	ADJ
cana-2739	162	7	-	-	ADJ
cana-2739	162	8	linear	linear	ADJ
cana-2739	162	9	dataset	dataset	NOUN
cana-2739	162	10	to	to	ADP
cana-2739	162	11	a	a	DET
cana-2739	162	12	high	high	ADV
cana-2739	162	13	-	-	PUNCT
cana-2739	162	14	dimensional	dimensional	ADJ
cana-2739	162	15	feature	feature	NOUN
cana-2739	162	16	space	space	NOUN
cana-2739	162	17	where	where	SCONJ
cana-2739	162	18	the	the	DET
cana-2739	162	19	transformed	transform	VERB
cana-2739	162	20	dataset	dataset	NOUN
cana-2739	162	21	becomes	become	VERB
cana-2739	162	22	more	more	ADV
cana-2739	162	23	separable	separable	ADJ
cana-2739	162	24	from	from	ADP
cana-2739	162	25	the	the	DET
cana-2739	162	26	original	original	ADJ
cana-2739	162	27	dataset	dataset	NOUN
cana-2739	162	28	.	.	PUNCT
cana-2739	163	1	linear	linear	ADJ
cana-2739	163	2	kernel	kernel	NOUN
cana-2739	163	3	,	,	PUNCT
cana-2739	163	4	radial	radial	ADJ
cana-2739	163	5	basis	basis	NOUN
cana-2739	163	6	function	function	NOUN
cana-2739	163	7	(	(	PUNCT
cana-2739	163	8	rbf	rbf	PROPN
cana-2739	163	9	)	)	PUNCT
cana-2739	163	10	and	and	CCONJ
cana-2739	163	11	polynomial	polynomial	ADJ
cana-2739	163	12	kernel	kernel	NOUN
cana-2739	163	13	are	be	AUX
cana-2739	163	14	some	some	PRON
cana-2739	163	15	of	of	ADP
cana-2739	163	16	the	the	DET
cana-2739	163	17	kernel	kernel	PROPN
cana-2739	163	18	functions	function	NOUN
cana-2739	163	19	to	to	PART
cana-2739	163	20	generate	generate	VERB
cana-2739	163	21	different	different	ADJ
cana-2739	163	22	types	type	NOUN
cana-2739	163	23	of	of	ADP
cana-2739	163	24	classifying	classify	VERB
cana-2739	163	25	hyperplanes	hyperplane	NOUN
cana-2739	163	26	in	in	ADP
cana-2739	163	27	svm	svm	PROPN
cana-2739	163	28	.	.	PROPN
cana-2739	163	29	linear	linear	PROPN
cana-2739	163	30	kernel	kernel	PROPN
cana-2739	163	31	𝐾(𝑥𝑖	𝐾(𝑥𝑖	PROPN
cana-2739	163	32	,	,	PUNCT
cana-2739	163	33	𝑥𝑗	𝑥𝑗	PROPN
cana-2739	163	34	)	)	PUNCT
cana-2739	163	35	=	=	SYM
cana-2739	163	36	𝑥𝑖	𝑥𝑖	PROPN
cana-2739	163	37	𝑇	𝑇	PROPN
cana-2739	163	38	⋅	⋅	PROPN
cana-2739	163	39	𝑥𝑗	𝑥𝑗	PROPN
cana-2739	163	40	(	(	PUNCT
cana-2739	163	41	7	7	NUM
cana-2739	163	42	)	)	PUNCT
cana-2739	163	43	rbf	rbf	PROPN
cana-2739	163	44	kernel	kernel	PROPN
cana-2739	163	45	𝐾(𝑥𝑖	𝐾(𝑥𝑖	PROPN
cana-2739	163	46	,	,	PUNCT
cana-2739	163	47	𝑥𝑗	𝑥𝑗	PROPN
cana-2739	163	48	)	)	PUNCT
cana-2739	163	49	=	=	NOUN
cana-2739	163	50	exp	exp	NOUN
cana-2739	163	51	(	(	PUNCT
cana-2739	163	52	−γ|𝑥𝑖	−γ|𝑥𝑖	PROPN
cana-2739	163	53	−	−	PROPN
cana-2739	163	54	𝑥𝑗|2	𝑥𝑗|2	VERB
cana-2739	163	55	)	)	PUNCT
cana-2739	163	56	,	,	PUNCT
cana-2739	163	57	γ	γ	X
cana-2739	163	58	>	>	X
cana-2739	163	59	0	0	NUM
cana-2739	164	1	(	(	PUNCT
cana-2739	164	2	8)	8)	NUM
cana-2739	164	3	polynomial	polynomial	ADJ
cana-2739	164	4	kernel	kernel	NOUN
cana-2739	164	5	𝐾(𝑥𝑖	𝐾(𝑥𝑖	PROPN
cana-2739	164	6	,	,	PUNCT
cana-2739	164	7	𝑥𝑗	𝑥𝑗	PROPN
cana-2739	164	8	)	)	PUNCT
cana-2739	164	9	=	=	SYM
cana-2739	165	1	𝑥𝑖	𝑥𝑖	PROPN
cana-2739	165	2	𝑇	𝑇	PROPN
cana-2739	165	3	⋅	⋅	PROPN
cana-2739	165	4	𝑥𝑗	𝑥𝑗	PROPN
cana-2739	165	5	+	+	PROPN
cana-2739	165	6	c	c	PROPN
cana-2739	165	7	𝑑	𝑑	NOUN
cana-2739	165	8	(	(	PUNCT
cana-2739	165	9	9	9	NUM
cana-2739	165	10	)	)	PUNCT
cana-2739	165	11	where	where	SCONJ
cana-2739	165	12	k(xi	k(xi	PROPN
cana-2739	165	13	,	,	PUNCT
cana-2739	165	14	xj	xj	NOUN
cana-2739	165	15	)	)	PUNCT
cana-2739	165	16	is	be	AUX
cana-2739	165	17	kernel	kernel	PROPN
cana-2739	165	18	function	function	PROPN
cana-2739	165	19	,	,	PUNCT
cana-2739	165	20	xi	xi	PROPN
cana-2739	165	21	,	,	PUNCT
cana-2739	165	22	xj	xj	PROPN
cana-2739	165	23	are	be	AUX
cana-2739	165	24	the	the	DET
cana-2739	165	25	i	i	PROPN
cana-2739	165	26	-	-	PUNCT
cana-2739	165	27	th	th	X
cana-2739	165	28	and	and	CCONJ
cana-2739	165	29	j	j	PROPN
cana-2739	165	30	-	-	PUNCT
cana-2739	165	31	th	th	X
cana-2739	165	32	data	datum	NOUN
cana-2739	165	33	,	,	PUNCT
cana-2739	165	34	c	c	PROPN
cana-2739	165	35	is	be	AUX
cana-2739	165	36	slack	slack	NOUN
cana-2739	165	37	variable	variable	ADJ
cana-2739	165	38	,	,	PUNCT
cana-2739	165	39	d	d	NOUN
cana-2739	165	40	for	for	ADP
cana-2739	165	41	degree	degree	NOUN
cana-2739	165	42	of	of	ADP
cana-2739	165	43	polynomial	polynomial	ADJ
cana-2739	165	44	kernel	kernel	NOUN
cana-2739	165	45	function	function	NOUN
cana-2739	165	46	and	and	CCONJ
cana-2739	165	47	γ	γ	PROPN
cana-2739	165	48	is	be	AUX
cana-2739	165	49	learning	learn	VERB
cana-2739	165	50	rate	rate	NOUN
cana-2739	165	51	.	.	PUNCT
cana-2739	166	1	3.3	3.3	NUM
cana-2739	166	2	evaluation	evaluation	NOUN
cana-2739	166	3	metrics	metric	NOUN
cana-2739	166	4	to	to	PART
cana-2739	166	5	assess	assess	VERB
cana-2739	166	6	decision	decision	NOUN
cana-2739	166	7	tree	tree	NOUN
cana-2739	166	8	,	,	PUNCT
cana-2739	166	9	logistic	logistic	ADJ
cana-2739	166	10	regression	regression	NOUN
cana-2739	166	11	and	and	CCONJ
cana-2739	166	12	svm	svm	ADJ
cana-2739	166	13	algorithms	algorithm	NOUN
cana-2739	166	14	,	,	PUNCT
cana-2739	166	15	a	a	DET
cana-2739	166	16	confusion	confusion	NOUN
cana-2739	166	17	matrix	matrix	NOUN
cana-2739	166	18	as	as	SCONJ
cana-2739	166	19	shown	show	VERB
cana-2739	166	20	in	in	ADP
cana-2739	166	21	table	table	NOUN
cana-2739	166	22	1	1	NUM
cana-2739	166	23	is	be	AUX
cana-2739	166	24	generated	generate	VERB
cana-2739	166	25	with	with	ADP
cana-2739	166	26	four	four	NUM
cana-2739	166	27	parameters	parameter	NOUN
cana-2739	166	28	namely	namely	ADV
cana-2739	166	29	,	,	PUNCT
cana-2739	166	30	true	true	ADJ
cana-2739	166	31	positive	positive	ADJ
cana-2739	166	32	(	(	PUNCT
cana-2739	166	33	tp	tp	NOUN
cana-2739	166	34	)	)	PUNCT
cana-2739	166	35	,	,	PUNCT
cana-2739	166	36	true	true	ADJ
cana-2739	166	37	negative	negative	ADJ
cana-2739	166	38	(	(	PUNCT
cana-2739	166	39	tn	tn	NOUN
cana-2739	166	40	)	)	PUNCT
cana-2739	166	41	,	,	PUNCT
cana-2739	166	42	false	false	ADJ
cana-2739	166	43	positive	positive	ADJ
cana-2739	166	44	(	(	PUNCT
cana-2739	166	45	fp	fp	NOUN
cana-2739	166	46	)	)	PUNCT
cana-2739	166	47	and	and	CCONJ
cana-2739	166	48	false	false	ADJ
cana-2739	166	49	negative	negative	ADJ
cana-2739	166	50	(	(	PUNCT
cana-2739	166	51	fn	fn	NOUN
cana-2739	166	52	)	)	PUNCT
cana-2739	166	53	.	.	PUNCT
cana-2739	167	1	tp	tp	NOUN
cana-2739	167	2	and	and	CCONJ
cana-2739	167	3	tn	tn	NOUN
cana-2739	167	4	refer	refer	VERB
cana-2739	167	5	to	to	ADP
cana-2739	167	6	the	the	DET
cana-2739	167	7	count	count	NOUN
cana-2739	167	8	of	of	ADP
cana-2739	167	9	data	datum	NOUN
cana-2739	167	10	that	that	PRON
cana-2739	167	11	is	be	AUX
cana-2739	167	12	correctly	correctly	ADV
cana-2739	167	13	identified	identify	VERB
cana-2739	167	14	as	as	ADP
cana-2739	167	15	normal	normal	ADJ
cana-2739	167	16	ovary	ovary	ADJ
cana-2739	167	17	and	and	CCONJ
cana-2739	167	18	cystic	cystic	ADJ
cana-2739	167	19	ovary	ovary	NOUN
cana-2739	167	20	in	in	ADP
cana-2739	167	21	the	the	DET
cana-2739	167	22	proposed	propose	VERB
cana-2739	167	23	model	model	NOUN
cana-2739	167	24	.	.	PUNCT
cana-2739	168	1	fp	fp	NOUN
cana-2739	168	2	and	and	CCONJ
cana-2739	168	3	fn	fn	PROPN
cana-2739	168	4	are	be	AUX
cana-2739	168	5	the	the	DET
cana-2739	168	6	count	count	NOUN
cana-2739	168	7	of	of	ADP
cana-2739	168	8	data	datum	NOUN
cana-2739	168	9	that	that	PRON
cana-2739	168	10	is	be	AUX
cana-2739	168	11	incorrectly	incorrectly	ADV
cana-2739	168	12	identified	identify	VERB
cana-2739	168	13	as	as	ADP
cana-2739	168	14	normal	normal	ADJ
cana-2739	168	15	ovary	ovary	ADJ
cana-2739	168	16	and	and	CCONJ
cana-2739	168	17	cystic	cystic	ADJ
cana-2739	168	18	ovary	ovary	NOUN
cana-2739	168	19	in	in	ADP
cana-2739	168	20	the	the	DET
cana-2739	168	21	proposed	propose	VERB
cana-2739	168	22	model	model	NOUN
cana-2739	168	23	.	.	PUNCT
cana-2739	169	1	using	use	VERB
cana-2739	169	2	these	these	DET
cana-2739	169	3	parameters	parameter	NOUN
cana-2739	169	4	,	,	PUNCT
cana-2739	169	5	five	five	NUM
cana-2739	169	6	evaluation	evaluation	NOUN
cana-2739	169	7	metrics	metric	NOUN
cana-2739	169	8	(	(	PUNCT
cana-2739	169	9	accuracy	accuracy	NOUN
cana-2739	169	10	,	,	PUNCT
cana-2739	169	11	precision	precision	NOUN
cana-2739	169	12	,	,	PUNCT
cana-2739	169	13	recall	recall	NOUN
cana-2739	169	14	,	,	PUNCT
cana-2739	169	15	f1	f1	NOUN
cana-2739	169	16	score	score	NOUN
cana-2739	169	17	and	and	CCONJ
cana-2739	169	18	specificity	specificity	NOUN
cana-2739	169	19	)	)	PUNCT
cana-2739	169	20	are	be	AUX
cana-2739	169	21	calculated	calculate	VERB
cana-2739	169	22	as	as	SCONJ
cana-2739	169	23	shown	show	VERB
cana-2739	169	24	in	in	ADP
cana-2739	169	25	table	table	NOUN
cana-2739	169	26	2	2	NUM
cana-2739	169	27	to	to	PART
cana-2739	169	28	analyse	analyse	VERB
cana-2739	169	29	the	the	DET
cana-2739	169	30	efficiency	efficiency	NOUN
cana-2739	169	31	of	of	ADP
cana-2739	169	32	algorithms	algorithm	NOUN
cana-2739	169	33	(	(	PUNCT
cana-2739	169	34	kalaiyarasi	kalaiyarasi	PROPN
cana-2739	169	35	et	et	PROPN
cana-2739	169	36	al	al	PROPN
cana-2739	169	37	.	.	PROPN
cana-2739	169	38	,	,	PUNCT
cana-2739	169	39	2020	2020	NUM
cana-2739	169	40	;	;	PUNCT
cana-2739	169	41	suganya	suganya	PROPN
cana-2739	169	42	et	et	PROPN
cana-2739	169	43	al	al	PROPN
cana-2739	169	44	.	.	PROPN
cana-2739	169	45	,	,	PUNCT
cana-2739	169	46	2022	2022	NUM
cana-2739	169	47	)	)	PUNCT
cana-2739	169	48	.	.	PUNCT
cana-2739	170	1	table	table	NOUN
cana-2739	170	2	1	1	NUM
cana-2739	170	3	confusion	confusion	NOUN
cana-2739	170	4	matrix	matrix	NOUN
cana-2739	170	5	.	.	PUNCT
cana-2739	171	1	real	real	ADJ
cana-2739	171	2	positive	positive	ADJ
cana-2739	171	3	real	real	ADJ
cana-2739	171	4	negative	negative	ADJ
cana-2739	171	5	predicted	predict	VERB
cana-2739	171	6	positive	positive	ADJ
cana-2739	171	7	true	true	ADJ
cana-2739	171	8	positive	positive	ADJ
cana-2739	171	9	(	(	PUNCT
cana-2739	171	10	tp	tp	NOUN
cana-2739	171	11	)	)	PUNCT
cana-2739	171	12	false	false	ADJ
cana-2739	171	13	positive	positive	ADJ
cana-2739	171	14	(	(	PUNCT
cana-2739	171	15	fp	fp	AUX
cana-2739	171	16	)	)	PUNCT
cana-2739	171	17	predicted	predict	VERB
cana-2739	171	18	negative	negative	ADJ
cana-2739	171	19	false	false	ADJ
cana-2739	171	20	negative	negative	ADJ
cana-2739	171	21	(	(	PUNCT
cana-2739	171	22	fn	fn	NOUN
cana-2739	171	23	)	)	PUNCT
cana-2739	171	24	true	true	ADJ
cana-2739	171	25	negative	negative	ADJ
cana-2739	171	26	(	(	PUNCT
cana-2739	171	27	tn	tn	NOUN
cana-2739	171	28	)	)	PUNCT
cana-2739	171	29	communications	communication	NOUN
cana-2739	171	30	on	on	ADP
cana-2739	171	31	applied	apply	VERB
cana-2739	171	32	nonlinear	nonlinear	ADJ
cana-2739	171	33	analysis	analysis	NOUN
cana-2739	171	34	issn	issn	NOUN
cana-2739	171	35	:	:	PUNCT
cana-2739	171	36	1074	1074	NUM
cana-2739	171	37	-	-	PUNCT
cana-2739	171	38	133x	133x	NUM
cana-2739	171	39	vol	vol	NOUN
cana-2739	171	40	32	32	NUM
cana-2739	171	41	no	no	NOUN
cana-2739	171	42	.	.	PUNCT
cana-2739	172	1	4s	4s	NUM
cana-2739	172	2	(	(	PUNCT
cana-2739	172	3	2025	2025	NUM
cana-2739	172	4	)	)	PUNCT
cana-2739	173	1	67	67	NUM
cana-2739	173	2	https://internationalpubls.com	https://internationalpubls.com	X
cana-2739	173	3	table	table	NOUN
cana-2739	173	4	2	2	NUM
cana-2739	173	5	evaluation	evaluation	NOUN
cana-2739	173	6	metrics	metric	NOUN
cana-2739	173	7	.	.	PUNCT
cana-2739	174	1	parameters	parameter	NOUN
cana-2739	174	2	formula	formula	NOUN
cana-2739	174	3	accuracy	accuracy	NOUN
cana-2739	174	4	𝑇𝑁	𝑇𝑁	PROPN
cana-2739	174	5	+	+	CCONJ
cana-2739	174	6	𝑇𝑃	𝑇𝑃	PROPN
cana-2739	174	7	𝑇𝑃	𝑇𝑃	PROPN
cana-2739	174	8	+	+	CCONJ
cana-2739	174	9	𝑇𝑁	𝑇𝑁	PROPN
cana-2739	174	10	+	+	NUM
cana-2739	174	11	𝐹𝑃	𝐹𝑃	NOUN
cana-2739	175	1	+	+	CCONJ
cana-2739	175	2	𝐹𝑁	𝐹𝑁	PROPN
cana-2739	175	3	precision	precision	NOUN
cana-2739	175	4	𝑇𝑃	𝑇𝑃	PROPN
cana-2739	175	5	𝑇𝑃	𝑇𝑃	PROPN
cana-2739	175	6	+	+	CCONJ
cana-2739	175	7	𝐹𝑃	𝐹𝑃	PROPN
cana-2739	175	8	recall	recall	NOUN
cana-2739	175	9	𝑇𝑃	𝑇𝑃	PROPN
cana-2739	175	10	𝑇𝑃	𝑇𝑃	PROPN
cana-2739	175	11	+	+	CCONJ
cana-2739	175	12	𝐹𝑁	𝐹𝑁	PROPN
cana-2739	175	13	f1	f1	NOUN
cana-2739	175	14	-	-	PUNCT
cana-2739	175	15	score	score	NOUN
cana-2739	175	16	2	2	NUM
cana-2739	175	17	∗	∗	NOUN
cana-2739	175	18	𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛	𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛	PROPN
cana-2739	175	19	∗	∗	NOUN
cana-2739	175	20	𝑅𝑒𝑐𝑎𝑙𝑙	𝑅𝑒𝑐𝑎𝑙𝑙	PROPN
cana-2739	175	21	𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛	𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛	PROPN
cana-2739	175	22	+	+	CCONJ
cana-2739	175	23	𝑅𝑒𝑐𝑎𝑙𝑙	𝑅𝑒𝑐𝑎𝑙𝑙	PROPN
cana-2739	175	24	specificity	specificity	NOUN
cana-2739	175	25	𝑇𝑁	𝑇𝑁	PROPN
cana-2739	175	26	𝑇𝑁	𝑇𝑁	PROPN
cana-2739	175	27	+	+	CCONJ
cana-2739	175	28	𝐹𝑃	𝐹𝑃	NOUN
cana-2739	175	29	receiver	receiver	NOUN
cana-2739	175	30	operating	operate	VERB
cana-2739	175	31	characteristic	characteristic	ADJ
cana-2739	175	32	curve	curve	NOUN
cana-2739	175	33	(	(	PUNCT
cana-2739	175	34	roc	roc	PROPN
cana-2739	175	35	)	)	PUNCT
cana-2739	175	36	is	be	AUX
cana-2739	175	37	also	also	ADV
cana-2739	175	38	determined	determine	VERB
cana-2739	175	39	with	with	ADP
cana-2739	175	40	area	area	NOUN
cana-2739	175	41	under	under	ADP
cana-2739	175	42	the	the	DET
cana-2739	175	43	roc	roc	PROPN
cana-2739	175	44	curve	curve	NOUN
cana-2739	175	45	(	(	PUNCT
cana-2739	175	46	auc	auc	NOUN
cana-2739	175	47	)	)	PUNCT
cana-2739	175	48	to	to	PART
cana-2739	175	49	evaluate	evaluate	VERB
cana-2739	175	50	the	the	DET
cana-2739	175	51	classifiers	classifier	NOUN
cana-2739	175	52	.	.	PUNCT
cana-2739	176	1	it	it	PRON
cana-2739	176	2	maps	map	VERB
cana-2739	176	3	true	true	ADJ
cana-2739	176	4	positive	positive	ADJ
cana-2739	176	5	rate	rate	NOUN
cana-2739	176	6	(	(	PUNCT
cana-2739	176	7	tpr	tpr	NOUN
cana-2739	176	8	)	)	PUNCT
cana-2739	176	9	versus	versus	ADP
cana-2739	176	10	false	false	ADJ
cana-2739	176	11	positive	positive	ADJ
cana-2739	176	12	rate	rate	NOUN
cana-2739	176	13	(	(	PUNCT
cana-2739	176	14	fpr	fpr	NOUN
cana-2739	176	15	)	)	PUNCT
cana-2739	176	16	on	on	ADP
cana-2739	176	17	different	different	ADJ
cana-2739	176	18	threshold	threshold	NOUN
cana-2739	176	19	values	value	NOUN
cana-2739	176	20	.	.	PUNCT
cana-2739	177	1	tpr	tpr	NOUN
cana-2739	177	2	and	and	CCONJ
cana-2739	177	3	fpr	fpr	NOUN
cana-2739	177	4	are	be	AUX
cana-2739	177	5	computed	compute	VERB
cana-2739	177	6	using	use	VERB
cana-2739	177	7	equations	equation	NOUN
cana-2739	177	8	(	(	PUNCT
cana-2739	177	9	10	10	NUM
cana-2739	177	10	)	)	PUNCT
cana-2739	177	11	and	and	CCONJ
cana-2739	177	12	(	(	PUNCT
cana-2739	177	13	11	11	NUM
cana-2739	177	14	)	)	PUNCT
cana-2739	177	15	,	,	PUNCT
cana-2739	177	16	respectively	respectively	ADV
cana-2739	177	17	.	.	PUNCT
cana-2739	178	1	𝑇𝑃𝑅	𝑇𝑃𝑅	PROPN
cana-2739	178	2	=	=	PUNCT
cana-2739	178	3	𝑇𝑃	𝑇𝑃	PROPN
cana-2739	178	4	𝑇𝑃	𝑇𝑃	PROPN
cana-2739	178	5	+	+	CCONJ
cana-2739	179	1	𝐹𝑁	𝐹𝑁	PROPN
cana-2739	179	2	(	(	PUNCT
cana-2739	179	3	10	10	NUM
cana-2739	179	4	)	)	PUNCT
cana-2739	179	5	𝐹𝑃𝑅	𝐹𝑃𝑅	PROPN
cana-2739	179	6	=	=	SYM
cana-2739	179	7	𝐹𝑃	𝐹𝑃	PROPN
cana-2739	179	8	𝐹𝑃	𝐹𝑃	PROPN
cana-2739	180	1	+	+	CCONJ
cana-2739	180	2	𝑇𝑁	𝑇𝑁	PROPN
cana-2739	180	3	(	(	PUNCT
cana-2739	180	4	11	11	NUM
cana-2739	180	5	)	)	PUNCT
cana-2739	180	6	it	it	PRON
cana-2739	180	7	is	be	AUX
cana-2739	180	8	an	an	DET
cana-2739	180	9	important	important	ADJ
cana-2739	180	10	performance	performance	NOUN
cana-2739	180	11	evaluation	evaluation	NOUN
cana-2739	180	12	criterion	criterion	NOUN
cana-2739	180	13	that	that	PRON
cana-2739	180	14	measures	measure	VERB
cana-2739	180	15	the	the	DET
cana-2739	180	16	effectiveness	effectiveness	NOUN
cana-2739	180	17	of	of	ADP
cana-2739	180	18	a	a	DET
cana-2739	180	19	machine	machine	NOUN
cana-2739	180	20	learning	learn	VERB
cana-2739	180	21	model	model	NOUN
cana-2739	180	22	.	.	PUNCT
cana-2739	181	1	it	it	PRON
cana-2739	181	2	specifies	specify	VERB
cana-2739	181	3	how	how	SCONJ
cana-2739	181	4	capable	capable	ADJ
cana-2739	181	5	the	the	DET
cana-2739	181	6	model	model	NOUN
cana-2739	181	7	is	be	AUX
cana-2739	181	8	in	in	ADP
cana-2739	181	9	classifying	classify	VERB
cana-2739	181	10	data	datum	NOUN
cana-2739	181	11	and	and	CCONJ
cana-2739	181	12	therefore	therefore	ADV
cana-2739	181	13	,	,	PUNCT
cana-2739	181	14	depicts	depict	VERB
cana-2739	181	15	the	the	DET
cana-2739	181	16	comprehensive	comprehensive	ADJ
cana-2739	181	17	performance	performance	NOUN
cana-2739	181	18	of	of	ADP
cana-2739	181	19	the	the	DET
cana-2739	181	20	model	model	NOUN
cana-2739	181	21	.	.	PUNCT
cana-2739	182	1	a	a	DET
cana-2739	182	2	value	value	NOUN
cana-2739	182	3	near	near	ADP
cana-2739	182	4	to	to	ADP
cana-2739	182	5	1	1	NUM
cana-2739	182	6	in	in	ADP
cana-2739	182	7	auc	auc	NOUN
cana-2739	182	8	plot	plot	NOUN
cana-2739	182	9	signifies	signifie	NOUN
cana-2739	182	10	that	that	SCONJ
cana-2739	182	11	the	the	DET
cana-2739	182	12	model	model	NOUN
cana-2739	182	13	performs	perform	VERB
cana-2739	182	14	better	well	ADV
cana-2739	182	15	in	in	ADP
cana-2739	182	16	classification	classification	NOUN
cana-2739	182	17	.	.	PUNCT
cana-2739	183	1	4	4	X
cana-2739	183	2	.	.	NUM
cana-2739	183	3	results	result	NOUN
cana-2739	183	4	and	and	CCONJ
cana-2739	183	5	discussion	discussion	NOUN
cana-2739	183	6	the	the	DET
cana-2739	183	7	proposed	propose	VERB
cana-2739	183	8	system	system	NOUN
cana-2739	183	9	is	be	AUX
cana-2739	183	10	assessed	assess	VERB
cana-2739	183	11	by	by	ADP
cana-2739	183	12	comparing	compare	VERB
cana-2739	183	13	the	the	DET
cana-2739	183	14	results	result	NOUN
cana-2739	183	15	obtained	obtain	VERB
cana-2739	183	16	through	through	ADP
cana-2739	183	17	decision	decision	NOUN
cana-2739	183	18	tree	tree	NOUN
cana-2739	183	19	,	,	PUNCT
cana-2739	183	20	logistic	logistic	ADJ
cana-2739	183	21	regression	regression	NOUN
cana-2739	183	22	and	and	CCONJ
cana-2739	183	23	svm	svm	ADJ
cana-2739	183	24	.	.	PUNCT
cana-2739	184	1	these	these	DET
cana-2739	184	2	algorithms	algorithm	NOUN
cana-2739	184	3	have	have	AUX
cana-2739	184	4	utilized	utilize	VERB
cana-2739	184	5	the	the	DET
cana-2739	184	6	textural	textural	ADJ
cana-2739	184	7	features	feature	NOUN
cana-2739	184	8	extracted	extract	VERB
cana-2739	184	9	from	from	ADP
cana-2739	184	10	the	the	DET
cana-2739	184	11	ultrasound	ultrasound	NOUN
cana-2739	184	12	images	image	NOUN
cana-2739	184	13	using	use	VERB
cana-2739	184	14	fractal	fractal	ADJ
cana-2739	184	15	dimension	dimension	NOUN
cana-2739	184	16	texture	texture	NOUN
cana-2739	184	17	analysis	analysis	NOUN
cana-2739	184	18	(	(	PUNCT
cana-2739	184	19	fdta	fdta	PROPN
cana-2739	184	20	)	)	PUNCT
cana-2739	184	21	and	and	CCONJ
cana-2739	184	22	local	local	ADJ
cana-2739	184	23	binary	binary	NOUN
cana-2739	184	24	patten	patten	NOUN
cana-2739	184	25	(	(	PUNCT
cana-2739	184	26	lbp	lbp	PROPN
cana-2739	184	27	)	)	PUNCT
cana-2739	184	28	.	.	PUNCT
cana-2739	185	1	the	the	DET
cana-2739	185	2	confusion	confusion	NOUN
cana-2739	185	3	matrix	matrix	NOUN
cana-2739	185	4	computed	compute	VERB
cana-2739	185	5	for	for	ADP
cana-2739	185	6	decision	decision	NOUN
cana-2739	185	7	tree	tree	NOUN
cana-2739	185	8	,	,	PUNCT
cana-2739	185	9	logistic	logistic	ADJ
cana-2739	185	10	regression	regression	NOUN
cana-2739	185	11	and	and	CCONJ
cana-2739	185	12	svm	svm	PROPN
cana-2739	185	13	using	use	VERB
cana-2739	185	14	fdta	fdta	PROPN
cana-2739	185	15	and	and	CCONJ
cana-2739	185	16	lbp	lbp	PROPN
cana-2739	185	17	as	as	SCONJ
cana-2739	185	18	a	a	DET
cana-2739	185	19	single	single	ADJ
cana-2739	185	20	feature	feature	NOUN
cana-2739	185	21	are	be	AUX
cana-2739	185	22	displayed	display	VERB
cana-2739	185	23	in	in	ADP
cana-2739	185	24	tables	table	NOUN
cana-2739	185	25	3	3	NUM
cana-2739	185	26	and	and	CCONJ
cana-2739	185	27	4	4	NUM
cana-2739	185	28	;	;	PUNCT
cana-2739	185	29	and	and	CCONJ
cana-2739	185	30	for	for	ADP
cana-2739	185	31	combined	combine	VERB
cana-2739	185	32	textural	textural	ADJ
cana-2739	185	33	features	feature	NOUN
cana-2739	185	34	of	of	ADP
cana-2739	185	35	fdta	fdta	PROPN
cana-2739	185	36	and	and	CCONJ
cana-2739	185	37	lbp	lbp	NOUN
cana-2739	185	38	are	be	AUX
cana-2739	185	39	displayed	display	VERB
cana-2739	185	40	in	in	ADP
cana-2739	185	41	table	table	NOUN
cana-2739	185	42	5	5	NUM
cana-2739	185	43	.	.	PUNCT
cana-2739	185	44	table	table	NOUN
cana-2739	185	45	3	3	NUM
cana-2739	185	46	confusion	confusion	NOUN
cana-2739	185	47	matrix	matrix	NOUN
cana-2739	185	48	for	for	ADP
cana-2739	185	49	decision	decision	NOUN
cana-2739	185	50	tree	tree	NOUN
cana-2739	185	51	,	,	PUNCT
cana-2739	185	52	logistic	logistic	ADJ
cana-2739	185	53	regression	regression	NOUN
cana-2739	185	54	and	and	CCONJ
cana-2739	185	55	svm	svm	ADJ
cana-2739	185	56	using	use	VERB
cana-2739	185	57	fdta	fdta	PROPN
cana-2739	185	58	.	.	PUNCT
cana-2739	186	1	real	real	ADJ
cana-2739	186	2	positive	positive	ADJ
cana-2739	186	3	real	real	ADJ
cana-2739	186	4	negative	negative	ADJ
cana-2739	186	5	decision	decision	NOUN
cana-2739	186	6	tree	tree	NOUN
cana-2739	186	7	logistic	logistic	ADJ
cana-2739	186	8	regression	regression	NOUN
cana-2739	186	9	svm	svm	ADJ
cana-2739	186	10	decision	decision	NOUN
cana-2739	186	11	tree	tree	NOUN
cana-2739	186	12	logistic	logistic	ADJ
cana-2739	186	13	regression	regression	NOUN
cana-2739	186	14	svm	svm	NOUN
cana-2739	186	15	predicted	predict	VERB
cana-2739	186	16	positive	positive	ADJ
cana-2739	186	17	125	125	NUM
cana-2739	186	18	149	149	NUM
cana-2739	186	19	149	149	NUM
cana-2739	186	20	24	24	NUM
cana-2739	186	21	0	0	NUM
cana-2739	186	22	0	0	NUM
cana-2739	186	23	predicted	predict	VERB
cana-2739	186	24	negative	negative	ADJ
cana-2739	186	25	13	13	NUM
cana-2739	186	26	40	40	NUM
cana-2739	186	27	34	34	NUM
cana-2739	186	28	31	31	NUM
cana-2739	186	29	4	4	NUM
cana-2739	186	30	10	10	NUM
cana-2739	186	31	communications	communication	NOUN
cana-2739	186	32	on	on	ADP
cana-2739	186	33	applied	apply	VERB
cana-2739	186	34	nonlinear	nonlinear	ADJ
cana-2739	186	35	analysis	analysis	NOUN
cana-2739	186	36	issn	issn	NOUN
cana-2739	186	37	:	:	PUNCT
cana-2739	186	38	1074	1074	NUM
cana-2739	186	39	-	-	PUNCT
cana-2739	186	40	133x	133x	NUM
cana-2739	186	41	vol	vol	NOUN
cana-2739	186	42	32	32	NUM
cana-2739	186	43	no	no	NOUN
cana-2739	186	44	.	.	PUNCT
cana-2739	187	1	4s	4s	NUM
cana-2739	187	2	(	(	PUNCT
cana-2739	187	3	2025	2025	NUM
cana-2739	187	4	)	)	PUNCT
cana-2739	188	1	68	68	NUM
cana-2739	188	2	https://internationalpubls.com	https://internationalpubls.com	X
cana-2739	188	3	table	table	NOUN
cana-2739	188	4	4	4	NUM
cana-2739	188	5	confusion	confusion	NOUN
cana-2739	188	6	matrix	matrix	NOUN
cana-2739	188	7	for	for	ADP
cana-2739	188	8	decision	decision	NOUN
cana-2739	188	9	tree	tree	NOUN
cana-2739	188	10	,	,	PUNCT
cana-2739	188	11	logistic	logistic	ADJ
cana-2739	188	12	regression	regression	NOUN
cana-2739	188	13	and	and	CCONJ
cana-2739	188	14	svm	svm	ADJ
cana-2739	188	15	using	use	VERB
cana-2739	188	16	lbp	lbp	NOUN
cana-2739	188	17	.	.	PUNCT
cana-2739	189	1	real	real	ADJ
cana-2739	189	2	positive	positive	ADJ
cana-2739	189	3	real	real	ADJ
cana-2739	189	4	negative	negative	ADJ
cana-2739	189	5	decision	decision	NOUN
cana-2739	189	6	tree	tree	NOUN
cana-2739	189	7	logistic	logistic	ADJ
cana-2739	189	8	regression	regression	NOUN
cana-2739	189	9	svm	svm	ADJ
cana-2739	189	10	decision	decision	NOUN
cana-2739	189	11	tree	tree	NOUN
cana-2739	189	12	logistic	logistic	ADJ
cana-2739	189	13	regression	regression	NOUN
cana-2739	189	14	svm	svm	NOUN
cana-2739	189	15	predicted	predict	VERB
cana-2739	189	16	positive	positive	ADJ
cana-2739	189	17	123	123	NUM
cana-2739	189	18	143	143	NUM
cana-2739	189	19	141	141	NUM
cana-2739	189	20	26	26	NUM
cana-2739	189	21	6	6	NUM
cana-2739	189	22	8	8	NUM
cana-2739	189	23	predicted	predict	VERB
cana-2739	189	24	negative	negative	ADJ
cana-2739	189	25	16	16	NUM
cana-2739	189	26	19	19	NUM
cana-2739	189	27	16	16	NUM
cana-2739	189	28	28	28	NUM
cana-2739	189	29	25	25	NUM
cana-2739	189	30	28	28	NUM
cana-2739	189	31	table	table	NOUN
cana-2739	189	32	5	5	NUM
cana-2739	189	33	confusion	confusion	NOUN
cana-2739	189	34	matrix	matrix	NOUN
cana-2739	189	35	for	for	ADP
cana-2739	189	36	decision	decision	NOUN
cana-2739	189	37	tree	tree	NOUN
cana-2739	189	38	,	,	PUNCT
cana-2739	189	39	logistic	logistic	ADJ
cana-2739	189	40	regression	regression	NOUN
cana-2739	189	41	and	and	CCONJ
cana-2739	189	42	svm	svm	PROPN
cana-2739	189	43	using	use	VERB
cana-2739	189	44	fdta+lbp	fdta+lbp	PROPN
cana-2739	189	45	.	.	PUNCT
cana-2739	190	1	real	real	ADJ
cana-2739	190	2	positive	positive	ADJ
cana-2739	190	3	real	real	ADJ
cana-2739	190	4	negative	negative	ADJ
cana-2739	190	5	decision	decision	NOUN
cana-2739	190	6	tree	tree	NOUN
cana-2739	190	7	logistic	logistic	ADJ
cana-2739	190	8	regression	regression	NOUN
cana-2739	190	9	svm	svm	ADJ
cana-2739	190	10	decision	decision	NOUN
cana-2739	190	11	tree	tree	NOUN
cana-2739	190	12	logistic	logistic	ADJ
cana-2739	190	13	regression	regression	NOUN
cana-2739	190	14	svm	svm	NOUN
cana-2739	190	15	predicted	predict	VERB
cana-2739	190	16	positive	positive	ADJ
cana-2739	190	17	129	129	NUM
cana-2739	190	18	145	145	NUM
cana-2739	190	19	146	146	NUM
cana-2739	190	20	20	20	NUM
cana-2739	190	21	4	4	NUM
cana-2739	190	22	3	3	NUM
cana-2739	190	23	predicted	predict	VERB
cana-2739	190	24	negative	negative	ADJ
cana-2739	190	25	12	12	NUM
cana-2739	190	26	17	17	NUM
cana-2739	190	27	14	14	NUM
cana-2739	190	28	32	32	NUM
cana-2739	190	29	27	27	NUM
cana-2739	190	30	30	30	NUM
cana-2739	190	31	using	use	VERB
cana-2739	190	32	confusion	confusion	NOUN
cana-2739	190	33	matrix	matrix	NOUN
cana-2739	190	34	parameters	parameter	NOUN
cana-2739	190	35	,	,	PUNCT
cana-2739	190	36	the	the	DET
cana-2739	190	37	evaluation	evaluation	NOUN
cana-2739	190	38	metrics	metric	NOUN
cana-2739	190	39	are	be	AUX
cana-2739	190	40	calculated	calculate	VERB
cana-2739	190	41	for	for	ADP
cana-2739	190	42	each	each	PRON
cana-2739	190	43	of	of	ADP
cana-2739	190	44	these	these	DET
cana-2739	190	45	three	three	NUM
cana-2739	190	46	algorithms	algorithm	NOUN
cana-2739	190	47	and	and	CCONJ
cana-2739	190	48	are	be	AUX
cana-2739	190	49	shown	show	VERB
cana-2739	190	50	in	in	ADP
cana-2739	190	51	table	table	NOUN
cana-2739	190	52	6	6	NUM
cana-2739	190	53	.	.	PUNCT
cana-2739	191	1	the	the	DET
cana-2739	191	2	table	table	NOUN
cana-2739	191	3	displays	display	VERB
cana-2739	191	4	the	the	DET
cana-2739	191	5	performance	performance	NOUN
cana-2739	191	6	metrics	metric	NOUN
cana-2739	191	7	in	in	ADP
cana-2739	191	8	relation	relation	NOUN
cana-2739	191	9	to	to	ADP
cana-2739	191	10	single	single	ADJ
cana-2739	191	11	feature	feature	NOUN
cana-2739	191	12	fdta	fdta	NOUN
cana-2739	191	13	and	and	CCONJ
cana-2739	191	14	lbp	lbp	PROPN
cana-2739	191	15	separately	separately	ADV
cana-2739	191	16	,	,	PUNCT
cana-2739	191	17	along	along	ADP
cana-2739	191	18	with	with	ADP
cana-2739	191	19	the	the	DET
cana-2739	191	20	combined	combined	ADJ
cana-2739	191	21	textural	textural	ADJ
cana-2739	191	22	features	feature	NOUN
cana-2739	191	23	(	(	PUNCT
cana-2739	191	24	fdta	fdta	X
cana-2739	191	25	+	+	NUM
cana-2739	191	26	lbp	lbp	NOUN
cana-2739	191	27	)	)	PUNCT
cana-2739	191	28	.	.	PUNCT
cana-2739	192	1	using	use	VERB
cana-2739	192	2	fdta	fdta	PROPN
cana-2739	192	3	as	as	ADP
cana-2739	192	4	a	a	DET
cana-2739	192	5	single	single	ADJ
cana-2739	192	6	feature	feature	NOUN
cana-2739	192	7	,	,	PUNCT
cana-2739	192	8	decision	decision	NOUN
cana-2739	192	9	tree	tree	NOUN
cana-2739	192	10	has	have	AUX
cana-2739	192	11	given	give	VERB
cana-2739	192	12	80.83	80.83	NUM
cana-2739	192	13	%	%	NOUN
cana-2739	192	14	as	as	ADP
cana-2739	192	15	accuracy	accuracy	NOUN
cana-2739	192	16	,	,	PUNCT
cana-2739	192	17	83.89	83.89	NUM
cana-2739	192	18	%	%	NOUN
cana-2739	192	19	as	as	ADP
cana-2739	192	20	precision	precision	NOUN
cana-2739	192	21	,	,	PUNCT
cana-2739	192	22	maximum	maximum	ADJ
cana-2739	192	23	recall	recall	NOUN
cana-2739	192	24	as	as	ADP
cana-2739	192	25	90.58	90.58	NUM
cana-2739	192	26	%	%	NOUN
cana-2739	192	27	,	,	PUNCT
cana-2739	192	28	f1score	f1score	NOUN
cana-2739	192	29	as	as	ADP
cana-2739	192	30	87.11	87.11	NUM
cana-2739	192	31	%	%	NOUN
cana-2739	192	32	and	and	CCONJ
cana-2739	192	33	specificity	specificity	NOUN
cana-2739	192	34	as	as	ADP
cana-2739	192	35	56.36	56.36	NUM
cana-2739	192	36	%	%	NOUN
cana-2739	192	37	.	.	PUNCT
cana-2739	193	1	similarly	similarly	ADV
cana-2739	193	2	,	,	PUNCT
cana-2739	193	3	logistic	logistic	ADJ
cana-2739	193	4	regression	regression	NOUN
cana-2739	193	5	has	have	AUX
cana-2739	193	6	secured	secure	VERB
cana-2739	193	7	79.27	79.27	NUM
cana-2739	193	8	%	%	NOUN
cana-2739	193	9	in	in	ADP
cana-2739	193	10	accuracy	accuracy	NOUN
cana-2739	193	11	,	,	PUNCT
cana-2739	193	12	maximum	maximum	ADJ
cana-2739	193	13	precision	precision	NOUN
cana-2739	193	14	and	and	CCONJ
cana-2739	193	15	specificity	specificity	NOUN
cana-2739	193	16	as	as	ADP
cana-2739	193	17	100	100	NUM
cana-2739	193	18	%	%	NOUN
cana-2739	193	19	,	,	PUNCT
cana-2739	193	20	78.83	78.83	NUM
cana-2739	193	21	%	%	NOUN
cana-2739	193	22	as	as	ADP
cana-2739	193	23	recall	recall	NOUN
cana-2739	193	24	and	and	CCONJ
cana-2739	193	25	88.16	88.16	NUM
cana-2739	193	26	%	%	NOUN
cana-2739	193	27	as	as	ADP
cana-2739	193	28	f1	f1	NOUN
cana-2739	193	29	-	-	PUNCT
cana-2739	193	30	score	score	NOUN
cana-2739	193	31	.	.	PUNCT
cana-2739	194	1	svm	svm	PROPN
cana-2739	194	2	outperforms	outperform	VERB
cana-2739	194	3	decision	decision	NOUN
cana-2739	194	4	tree	tree	NOUN
cana-2739	194	5	and	and	CCONJ
cana-2739	194	6	logistic	logistic	ADJ
cana-2739	194	7	regression	regression	NOUN
cana-2739	194	8	in	in	ADP
cana-2739	194	9	four	four	NUM
cana-2739	194	10	metrics	metric	NOUN
cana-2739	194	11	namely	namely	ADV
cana-2739	194	12	,	,	PUNCT
cana-2739	194	13	accuracy	accuracy	NOUN
cana-2739	194	14	(	(	PUNCT
cana-2739	194	15	82.38	82.38	NUM
cana-2739	194	16	%	%	NOUN
cana-2739	194	17	)	)	PUNCT
cana-2739	194	18	,	,	PUNCT
cana-2739	194	19	precision	precision	NOUN
cana-2739	194	20	(	(	PUNCT
cana-2739	194	21	100	100	NUM
cana-2739	194	22	%	%	NOUN
cana-2739	194	23	)	)	PUNCT
cana-2739	194	24	,	,	PUNCT
cana-2739	194	25	f1	f1	NOUN
cana-2739	194	26	score	score	NOUN
cana-2739	194	27	(	(	PUNCT
cana-2739	194	28	89.76	89.76	NUM
cana-2739	194	29	%	%	NOUN
cana-2739	194	30	)	)	PUNCT
cana-2739	194	31	and	and	CCONJ
cana-2739	194	32	specificity	specificity	NOUN
cana-2739	194	33	(	(	PUNCT
cana-2739	194	34	100	100	NUM
cana-2739	194	35	%	%	NOUN
cana-2739	194	36	)	)	PUNCT
cana-2739	194	37	.	.	PUNCT
cana-2739	195	1	in	in	ADP
cana-2739	195	2	case	case	NOUN
cana-2739	195	3	of	of	ADP
cana-2739	195	4	lbp	lbp	NOUN
cana-2739	195	5	,	,	PUNCT
cana-2739	195	6	decision	decision	NOUN
cana-2739	195	7	tree	tree	NOUN
cana-2739	195	8	has	have	AUX
cana-2739	195	9	obtained	obtain	VERB
cana-2739	195	10	minimum	minimum	NOUN
cana-2739	195	11	in	in	ADP
cana-2739	195	12	all	all	DET
cana-2739	195	13	the	the	DET
cana-2739	195	14	five	five	NUM
cana-2739	195	15	metrics	metric	NOUN
cana-2739	195	16	,	,	PUNCT
cana-2739	195	17	accuracy	accuracy	NOUN
cana-2739	195	18	(	(	PUNCT
cana-2739	195	19	78.24	78.24	NUM
cana-2739	195	20	%	%	NOUN
cana-2739	195	21	)	)	PUNCT
cana-2739	195	22	,	,	PUNCT
cana-2739	195	23	precision	precision	NOUN
cana-2739	195	24	(	(	PUNCT
cana-2739	195	25	82.55	82.55	NUM
cana-2739	195	26	%	%	NOUN
cana-2739	195	27	)	)	PUNCT
cana-2739	195	28	,	,	PUNCT
cana-2739	195	29	recall	recall	INTJ
cana-2739	195	30	(	(	PUNCT
cana-2739	195	31	88.49	88.49	NUM
cana-2739	195	32	%	%	NOUN
cana-2739	195	33	)	)	PUNCT
cana-2739	195	34	,	,	PUNCT
cana-2739	195	35	f1	f1	NOUN
cana-2739	195	36	score	score	NOUN
cana-2739	195	37	(	(	PUNCT
cana-2739	195	38	85.42	85.42	NUM
cana-2739	195	39	%	%	NOUN
cana-2739	195	40	)	)	PUNCT
cana-2739	195	41	and	and	CCONJ
cana-2739	195	42	specificity	specificity	NOUN
cana-2739	195	43	(	(	PUNCT
cana-2739	195	44	51.85	51.85	NUM
cana-2739	195	45	%	%	NOUN
cana-2739	195	46	)	)	PUNCT
cana-2739	195	47	.	.	PUNCT
cana-2739	196	1	logistic	logistic	ADJ
cana-2739	196	2	regression	regression	NOUN
cana-2739	196	3	has	have	AUX
cana-2739	196	4	come	come	VERB
cana-2739	196	5	out	out	ADP
cana-2739	196	6	to	to	PART
cana-2739	196	7	be	be	AUX
cana-2739	196	8	better	well	ADJ
cana-2739	196	9	in	in	ADP
cana-2739	196	10	two	two	NUM
cana-2739	196	11	metrics	metric	NOUN
cana-2739	196	12	(	(	PUNCT
cana-2739	196	13	precision	precision	NOUN
cana-2739	196	14	95.97	95.97	NUM
cana-2739	196	15	%	%	NOUN
cana-2739	196	16	and	and	CCONJ
cana-2739	196	17	specificity	specificity	NOUN
cana-2739	196	18	80.64	80.64	NUM
cana-2739	196	19	%	%	NOUN
cana-2739	196	20	)	)	PUNCT
cana-2739	196	21	.	.	PUNCT
cana-2739	197	1	it	it	PRON
cana-2739	197	2	has	have	AUX
cana-2739	197	3	achieved	achieve	VERB
cana-2739	197	4	87.05	87.05	NUM
cana-2739	197	5	%	%	NOUN
cana-2739	197	6	as	as	ADP
cana-2739	197	7	accuracy	accuracy	NOUN
cana-2739	197	8	,	,	PUNCT
cana-2739	197	9	88.27	88.27	NUM
cana-2739	197	10	%	%	NOUN
cana-2739	197	11	as	as	ADP
cana-2739	197	12	recall	recall	NOUN
cana-2739	197	13	and	and	CCONJ
cana-2739	197	14	91.96	91.96	NUM
cana-2739	197	15	%	%	NOUN
cana-2739	197	16	as	as	ADP
cana-2739	197	17	f1score	f1score	NOUN
cana-2739	197	18	.	.	PUNCT
cana-2739	198	1	svm	svm	PROPN
cana-2739	198	2	has	have	AUX
cana-2739	198	3	reached	reach	VERB
cana-2739	198	4	87.56	87.56	NUM
cana-2739	198	5	%	%	NOUN
cana-2739	198	6	in	in	ADP
cana-2739	198	7	accuracy	accuracy	NOUN
cana-2739	198	8	,	,	PUNCT
cana-2739	198	9	89.80	89.80	NUM
cana-2739	198	10	%	%	NOUN
cana-2739	198	11	in	in	ADP
cana-2739	198	12	recall	recall	NOUN
cana-2739	198	13	,	,	PUNCT
cana-2739	198	14	92.15	92.15	NUM
cana-2739	198	15	%	%	NOUN
cana-2739	198	16	in	in	ADP
cana-2739	198	17	f1	f1	ADJ
cana-2739	198	18	score	score	NOUN
cana-2739	198	19	as	as	ADP
cana-2739	198	20	maximum	maximum	ADJ
cana-2739	198	21	values	value	NOUN
cana-2739	198	22	for	for	ADP
cana-2739	198	23	lbp	lbp	NOUN
cana-2739	198	24	.	.	PUNCT
cana-2739	199	1	it	it	PRON
cana-2739	199	2	has	have	AUX
cana-2739	199	3	attained	attain	VERB
cana-2739	199	4	94.63	94.63	NUM
cana-2739	199	5	%	%	NOUN
cana-2739	199	6	as	as	ADP
cana-2739	199	7	precision	precision	NOUN
cana-2739	199	8	and	and	CCONJ
cana-2739	199	9	77.78	77.78	NUM
cana-2739	199	10	%	%	NOUN
cana-2739	199	11	as	as	ADP
cana-2739	199	12	specificity	specificity	NOUN
cana-2739	199	13	.	.	PUNCT
cana-2739	200	1	communications	communication	NOUN
cana-2739	200	2	on	on	ADP
cana-2739	200	3	applied	apply	VERB
cana-2739	200	4	nonlinear	nonlinear	ADJ
cana-2739	200	5	analysis	analysis	NOUN
cana-2739	200	6	issn	issn	NOUN
cana-2739	200	7	:	:	PUNCT
cana-2739	200	8	1074	1074	NUM
cana-2739	200	9	-	-	PUNCT
cana-2739	200	10	133x	133x	NUM
cana-2739	200	11	vol	vol	NOUN
cana-2739	200	12	32	32	NUM
cana-2739	200	13	no	no	NOUN
cana-2739	200	14	.	.	PUNCT
cana-2739	201	1	4s	4s	NUM
cana-2739	201	2	(	(	PUNCT
cana-2739	201	3	2025	2025	NUM
cana-2739	201	4	)	)	PUNCT
cana-2739	201	5	69	69	NUM
cana-2739	202	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-2739	202	2	table	table	NOUN
cana-2739	202	3	6	6	NUM
cana-2739	202	4	evaluation	evaluation	NOUN
cana-2739	202	5	metrics	metric	NOUN
cana-2739	202	6	for	for	ADP
cana-2739	202	7	decision	decision	NOUN
cana-2739	202	8	tree	tree	NOUN
cana-2739	202	9	,	,	PUNCT
cana-2739	202	10	logistic	logistic	ADJ
cana-2739	202	11	regression	regression	NOUN
cana-2739	202	12	and	and	CCONJ
cana-2739	202	13	svm	svm	PROPN
cana-2739	202	14	.	.	PROPN
cana-2739	202	15	evaluati	evaluati	NOUN
cana-2739	202	16	on	on	ADP
cana-2739	202	17	metrics	metric	NOUN
cana-2739	202	18	(	(	PUNCT
cana-2739	202	19	%	%	INTJ
cana-2739	202	20	)	)	PUNCT
cana-2739	202	21	fractal	fractal	ADJ
cana-2739	202	22	dimension	dimension	NOUN
cana-2739	202	23	texture	texture	NOUN
cana-2739	202	24	analysis	analysis	NOUN
cana-2739	202	25	(	(	PUNCT
cana-2739	202	26	fdta	fdta	NOUN
cana-2739	202	27	)	)	PUNCT
cana-2739	202	28	local	local	ADJ
cana-2739	202	29	binary	binary	NOUN
cana-2739	202	30	pattern	pattern	NOUN
cana-2739	202	31	(	(	PUNCT
cana-2739	202	32	lbp	lbp	NOUN
cana-2739	202	33	)	)	PUNCT
cana-2739	202	34	fdta	fdta	PROPN
cana-2739	202	35	+	+	CCONJ
cana-2739	202	36	lbp	lbp	PROPN
cana-2739	202	37	decisio	decisio	PROPN
cana-2739	202	38	n	n	PRON
cana-2739	202	39	tree	tree	NOUN
cana-2739	202	40	logistic	logistic	ADJ
cana-2739	202	41	regressi	regressi	NOUN
cana-2739	202	42	on	on	ADP
cana-2739	202	43	sv	sv	PROPN
cana-2739	202	44	m	m	PROPN
cana-2739	202	45	decisio	decisio	PROPN
cana-2739	202	46	n	n	PRON
cana-2739	202	47	tree	tree	NOUN
cana-2739	202	48	logistic	logistic	ADJ
cana-2739	202	49	regressi	regressi	NOUN
cana-2739	202	50	on	on	ADP
cana-2739	202	51	sv	sv	PROPN
cana-2739	202	52	m	m	PROPN
cana-2739	202	53	decisio	decisio	PROPN
cana-2739	202	54	n	n	PRON
cana-2739	202	55	tree	tree	NOUN
cana-2739	202	56	logistic	logistic	ADJ
cana-2739	202	57	regressi	regressi	NOUN
cana-2739	202	58	on	on	ADP
cana-2739	202	59	sv	sv	PROPN
cana-2739	202	60	m	m	PROPN
cana-2739	202	61	accuracy	accuracy	NOUN
cana-2739	203	1	80.83	80.83	NUM
cana-2739	203	2	79.27	79.27	NUM
cana-2739	203	3	82.3	82.3	NUM
cana-2739	203	4	8	8	NUM
cana-2739	203	5	78.24	78.24	NUM
cana-2739	203	6	87.05	87.05	NUM
cana-2739	203	7	87.5	87.5	NUM
cana-2739	203	8	6	6	NUM
cana-2739	203	9	83.42	83.42	NUM
cana-2739	203	10	89.12	89.12	NUM
cana-2739	203	11	91.1	91.1	NUM
cana-2739	203	12	9	9	NUM
cana-2739	203	13	precision	precision	NOUN
cana-2739	203	14	83.89	83.89	NUM
cana-2739	203	15	100	100	NUM
cana-2739	203	16	100	100	NUM
cana-2739	203	17	82.55	82.55	NUM
cana-2739	203	18	95.97	95.97	NUM
cana-2739	203	19	94.6	94.6	NUM
cana-2739	203	20	3	3	NUM
cana-2739	203	21	86.58	86.58	NUM
cana-2739	203	22	97.31	97.31	NUM
cana-2739	203	23	97.9	97.9	NUM
cana-2739	203	24	8	8	NUM
cana-2739	203	25	recall	recall	VERB
cana-2739	203	26	90.58	90.58	NUM
cana-2739	203	27	78.83	78.83	NUM
cana-2739	203	28	81.4	81.4	NUM
cana-2739	203	29	2	2	NUM
cana-2739	203	30	88.49	88.49	NUM
cana-2739	203	31	88.27	88.27	NUM
cana-2739	203	32	89.8	89.8	NUM
cana-2739	203	33	0	0	NUM
cana-2739	203	34	91.49	91.49	NUM
cana-2739	203	35	89.51	89.51	NUM
cana-2739	203	36	91.2	91.2	NUM
cana-2739	203	37	5	5	NUM
cana-2739	203	38	f1	f1	NOUN
cana-2739	203	39	-	-	PUNCT
cana-2739	203	40	score	score	NOUN
cana-2739	203	41	87.11	87.11	NUM
cana-2739	203	42	88.16	88.16	NUM
cana-2739	203	43	89.7	89.7	NUM
cana-2739	203	44	6	6	NUM
cana-2739	203	45	85.42	85.42	NUM
cana-2739	203	46	91.96	91.96	NUM
cana-2739	203	47	92.1	92.1	NUM
cana-2739	203	48	5	5	NUM
cana-2739	203	49	88.97	88.97	NUM
cana-2739	203	50	93.45	93.45	NUM
cana-2739	203	51	94.4	94.4	NUM
cana-2739	203	52	9	9	NUM
cana-2739	203	53	specificit	specificit	NOUN
cana-2739	203	54	y	y	PROPN
cana-2739	203	55	56.36	56.36	NUM
cana-2739	203	56	100	100	NUM
cana-2739	203	57	100	100	NUM
cana-2739	203	58	51.85	51.85	NUM
cana-2739	203	59	80.64	80.64	NUM
cana-2739	203	60	77.7	77.7	NUM
cana-2739	203	61	8	8	NUM
cana-2739	203	62	61.54	61.54	NUM
cana-2739	203	63	87.09	87.09	NUM
cana-2739	203	64	90.9	90.9	NUM
cana-2739	203	65	0	0	NUM
cana-2739	203	66	multiple	multiple	ADJ
cana-2739	203	67	features	feature	NOUN
cana-2739	203	68	yield	yield	VERB
cana-2739	203	69	a	a	DET
cana-2739	203	70	better	well	ADJ
cana-2739	203	71	method	method	NOUN
cana-2739	203	72	in	in	ADP
cana-2739	203	73	identification	identification	NOUN
cana-2739	203	74	of	of	ADP
cana-2739	203	75	cystic	cystic	ADJ
cana-2739	203	76	ovaries	ovary	NOUN
cana-2739	203	77	from	from	ADP
cana-2739	203	78	normal	normal	ADJ
cana-2739	203	79	ovaries	ovary	NOUN
cana-2739	203	80	.	.	PUNCT
cana-2739	204	1	thus	thus	ADV
cana-2739	204	2	,	,	PUNCT
cana-2739	204	3	the	the	DET
cana-2739	204	4	computer	computer	NOUN
cana-2739	204	5	aided	aid	VERB
cana-2739	204	6	diagnosis	diagnosis	NOUN
cana-2739	204	7	system	system	NOUN
cana-2739	204	8	built	build	VERB
cana-2739	204	9	on	on	ADP
cana-2739	204	10	multiple	multiple	ADJ
cana-2739	204	11	features	feature	NOUN
cana-2739	204	12	with	with	ADP
cana-2739	204	13	one	one	NUM
cana-2739	204	14	machine	machine	NOUN
cana-2739	204	15	learning	learn	VERB
cana-2739	204	16	classifier	classifier	NOUN
cana-2739	204	17	is	be	AUX
cana-2739	204	18	explored	explore	VERB
cana-2739	204	19	in	in	ADP
cana-2739	204	20	detail	detail	NOUN
cana-2739	204	21	.	.	PUNCT
cana-2739	205	1	the	the	DET
cana-2739	205	2	result	result	NOUN
cana-2739	205	3	secured	secure	VERB
cana-2739	205	4	in	in	ADP
cana-2739	205	5	combination	combination	NOUN
cana-2739	205	6	of	of	ADP
cana-2739	205	7	fdta	fdta	NOUN
cana-2739	205	8	and	and	CCONJ
cana-2739	205	9	lbp	lbp	NOUN
cana-2739	205	10	with	with	ADP
cana-2739	205	11	svm	svm	PROPN
cana-2739	205	12	as	as	SCONJ
cana-2739	205	13	classifier	classifier	NOUN
cana-2739	205	14	has	have	AUX
cana-2739	205	15	achieved	achieve	VERB
cana-2739	205	16	the	the	DET
cana-2739	205	17	maximum	maximum	ADJ
cana-2739	205	18	accuracy	accuracy	NOUN
cana-2739	205	19	.	.	PUNCT
cana-2739	206	1	decision	decision	NOUN
cana-2739	206	2	tree	tree	NOUN
cana-2739	206	3	has	have	AUX
cana-2739	206	4	obtained	obtain	VERB
cana-2739	206	5	83.42	83.42	NUM
cana-2739	206	6	%	%	NOUN
cana-2739	206	7	as	as	ADP
cana-2739	206	8	accuracy	accuracy	NOUN
cana-2739	206	9	,	,	PUNCT
cana-2739	206	10	86.58	86.58	NUM
cana-2739	206	11	%	%	NOUN
cana-2739	206	12	as	as	ADP
cana-2739	206	13	precision	precision	NOUN
cana-2739	206	14	,	,	PUNCT
cana-2739	206	15	91.49	91.49	NUM
cana-2739	206	16	%	%	NOUN
cana-2739	206	17	as	as	ADP
cana-2739	206	18	recall	recall	NOUN
cana-2739	206	19	,	,	PUNCT
cana-2739	206	20	88.97	88.97	NUM
cana-2739	206	21	%	%	NOUN
cana-2739	206	22	as	as	ADP
cana-2739	206	23	f1	f1	NOUN
cana-2739	206	24	-	-	PUNCT
cana-2739	206	25	score	score	NOUN
cana-2739	206	26	and	and	CCONJ
cana-2739	206	27	61.54	61.54	NUM
cana-2739	206	28	%	%	NOUN
cana-2739	206	29	as	as	ADP
cana-2739	206	30	specificity	specificity	NOUN
cana-2739	206	31	.	.	PUNCT
cana-2739	207	1	logistic	logistic	ADJ
cana-2739	207	2	regression	regression	NOUN
cana-2739	207	3	has	have	AUX
cana-2739	207	4	attained	attain	VERB
cana-2739	207	5	89.12	89.12	NUM
cana-2739	207	6	%	%	NOUN
cana-2739	207	7	,	,	PUNCT
cana-2739	207	8	97.31	97.31	NUM
cana-2739	207	9	%	%	NOUN
cana-2739	207	10	89.51	89.51	NUM
cana-2739	207	11	%	%	NOUN
cana-2739	207	12	,	,	PUNCT
cana-2739	207	13	93.45	93.45	NUM
cana-2739	207	14	%	%	NOUN
cana-2739	207	15	and	and	CCONJ
cana-2739	207	16	87.09	87.09	NUM
cana-2739	207	17	%	%	NOUN
cana-2739	207	18	as	as	ADP
cana-2739	207	19	accuracy	accuracy	NOUN
cana-2739	207	20	,	,	PUNCT
cana-2739	207	21	precision	precision	NOUN
cana-2739	207	22	,	,	PUNCT
cana-2739	207	23	recall	recall	NOUN
cana-2739	207	24	,	,	PUNCT
cana-2739	207	25	f1	f1	NOUN
cana-2739	207	26	-	-	PUNCT
cana-2739	207	27	score	score	NOUN
cana-2739	207	28	and	and	CCONJ
cana-2739	207	29	specificity	specificity	NOUN
cana-2739	207	30	,	,	PUNCT
cana-2739	207	31	respectively	respectively	ADV
cana-2739	207	32	.	.	PUNCT
cana-2739	208	1	svm	svm	PROPN
cana-2739	208	2	has	have	AUX
cana-2739	208	3	secured	secure	VERB
cana-2739	208	4	91.19	91.19	NUM
cana-2739	208	5	%	%	NOUN
cana-2739	208	6	,	,	PUNCT
cana-2739	208	7	97.98	97.98	NUM
cana-2739	208	8	%	%	NOUN
cana-2739	208	9	,	,	PUNCT
cana-2739	208	10	91.25	91.25	NUM
cana-2739	208	11	%	%	NOUN
cana-2739	208	12	,	,	PUNCT
cana-2739	208	13	94.49	94.49	NUM
cana-2739	208	14	%	%	NOUN
cana-2739	208	15	and	and	CCONJ
cana-2739	208	16	90.90	90.90	NUM
cana-2739	208	17	%	%	NOUN
cana-2739	208	18	sequentially	sequentially	ADV
cana-2739	208	19	.	.	PUNCT
cana-2739	209	1	it	it	PRON
cana-2739	209	2	is	be	AUX
cana-2739	209	3	evident	evident	ADJ
cana-2739	209	4	that	that	SCONJ
cana-2739	209	5	svm	svm	PROPN
cana-2739	209	6	has	have	AUX
cana-2739	209	7	shown	show	VERB
cana-2739	209	8	best	best	ADJ
cana-2739	209	9	performance	performance	NOUN
cana-2739	209	10	in	in	ADP
cana-2739	209	11	four	four	NUM
cana-2739	209	12	evaluation	evaluation	NOUN
cana-2739	209	13	metrics	metric	NOUN
cana-2739	209	14	(	(	PUNCT
cana-2739	209	15	accuracy	accuracy	NOUN
cana-2739	209	16	91.19	91.19	NUM
cana-2739	209	17	%	%	NOUN
cana-2739	209	18	,	,	PUNCT
cana-2739	209	19	precision	precision	NOUN
cana-2739	209	20	97.98	97.98	NUM
cana-2739	209	21	%	%	NOUN
cana-2739	209	22	,	,	PUNCT
cana-2739	209	23	f1	f1	NOUN
cana-2739	209	24	-	-	PUNCT
cana-2739	209	25	score	score	NOUN
cana-2739	209	26	94.49	94.49	NUM
cana-2739	209	27	%	%	NOUN
cana-2739	209	28	and	and	CCONJ
cana-2739	209	29	specificity	specificity	NOUN
cana-2739	209	30	90.90	90.90	NUM
cana-2739	209	31	%	%	NOUN
cana-2739	209	32	)	)	PUNCT
cana-2739	209	33	and	and	CCONJ
cana-2739	209	34	decision	decision	NOUN
cana-2739	209	35	tree	tree	NOUN
cana-2739	209	36	in	in	ADP
cana-2739	209	37	one	one	NUM
cana-2739	209	38	(	(	PUNCT
cana-2739	209	39	recall	recall	VERB
cana-2739	209	40	91.49	91.49	NUM
cana-2739	209	41	%	%	NOUN
cana-2739	209	42	)	)	PUNCT
cana-2739	209	43	.	.	PUNCT
cana-2739	210	1	figure	figure	VERB
cana-2739	210	2	3	3	NUM
cana-2739	210	3	depicts	depict	VERB
cana-2739	210	4	the	the	DET
cana-2739	210	5	accuracy	accuracy	NOUN
cana-2739	210	6	performance	performance	NOUN
cana-2739	210	7	metric	metric	ADJ
cana-2739	210	8	for	for	ADP
cana-2739	210	9	fdta	fdta	PROPN
cana-2739	210	10	,	,	PUNCT
cana-2739	210	11	lbp	lbp	PROPN
cana-2739	210	12	and	and	CCONJ
cana-2739	210	13	fdta	fdta	PROPN
cana-2739	210	14	+	+	CCONJ
cana-2739	210	15	lbp	lbp	NOUN
cana-2739	210	16	models	model	NOUN
cana-2739	210	17	.	.	PUNCT
cana-2739	211	1	the	the	DET
cana-2739	211	2	graph	graph	NOUN
cana-2739	211	3	clearly	clearly	ADV
cana-2739	211	4	delineates	delineate	VERB
cana-2739	211	5	that	that	DET
cana-2739	211	6	combination	combination	NOUN
cana-2739	211	7	of	of	ADP
cana-2739	211	8	fdta	fdta	PROPN
cana-2739	211	9	and	and	CCONJ
cana-2739	211	10	lbp	lbp	PROPN
cana-2739	211	11	textural	textural	NOUN
cana-2739	211	12	features	feature	NOUN
cana-2739	211	13	performs	perform	VERB
cana-2739	211	14	better	well	ADJ
cana-2739	211	15	than	than	ADP
cana-2739	211	16	single	single	ADJ
cana-2739	211	17	features	feature	NOUN
cana-2739	211	18	.	.	PUNCT
cana-2739	212	1	the	the	DET
cana-2739	212	2	comparative	comparative	ADJ
cana-2739	212	3	analysis	analysis	NOUN
cana-2739	212	4	of	of	ADP
cana-2739	212	5	remaining	remain	VERB
cana-2739	212	6	performance	performance	NOUN
cana-2739	212	7	metrics	metric	NOUN
cana-2739	212	8	is	be	AUX
cana-2739	212	9	represented	represent	VERB
cana-2739	212	10	graphically	graphically	ADV
cana-2739	212	11	on	on	ADP
cana-2739	212	12	left	left	ADJ
cana-2739	212	13	hand	hand	NOUN
cana-2739	212	14	side	side	NOUN
cana-2739	212	15	for	for	ADP
cana-2739	212	16	fdta	fdta	NOUN
cana-2739	212	17	in	in	ADP
cana-2739	212	18	figure	figure	NOUN
cana-2739	212	19	4	4	NUM
cana-2739	212	20	,	,	PUNCT
cana-2739	212	21	lbp	lbp	NOUN
cana-2739	212	22	in	in	ADP
cana-2739	212	23	figure	figure	NOUN
cana-2739	212	24	5	5	NUM
cana-2739	212	25	and	and	CCONJ
cana-2739	212	26	fdta	fdta	NOUN
cana-2739	212	27	+	+	CCONJ
cana-2739	212	28	lbp	lbp	NOUN
cana-2739	212	29	in	in	ADP
cana-2739	212	30	figure	figure	NOUN
cana-2739	212	31	6	6	NUM
cana-2739	212	32	.	.	PUNCT
cana-2739	213	1	figure	figure	VERB
cana-2739	213	2	3	3	NUM
cana-2739	213	3	accuracy	accuracy	NOUN
cana-2739	213	4	graph	graph	NOUN
cana-2739	213	5	analysis	analysis	NOUN
cana-2739	213	6	for	for	ADP
cana-2739	213	7	fdta	fdta	PROPN
cana-2739	213	8	,	,	PUNCT
cana-2739	213	9	lbp	lbp	PROPN
cana-2739	213	10	and	and	CCONJ
cana-2739	213	11	fdta	fdta	PROPN
cana-2739	213	12	+	+	CCONJ
cana-2739	213	13	lbp	lbp	NOUN
cana-2739	213	14	.	.	PUNCT
cana-2739	214	1	communications	communication	NOUN
cana-2739	214	2	on	on	ADP
cana-2739	214	3	applied	apply	VERB
cana-2739	214	4	nonlinear	nonlinear	ADJ
cana-2739	214	5	analysis	analysis	NOUN
cana-2739	214	6	issn	issn	NOUN
cana-2739	214	7	:	:	PUNCT
cana-2739	214	8	1074	1074	NUM
cana-2739	214	9	-	-	PUNCT
cana-2739	214	10	133x	133x	NUM
cana-2739	214	11	vol	vol	NOUN
cana-2739	214	12	32	32	NUM
cana-2739	214	13	no	no	NOUN
cana-2739	214	14	.	.	PUNCT
cana-2739	215	1	4s	4s	NUM
cana-2739	215	2	(	(	PUNCT
cana-2739	215	3	2025	2025	NUM
cana-2739	215	4	)	)	PUNCT
cana-2739	215	5	70	70	NUM
cana-2739	215	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2739	215	7	the	the	DET
cana-2739	215	8	roc	roc	PROPN
cana-2739	215	9	-	-	PUNCT
cana-2739	215	10	auc	auc	NOUN
cana-2739	215	11	curves	curve	NOUN
cana-2739	215	12	for	for	ADP
cana-2739	215	13	decision	decision	NOUN
cana-2739	215	14	tree	tree	NOUN
cana-2739	215	15	,	,	PUNCT
cana-2739	215	16	logistic	logistic	ADJ
cana-2739	215	17	regression	regression	NOUN
cana-2739	215	18	and	and	CCONJ
cana-2739	215	19	svm	svm	VERB
cana-2739	215	20	for	for	ADP
cana-2739	215	21	ovarian	ovarian	ADJ
cana-2739	215	22	cyst	cyst	NOUN
cana-2739	215	23	classification	classification	NOUN
cana-2739	215	24	for	for	ADP
cana-2739	215	25	fdta	fdta	PROPN
cana-2739	215	26	,	,	PUNCT
cana-2739	215	27	lbp	lbp	PROPN
cana-2739	215	28	and	and	CCONJ
cana-2739	215	29	combined	combine	VERB
cana-2739	215	30	feature	feature	NOUN
cana-2739	215	31	of	of	ADP
cana-2739	215	32	fdta	fdta	NOUN
cana-2739	215	33	+	+	CCONJ
cana-2739	215	34	lbp	lbp	NOUN
cana-2739	215	35	are	be	AUX
cana-2739	215	36	shown	show	VERB
cana-2739	215	37	on	on	ADP
cana-2739	215	38	right	right	ADJ
cana-2739	215	39	hand	hand	NOUN
cana-2739	215	40	side	side	NOUN
cana-2739	215	41	in	in	ADP
cana-2739	215	42	figure	figure	NOUN
cana-2739	215	43	4	4	NUM
cana-2739	215	44	,	,	PUNCT
cana-2739	215	45	figure	figure	VERB
cana-2739	215	46	5	5	NUM
cana-2739	215	47	and	and	CCONJ
cana-2739	215	48	figure	figure	VERB
cana-2739	215	49	6	6	NUM
cana-2739	215	50	.	.	PUNCT
cana-2739	216	1	for	for	ADP
cana-2739	216	2	fdta	fdta	PROPN
cana-2739	216	3	,	,	PUNCT
cana-2739	216	4	svm	svm	PROPN
cana-2739	216	5	has	have	VERB
cana-2739	216	6	maximum	maximum	ADJ
cana-2739	216	7	area	area	NOUN
cana-2739	216	8	under	under	ADP
cana-2739	216	9	curve	curve	NOUN
cana-2739	216	10	with	with	ADP
cana-2739	216	11	value	value	NOUN
cana-2739	216	12	of	of	ADP
cana-2739	216	13	0.8705	0.8705	NUM
cana-2739	216	14	.	.	PUNCT
cana-2739	217	1	in	in	ADP
cana-2739	217	2	case	case	NOUN
cana-2739	217	3	of	of	ADP
cana-2739	217	4	lbp	lbp	PROPN
cana-2739	217	5	,	,	PUNCT
cana-2739	217	6	the	the	DET
cana-2739	217	7	auc	auc	NOUN
cana-2739	217	8	value	value	NOUN
cana-2739	217	9	has	have	AUX
cana-2739	217	10	increased	increase	VERB
cana-2739	217	11	for	for	ADP
cana-2739	217	12	both	both	CCONJ
cana-2739	217	13	logistic	logistic	ADJ
cana-2739	217	14	regression	regression	NOUN
cana-2739	217	15	(	(	PUNCT
cana-2739	217	16	0.8969	0.8969	NUM
cana-2739	217	17	)	)	PUNCT
cana-2739	217	18	and	and	CCONJ
cana-2739	217	19	svm	svm	ADJ
cana-2739	217	20	(	(	PUNCT
cana-2739	217	21	0.9091	0.9091	NUM
cana-2739	217	22	)	)	PUNCT
cana-2739	217	23	but	but	CCONJ
cana-2739	217	24	reduced	reduce	VERB
cana-2739	217	25	for	for	ADP
cana-2739	217	26	decision	decision	NOUN
cana-2739	217	27	tree	tree	NOUN
cana-2739	217	28	(	(	PUNCT
cana-2739	217	29	0.7309	0.7309	NUM
cana-2739	217	30	)	)	PUNCT
cana-2739	217	31	as	as	SCONJ
cana-2739	217	32	compared	compare	VERB
cana-2739	217	33	to	to	ADP
cana-2739	217	34	fdta	fdta	NOUN
cana-2739	217	35	value	value	NOUN
cana-2739	217	36	of	of	ADP
cana-2739	217	37	0.7717	0.7717	NUM
cana-2739	217	38	.	.	PUNCT
cana-2739	218	1	for	for	ADP
cana-2739	218	2	hybrid	hybrid	ADJ
cana-2739	218	3	textural	textural	ADJ
cana-2739	218	4	features	feature	NOUN
cana-2739	218	5	(	(	PUNCT
cana-2739	218	6	fdta	fdta	X
cana-2739	218	7	+	+	NUM
cana-2739	218	8	lbp	lbp	NOUN
cana-2739	218	9	)	)	PUNCT
cana-2739	218	10	,	,	PUNCT
cana-2739	218	11	the	the	DET
cana-2739	218	12	area	area	NOUN
cana-2739	218	13	under	under	ADP
cana-2739	218	14	curve	curve	NOUN
cana-2739	218	15	for	for	ADP
cana-2739	218	16	svm	svm	PROPN
cana-2739	218	17	is	be	AUX
cana-2739	218	18	0.93	0.93	NUM
cana-2739	218	19	which	which	PRON
cana-2739	218	20	is	be	AUX
cana-2739	218	21	better	well	ADJ
cana-2739	218	22	than	than	ADP
cana-2739	218	23	decision	decision	NOUN
cana-2739	218	24	tree	tree	NOUN
cana-2739	218	25	(	(	PUNCT
cana-2739	218	26	0.7965	0.7965	NUM
cana-2739	218	27	)	)	PUNCT
cana-2739	218	28	and	and	CCONJ
cana-2739	218	29	logistic	logistic	ADJ
cana-2739	218	30	regression	regression	NOUN
cana-2739	218	31	(	(	PUNCT
cana-2739	218	32	0.914	0.914	NUM
cana-2739	218	33	)	)	PUNCT
cana-2739	218	34	.	.	PUNCT
cana-2739	219	1	figure	figure	VERB
cana-2739	219	2	4	4	NUM
cana-2739	219	3	comparative	comparative	ADJ
cana-2739	219	4	analysis	analysis	NOUN
cana-2739	219	5	in	in	ADP
cana-2739	219	6	terms	term	NOUN
cana-2739	219	7	of	of	ADP
cana-2739	219	8	evaluation	evaluation	NOUN
cana-2739	219	9	metrics	metric	NOUN
cana-2739	219	10	and	and	CCONJ
cana-2739	219	11	roc	roc	NOUN
cana-2739	219	12	-	-	PUNCT
cana-2739	219	13	auc	auc	NOUN
cana-2739	219	14	curve	curve	NOUN
cana-2739	219	15	for	for	ADP
cana-2739	219	16	fdta	fdta	PROPN
cana-2739	219	17	.	.	PUNCT
cana-2739	220	1	figure	figure	VERB
cana-2739	220	2	5	5	NUM
cana-2739	220	3	comparative	comparative	ADJ
cana-2739	220	4	analysis	analysis	NOUN
cana-2739	220	5	in	in	ADP
cana-2739	220	6	terms	term	NOUN
cana-2739	220	7	of	of	ADP
cana-2739	220	8	evaluation	evaluation	NOUN
cana-2739	220	9	metrics	metric	NOUN
cana-2739	220	10	and	and	CCONJ
cana-2739	220	11	roc	roc	NOUN
cana-2739	220	12	-	-	PUNCT
cana-2739	220	13	auc	auc	NOUN
cana-2739	220	14	curve	curve	NOUN
cana-2739	220	15	for	for	ADP
cana-2739	220	16	lbp	lbp	PROPN
cana-2739	220	17	.	.	PUNCT
cana-2739	221	1	figure	figure	VERB
cana-2739	221	2	6	6	NUM
cana-2739	221	3	comparative	comparative	ADJ
cana-2739	221	4	analysis	analysis	NOUN
cana-2739	221	5	in	in	ADP
cana-2739	221	6	terms	term	NOUN
cana-2739	221	7	of	of	ADP
cana-2739	221	8	evaluation	evaluation	NOUN
cana-2739	221	9	metrics	metric	NOUN
cana-2739	221	10	and	and	CCONJ
cana-2739	221	11	roc	roc	NOUN
cana-2739	221	12	-	-	PUNCT
cana-2739	221	13	auc	auc	NOUN
cana-2739	221	14	curve	curve	NOUN
cana-2739	221	15	for	for	ADP
cana-2739	221	16	fdta	fdta	NOUN
cana-2739	221	17	+	+	CCONJ
cana-2739	221	18	lbp	lbp	NOUN
cana-2739	221	19	.	.	PUNCT
cana-2739	222	1	communications	communication	NOUN
cana-2739	222	2	on	on	ADP
cana-2739	222	3	applied	apply	VERB
cana-2739	222	4	nonlinear	nonlinear	ADJ
cana-2739	222	5	analysis	analysis	NOUN
cana-2739	222	6	issn	issn	NOUN
cana-2739	222	7	:	:	PUNCT
cana-2739	222	8	1074	1074	NUM
cana-2739	222	9	-	-	PUNCT
cana-2739	222	10	133x	133x	NUM
cana-2739	222	11	vol	vol	NOUN
cana-2739	222	12	32	32	NUM
cana-2739	222	13	no	no	NOUN
cana-2739	222	14	.	.	PUNCT
cana-2739	223	1	4s	4s	NUM
cana-2739	223	2	(	(	PUNCT
cana-2739	223	3	2025	2025	NUM
cana-2739	223	4	)	)	PUNCT
cana-2739	223	5	71	71	NUM
cana-2739	223	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2739	223	7	comparing	compare	VERB
cana-2739	223	8	the	the	DET
cana-2739	223	9	experimental	experimental	ADJ
cana-2739	223	10	results	result	NOUN
cana-2739	223	11	,	,	PUNCT
cana-2739	223	12	it	it	PRON
cana-2739	223	13	is	be	AUX
cana-2739	223	14	obvious	obvious	ADJ
cana-2739	223	15	that	that	SCONJ
cana-2739	223	16	svm	svm	PROPN
cana-2739	223	17	effectively	effectively	ADV
cana-2739	223	18	classifies	classify	VERB
cana-2739	223	19	ovarian	ovarian	ADJ
cana-2739	223	20	ultrasound	ultrasound	NOUN
cana-2739	223	21	images	image	NOUN
cana-2739	223	22	as	as	ADP
cana-2739	223	23	normal	normal	ADJ
cana-2739	223	24	and	and	CCONJ
cana-2739	223	25	cystic	cystic	ADJ
cana-2739	223	26	with	with	ADP
cana-2739	223	27	an	an	DET
cana-2739	223	28	accuracy	accuracy	NOUN
cana-2739	223	29	of	of	ADP
cana-2739	223	30	91.19	91.19	NUM
cana-2739	223	31	%	%	NOUN
cana-2739	223	32	and	and	CCONJ
cana-2739	223	33	auc	auc	NOUN
cana-2739	223	34	score	score	NOUN
cana-2739	223	35	of	of	ADP
cana-2739	223	36	0.93	0.93	NUM
cana-2739	223	37	using	use	VERB
cana-2739	223	38	multiple	multiple	ADJ
cana-2739	223	39	textural	textural	ADJ
cana-2739	223	40	features	feature	NOUN
cana-2739	223	41	of	of	ADP
cana-2739	223	42	fdta	fdta	PROPN
cana-2739	223	43	and	and	CCONJ
cana-2739	223	44	lbp	lbp	NOUN
cana-2739	223	45	.	.	PUNCT
cana-2739	224	1	it	it	PRON
cana-2739	224	2	has	have	AUX
cana-2739	224	3	proved	prove	VERB
cana-2739	224	4	to	to	PART
cana-2739	224	5	be	be	AUX
cana-2739	224	6	a	a	DET
cana-2739	224	7	productive	productive	ADJ
cana-2739	224	8	machine	machine	NOUN
cana-2739	224	9	learning	learn	VERB
cana-2739	224	10	algorithm	algorithm	NOUN
cana-2739	224	11	for	for	ADP
cana-2739	224	12	classification	classification	NOUN
cana-2739	224	13	of	of	ADP
cana-2739	224	14	ovarian	ovarian	ADJ
cana-2739	224	15	cyst	cyst	NOUN
cana-2739	224	16	from	from	ADP
cana-2739	224	17	normal	normal	ADJ
cana-2739	224	18	ovaries	ovary	NOUN
cana-2739	224	19	with	with	ADP
cana-2739	224	20	precision	precision	NOUN
cana-2739	224	21	,	,	PUNCT
cana-2739	224	22	recall	recall	NOUN
cana-2739	224	23	,	,	PUNCT
cana-2739	224	24	f1	f1	NOUN
cana-2739	224	25	-	-	PUNCT
cana-2739	224	26	score	score	NOUN
cana-2739	224	27	and	and	CCONJ
cana-2739	224	28	specificity	specificity	NOUN
cana-2739	224	29	as	as	ADP
cana-2739	224	30	97.98	97.98	NUM
cana-2739	224	31	%	%	NOUN
cana-2739	224	32	,	,	PUNCT
cana-2739	224	33	91.25	91.25	NUM
cana-2739	224	34	%	%	NOUN
cana-2739	224	35	,	,	PUNCT
cana-2739	224	36	94.49	94.49	NUM
cana-2739	224	37	%	%	NOUN
cana-2739	224	38	and	and	CCONJ
cana-2739	224	39	90.90	90.90	NUM
cana-2739	224	40	%	%	NOUN
cana-2739	224	41	,	,	PUNCT
cana-2739	224	42	respectively	respectively	ADV
cana-2739	224	43	.	.	PUNCT
cana-2739	225	1	thus	thus	ADV
cana-2739	225	2	,	,	PUNCT
cana-2739	225	3	svm	svm	PROPN
cana-2739	225	4	is	be	AUX
cana-2739	225	5	chosen	choose	VERB
cana-2739	225	6	as	as	ADP
cana-2739	225	7	the	the	DET
cana-2739	225	8	compared	compare	VERB
cana-2739	225	9	machine	machine	NOUN
cana-2739	225	10	learning	learning	NOUN
cana-2739	225	11	model	model	NOUN
cana-2739	225	12	with	with	ADP
cana-2739	225	13	other	other	ADJ
cana-2739	225	14	models	model	NOUN
cana-2739	225	15	.	.	PUNCT
cana-2739	226	1	this	this	DET
cana-2739	226	2	research	research	NOUN
cana-2739	226	3	further	far	ADV
cana-2739	226	4	compares	compare	VERB
cana-2739	226	5	the	the	DET
cana-2739	226	6	svm	svm	ADJ
cana-2739	226	7	model	model	NOUN
cana-2739	226	8	results	result	VERB
cana-2739	226	9	with	with	ADP
cana-2739	226	10	the	the	DET
cana-2739	226	11	latest	late	ADJ
cana-2739	226	12	ovarian	ovarian	ADJ
cana-2739	226	13	cyst	cyst	NOUN
cana-2739	226	14	classification	classification	NOUN
cana-2739	226	15	machine	machine	NOUN
cana-2739	226	16	learning	learning	NOUN
cana-2739	226	17	models	model	NOUN
cana-2739	226	18	mentioned	mention	VERB
cana-2739	226	19	in	in	ADP
cana-2739	226	20	the	the	DET
cana-2739	226	21	literature	literature	NOUN
cana-2739	226	22	that	that	PRON
cana-2739	226	23	considered	consider	VERB
cana-2739	226	24	textural	textural	ADJ
cana-2739	226	25	features	feature	NOUN
cana-2739	226	26	as	as	ADP
cana-2739	226	27	a	a	DET
cana-2739	226	28	basis	basis	NOUN
cana-2739	226	29	for	for	ADP
cana-2739	226	30	classification	classification	NOUN
cana-2739	226	31	in	in	ADP
cana-2739	226	32	images	image	NOUN
cana-2739	226	33	.	.	PUNCT
cana-2739	227	1	a	a	DET
cana-2739	227	2	summary	summary	NOUN
cana-2739	227	3	of	of	ADP
cana-2739	227	4	this	this	DET
cana-2739	227	5	contrast	contrast	NOUN
cana-2739	227	6	is	be	AUX
cana-2739	227	7	shown	show	VERB
cana-2739	227	8	in	in	ADP
cana-2739	227	9	table	table	NOUN
cana-2739	227	10	7	7	NUM
cana-2739	227	11	.	.	PUNCT
cana-2739	228	1	wei	wei	PROPN
cana-2739	228	2	et	et	PROPN
cana-2739	228	3	al	al	PROPN
cana-2739	228	4	.	.	PROPN
cana-2739	228	5	extracted	extract	VERB
cana-2739	228	6	lbp	lbp	NOUN
cana-2739	228	7	,	,	PUNCT
cana-2739	228	8	glcm	glcm	PROPN
cana-2739	228	9	and	and	CCONJ
cana-2739	228	10	hog	hog	NOUN
cana-2739	228	11	features	feature	NOUN
cana-2739	228	12	from	from	ADP
cana-2739	228	13	448	448	NUM
cana-2739	228	14	images	image	NOUN
cana-2739	228	15	and	and	CCONJ
cana-2739	228	16	achieved	achieve	VERB
cana-2739	228	17	91.11	91.11	NUM
cana-2739	228	18	%	%	NOUN
cana-2739	228	19	as	as	ADP
cana-2739	228	20	classification	classification	NOUN
cana-2739	228	21	accuracy	accuracy	NOUN
cana-2739	228	22	.	.	PUNCT
cana-2739	229	1	using	use	VERB
cana-2739	229	2	ensemble	ensemble	ADJ
cana-2739	229	3	model	model	NOUN
cana-2739	229	4	,	,	PUNCT
cana-2739	229	5	rocha	rocha	PROPN
cana-2739	229	6	et	et	PROPN
cana-2739	229	7	al	al	PROPN
cana-2739	229	8	.	.	PROPN
cana-2739	229	9	classified	classify	VERB
cana-2739	229	10	images	image	NOUN
cana-2739	229	11	with	with	ADP
cana-2739	229	12	85.9	85.9	NUM
cana-2739	229	13	%	%	NOUN
cana-2739	229	14	accuracy	accuracy	NOUN
cana-2739	229	15	by	by	ADP
cana-2739	229	16	extracting	extract	VERB
cana-2739	229	17	features	feature	NOUN
cana-2739	229	18	with	with	ADP
cana-2739	229	19	the	the	DET
cana-2739	229	20	help	help	NOUN
cana-2739	229	21	of	of	ADP
cana-2739	229	22	lbp	lbp	NOUN
cana-2739	229	23	and	and	CCONJ
cana-2739	229	24	convolutional	convolutional	ADJ
cana-2739	229	25	neural	neural	ADJ
cana-2739	229	26	network	network	NOUN
cana-2739	229	27	.	.	PUNCT
cana-2739	230	1	the	the	DET
cana-2739	230	2	svm	svm	PROPN
cana-2739	230	3	model	model	NOUN
cana-2739	230	4	proposed	propose	VERB
cana-2739	230	5	in	in	ADP
cana-2739	230	6	this	this	DET
cana-2739	230	7	research	research	NOUN
cana-2739	230	8	outperforms	outperform	VERB
cana-2739	230	9	all	all	DET
cana-2739	230	10	other	other	ADJ
cana-2739	230	11	models	model	NOUN
cana-2739	230	12	extracting	extract	VERB
cana-2739	230	13	textural	textural	ADJ
cana-2739	230	14	features	feature	NOUN
cana-2739	230	15	with	with	ADP
cana-2739	230	16	an	an	DET
cana-2739	230	17	accuracy	accuracy	NOUN
cana-2739	230	18	of	of	ADP
cana-2739	230	19	91.19	91.19	NUM
cana-2739	230	20	%	%	NOUN
cana-2739	230	21	on	on	ADP
cana-2739	230	22	1203	1203	NUM
cana-2739	230	23	ultrasound	ultrasound	NOUN
cana-2739	230	24	images	image	NOUN
cana-2739	230	25	.	.	PUNCT
cana-2739	231	1	table	table	NOUN
cana-2739	231	2	7	7	NUM
cana-2739	231	3	comparsion	comparsion	NOUN
cana-2739	231	4	between	between	ADP
cana-2739	231	5	findings	finding	NOUN
cana-2739	231	6	of	of	ADP
cana-2739	231	7	proposed	propose	VERB
cana-2739	231	8	model	model	NOUN
cana-2739	231	9	with	with	ADP
cana-2739	231	10	recent	recent	ADJ
cana-2739	231	11	machine	machine	NOUN
cana-2739	231	12	learning	learning	NOUN
cana-2739	231	13	models	model	NOUN
cana-2739	231	14	.	.	PUNCT
cana-2739	232	1	references	reference	NOUN
cana-2739	232	2	feature	feature	VERB
cana-2739	232	3	extration	extration	NOUN
cana-2739	232	4	models	model	NOUN
cana-2739	232	5	accuracy	accuracy	NOUN
cana-2739	232	6	(	(	PUNCT
cana-2739	232	7	%	%	INTJ
cana-2739	232	8	)	)	PUNCT
cana-2739	232	9	(	(	PUNCT
cana-2739	232	10	wei	wei	PROPN
cana-2739	232	11	et	et	PROPN
cana-2739	232	12	al	al	PROPN
cana-2739	232	13	.	.	PROPN
cana-2739	232	14	,	,	PUNCT
cana-2739	232	15	2020	2020	NUM
cana-2739	232	16	)	)	PUNCT
cana-2739	232	17	lbp	lbp	PROPN
cana-2739	232	18	,	,	PUNCT
cana-2739	232	19	glcm	glcm	PROPN
cana-2739	232	20	and	and	CCONJ
cana-2739	232	21	hog	hog	NOUN
cana-2739	232	22	svm	svm	PROPN
cana-2739	232	23	and	and	CCONJ
cana-2739	232	24	naïve	naïve	ADJ
cana-2739	232	25	bayes	bayes	NOUN
cana-2739	232	26	91.11	91.11	NUM
cana-2739	232	27	%	%	NOUN
cana-2739	232	28	(	(	PUNCT
cana-2739	232	29	y.	y.	PROPN
cana-2739	232	30	c.	c.	PROPN
cana-2739	232	31	chang	chang	PROPN
cana-2739	232	32	&	&	CCONJ
cana-2739	232	33	jeng	jeng	PROPN
cana-2739	232	34	,	,	PUNCT
cana-2739	232	35	2023	2023	NUM
cana-2739	232	36	)	)	PUNCT
cana-2739	232	37	hurst	hurst	PROPN
cana-2739	232	38	exponent	exponent	PROPN
cana-2739	232	39	logistic	logistic	PROPN
cana-2739	232	40	regression	regression	PROPN
cana-2739	232	41	,	,	PUNCT
cana-2739	232	42	knn	knn	PROPN
cana-2739	232	43	,	,	PUNCT
cana-2739	232	44	decision	decision	NOUN
cana-2739	232	45	tree	tree	NOUN
cana-2739	232	46	and	and	CCONJ
cana-2739	232	47	svm	svm	VERB
cana-2739	232	48	lr	lr	NOUN
cana-2739	232	49	–	–	PUNCT
cana-2739	232	50	83.7	83.7	NUM
cana-2739	232	51	%	%	NOUN
cana-2739	232	52	,	,	PUNCT
cana-2739	232	53	knn-81.3	knn-81.3	PROPN
cana-2739	232	54	%	%	NOUN
cana-2739	232	55	,	,	PUNCT
cana-2739	232	56	dt	dt	PROPN
cana-2739	232	57	–	–	PUNCT
cana-2739	232	58	82.6	82.6	NUM
cana-2739	232	59	%	%	NOUN
cana-2739	232	60	,	,	PUNCT
cana-2739	232	61	svm	svm	VERB
cana-2739	232	62	83.9	83.9	NUM
cana-2739	232	63	%	%	NOUN
cana-2739	232	64	(	(	PUNCT
cana-2739	232	65	bayzitov	bayzitov	PROPN
cana-2739	232	66	et	et	PROPN
cana-2739	232	67	al	al	PROPN
cana-2739	232	68	.	.	PROPN
cana-2739	232	69	,	,	PUNCT
cana-2739	232	70	2023	2023	NUM
cana-2739	232	71	)	)	PUNCT
cana-2739	232	72	lbp	lbp	PROPN
cana-2739	232	73	adaboost	adaboost	PROPN
cana-2739	232	74	classifiers	classifier	NOUN
cana-2739	232	75	with	with	ADP
cana-2739	232	76	lbp	lbp	PROPN
cana-2739	232	77	85.4	85.4	NUM
cana-2739	232	78	%	%	NOUN
cana-2739	232	79	(	(	PUNCT
cana-2739	232	80	rocha	rocha	PROPN
cana-2739	232	81	et	et	PROPN
cana-2739	232	82	al	al	PROPN
cana-2739	232	83	.	.	PROPN
cana-2739	232	84	,	,	PUNCT
cana-2739	232	85	2023	2023	NUM
cana-2739	232	86	)	)	PUNCT
cana-2739	232	87	hybrid	hybrid	ADJ
cana-2739	232	88	convolutional	convolutional	ADJ
cana-2739	232	89	neural	neural	ADJ
cana-2739	232	90	networks	network	NOUN
cana-2739	232	91	(	(	PUNCT
cana-2739	232	92	combination	combination	NOUN
cana-2739	232	93	of	of	ADP
cana-2739	232	94	convolutional	convolutional	ADJ
cana-2739	232	95	neural	neural	ADJ
cana-2739	232	96	networks	network	NOUN
cana-2739	232	97	and	and	CCONJ
cana-2739	232	98	lbp	lbp	PROPN
cana-2739	232	99	)	)	PUNCT
cana-2739	232	100	ensemble	ensemble	ADJ
cana-2739	232	101	model	model	NOUN
cana-2739	232	102	(	(	PUNCT
cana-2739	232	103	svm	svm	ADJ
cana-2739	232	104	,	,	PUNCT
cana-2739	232	105	random	random	ADJ
cana-2739	232	106	forest	forest	NOUN
cana-2739	232	107	,	,	PUNCT
cana-2739	232	108	knn	knn	PROPN
cana-2739	232	109	,	,	PUNCT
cana-2739	232	110	linear	linear	VERB
cana-2739	232	111	discriminant	discriminant	ADJ
cana-2739	232	112	analysis	analysis	NOUN
cana-2739	232	113	)	)	PUNCT
cana-2739	232	114	85.9	85.9	NUM
cana-2739	232	115	%	%	NOUN
cana-2739	232	116	proposed	propose	VERB
cana-2739	232	117	model	model	NOUN
cana-2739	232	118	fdta	fdta	PROPN
cana-2739	232	119	and	and	CCONJ
cana-2739	232	120	lbp	lbp	PROPN
cana-2739	232	121	svm	svm	PROPN
cana-2739	232	122	91.19	91.19	NUM
cana-2739	232	123	%	%	NOUN
cana-2739	232	124	5	5	NUM
cana-2739	232	125	.	.	PUNCT
cana-2739	232	126	conclusion	conclusion	NOUN
cana-2739	232	127	the	the	DET
cana-2739	232	128	research	research	NOUN
cana-2739	232	129	suggests	suggest	VERB
cana-2739	232	130	a	a	DET
cana-2739	232	131	computer	computer	NOUN
cana-2739	232	132	aided	aid	VERB
cana-2739	232	133	diagnosis	diagnosis	NOUN
cana-2739	232	134	system	system	NOUN
cana-2739	232	135	for	for	ADP
cana-2739	232	136	early	early	ADJ
cana-2739	232	137	detection	detection	NOUN
cana-2739	232	138	of	of	ADP
cana-2739	232	139	ovarian	ovarian	ADJ
cana-2739	232	140	cyst	cyst	NOUN
cana-2739	232	141	using	use	VERB
cana-2739	232	142	machine	machine	NOUN
cana-2739	232	143	learning	learning	NOUN
cana-2739	232	144	algorithms	algorithm	NOUN
cana-2739	232	145	.	.	PUNCT
cana-2739	233	1	the	the	DET
cana-2739	233	2	ultrasound	ultrasound	NOUN
cana-2739	233	3	images	image	NOUN
cana-2739	233	4	are	be	AUX
cana-2739	233	5	pre	pre	ADJ
cana-2739	233	6	-	-	VERB
cana-2739	233	7	processed	processed	ADJ
cana-2739	233	8	to	to	PART
cana-2739	233	9	improve	improve	VERB
cana-2739	233	10	the	the	DET
cana-2739	233	11	quality	quality	NOUN
cana-2739	233	12	of	of	ADP
cana-2739	233	13	images	image	NOUN
cana-2739	233	14	to	to	PART
cana-2739	233	15	achieve	achieve	VERB
cana-2739	233	16	better	well	ADJ
cana-2739	233	17	classification	classification	NOUN
cana-2739	233	18	model	model	NOUN
cana-2739	233	19	.	.	PUNCT
cana-2739	234	1	they	they	PRON
cana-2739	234	2	are	be	AUX
cana-2739	234	3	transformed	transform	VERB
cana-2739	234	4	to	to	ADP
cana-2739	234	5	grey	grey	ADJ
cana-2739	234	6	scale	scale	NOUN
cana-2739	234	7	images	image	NOUN
cana-2739	234	8	from	from	ADP
cana-2739	234	9	rgb	rgb	PROPN
cana-2739	234	10	scale	scale	NOUN
cana-2739	234	11	followed	follow	VERB
cana-2739	234	12	by	by	ADP
cana-2739	234	13	region	region	NOUN
cana-2739	234	14	of	of	ADP
cana-2739	234	15	interest	interest	NOUN
cana-2739	234	16	extraction	extraction	NOUN
cana-2739	234	17	to	to	PART
cana-2739	234	18	remove	remove	VERB
cana-2739	234	19	complexities	complexity	NOUN
cana-2739	234	20	from	from	ADP
cana-2739	234	21	computer	computer	NOUN
cana-2739	234	22	aided	aid	VERB
cana-2739	234	23	diagnosis	diagnosis	NOUN
cana-2739	234	24	system	system	NOUN
cana-2739	234	25	.	.	PUNCT
cana-2739	235	1	for	for	ADP
cana-2739	235	2	further	further	ADJ
cana-2739	235	3	enhancement	enhancement	NOUN
cana-2739	235	4	,	,	PUNCT
cana-2739	235	5	speckle	speckle	NOUN
cana-2739	235	6	noise	noise	NOUN
cana-2739	235	7	is	be	AUX
cana-2739	235	8	discarded	discard	VERB
cana-2739	235	9	from	from	ADP
cana-2739	235	10	ultrasound	ultrasound	NOUN
cana-2739	235	11	images	image	NOUN
cana-2739	235	12	with	with	ADP
cana-2739	235	13	the	the	DET
cana-2739	235	14	assistance	assistance	NOUN
cana-2739	235	15	of	of	ADP
cana-2739	235	16	fast	fast	ADJ
cana-2739	235	17	nl	nl	NOUN
cana-2739	235	18	filter	filter	NOUN
cana-2739	235	19	.	.	PUNCT
cana-2739	236	1	then	then	ADV
cana-2739	236	2	,	,	PUNCT
cana-2739	236	3	14	14	NUM
cana-2739	236	4	textural	textural	ADJ
cana-2739	236	5	features	feature	NOUN
cana-2739	236	6	are	be	AUX
cana-2739	236	7	acquired	acquire	VERB
cana-2739	236	8	from	from	ADP
cana-2739	236	9	images	image	NOUN
cana-2739	236	10	using	use	VERB
cana-2739	236	11	local	local	ADJ
cana-2739	236	12	binary	binary	ADJ
cana-2739	236	13	pattern	pattern	NOUN
cana-2739	236	14	and	and	CCONJ
cana-2739	236	15	fractal	fractal	ADJ
cana-2739	236	16	dimensions	dimension	NOUN
cana-2739	236	17	.	.	PUNCT
cana-2739	237	1	the	the	DET
cana-2739	237	2	research	research	NOUN
cana-2739	237	3	has	have	AUX
cana-2739	237	4	utilized	utilize	VERB
cana-2739	237	5	three	three	NUM
cana-2739	237	6	machine	machine	NOUN
cana-2739	237	7	learning	learn	VERB
cana-2739	237	8	algorithms	algorithm	NOUN
cana-2739	237	9	,	,	PUNCT
cana-2739	237	10	namely	namely	ADV
cana-2739	237	11	decision	decision	NOUN
cana-2739	237	12	tree	tree	NOUN
cana-2739	237	13	,	,	PUNCT
cana-2739	237	14	logistic	logistic	ADJ
cana-2739	237	15	regression	regression	NOUN
cana-2739	237	16	and	and	CCONJ
cana-2739	237	17	svm	svm	VERB
cana-2739	237	18	to	to	PART
cana-2739	237	19	differentiate	differentiate	VERB
cana-2739	237	20	images	image	NOUN
cana-2739	237	21	having	have	VERB
cana-2739	237	22	cystic	cystic	ADJ
cana-2739	237	23	ovaries	ovary	NOUN
cana-2739	237	24	from	from	ADP
cana-2739	237	25	normal	normal	ADJ
cana-2739	237	26	ovaries	ovary	NOUN
cana-2739	237	27	.	.	PUNCT
cana-2739	238	1	specificity	specificity	NOUN
cana-2739	238	2	,	,	PUNCT
cana-2739	238	3	f1	f1	NOUN
cana-2739	238	4	-	-	PUNCT
cana-2739	238	5	score	score	NOUN
cana-2739	238	6	,	,	PUNCT
cana-2739	238	7	recall	recall	NOUN
cana-2739	238	8	,	,	PUNCT
cana-2739	238	9	accuracy	accuracy	NOUN
cana-2739	238	10	,	,	PUNCT
cana-2739	238	11	precision	precision	NOUN
cana-2739	238	12	and	and	CCONJ
cana-2739	238	13	roc	roc	NOUN
cana-2739	238	14	-	-	PUNCT
cana-2739	238	15	auc	auc	NOUN
cana-2739	238	16	curve	curve	NOUN
cana-2739	238	17	are	be	AUX
cana-2739	238	18	employed	employ	VERB
cana-2739	238	19	to	to	PART
cana-2739	238	20	check	check	VERB
cana-2739	238	21	the	the	DET
cana-2739	238	22	performance	performance	NOUN
cana-2739	238	23	of	of	ADP
cana-2739	238	24	the	the	DET
cana-2739	238	25	algorithms	algorithm	NOUN
cana-2739	238	26	.	.	PUNCT
cana-2739	239	1	svm	svm	PROPN
cana-2739	239	2	has	have	AUX
cana-2739	239	3	achieved	achieve	VERB
cana-2739	239	4	better	well	ADJ
cana-2739	239	5	performance	performance	NOUN
cana-2739	239	6	:	:	PUNCT
cana-2739	239	7	accuracy	accuracy	NOUN
cana-2739	239	8	(	(	PUNCT
cana-2739	239	9	91.19	91.19	NUM
cana-2739	239	10	%	%	NOUN
cana-2739	239	11	)	)	PUNCT
cana-2739	239	12	,	,	PUNCT
cana-2739	239	13	communications	communication	NOUN
cana-2739	239	14	on	on	ADP
cana-2739	239	15	applied	apply	VERB
cana-2739	239	16	nonlinear	nonlinear	ADJ
cana-2739	239	17	analysis	analysis	NOUN
cana-2739	239	18	issn	issn	NOUN
cana-2739	239	19	:	:	PUNCT
cana-2739	239	20	1074	1074	NUM
cana-2739	239	21	-	-	PUNCT
cana-2739	239	22	133x	133x	NUM
cana-2739	239	23	vol	vol	NOUN
cana-2739	239	24	32	32	NUM
cana-2739	239	25	no	no	NOUN
cana-2739	239	26	.	.	PUNCT
cana-2739	240	1	4s	4s	NUM
cana-2739	240	2	(	(	PUNCT
cana-2739	240	3	2025	2025	NUM
cana-2739	240	4	)	)	PUNCT
cana-2739	240	5	72	72	NUM
cana-2739	240	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2739	240	7	precision	precision	NOUN
cana-2739	240	8	(	(	PUNCT
cana-2739	240	9	97.98	97.98	NUM
cana-2739	240	10	%	%	NOUN
cana-2739	240	11	)	)	PUNCT
cana-2739	240	12	,	,	PUNCT
cana-2739	240	13	f1	f1	NOUN
cana-2739	240	14	-	-	PUNCT
cana-2739	240	15	score	score	NOUN
cana-2739	240	16	(	(	PUNCT
cana-2739	240	17	94.49	94.49	NUM
cana-2739	240	18	%	%	NOUN
cana-2739	240	19	)	)	PUNCT
cana-2739	240	20	,	,	PUNCT
cana-2739	240	21	specificity	specificity	NOUN
cana-2739	240	22	(	(	PUNCT
cana-2739	240	23	90.90	90.90	NUM
cana-2739	240	24	%	%	NOUN
cana-2739	240	25	)	)	PUNCT
cana-2739	240	26	and	and	CCONJ
cana-2739	240	27	auc	auc	X
cana-2739	240	28	(	(	PUNCT
cana-2739	240	29	0.93	0.93	NUM
cana-2739	240	30	)	)	PUNCT
cana-2739	240	31	compared	compare	VERB
cana-2739	240	32	to	to	ADP
cana-2739	240	33	decision	decision	NOUN
cana-2739	240	34	tree	tree	NOUN
cana-2739	240	35	and	and	CCONJ
cana-2739	240	36	logistic	logistic	ADJ
cana-2739	240	37	regression	regression	NOUN
cana-2739	240	38	.	.	PUNCT
cana-2739	241	1	thus	thus	ADV
cana-2739	241	2	,	,	PUNCT
cana-2739	241	3	it	it	PRON
cana-2739	241	4	can	can	AUX
cana-2739	241	5	be	be	AUX
cana-2739	241	6	utilized	utilize	VERB
cana-2739	241	7	as	as	ADP
cana-2739	241	8	a	a	DET
cana-2739	241	9	decision	decision	NOUN
cana-2739	241	10	tool	tool	NOUN
cana-2739	241	11	to	to	PART
cana-2739	241	12	help	help	VERB
cana-2739	241	13	healthcare	healthcare	NOUN
cana-2739	241	14	providers	provider	NOUN
cana-2739	241	15	for	for	ADP
cana-2739	241	16	the	the	DET
cana-2739	241	17	classification	classification	NOUN
cana-2739	241	18	of	of	ADP
cana-2739	241	19	ovarian	ovarian	ADJ
cana-2739	241	20	cysts	cyst	NOUN
cana-2739	241	21	from	from	ADP
cana-2739	241	22	ultrasound	ultrasound	NOUN
cana-2739	241	23	images	image	NOUN
cana-2739	241	24	.	.	PUNCT
cana-2739	242	1	in	in	ADP
cana-2739	242	2	future	future	NOUN
cana-2739	242	3	,	,	PUNCT
cana-2739	242	4	this	this	DET
cana-2739	242	5	research	research	NOUN
cana-2739	242	6	can	can	AUX
cana-2739	242	7	be	be	AUX
cana-2739	242	8	extended	extend	VERB
cana-2739	242	9	to	to	PART
cana-2739	242	10	ascertain	ascertain	VERB
cana-2739	242	11	similar	similar	ADJ
cana-2739	242	12	types	type	NOUN
cana-2739	242	13	of	of	ADP
cana-2739	242	14	diseases	disease	NOUN
cana-2739	242	15	using	use	VERB
cana-2739	242	16	ultrasound	ultrasound	NOUN
cana-2739	242	17	images	image	NOUN
cana-2739	242	18	with	with	ADP
cana-2739	242	19	better	well	ADJ
cana-2739	242	20	accuracy	accuracy	NOUN
cana-2739	242	21	.	.	PUNCT
cana-2739	243	1	it	it	PRON
cana-2739	243	2	can	can	AUX
cana-2739	243	3	be	be	AUX
cana-2739	243	4	additionally	additionally	ADV
cana-2739	243	5	expanded	expand	VERB
cana-2739	243	6	by	by	ADP
cana-2739	243	7	including	include	VERB
cana-2739	243	8	a	a	DET
cana-2739	243	9	greater	great	ADJ
cana-2739	243	10	number	number	NOUN
cana-2739	243	11	of	of	ADP
cana-2739	243	12	ultrasound	ultrasound	ADJ
cana-2739	243	13	images	image	NOUN
cana-2739	243	14	as	as	ADP
cana-2739	243	15	input	input	NOUN
cana-2739	243	16	and	and	CCONJ
cana-2739	243	17	verifying	verify	VERB
cana-2739	243	18	its	its	PRON
cana-2739	243	19	effect	effect	NOUN
cana-2739	243	20	on	on	ADP
cana-2739	243	21	the	the	DET
cana-2739	243	22	overall	overall	ADJ
cana-2739	243	23	performance	performance	NOUN
cana-2739	243	24	of	of	ADP
cana-2739	243	25	the	the	DET
cana-2739	243	26	computer	computer	NOUN
cana-2739	243	27	aided	aid	VERB
cana-2739	243	28	diagnosis	diagnosis	NOUN
cana-2739	243	29	system	system	NOUN
cana-2739	243	30	.	.	PUNCT
cana-2739	244	1	it	it	PRON
cana-2739	244	2	may	may	AUX
cana-2739	244	3	also	also	ADV
cana-2739	244	4	classify	classify	VERB
cana-2739	244	5	different	different	ADJ
cana-2739	244	6	types	type	NOUN
cana-2739	244	7	of	of	ADP
cana-2739	244	8	ovarian	ovarian	ADJ
cana-2739	244	9	cysts	cyst	NOUN
cana-2739	244	10	found	find	VERB
cana-2739	244	11	in	in	ADP
cana-2739	244	12	women	woman	NOUN
cana-2739	244	13	.	.	PUNCT
cana-2739	245	1	this	this	DET
cana-2739	245	2	research	research	NOUN
cana-2739	245	3	is	be	AUX
cana-2739	245	4	certainly	certainly	ADV
cana-2739	245	5	beneficial	beneficial	ADJ
cana-2739	245	6	to	to	ADP
cana-2739	245	7	healthcare	healthcare	NOUN
cana-2739	245	8	providers	provider	NOUN
cana-2739	245	9	in	in	ADP
cana-2739	245	10	providing	provide	VERB
cana-2739	245	11	accurate	accurate	ADJ
cana-2739	245	12	clinical	clinical	ADJ
cana-2739	245	13	diagnosis	diagnosis	NOUN
cana-2739	245	14	and	and	CCONJ
cana-2739	245	15	medical	medical	ADJ
cana-2739	245	16	care	care	NOUN
cana-2739	245	17	for	for	ADP
cana-2739	245	18	patients	patient	NOUN
cana-2739	245	19	.	.	PUNCT
cana-2739	246	1	acknowledgment	acknowledgment	NOUN
cana-2739	246	2	the	the	DET
cana-2739	246	3	authors	author	NOUN
cana-2739	246	4	express	express	VERB
cana-2739	246	5	their	their	PRON
cana-2739	246	6	gratitude	gratitude	NOUN
cana-2739	246	7	to	to	ADP
cana-2739	246	8	dr	dr	PROPN
cana-2739	246	9	.	.	PROPN
cana-2739	246	10	pallavi	pallavi	PROPN
cana-2739	246	11	goyal	goyal	PROPN
cana-2739	246	12	for	for	ADP
cana-2739	246	13	generously	generously	ADV
cana-2739	246	14	providing	provide	VERB
cana-2739	246	15	a	a	DET
cana-2739	246	16	collection	collection	NOUN
cana-2739	246	17	of	of	ADP
cana-2739	246	18	anonymized	anonymize	VERB
cana-2739	246	19	ultrasound	ultrasound	NOUN
cana-2739	246	20	images	image	NOUN
cana-2739	246	21	and	and	CCONJ
cana-2739	246	22	for	for	ADP
cana-2739	246	23	offering	offer	VERB
cana-2739	246	24	valuable	valuable	ADJ
cana-2739	246	25	insights	insight	NOUN
cana-2739	246	26	into	into	ADP
cana-2739	246	27	the	the	DET
cana-2739	246	28	medical	medical	ADJ
cana-2739	246	29	interpretation	interpretation	NOUN
cana-2739	246	30	of	of	ADP
cana-2739	246	31	these	these	DET
cana-2739	246	32	images	image	NOUN
cana-2739	246	33	.	.	PUNCT
cana-2739	247	1	references	reference	NOUN
cana-2739	247	2	[	[	X
cana-2739	247	3	1	1	NUM
cana-2739	247	4	]	]	X
cana-2739	247	5	alzubi	alzubi	ADJ
cana-2739	247	6	,	,	PUNCT
cana-2739	247	7	a.	a.	NOUN
cana-2739	247	8	a.	a.	PROPN
cana-2739	247	9	,	,	PUNCT
cana-2739	247	10	moghrabi	moghrabi	PROPN
cana-2739	247	11	,	,	PUNCT
cana-2739	247	12	h.	h.	PROPN
cana-2739	247	13	al	al	PROPN
cana-2739	247	14	,	,	PUNCT
cana-2739	247	15	alzubi	alzubi	PROPN
cana-2739	247	16	,	,	PUNCT
cana-2739	247	17	m.	m.	NOUN
cana-2739	247	18	a.	a.	PROPN
cana-2739	247	19	,	,	PUNCT
cana-2739	247	20	alzubi	alzubi	PROPN
cana-2739	247	21	,	,	PUNCT
cana-2739	247	22	s.	s.	PROPN
cana-2739	247	23	a.	a.	PROPN
cana-2739	247	24	,	,	PUNCT
cana-2739	247	25	&	&	CCONJ
cana-2739	247	26	arabia	arabia	PROPN
cana-2739	247	27	,	,	PUNCT
cana-2739	247	28	s.	s.	PROPN
cana-2739	247	29	(	(	PUNCT
cana-2739	247	30	2023	2023	NUM
cana-2739	247	31	)	)	PUNCT
cana-2739	247	32	.	.	PUNCT
cana-2739	248	1	methods	method	NOUN
cana-2739	248	2	for	for	ADP
cana-2739	248	3	automatic	automatic	ADJ
cana-2739	248	4	cyst	cyst	NOUN
cana-2739	248	5	detection	detection	NOUN
cana-2739	248	6	and	and	CCONJ
cana-2739	248	7	classification	classification	NOUN
cana-2739	248	8	in	in	ADP
cana-2739	248	9	ultrasound	ultrasound	ADJ
cana-2739	248	10	images	image	NOUN
cana-2739	248	11	of	of	ADP
cana-2739	248	12	the	the	DET
cana-2739	248	13	female	female	ADJ
cana-2739	248	14	genitalia	genitalia	NOUN
cana-2739	248	15	using	use	VERB
cana-2739	248	16	image	image	NOUN
cana-2739	248	17	processing	processing	NOUN
cana-2739	248	18	.	.	PUNCT
cana-2739	249	1	journal	journal	PROPN
cana-2739	249	2	of	of	ADP
cana-2739	249	3	population	population	NOUN
cana-2739	249	4	therapeutics	therapeutic	NOUN
cana-2739	249	5	and	and	CCONJ
cana-2739	249	6	clinical	clinical	ADJ
cana-2739	249	7	pharmacology	pharmacology	NOUN
cana-2739	249	8	,	,	PUNCT
cana-2739	249	9	30(6	30(6	NUM
cana-2739	249	10	)	)	PUNCT
cana-2739	249	11	,	,	PUNCT
cana-2739	249	12	297–305	297–305	NUM
cana-2739	249	13	.	.	PUNCT
cana-2739	250	1	https://doi.org/10.47750/jptcp.2023.30.06.035	https://doi.org/10.47750/jptcp.2023.30.06.035	X
cana-2739	251	1	[	[	X
cana-2739	251	2	2	2	NUM
cana-2739	251	3	]	]	X
cana-2739	251	4	bao	bao	PROPN
cana-2739	251	5	,	,	PUNCT
cana-2739	251	6	x.	x.	NOUN
cana-2739	251	7	x.	x.	PROPN
cana-2739	251	8	,	,	PUNCT
cana-2739	251	9	zhao	zhao	PROPN
cana-2739	251	10	,	,	PUNCT
cana-2739	251	11	c.	c.	PROPN
cana-2739	251	12	,	,	PUNCT
cana-2739	251	13	bao	bao	PROPN
cana-2739	251	14	,	,	PUNCT
cana-2739	251	15	s.	s.	PROPN
cana-2739	251	16	s.	s.	PROPN
cana-2739	251	17	,	,	PUNCT
cana-2739	251	18	rao	rao	PROPN
cana-2739	251	19	,	,	PUNCT
cana-2739	251	20	j.	j.	PROPN
cana-2739	251	21	s.	s.	PROPN
cana-2739	251	22	,	,	PUNCT
cana-2739	251	23	yang	yang	PROPN
cana-2739	251	24	,	,	PUNCT
cana-2739	251	25	z.	z.	PROPN
cana-2739	251	26	y.	y.	PROPN
cana-2739	251	27	,	,	PUNCT
cana-2739	251	28	&	&	CCONJ
cana-2739	251	29	li	li	PROPN
cana-2739	251	30	,	,	PUNCT
cana-2739	251	31	x.	x.	PROPN
cana-2739	251	32	g.	g.	PROPN
cana-2739	251	33	(	(	PUNCT
cana-2739	251	34	2023	2023	NUM
cana-2739	251	35	)	)	PUNCT
cana-2739	251	36	.	.	PUNCT
cana-2739	252	1	recognition	recognition	NOUN
cana-2739	252	2	of	of	ADP
cana-2739	252	3	necrotic	necrotic	ADJ
cana-2739	252	4	regions	region	NOUN
cana-2739	252	5	in	in	ADP
cana-2739	252	6	mri	mri	NOUN
cana-2739	252	7	images	image	NOUN
cana-2739	252	8	of	of	ADP
cana-2739	252	9	chronic	chronic	ADJ
cana-2739	252	10	spinal	spinal	NOUN
cana-2739	252	11	cord	cord	NOUN
cana-2739	252	12	injury	injury	NOUN
cana-2739	252	13	based	base	VERB
cana-2739	252	14	on	on	ADP
cana-2739	252	15	superpixel	superpixel	ADJ
cana-2739	252	16	.	.	PUNCT
cana-2739	253	1	computer	computer	NOUN
cana-2739	253	2	methods	method	NOUN
cana-2739	253	3	and	and	CCONJ
cana-2739	253	4	programs	program	NOUN
cana-2739	253	5	in	in	ADP
cana-2739	253	6	biomedicine	biomedicine	NOUN
cana-2739	253	7	,	,	PUNCT
cana-2739	253	8	228	228	NUM
cana-2739	253	9	,	,	PUNCT
cana-2739	253	10	107252	107252	NUM
cana-2739	253	11	.	.	PUNCT
cana-2739	254	1	https://doi.org/10.1016/j.cmpb.2022.107252	https://doi.org/10.1016/j.cmpb.2022.107252	VERB
cana-2739	255	1	[	[	X
cana-2739	255	2	3	3	NUM
cana-2739	255	3	]	]	X
cana-2739	255	4	bayzitov	bayzitov	PROPN
cana-2739	255	5	,	,	PUNCT
cana-2739	255	6	d.	d.	PROPN
cana-2739	255	7	m.	m.	PROPN
cana-2739	255	8	,	,	PUNCT
cana-2739	255	9	liashenko	liashenko	PROPN
cana-2739	255	10	,	,	PUNCT
cana-2739	255	11	a.	a.	NOUN
cana-2739	255	12	v.	v.	PROPN
cana-2739	255	13	,	,	PUNCT
cana-2739	255	14	bayazitov	bayazitov	NOUN
cana-2739	255	15	,	,	PUNCT
cana-2739	255	16	m.	m.	PROPN
cana-2739	255	17	r.	r.	PROPN
cana-2739	255	18	,	,	PUNCT
cana-2739	255	19	bidnyuk	bidnyuk	PROPN
cana-2739	255	20	,	,	PUNCT
cana-2739	255	21	k.	k.	PROPN
cana-2739	255	22	a.	a.	PROPN
cana-2739	255	23	,	,	PUNCT
cana-2739	255	24	&	&	CCONJ
cana-2739	255	25	godlevska	godlevska	PROPN
cana-2739	255	26	,	,	PUNCT
cana-2739	255	27	t.	t.	PROPN
cana-2739	255	28	l.	l.	PROPN
cana-2739	255	29	(	(	PUNCT
cana-2739	255	30	2023	2023	NUM
cana-2739	255	31	)	)	PUNCT
cana-2739	255	32	.	.	PUNCT
cana-2739	256	1	digital	digital	ADJ
cana-2739	256	2	images	image	NOUN
cana-2739	256	3	classification	classification	NOUN
cana-2739	256	4	in	in	ADP
cana-2739	256	5	automatic	automatic	ADJ
cana-2739	256	6	laparoscopic	laparoscopic	NOUN
cana-2739	256	7	diagnostics	diagnostic	NOUN
cana-2739	256	8	.	.	PUNCT
cana-2739	257	1	wiadomości	wiadomości	NOUN
cana-2739	257	2	lekarskie	lekarskie	PROPN
cana-2739	257	3	,	,	PUNCT
cana-2739	257	4	76(2	76(2	NUM
cana-2739	257	5	)	)	PUNCT
cana-2739	257	6	,	,	PUNCT
cana-2739	257	7	251–256	251–256	NUM
cana-2739	257	8	.	.	PUNCT
cana-2739	258	1	https://doi.org/10.36740/wlek202302102	https://doi.org/10.36740/wlek202302102	PROPN
cana-2739	258	2	[	[	X
cana-2739	258	3	4	4	NUM
cana-2739	258	4	]	]	X
cana-2739	258	5	benazir	benazir	PROPN
cana-2739	258	6	begam	begam	PROPN
cana-2739	258	7	,	,	PUNCT
cana-2739	258	8	r.	r.	PROPN
cana-2739	258	9	,	,	PUNCT
cana-2739	258	10	yogalakshmi	yogalakshmi	PROPN
cana-2739	258	11	,	,	PUNCT
cana-2739	258	12	v.	v.	PROPN
cana-2739	258	13	,	,	PUNCT
cana-2739	258	14	saranya	saranya	PROPN
cana-2739	258	15	,	,	PUNCT
cana-2739	258	16	g.	g.	PROPN
cana-2739	258	17	,	,	PUNCT
cana-2739	258	18	gururaj	gururaj	PROPN
cana-2739	258	19	,	,	PUNCT
cana-2739	258	20	d.	d.	PROPN
cana-2739	258	21	,	,	PUNCT
cana-2739	258	22	jagtap	jagtap	PROPN
cana-2739	258	23	,	,	PUNCT
cana-2739	258	24	s.	s.	PROPN
cana-2739	258	25	,	,	PUNCT
cana-2739	258	26	&	&	CCONJ
cana-2739	258	27	ravanan	ravanan	PROPN
cana-2739	258	28	,	,	PUNCT
cana-2739	258	29	v.	v.	PROPN
cana-2739	258	30	(	(	PUNCT
cana-2739	258	31	2022	2022	NUM
cana-2739	258	32	)	)	PUNCT
cana-2739	258	33	.	.	PUNCT
cana-2739	259	1	ovarian	ovarian	ADJ
cana-2739	259	2	cyst	cyst	NOUN
cana-2739	259	3	detection	detection	NOUN
cana-2739	259	4	using	use	VERB
cana-2739	259	5	neural	neural	ADJ
cana-2739	259	6	networks	network	NOUN
cana-2739	259	7	.	.	PUNCT
cana-2739	260	1	proceedings	proceeding	NOUN
cana-2739	260	2	of	of	ADP
cana-2739	260	3	the	the	DET
cana-2739	260	4	international	international	ADJ
cana-2739	260	5	conference	conference	NOUN
cana-2739	260	6	on	on	ADP
cana-2739	260	7	electronics	electronic	NOUN
cana-2739	260	8	and	and	CCONJ
cana-2739	260	9	renewable	renewable	ADJ
cana-2739	260	10	systems	system	NOUN
cana-2739	260	11	,	,	PUNCT
cana-2739	260	12	icears	icear	NOUN
cana-2739	260	13	2022	2022	NUM
cana-2739	260	14	,	,	PUNCT
cana-2739	260	15	icears	icear	NOUN
cana-2739	260	16	,	,	PUNCT
cana-2739	260	17	1827–1830	1827–1830	NUM
cana-2739	260	18	.	.	PUNCT
cana-2739	261	1	https://doi.org/10.1109/icears53579.2022.9752205	https://doi.org/10.1109/icears53579.2022.9752205	PROPN
cana-2739	262	1	[	[	X
cana-2739	262	2	5	5	NUM
cana-2739	262	3	]	]	X
cana-2739	262	4	chang	chang	PROPN
cana-2739	262	5	,	,	PUNCT
cana-2739	262	6	c.	c.	PROPN
cana-2739	262	7	c.	c.	PROPN
cana-2739	262	8	,	,	PUNCT
cana-2739	262	9	&	&	CCONJ
cana-2739	262	10	lin	lin	PROPN
cana-2739	262	11	,	,	PUNCT
cana-2739	262	12	c.	c.	PROPN
cana-2739	262	13	j.	j.	PROPN
cana-2739	262	14	(	(	PUNCT
cana-2739	262	15	2011	2011	NUM
cana-2739	262	16	)	)	PUNCT
cana-2739	262	17	.	.	PUNCT
cana-2739	263	1	libsvm	libsvm	VERB
cana-2739	263	2	:	:	PUNCT
cana-2739	263	3	a	a	DET
cana-2739	263	4	library	library	NOUN
cana-2739	263	5	for	for	ADP
cana-2739	263	6	support	support	NOUN
cana-2739	263	7	vector	vector	NOUN
cana-2739	263	8	machines	machine	NOUN
cana-2739	263	9	.	.	PUNCT
cana-2739	264	1	acm	acm	PROPN
cana-2739	264	2	transactions	transaction	NOUN
cana-2739	264	3	on	on	ADP
cana-2739	264	4	intelligent	intelligent	ADJ
cana-2739	264	5	systems	system	NOUN
cana-2739	264	6	and	and	CCONJ
cana-2739	264	7	technology	technology	NOUN
cana-2739	264	8	,	,	PUNCT
cana-2739	264	9	2(3	2(3	NUM
cana-2739	264	10	)	)	PUNCT
cana-2739	264	11	.	.	PUNCT
cana-2739	265	1	https://doi.org/10.1145/1961189.1961199	https://doi.org/10.1145/1961189.1961199	PROPN
cana-2739	265	2	[	[	X
cana-2739	265	3	6	6	NUM
cana-2739	265	4	]	]	X
cana-2739	265	5	chang	chang	PROPN
cana-2739	265	6	,	,	PUNCT
cana-2739	265	7	y.	y.	PROPN
cana-2739	265	8	c.	c.	PROPN
cana-2739	265	9	,	,	PUNCT
cana-2739	265	10	&	&	CCONJ
cana-2739	265	11	jeng	jeng	PROPN
cana-2739	265	12	,	,	PUNCT
cana-2739	265	13	j.	j.	PROPN
cana-2739	265	14	t.	t.	PROPN
cana-2739	265	15	(	(	PUNCT
cana-2739	265	16	2023	2023	NUM
cana-2739	265	17	)	)	PUNCT
cana-2739	265	18	.	.	PUNCT
cana-2739	266	1	classifying	classify	VERB
cana-2739	266	2	images	image	NOUN
cana-2739	266	3	of	of	ADP
cana-2739	266	4	two	two	NUM
cana-2739	266	5	-	-	PUNCT
cana-2739	266	6	dimensional	dimensional	ADJ
cana-2739	266	7	fractional	fractional	ADJ
cana-2739	266	8	brownian	brownian	ADJ
cana-2739	266	9	motion	motion	NOUN
cana-2739	266	10	through	through	ADP
cana-2739	266	11	deep	deep	ADJ
cana-2739	266	12	learning	learning	NOUN
cana-2739	266	13	and	and	CCONJ
cana-2739	266	14	its	its	PRON
cana-2739	266	15	applications	application	NOUN
cana-2739	266	16	.	.	PUNCT
cana-2739	267	1	applied	apply	VERB
cana-2739	267	2	sciences	sciences	PROPN
cana-2739	267	3	(	(	PUNCT
cana-2739	267	4	switzerland	switzerland	PROPN
cana-2739	267	5	)	)	PUNCT
cana-2739	267	6	,	,	PUNCT
cana-2739	267	7	13(2	13(2	NOUN
cana-2739	267	8	)	)	PUNCT
cana-2739	267	9	.	.	PUNCT
cana-2739	268	1	https://doi.org/10.3390/app13020803	https://doi.org/10.3390/app13020803	PROPN
cana-2739	268	2	[	[	X
cana-2739	268	3	7	7	NUM
cana-2739	268	4	]	]	X
cana-2739	268	5	cramer	cramer	PROPN
cana-2739	268	6	,	,	PUNCT
cana-2739	268	7	j.	j.	PROPN
cana-2739	268	8	s.	s.	PROPN
cana-2739	268	9	(	(	PUNCT
cana-2739	268	10	2003	2003	NUM
cana-2739	268	11	)	)	PUNCT
cana-2739	268	12	.	.	PUNCT
cana-2739	269	1	the	the	DET
cana-2739	269	2	origins	origin	NOUN
cana-2739	269	3	of	of	ADP
cana-2739	269	4	logistic	logistic	ADJ
cana-2739	269	5	regression	regression	NOUN
cana-2739	269	6	.	.	PUNCT
cana-2739	270	1	ssrn	ssrn	PROPN
cana-2739	270	2	electronic	electronic	ADJ
cana-2739	270	3	journal	journal	PROPN
cana-2739	270	4	.	.	PUNCT
cana-2739	271	1	https://doi.org/10.2139/ssrn.360300	https://doi.org/10.2139/ssrn.360300	X
cana-2739	272	1	[	[	X
cana-2739	272	2	8	8	NUM
cana-2739	272	3	]	]	X
cana-2739	272	4	dalal	dalal	PROPN
cana-2739	272	5	,	,	PUNCT
cana-2739	272	6	n.	n.	NOUN
cana-2739	272	7	,	,	PUNCT
cana-2739	272	8	&	&	CCONJ
cana-2739	272	9	triggs	triggs	PROPN
cana-2739	272	10	,	,	PUNCT
cana-2739	272	11	b.	b.	PROPN
cana-2739	272	12	(	(	PUNCT
cana-2739	272	13	2005	2005	NUM
cana-2739	272	14	)	)	PUNCT
cana-2739	272	15	.	.	PUNCT
cana-2739	273	1	histograms	histogram	NOUN
cana-2739	273	2	of	of	ADP
cana-2739	273	3	oriented	orient	VERB
cana-2739	273	4	gradients	gradient	NOUN
cana-2739	273	5	for	for	ADP
cana-2739	273	6	human	human	ADJ
cana-2739	273	7	detection	detection	NOUN
cana-2739	273	8	.	.	PUNCT
cana-2739	274	1	ieee	ieee	NOUN
cana-2739	274	2	computer	computer	PROPN
cana-2739	274	3	society	society	PROPN
cana-2739	274	4	conference	conference	NOUN
cana-2739	274	5	on	on	ADP
cana-2739	274	6	computer	computer	NOUN
cana-2739	274	7	vision	vision	NOUN
cana-2739	274	8	and	and	CCONJ
cana-2739	274	9	pattern	pattern	NOUN
cana-2739	274	10	recognition	recognition	NOUN
cana-2739	274	11	,	,	PUNCT
cana-2739	274	12	1	1	NUM
cana-2739	274	13	,	,	PUNCT
cana-2739	274	14	886–893	886–893	NUM
cana-2739	274	15	.	.	PUNCT
cana-2739	275	1	[	[	X
cana-2739	275	2	9	9	NUM
cana-2739	275	3	]	]	SYM
cana-2739	275	4	decreusefond	decreusefond	NOUN
cana-2739	275	5	,	,	PUNCT
cana-2739	275	6	l.	l.	PROPN
cana-2739	275	7	,	,	PUNCT
cana-2739	275	8	&	&	CCONJ
cana-2739	275	9	üstünel	üstünel	PROPN
cana-2739	275	10	,	,	PUNCT
cana-2739	275	11	a.	a.	PROPN
cana-2739	275	12	s.	s.	PROPN
cana-2739	275	13	(	(	PUNCT
cana-2739	275	14	1998	1998	NUM
cana-2739	275	15	)	)	PUNCT
cana-2739	275	16	.	.	PUNCT
cana-2739	276	1	fractional	fractional	ADJ
cana-2739	276	2	brownian	brownian	ADJ
cana-2739	276	3	motion	motion	NOUN
cana-2739	276	4	:	:	PUNCT
cana-2739	276	5	theory	theory	NOUN
cana-2739	276	6	and	and	CCONJ
cana-2739	276	7	applications	application	NOUN
cana-2739	276	8	.	.	PUNCT
cana-2739	277	1	esaim	esaim	NOUN
cana-2739	277	2	:	:	PUNCT
cana-2739	277	3	proceedings	proceeding	NOUN
cana-2739	277	4	,	,	PUNCT
cana-2739	277	5	5	5	NUM
cana-2739	277	6	,	,	PUNCT
cana-2739	277	7	75–86	75–86	NUM
cana-2739	277	8	.	.	PUNCT
cana-2739	278	1	https://doi.org/10.1051/proc:1998014	https://doi.org/10.1051/proc:1998014	NOUN
cana-2739	279	1	[	[	X
cana-2739	279	2	10	10	NUM
cana-2739	279	3	]	]	X
cana-2739	279	4	edwards	edwards	PROPN
cana-2739	279	5	.	.	PROPN
cana-2739	279	6	,	,	PUNCT
cana-2739	279	7	d.	d.	PROPN
cana-2739	279	8	von	von	PROPN
cana-2739	279	9	w.	w.	PROPN
cana-2739	279	10	and	and	CCONJ
cana-2739	279	11	w.	w.	PROPN
cana-2739	279	12	(	(	PUNCT
cana-2739	279	13	1987	1987	NUM
cana-2739	279	14	)	)	PUNCT
cana-2739	279	15	.	.	PUNCT
cana-2739	280	1	a	a	DET
cana-2739	280	2	review	review	NOUN
cana-2739	280	3	of	of	ADP
cana-2739	280	4	:	:	PUNCT
cana-2739	280	5	“	"	PUNCT
cana-2739	280	6	decision	decision	NOUN
cana-2739	280	7	analysis	analysis	NOUN
cana-2739	280	8	and	and	CCONJ
cana-2739	280	9	behavioral	behavioral	ADJ
cana-2739	280	10	research	research	NOUN
cana-2739	280	11	.	.	PUNCT
cana-2739	281	1	in	in	ADP
cana-2739	281	2	the	the	DET
cana-2739	281	3	engineering	engineering	NOUN
cana-2739	281	4	economist	economist	NOUN
cana-2739	281	5	(	(	PUNCT
cana-2739	281	6	vol	vol	NOUN
cana-2739	281	7	.	.	PROPN
cana-2739	281	8	33	33	NUM
cana-2739	281	9	,	,	PUNCT
cana-2739	281	10	issue	issue	NOUN
cana-2739	281	11	1	1	NUM
cana-2739	281	12	)	)	PUNCT
cana-2739	281	13	.	.	PUNCT
cana-2739	282	1	cambridge	cambridge	PROPN
cana-2739	282	2	university	university	PROPN
cana-2739	282	3	press	press	PROPN
cana-2739	282	4	,	,	PUNCT
cana-2739	282	5	new	new	PROPN
cana-2739	282	6	york	york	PROPN
cana-2739	282	7	.	.	PUNCT
cana-2739	283	1	[	[	X
cana-2739	283	2	11	11	NUM
cana-2739	283	3	]	]	PUNCT
cana-2739	283	4	gopalakrishnan	gopalakrishnan	NOUN
cana-2739	283	5	,	,	PUNCT
cana-2739	283	6	c.	c.	PROPN
cana-2739	283	7	,	,	PUNCT
cana-2739	283	8	&	&	CCONJ
cana-2739	283	9	iyapparaja	iyapparaja	PROPN
cana-2739	283	10	,	,	PUNCT
cana-2739	283	11	m.	m.	NOUN
cana-2739	283	12	(	(	PUNCT
cana-2739	283	13	2021	2021	NUM
cana-2739	283	14	)	)	PUNCT
cana-2739	283	15	.	.	PUNCT
cana-2739	284	1	multilevel	multilevel	NOUN
cana-2739	284	2	thresholding	thresholding	NOUN
cana-2739	284	3	based	base	VERB
cana-2739	284	4	follicle	follicle	NOUN
cana-2739	284	5	detection	detection	NOUN
cana-2739	284	6	and	and	CCONJ
cana-2739	284	7	classification	classification	NOUN
cana-2739	284	8	of	of	ADP
cana-2739	284	9	polycystic	polycystic	ADJ
cana-2739	284	10	ovary	ovary	ADJ
cana-2739	284	11	syndrome	syndrome	NOUN
cana-2739	284	12	from	from	ADP
cana-2739	284	13	the	the	DET
cana-2739	284	14	ultrasound	ultrasound	NOUN
cana-2739	284	15	images	image	NOUN
cana-2739	284	16	using	use	VERB
cana-2739	284	17	machine	machine	NOUN
cana-2739	284	18	learning	learning	NOUN
cana-2739	284	19	.	.	PUNCT
cana-2739	285	1	international	international	ADJ
cana-2739	285	2	journal	journal	PROPN
cana-2739	285	3	of	of	ADP
cana-2739	285	4	systems	system	NOUN
cana-2739	285	5	assurance	assurance	NOUN
cana-2739	285	6	engineering	engineering	NOUN
cana-2739	285	7	and	and	CCONJ
cana-2739	285	8	management	management	NOUN
cana-2739	285	9	.	.	PUNCT
cana-2739	286	1	https://doi.org/10.1007/s13198-021-01203-x	https://doi.org/10.1007/s13198-021-01203-x	PROPN
cana-2739	287	1	[	[	X
cana-2739	287	2	12	12	NUM
cana-2739	287	3	]	]	X
cana-2739	287	4	gupta	gupta	PROPN
cana-2739	287	5	,	,	PUNCT
cana-2739	287	6	a.	a.	NOUN
cana-2739	287	7	,	,	PUNCT
cana-2739	287	8	&	&	CCONJ
cana-2739	287	9	fatima	fatima	PROPN
cana-2739	287	10	,	,	PUNCT
cana-2739	287	11	h.	h.	PROPN
cana-2739	287	12	(	(	PUNCT
cana-2739	287	13	2023	2023	NUM
cana-2739	287	14	)	)	PUNCT
cana-2739	287	15	.	.	PUNCT
cana-2739	288	1	a	a	DET
cana-2739	288	2	systematic	systematic	ADJ
cana-2739	288	3	review	review	NOUN
cana-2739	288	4	of	of	ADP
cana-2739	288	5	machine	machine	NOUN
cana-2739	288	6	learning	learn	VERB
cana-2739	288	7	for	for	ADP
cana-2739	288	8	ovarian	ovarian	ADJ
cana-2739	288	9	cyst	cyst	NOUN
cana-2739	288	10	detection	detection	NOUN
cana-2739	288	11	using	use	VERB
cana-2739	288	12	ultrasound	ultrasound	NOUN
cana-2739	288	13	images	image	NOUN
cana-2739	288	14	.	.	PUNCT
cana-2739	289	1	2023	2023	NUM
cana-2739	289	2	2nd	2nd	ADJ
cana-2739	289	3	international	international	ADJ
cana-2739	289	4	conference	conference	NOUN
cana-2739	289	5	on	on	ADP
cana-2739	289	6	applied	apply	VERB
cana-2739	289	7	artificial	artificial	ADJ
cana-2739	289	8	intelligence	intelligence	NOUN
cana-2739	289	9	and	and	CCONJ
cana-2739	289	10	computing	computing	NOUN
cana-2739	289	11	(	(	PUNCT
cana-2739	289	12	icaaic	icaaic	PROPN
cana-2739	289	13	)	)	PUNCT
cana-2739	289	14	,	,	PUNCT
cana-2739	289	15	201–206	201–206	NUM
cana-2739	289	16	.	.	PUNCT
cana-2739	289	17	https://doi.org/10.1109/icaaic56838.2023.10140444	https://doi.org/10.1109/icaaic56838.2023.10140444	PROPN
cana-2739	290	1	[	[	X
cana-2739	290	2	13	13	NUM
cana-2739	290	3	]	]	SYM
cana-2739	290	4	haralick	haralick	NOUN
cana-2739	290	5	,	,	PUNCT
cana-2739	290	6	r.	r.	PROPN
cana-2739	290	7	m.	m.	PROPN
cana-2739	290	8	,	,	PUNCT
cana-2739	290	9	dinstein	dinstein	PROPN
cana-2739	290	10	,	,	PUNCT
cana-2739	290	11	i.	i.	PROPN
cana-2739	290	12	,	,	PUNCT
cana-2739	290	13	&	&	CCONJ
cana-2739	290	14	shanmugam	shanmugam	PROPN
cana-2739	290	15	,	,	PUNCT
cana-2739	290	16	k.	k.	PROPN
cana-2739	290	17	(	(	PUNCT
cana-2739	290	18	1973	1973	NUM
cana-2739	290	19	)	)	PUNCT
cana-2739	290	20	.	.	PUNCT
cana-2739	291	1	textural	textural	ADJ
cana-2739	291	2	features	feature	NOUN
cana-2739	291	3	for	for	ADP
cana-2739	291	4	image	image	NOUN
cana-2739	291	5	classification	classification	NOUN
cana-2739	291	6	.	.	PUNCT
cana-2739	292	1	ieee	ieee	NOUN
cana-2739	292	2	transactions	transaction	NOUN
cana-2739	292	3	on	on	ADP
cana-2739	292	4	systems	system	NOUN
cana-2739	292	5	,	,	PUNCT
cana-2739	292	6	man	man	NOUN
cana-2739	292	7	and	and	CCONJ
cana-2739	292	8	cybernetics	cybernetic	NOUN
cana-2739	292	9	,	,	PUNCT
cana-2739	292	10	smc-3(6	smc-3(6	PROPN
cana-2739	292	11	)	)	PUNCT
cana-2739	292	12	,	,	PUNCT
cana-2739	292	13	610–621	610–621	NUM
cana-2739	292	14	.	.	PUNCT
cana-2739	293	1	https://doi.org/10.1109/tsmc.1973.4309314	https://doi.org/10.1109/tsmc.1973.4309314	PROPN
cana-2739	294	1	[	[	X
cana-2739	294	2	14	14	NUM
cana-2739	294	3	]	]	X
cana-2739	294	4	humeau	humeau	PROPN
cana-2739	294	5	-	-	PUNCT
cana-2739	294	6	heurtier	heurtier	NOUN
cana-2739	294	7	,	,	PUNCT
cana-2739	294	8	a.	a.	NOUN
cana-2739	294	9	(	(	PUNCT
cana-2739	294	10	2019	2019	NUM
cana-2739	294	11	)	)	PUNCT
cana-2739	294	12	.	.	PUNCT
cana-2739	295	1	texture	texture	ADJ
cana-2739	295	2	feature	feature	NOUN
cana-2739	295	3	extraction	extraction	NOUN
cana-2739	295	4	methods	method	NOUN
cana-2739	295	5	:	:	PUNCT
cana-2739	295	6	a	a	DET
cana-2739	295	7	survey	survey	NOUN
cana-2739	295	8	.	.	PUNCT
cana-2739	296	1	ieee	ieee	NOUN
cana-2739	296	2	access	access	NOUN
cana-2739	296	3	,	,	PUNCT
cana-2739	296	4	7	7	NUM
cana-2739	296	5	,	,	PUNCT
cana-2739	296	6	8975–9000	8975–9000	NUM
cana-2739	296	7	.	.	PUNCT
cana-2739	297	1	communications	communication	NOUN
cana-2739	297	2	on	on	ADP
cana-2739	297	3	applied	apply	VERB
cana-2739	297	4	nonlinear	nonlinear	ADJ
cana-2739	297	5	analysis	analysis	NOUN
cana-2739	297	6	issn	issn	NOUN
cana-2739	297	7	:	:	PUNCT
cana-2739	297	8	1074	1074	NUM
cana-2739	297	9	-	-	PUNCT
cana-2739	297	10	133x	133x	NUM
cana-2739	297	11	vol	vol	NOUN
cana-2739	297	12	32	32	NUM
cana-2739	297	13	no	no	NOUN
cana-2739	297	14	.	.	PUNCT
cana-2739	298	1	4s	4s	NUM
cana-2739	298	2	(	(	PUNCT
cana-2739	298	3	2025	2025	NUM
cana-2739	298	4	)	)	PUNCT
cana-2739	298	5	73	73	NUM
cana-2739	298	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2739	298	7	https://doi.org/10.1109/access.2018.2890743	https://doi.org/10.1109/access.2018.2890743	PROPN
cana-2739	298	8	[	[	X
cana-2739	298	9	15	15	NUM
cana-2739	298	10	]	]	X
cana-2739	298	11	hurst	hurst	PROPN
cana-2739	298	12	,	,	PUNCT
cana-2739	298	13	h.	h.	PROPN
cana-2739	298	14	e.	e.	PROPN
cana-2739	298	15	(	(	PUNCT
cana-2739	298	16	1951	1951	NUM
cana-2739	298	17	)	)	PUNCT
cana-2739	298	18	.	.	PUNCT
cana-2739	299	1	long	long	ADJ
cana-2739	299	2	term	term	NOUN
cana-2739	299	3	storage	storage	NOUN
cana-2739	299	4	of	of	ADP
cana-2739	299	5	reservoirs	reservoir	NOUN
cana-2739	299	6	:	:	PUNCT
cana-2739	299	7	an	an	DET
cana-2739	299	8	experimental	experimental	ADJ
cana-2739	299	9	study	study	NOUN
cana-2739	299	10	.	.	PUNCT
cana-2739	300	1	transactions	transaction	NOUN
cana-2739	300	2	of	of	ADP
cana-2739	300	3	the	the	DET
cana-2739	300	4	american	american	ADJ
cana-2739	300	5	society	society	NOUN
cana-2739	300	6	of	of	ADP
cana-2739	300	7	civil	civil	ADJ
cana-2739	300	8	engineers	engineer	NOUN
cana-2739	300	9	,	,	PUNCT
cana-2739	300	10	116	116	NUM
cana-2739	300	11	,	,	PUNCT
cana-2739	300	12	770–799	770–799	NUM
cana-2739	300	13	.	.	PUNCT
cana-2739	301	1	[	[	X
cana-2739	301	2	16	16	NUM
cana-2739	301	3	]	]	X
cana-2739	301	4	kadhim	kadhim	NOUN
cana-2739	301	5	,	,	PUNCT
cana-2739	301	6	m.	m.	NOUN
cana-2739	301	7	a.	a.	NOUN
cana-2739	301	8	(	(	PUNCT
cana-2739	301	9	2022	2022	NUM
cana-2739	301	10	)	)	PUNCT
cana-2739	301	11	.	.	PUNCT
cana-2739	302	1	analyzing	analyze	VERB
cana-2739	302	2	and	and	CCONJ
cana-2739	302	3	processing	process	VERB
cana-2739	302	4	medical	medical	ADJ
cana-2739	302	5	images	image	NOUN
cana-2739	302	6	with	with	ADP
cana-2739	302	7	increased	increase	VERB
cana-2739	302	8	performance	performance	NOUN
cana-2739	302	9	using	use	VERB
cana-2739	302	10	fractal	fractal	ADJ
cana-2739	302	11	geometry	geometry	NOUN
cana-2739	302	12	.	.	PUNCT
cana-2739	303	1	periodicals	periodical	NOUN
cana-2739	303	2	of	of	ADP
cana-2739	303	3	engineering	engineering	NOUN
cana-2739	303	4	and	and	CCONJ
cana-2739	303	5	natural	natural	ADJ
cana-2739	303	6	sciences	science	NOUN
cana-2739	303	7	,	,	PUNCT
cana-2739	303	8	10(1	10(1	NUM
cana-2739	303	9	)	)	PUNCT
cana-2739	303	10	,	,	PUNCT
cana-2739	303	11	544–556	544–556	NUM
cana-2739	303	12	.	.	PUNCT
cana-2739	304	1	https://doi.org/10.21533/pen.v10i1.2683	https://doi.org/10.21533/pen.v10i1.2683	PROPN
cana-2739	304	2	[	[	X
cana-2739	304	3	17	17	NUM
cana-2739	304	4	]	]	X
cana-2739	304	5	kalaiyarasi	kalaiyarasi	NOUN
cana-2739	304	6	,	,	PUNCT
cana-2739	304	7	m.	m.	NOUN
cana-2739	304	8	,	,	PUNCT
cana-2739	304	9	dhanasekar	dhanasekar	PROPN
cana-2739	304	10	,	,	PUNCT
cana-2739	304	11	r.	r.	PROPN
cana-2739	304	12	,	,	PUNCT
cana-2739	304	13	sakthiya	sakthiya	PROPN
cana-2739	304	14	ram	ram	PROPN
cana-2739	304	15	,	,	PUNCT
cana-2739	304	16	s.	s.	PROPN
cana-2739	304	17	,	,	PUNCT
cana-2739	304	18	&	&	CCONJ
cana-2739	304	19	vaishnavi	vaishnavi	PROPN
cana-2739	304	20	,	,	PUNCT
cana-2739	304	21	p.	p.	NOUN
cana-2739	304	22	(	(	PUNCT
cana-2739	304	23	2020	2020	NUM
cana-2739	304	24	)	)	PUNCT
cana-2739	304	25	.	.	PUNCT
cana-2739	305	1	classification	classification	NOUN
cana-2739	305	2	of	of	ADP
cana-2739	305	3	benign	benign	ADJ
cana-2739	305	4	or	or	CCONJ
cana-2739	305	5	malignant	malignant	ADJ
cana-2739	305	6	tumor	tumor	NOUN
cana-2739	305	7	using	use	VERB
cana-2739	305	8	machine	machine	NOUN
cana-2739	305	9	learning	learning	NOUN
cana-2739	305	10	.	.	PUNCT
cana-2739	306	1	iop	iop	PROPN
cana-2739	306	2	conference	conference	PROPN
cana-2739	306	3	series	series	PROPN
cana-2739	306	4	:	:	PUNCT
cana-2739	306	5	materials	material	NOUN
cana-2739	306	6	science	science	NOUN
cana-2739	306	7	and	and	CCONJ
cana-2739	306	8	engineering	engineering	NOUN
cana-2739	306	9	,	,	PUNCT
cana-2739	306	10	995(1	995(1	NUM
cana-2739	306	11	)	)	PUNCT
cana-2739	306	12	.	.	PUNCT
cana-2739	307	1	https://doi.org/10.1088/1757-899x/995/1/012028	https://doi.org/10.1088/1757-899x/995/1/012028	ADJ
cana-2739	307	2	[	[	X
cana-2739	307	3	18	18	NUM
cana-2739	307	4	]	]	X
cana-2739	307	5	kiruthika	kiruthika	X
cana-2739	307	6	,	,	PUNCT
cana-2739	307	7	v.	v.	PROPN
cana-2739	307	8	,	,	PUNCT
cana-2739	307	9	sathiya	sathiya	PROPN
cana-2739	307	10	,	,	PUNCT
cana-2739	307	11	s.	s.	PROPN
cana-2739	307	12	,	,	PUNCT
cana-2739	307	13	ramya	ramya	PROPN
cana-2739	307	14	,	,	PUNCT
cana-2739	307	15	m.	m.	NOUN
cana-2739	307	16	m.	m.	NOUN
cana-2739	307	17	,	,	PUNCT
cana-2739	307	18	&	&	CCONJ
cana-2739	307	19	sankaran	sankaran	PROPN
cana-2739	307	20	,	,	PUNCT
cana-2739	307	21	k.	k.	PROPN
cana-2739	307	22	s.	s.	PROPN
cana-2739	307	23	(	(	PUNCT
cana-2739	307	24	2023	2023	NUM
cana-2739	307	25	)	)	PUNCT
cana-2739	307	26	.	.	PUNCT
cana-2739	308	1	an	an	DET
cana-2739	308	2	intelligent	intelligent	ADJ
cana-2739	308	3	machine	machine	NOUN
cana-2739	308	4	learning	learn	VERB
cana-2739	308	5	approach	approach	NOUN
cana-2739	308	6	for	for	ADP
cana-2739	308	7	ovarian	ovarian	ADJ
cana-2739	308	8	detection	detection	NOUN
cana-2739	308	9	and	and	CCONJ
cana-2739	308	10	classification	classification	NOUN
cana-2739	308	11	system	system	NOUN
cana-2739	308	12	using	use	VERB
cana-2739	308	13	ultrasonogram	ultrasonogram	NOUN
cana-2739	308	14	images	image	NOUN
cana-2739	308	15	.	.	PUNCT
cana-2739	309	1	engineered	engineer	VERB
cana-2739	309	2	science	science	NOUN
cana-2739	309	3	,	,	PUNCT
cana-2739	309	4	23	23	NUM
cana-2739	309	5	,	,	PUNCT
cana-2739	309	6	1–11	1–11	PROPN
cana-2739	309	7	.	.	PUNCT
cana-2739	310	1	https://doi.org/10.30919/es8d879	https://doi.org/10.30919/es8d879	X
cana-2739	311	1	[	[	X
cana-2739	311	2	19	19	NUM
cana-2739	311	3	]	]	PUNCT
cana-2739	311	4	madhumitha	madhumitha	NOUN
cana-2739	311	5	,	,	PUNCT
cana-2739	311	6	j.	j.	PROPN
cana-2739	311	7	,	,	PUNCT
cana-2739	311	8	kalaiyarasi	kalaiyarasi	NOUN
cana-2739	311	9	,	,	PUNCT
cana-2739	311	10	m.	m.	NOUN
cana-2739	311	11	,	,	PUNCT
cana-2739	311	12	&	&	CCONJ
cana-2739	311	13	ram	ram	PROPN
cana-2739	311	14	,	,	PUNCT
cana-2739	311	15	s.	s.	PROPN
cana-2739	311	16	s.	s.	PROPN
cana-2739	311	17	(	(	PUNCT
cana-2739	311	18	2021	2021	NUM
cana-2739	311	19	)	)	PUNCT
cana-2739	311	20	.	.	PUNCT
cana-2739	312	1	automated	automate	VERB
cana-2739	312	2	polycystic	polycystic	ADJ
cana-2739	312	3	ovarian	ovarian	ADJ
cana-2739	312	4	syndrome	syndrome	NOUN
cana-2739	312	5	identification	identification	NOUN
cana-2739	312	6	with	with	ADP
cana-2739	312	7	follicle	follicle	NOUN
cana-2739	312	8	recognition	recognition	NOUN
cana-2739	312	9	.	.	PUNCT
cana-2739	313	1	2021	2021	NUM
cana-2739	314	1	3rd	3rd	ADJ
cana-2739	314	2	international	international	ADJ
cana-2739	314	3	conference	conference	NOUN
cana-2739	314	4	on	on	ADP
cana-2739	314	5	signal	signal	ADJ
cana-2739	314	6	processing	processing	NOUN
cana-2739	314	7	and	and	CCONJ
cana-2739	314	8	communication	communication	NOUN
cana-2739	314	9	,	,	PUNCT
cana-2739	314	10	icpsc	icpsc	NOUN
cana-2739	314	11	2021	2021	NUM
cana-2739	314	12	,	,	PUNCT
cana-2739	314	13	may	may	AUX
cana-2739	314	14	,	,	PUNCT
cana-2739	314	15	98–102	98–102	NUM
cana-2739	314	16	.	.	PUNCT
cana-2739	315	1	https://doi.org/10.1109/icspc51351.2021.9451720	https://doi.org/10.1109/icspc51351.2021.9451720	PROPN
cana-2739	316	1	[	[	X
cana-2739	316	2	20	20	NUM
cana-2739	316	3	]	]	PUNCT
cana-2739	316	4	nabilah	nabilah	PROPN
cana-2739	316	5	,	,	PUNCT
cana-2739	316	6	a.	a.	PROPN
cana-2739	316	7	,	,	PUNCT
cana-2739	316	8	sigit	sigit	PROPN
cana-2739	316	9	,	,	PUNCT
cana-2739	316	10	r.	r.	PROPN
cana-2739	316	11	,	,	PUNCT
cana-2739	316	12	harsono	harsono	PROPN
cana-2739	316	13	,	,	PUNCT
cana-2739	316	14	t.	t.	PROPN
cana-2739	316	15	,	,	PUNCT
cana-2739	316	16	&	&	CCONJ
cana-2739	316	17	anwar	anwar	PROPN
cana-2739	316	18	,	,	PUNCT
cana-2739	316	19	a.	a.	NOUN
cana-2739	316	20	(	(	PUNCT
cana-2739	316	21	2020	2020	NUM
cana-2739	316	22	)	)	PUNCT
cana-2739	316	23	.	.	PUNCT
cana-2739	317	1	classification	classification	NOUN
cana-2739	317	2	of	of	ADP
cana-2739	317	3	ovarian	ovarian	ADJ
cana-2739	317	4	cysts	cyst	NOUN
cana-2739	317	5	on	on	ADP
cana-2739	317	6	ultrasound	ultrasound	NOUN
cana-2739	317	7	images	image	NOUN
cana-2739	317	8	using	use	VERB
cana-2739	317	9	watershed	watershed	ADJ
cana-2739	317	10	segmentation	segmentation	NOUN
cana-2739	317	11	and	and	CCONJ
cana-2739	317	12	contour	contour	NOUN
cana-2739	317	13	analysis	analysis	NOUN
cana-2739	317	14	.	.	PUNCT
cana-2739	318	1	ies	ies	PROPN
cana-2739	318	2	2020	2020	NUM
cana-2739	318	3	international	international	ADJ
cana-2739	318	4	electronics	electronic	NOUN
cana-2739	318	5	symposium	symposium	NOUN
cana-2739	318	6	:	:	PUNCT
cana-2739	318	7	the	the	DET
cana-2739	318	8	role	role	NOUN
cana-2739	318	9	of	of	ADP
cana-2739	318	10	autonomous	autonomous	ADJ
cana-2739	318	11	and	and	CCONJ
cana-2739	318	12	intelligent	intelligent	ADJ
cana-2739	318	13	systems	system	NOUN
cana-2739	318	14	for	for	ADP
cana-2739	318	15	human	human	ADJ
cana-2739	318	16	life	life	NOUN
cana-2739	318	17	and	and	CCONJ
cana-2739	318	18	comfort	comfort	NOUN
cana-2739	318	19	,	,	PUNCT
cana-2739	318	20	513–519	513–519	NUM
cana-2739	318	21	.	.	PUNCT
cana-2739	319	1	https://doi.org/10.1109/ies50839.2020.9231695	https://doi.org/10.1109/ies50839.2020.9231695	NOUN
cana-2739	319	2	[	[	X
cana-2739	319	3	21	21	NUM
cana-2739	319	4	]	]	X
cana-2739	319	5	ojala	ojala	X
cana-2739	319	6	,	,	PUNCT
cana-2739	319	7	t.	t.	PROPN
cana-2739	319	8	,	,	PUNCT
cana-2739	319	9	pietikäinen	pietikäinen	NOUN
cana-2739	319	10	,	,	PUNCT
cana-2739	319	11	m.	m.	NOUN
cana-2739	319	12	,	,	PUNCT
cana-2739	319	13	&	&	CCONJ
cana-2739	319	14	harwood	harwood	PROPN
cana-2739	319	15	,	,	PUNCT
cana-2739	319	16	d.	d.	PROPN
cana-2739	319	17	(	(	PUNCT
cana-2739	319	18	1996	1996	NUM
cana-2739	319	19	)	)	PUNCT
cana-2739	319	20	.	.	PUNCT
cana-2739	320	1	a	a	DET
cana-2739	320	2	comparative	comparative	ADJ
cana-2739	320	3	study	study	NOUN
cana-2739	320	4	of	of	ADP
cana-2739	320	5	texture	texture	ADJ
cana-2739	320	6	measures	measure	NOUN
cana-2739	320	7	with	with	ADP
cana-2739	320	8	classification	classification	NOUN
cana-2739	320	9	based	base	VERB
cana-2739	320	10	on	on	ADP
cana-2739	320	11	feature	feature	NOUN
cana-2739	320	12	distributions	distribution	NOUN
cana-2739	320	13	.	.	PUNCT
cana-2739	321	1	pattern	pattern	NOUN
cana-2739	321	2	recognition	recognition	NOUN
cana-2739	321	3	,	,	PUNCT
cana-2739	321	4	29(1	29(1	NUM
cana-2739	321	5	)	)	PUNCT
cana-2739	321	6	,	,	PUNCT
cana-2739	321	7	51–59	51–59	NUM
cana-2739	321	8	.	.	PUNCT
cana-2739	322	1	https://doi.org/10.1016/0031-3203(95)00067-4	https://doi.org/10.1016/0031-3203(95)00067-4	NOUN
cana-2739	322	2	[	[	X
cana-2739	322	3	22	22	NUM
cana-2739	322	4	]	]	PUNCT
cana-2739	322	5	pang	pang	NOUN
cana-2739	322	6	,	,	PUNCT
cana-2739	322	7	c.	c.	PROPN
cana-2739	322	8	,	,	PUNCT
cana-2739	322	9	c.au	c.au	PROPN
cana-2739	322	10	,	,	PUNCT
cana-2739	322	11	o.	o.	PROPN
cana-2739	322	12	,	,	PUNCT
cana-2739	322	13	dai	dai	PROPN
cana-2739	322	14	,	,	PUNCT
cana-2739	322	15	j.	j.	PROPN
cana-2739	322	16	,	,	PUNCT
cana-2739	322	17	yang	yang	PROPN
cana-2739	322	18	,	,	PUNCT
cana-2739	322	19	w.	w.	PROPN
cana-2739	322	20	,	,	PUNCT
cana-2739	322	21	&	&	CCONJ
cana-2739	322	22	zou	zou	PROPN
cana-2739	322	23	,	,	PUNCT
cana-2739	322	24	f.	f.	PROPN
cana-2739	322	25	(	(	PUNCT
cana-2739	322	26	2009	2009	NUM
cana-2739	322	27	)	)	PUNCT
cana-2739	322	28	.	.	PUNCT
cana-2739	323	1	a	a	DET
cana-2739	323	2	fast	fast	ADJ
cana-2739	323	3	nl	nl	NOUN
cana-2739	323	4	-	-	PUNCT
cana-2739	323	5	means	means	NOUN
cana-2739	323	6	method	method	NOUN
cana-2739	323	7	in	in	ADP
cana-2739	323	8	image	image	NOUN
cana-2739	323	9	denoising	denoising	NOUN
cana-2739	323	10	based	base	VERB
cana-2739	323	11	on	on	ADP
cana-2739	323	12	the	the	DET
cana-2739	323	13	similarity	similarity	NOUN
cana-2739	323	14	of	of	ADP
cana-2739	323	15	spatially	spatially	ADV
cana-2739	323	16	sampled	sample	VERB
cana-2739	323	17	pixels	pixel	NOUN
cana-2739	323	18	.	.	PUNCT
cana-2739	324	1	2009	2009	NUM
cana-2739	324	2	ieee	ieee	PROPN
cana-2739	324	3	international	international	ADJ
cana-2739	324	4	workshop	workshop	NOUN
cana-2739	324	5	on	on	ADP
cana-2739	324	6	multimedia	multimedia	NOUN
cana-2739	324	7	signal	signal	NOUN
cana-2739	324	8	processing	processing	NOUN
cana-2739	324	9	,	,	PUNCT
cana-2739	324	10	mmsp	mmsp	NOUN
cana-2739	324	11	’	'	PUNCT
cana-2739	324	12	09	09	NUM
cana-2739	324	13	,	,	PUNCT
cana-2739	324	14	3–6	3–6	NUM
cana-2739	324	15	.	.	PUNCT
cana-2739	325	1	https://doi.org/10.1109/mmsp.2009.5293567	https://doi.org/10.1109/mmsp.2009.5293567	PROPN
cana-2739	326	1	[	[	X
cana-2739	326	2	23	23	NUM
cana-2739	326	3	]	]	X
cana-2739	326	4	parekh	parekh	PROPN
cana-2739	326	5	,	,	PUNCT
cana-2739	326	6	a.	a.	NOUN
cana-2739	326	7	m.	m.	NOUN
cana-2739	326	8	,	,	PUNCT
cana-2739	326	9	&	&	CCONJ
cana-2739	326	10	shah	shah	PROPN
cana-2739	326	11	,	,	PUNCT
cana-2739	326	12	n.	n.	PROPN
cana-2739	326	13	b.	b.	PROPN
cana-2739	326	14	(	(	PUNCT
cana-2739	326	15	2017	2017	NUM
cana-2739	326	16	)	)	PUNCT
cana-2739	326	17	.	.	PUNCT
cana-2739	327	1	classification	classification	NOUN
cana-2739	327	2	of	of	ADP
cana-2739	327	3	ovarian	ovarian	ADJ
cana-2739	327	4	cyst	cyst	NOUN
cana-2739	327	5	using	use	VERB
cana-2739	327	6	soft	soft	ADJ
cana-2739	327	7	computing	computing	NOUN
cana-2739	327	8	technique	technique	NOUN
cana-2739	327	9	.	.	PUNCT
cana-2739	328	1	8th	8th	ADJ
cana-2739	328	2	international	international	ADJ
cana-2739	328	3	conference	conference	NOUN
cana-2739	328	4	on	on	ADP
cana-2739	328	5	computing	computing	NOUN
cana-2739	328	6	,	,	PUNCT
cana-2739	328	7	communications	communication	NOUN
cana-2739	328	8	and	and	CCONJ
cana-2739	328	9	networking	networking	NOUN
cana-2739	328	10	technologies	technology	NOUN
cana-2739	328	11	,	,	PUNCT
cana-2739	328	12	icccnt	icccnt	ADJ
cana-2739	328	13	2017	2017	NUM
cana-2739	328	14	,	,	PUNCT
cana-2739	328	15	september	september	PROPN
cana-2739	328	16	.	.	PUNCT
cana-2739	329	1	https://doi.org/10.1109/icccnt.2017.8203965	https://doi.org/10.1109/icccnt.2017.8203965	NOUN
cana-2739	329	2	[	[	X
cana-2739	329	3	24	24	NUM
cana-2739	329	4	]	]	X
cana-2739	329	5	pentland	pentland	PROPN
cana-2739	329	6	,	,	PUNCT
cana-2739	329	7	a.	a.	NOUN
cana-2739	329	8	p.	p.	NOUN
cana-2739	329	9	(	(	PUNCT
cana-2739	329	10	1984	1984	NUM
cana-2739	329	11	)	)	PUNCT
cana-2739	329	12	.	.	PUNCT
cana-2739	330	1	fractal	fractal	NOUN
cana-2739	330	2	-	-	PUNCT
cana-2739	330	3	based	base	VERB
cana-2739	330	4	description	description	NOUN
cana-2739	330	5	of	of	ADP
cana-2739	330	6	natural	natural	ADJ
cana-2739	330	7	scenes	scene	NOUN
cana-2739	330	8	.	.	PUNCT
cana-2739	331	1	ieee	ieee	NOUN
cana-2739	331	2	transactions	transaction	NOUN
cana-2739	331	3	on	on	ADP
cana-2739	331	4	pattern	pattern	NOUN
cana-2739	331	5	analysis	analysis	NOUN
cana-2739	331	6	and	and	CCONJ
cana-2739	331	7	machine	machine	NOUN
cana-2739	331	8	intelligence	intelligence	NOUN
cana-2739	331	9	,	,	PUNCT
cana-2739	331	10	pami-6(6	pami-6(6	NOUN
cana-2739	331	11	)	)	PUNCT
cana-2739	331	12	,	,	PUNCT
cana-2739	331	13	661–674	661–674	NUM
cana-2739	331	14	.	.	PUNCT
cana-2739	332	1	https://doi.org/10.1109/tpami.1984.4767591	https://doi.org/10.1109/tpami.1984.4767591	PROPN
cana-2739	332	2	[	[	X
cana-2739	332	3	25	25	NUM
cana-2739	332	4	]	]	X
cana-2739	332	5	pradeep	pradeep	PROPN
cana-2739	332	6	,	,	PUNCT
cana-2739	332	7	s.	s.	PROPN
cana-2739	332	8	,	,	PUNCT
cana-2739	332	9	&	&	CCONJ
cana-2739	332	10	nirmaladevi	nirmaladevi	PROPN
cana-2739	332	11	,	,	PUNCT
cana-2739	332	12	p.	p.	NOUN
cana-2739	332	13	(	(	PUNCT
cana-2739	332	14	2021	2021	NUM
cana-2739	332	15	)	)	PUNCT
cana-2739	332	16	.	.	PUNCT
cana-2739	333	1	a	a	DET
cana-2739	333	2	review	review	NOUN
cana-2739	333	3	on	on	ADP
cana-2739	333	4	speckle	speckle	NOUN
cana-2739	333	5	noise	noise	NOUN
cana-2739	333	6	reduction	reduction	NOUN
cana-2739	333	7	techniques	technique	NOUN
cana-2739	333	8	in	in	ADP
cana-2739	333	9	ultrasound	ultrasound	ADJ
cana-2739	333	10	medical	medical	ADJ
cana-2739	333	11	images	image	NOUN
cana-2739	333	12	based	base	VERB
cana-2739	333	13	on	on	ADP
cana-2739	333	14	spatial	spatial	ADJ
cana-2739	333	15	domain	domain	NOUN
cana-2739	333	16	,	,	PUNCT
cana-2739	333	17	transform	transform	VERB
cana-2739	333	18	domain	domain	NOUN
cana-2739	333	19	and	and	CCONJ
cana-2739	333	20	cnn	cnn	PROPN
cana-2739	333	21	methods	method	NOUN
cana-2739	333	22	.	.	PUNCT
cana-2739	334	1	iop	iop	PROPN
cana-2739	334	2	conference	conference	PROPN
cana-2739	334	3	series	series	PROPN
cana-2739	334	4	:	:	PUNCT
cana-2739	334	5	materials	material	NOUN
cana-2739	334	6	science	science	NOUN
cana-2739	334	7	and	and	CCONJ
cana-2739	334	8	engineering	engineering	NOUN
cana-2739	334	9	,	,	PUNCT
cana-2739	334	10	1055(1	1055(1	NUM
cana-2739	334	11	)	)	PUNCT
cana-2739	334	12	,	,	PUNCT
cana-2739	334	13	012116	012116	NUM
cana-2739	334	14	.	.	PUNCT
cana-2739	334	15	https://doi.org/10.1088/1757-899x/1055/1/012116	https://doi.org/10.1088/1757-899x/1055/1/012116	PROPN
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cana-2739	335	2	26	26	NUM
cana-2739	335	3	]	]	X
cana-2739	335	4	rocha	rocha	PROPN
cana-2739	335	5	,	,	PUNCT
cana-2739	335	6	m.	m.	NOUN
cana-2739	335	7	m.	m.	NOUN
cana-2739	335	8	m.	m.	PROPN
cana-2739	335	9	,	,	PUNCT
cana-2739	335	10	landini	landini	PROPN
cana-2739	335	11	,	,	PUNCT
cana-2739	335	12	g.	g.	PROPN
cana-2739	335	13	,	,	PUNCT
cana-2739	335	14	&	&	CCONJ
cana-2739	335	15	florindo	florindo	PROPN
cana-2739	335	16	,	,	PUNCT
cana-2739	335	17	j.	j.	PROPN
cana-2739	335	18	b.	b.	PROPN
cana-2739	335	19	(	(	PUNCT
cana-2739	335	20	2023	2023	NUM
cana-2739	335	21	)	)	PUNCT
cana-2739	335	22	.	.	PUNCT
cana-2739	336	1	medical	medical	ADJ
cana-2739	336	2	image	image	NOUN
cana-2739	336	3	classification	classification	NOUN
cana-2739	336	4	using	use	VERB
cana-2739	336	5	a	a	DET
cana-2739	336	6	combination	combination	NOUN
cana-2739	336	7	of	of	ADP
cana-2739	336	8	features	feature	NOUN
cana-2739	336	9	from	from	ADP
cana-2739	336	10	convolutional	convolutional	ADJ
cana-2739	336	11	neural	neural	ADJ
cana-2739	336	12	networks	network	NOUN
cana-2739	336	13	.	.	PUNCT
cana-2739	337	1	multimedia	multimedia	NOUN
cana-2739	337	2	tools	tool	NOUN
cana-2739	337	3	and	and	CCONJ
cana-2739	337	4	applications	application	NOUN
cana-2739	337	5	,	,	PUNCT
cana-2739	337	6	82(13	82(13	NUM
cana-2739	337	7	)	)	PUNCT
cana-2739	337	8	,	,	PUNCT
cana-2739	337	9	19299–19322	19299–19322	NUM
cana-2739	337	10	.	.	PUNCT
cana-2739	338	1	https://doi.org/10.1007/s11042-022-14206-y	https://doi.org/10.1007/s11042-022-14206-y	PROPN
cana-2739	338	2	[	[	X
cana-2739	338	3	27	27	NUM
cana-2739	338	4	]	]	X
cana-2739	338	5	salman	salman	PROPN
cana-2739	338	6	hosain	hosain	PROPN
cana-2739	338	7	,	,	PUNCT
cana-2739	338	8	a.	a.	PROPN
cana-2739	338	9	k.	k.	PROPN
cana-2739	338	10	m.	m.	PROPN
cana-2739	338	11	,	,	PUNCT
cana-2739	338	12	mehedi	mehedi	PROPN
cana-2739	338	13	,	,	PUNCT
cana-2739	338	14	m.	m.	NOUN
cana-2739	338	15	h.	h.	PROPN
cana-2739	338	16	k.	k.	PROPN
cana-2739	338	17	,	,	PUNCT
cana-2739	338	18	&	&	CCONJ
cana-2739	338	19	kabir	kabir	PROPN
cana-2739	338	20	,	,	PUNCT
cana-2739	338	21	i.	i.	PROPN
cana-2739	338	22	e.	e.	PROPN
cana-2739	338	23	(	(	PUNCT
cana-2739	338	24	2023	2023	NUM
cana-2739	338	25	)	)	PUNCT
cana-2739	338	26	.	.	PUNCT
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cana-2739	339	2	:	:	PUNCT
cana-2739	339	3	a	a	DET
cana-2739	339	4	convolutional	convolutional	ADJ
cana-2739	339	5	neural	neural	ADJ
cana-2739	339	6	network	network	NOUN
cana-2739	339	7	architecture	architecture	NOUN
cana-2739	339	8	to	to	PART
cana-2739	339	9	detect	detect	VERB
cana-2739	339	10	polycystic	polycystic	ADJ
cana-2739	339	11	ovary	ovary	ADJ
cana-2739	339	12	syndrome	syndrome	NOUN
cana-2739	339	13	(	(	PUNCT
cana-2739	339	14	pcos	pcos	PROPN
cana-2739	339	15	)	)	PUNCT
cana-2739	339	16	from	from	ADP
cana-2739	339	17	ovarian	ovarian	ADJ
cana-2739	339	18	ultrasound	ultrasound	NOUN
cana-2739	339	19	images	image	NOUN
cana-2739	339	20	.	.	PUNCT
cana-2739	340	1	october	october	PROPN
cana-2739	340	2	,	,	PUNCT
cana-2739	340	3	1–6	1–6	NUM
cana-2739	340	4	.	.	PUNCT
cana-2739	341	1	https://doi.org/10.1109/iceet56468.2022.10007353	https://doi.org/10.1109/iceet56468.2022.10007353	PROPN
cana-2739	342	1	[	[	X
cana-2739	342	2	28	28	NUM
cana-2739	342	3	]	]	X
cana-2739	342	4	sheela	sheela	PROPN
cana-2739	342	5	,	,	PUNCT
cana-2739	342	6	s.	s.	PROPN
cana-2739	342	7	,	,	PUNCT
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cana-2739	342	9	,	,	PUNCT
cana-2739	342	10	m.	m.	NOUN
cana-2739	342	11	,	,	PUNCT
cana-2739	342	12	sumathy	sumathy	PROPN
cana-2739	342	13	,	,	PUNCT
cana-2739	342	14	s.	s.	PROPN
cana-2739	342	15	,	,	PUNCT
cana-2739	342	16	thirumoorthy	thirumoorthy	ADJ
cana-2739	342	17	,	,	PUNCT
cana-2739	342	18	s.	s.	PROPN
cana-2739	342	19	,	,	PUNCT
cana-2739	342	20	&	&	CCONJ
cana-2739	342	21	subalakshmi	subalakshmi	PROPN
cana-2739	342	22	,	,	PUNCT
cana-2739	342	23	e.	e.	PROPN
cana-2739	342	24	(	(	PUNCT
cana-2739	342	25	2020	2020	NUM
cana-2739	342	26	)	)	PUNCT
cana-2739	342	27	.	.	PUNCT
cana-2739	343	1	analysis	analysis	NOUN
cana-2739	343	2	of	of	ADP
cana-2739	343	3	various	various	ADJ
cana-2739	343	4	textural	textural	ADJ
cana-2739	343	5	descriptors	descriptor	NOUN
cana-2739	343	6	for	for	ADP
cana-2739	343	7	ovarian	ovarian	ADJ
cana-2739	343	8	cyst	cyst	NOUN
cana-2739	343	9	classification	classification	NOUN
cana-2739	343	10	.	.	PUNCT
cana-2739	344	1	advances	advance	NOUN
cana-2739	344	2	in	in	ADP
cana-2739	344	3	parallel	parallel	ADJ
cana-2739	344	4	computing	computing	NOUN
cana-2739	344	5	,	,	PUNCT
cana-2739	344	6	37	37	NUM
cana-2739	344	7	,	,	PUNCT
cana-2739	344	8	599–605	599–605	NUM
cana-2739	344	9	.	.	PUNCT
cana-2739	344	10	https://doi.org/10.3233/apc200208	https://doi.org/10.3233/apc200208	PROPN
cana-2739	344	11	[	[	X
cana-2739	344	12	29	29	NUM
cana-2739	344	13	]	]	X
cana-2739	344	14	suganya	suganya	ADJ
cana-2739	344	15	,	,	PUNCT
cana-2739	344	16	y.	y.	PROPN
cana-2739	344	17	,	,	PUNCT
cana-2739	344	18	ganesan	ganesan	PROPN
cana-2739	344	19	,	,	PUNCT
cana-2739	344	20	s.	s.	PROPN
cana-2739	344	21	,	,	PUNCT
cana-2739	344	22	&	&	CCONJ
cana-2739	344	23	valarmathi	valarmathi	PROPN
cana-2739	344	24	,	,	PUNCT
cana-2739	344	25	p.	p.	NOUN
cana-2739	344	26	(	(	PUNCT
cana-2739	344	27	2022	2022	NUM
cana-2739	344	28	)	)	PUNCT
cana-2739	344	29	.	.	PUNCT
cana-2739	345	1	ultrasound	ultrasound	ADJ
cana-2739	345	2	ovary	ovary	ADJ
cana-2739	345	3	cyst	cyst	NOUN
cana-2739	345	4	image	image	NOUN
cana-2739	345	5	classification	classification	NOUN
cana-2739	345	6	with	with	ADP
cana-2739	345	7	deep	deep	ADJ
cana-2739	345	8	learning	learn	VERB
cana-2739	345	9	neural	neural	ADJ
cana-2739	345	10	network	network	NOUN
cana-2739	345	11	with	with	ADP
cana-2739	345	12	support	support	NOUN
cana-2739	345	13	vector	vector	NOUN
cana-2739	345	14	machine	machine	NOUN
cana-2739	345	15	.	.	PUNCT
cana-2739	346	1	international	international	ADJ
cana-2739	346	2	journal	journal	PROPN
cana-2739	346	3	of	of	ADP
cana-2739	346	4	health	health	PROPN
cana-2739	346	5	sciences	science	NOUN
cana-2739	346	6	,	,	PUNCT
cana-2739	346	7	6(april	6(april	NUM
cana-2739	346	8	)	)	PUNCT
cana-2739	346	9	,	,	PUNCT
cana-2739	346	10	8811–8818	8811–8818	NUM
cana-2739	346	11	.	.	PUNCT
cana-2739	347	1	https://doi.org/10.53730/ijhs.v6ns2.7304	https://doi.org/10.53730/ijhs.v6ns2.7304	PROPN
cana-2739	347	2	[	[	X
cana-2739	347	3	30	30	NUM
cana-2739	347	4	]	]	X
cana-2739	347	5	wei	wei	PROPN
cana-2739	347	6	,	,	PUNCT
cana-2739	347	7	m.	m.	NOUN
cana-2739	347	8	,	,	PUNCT
cana-2739	347	9	du	du	PROPN
cana-2739	347	10	,	,	PUNCT
cana-2739	347	11	y.	y.	PROPN
cana-2739	347	12	,	,	PUNCT
cana-2739	347	13	wu	wu	PROPN
cana-2739	347	14	,	,	PUNCT
cana-2739	347	15	x.	x.	PROPN
cana-2739	347	16	,	,	PUNCT
cana-2739	347	17	su	su	PROPN
cana-2739	347	18	,	,	PUNCT
cana-2739	347	19	q.	q.	PROPN
cana-2739	347	20	,	,	PUNCT
cana-2739	347	21	zhu	zhu	PROPN
cana-2739	347	22	,	,	PUNCT
cana-2739	347	23	j.	j.	PROPN
cana-2739	347	24	,	,	PUNCT
cana-2739	347	25	zheng	zheng	PROPN
cana-2739	347	26	,	,	PUNCT
cana-2739	347	27	l.	l.	PROPN
cana-2739	347	28	,	,	PUNCT
cana-2739	347	29	lv	lv	PROPN
cana-2739	347	30	,	,	PUNCT
cana-2739	347	31	g.	g.	PROPN
cana-2739	347	32	,	,	PUNCT
cana-2739	347	33	&	&	CCONJ
cana-2739	347	34	zhuang	zhuang	PROPN
cana-2739	347	35	,	,	PUNCT
cana-2739	347	36	j.	j.	PROPN
cana-2739	347	37	(	(	PUNCT
cana-2739	347	38	2020	2020	NUM
cana-2739	347	39	)	)	PUNCT
cana-2739	347	40	.	.	PUNCT
cana-2739	348	1	a	a	DET
cana-2739	348	2	benign	benign	ADJ
cana-2739	348	3	and	and	CCONJ
cana-2739	348	4	malignant	malignant	ADJ
cana-2739	348	5	breast	breast	NOUN
cana-2739	348	6	tumor	tumor	NOUN
cana-2739	348	7	classification	classification	NOUN
cana-2739	348	8	method	method	NOUN
cana-2739	348	9	via	via	ADP
cana-2739	348	10	efficiently	efficiently	ADV
cana-2739	348	11	combining	combine	VERB
cana-2739	348	12	texture	texture	NOUN
cana-2739	348	13	and	and	CCONJ
cana-2739	348	14	morphological	morphological	ADJ
cana-2739	348	15	features	feature	NOUN
cana-2739	348	16	on	on	ADP
cana-2739	348	17	ultrasound	ultrasound	NOUN
cana-2739	348	18	images	image	NOUN
cana-2739	348	19	.	.	PUNCT
cana-2739	349	1	computational	computational	ADJ
cana-2739	349	2	and	and	CCONJ
cana-2739	349	3	mathematical	mathematical	ADJ
cana-2739	349	4	methods	method	NOUN
cana-2739	349	5	in	in	ADP
cana-2739	349	6	medicine	medicine	NOUN
cana-2739	349	7	,	,	PUNCT
cana-2739	349	8	2020	2020	NUM
cana-2739	349	9	.	.	PUNCT
cana-2739	350	1	https://doi.org/10.1155/2020/5894010	https://doi.org/10.1155/2020/5894010	PROPN
cana-2739	351	1	[	[	X
cana-2739	351	2	31	31	NUM
cana-2739	351	3	]	]	X
cana-2739	351	4	yakubu	yakubu	PROPN
cana-2739	351	5	,	,	PUNCT
cana-2739	351	6	i.	i.	PROPN
cana-2739	351	7	,	,	PUNCT
cana-2739	351	8	ziggah	ziggah	PROPN
cana-2739	351	9	,	,	PUNCT
cana-2739	351	10	y.	y.	PROPN
cana-2739	351	11	y.	y.	PROPN
cana-2739	351	12	,	,	PUNCT
cana-2739	351	13	&	&	CCONJ
cana-2739	351	14	yeboah	yeboah	NOUN
cana-2739	351	15	,	,	PUNCT
cana-2739	351	16	c.	c.	PROPN
cana-2739	351	17	(	(	PUNCT
cana-2739	351	18	2019	2019	NUM
cana-2739	351	19	)	)	PUNCT
cana-2739	351	20	.	.	PUNCT
cana-2739	352	1	evaluating	evaluate	VERB
cana-2739	352	2	hurst	hurst	PROPN
cana-2739	352	3	parameters	parameter	NOUN
cana-2739	352	4	and	and	CCONJ
cana-2739	352	5	fractal	fractal	ADJ
cana-2739	352	6	dimensions	dimension	NOUN
cana-2739	352	7	of	of	ADP
cana-2739	352	8	surveyed	survey	VERB
cana-2739	352	9	dataset	dataset	NOUN
cana-2739	352	10	of	of	ADP
cana-2739	352	11	tailings	tailing	NOUN
cana-2739	352	12	dam	dam	NOUN
cana-2739	352	13	embankment	embankment	NOUN
cana-2739	352	14	.	.	PUNCT
cana-2739	353	1	april	april	PROPN
cana-2739	353	2	.	.	PUNCT