Automatic detection of small bowel lesions with different bleeding risks based on deep learning models

Table 1 Training and test sets image classification and the number of each classification

Set	Type of CE	Type of pictures
		N	P0Lk	P0Lz	P0X	P1E	P1U	P1P	P2U	P2P	P2V	B	I	Total
Training set	PillCam	14026	2086	1505	551	1851	3867	687	1564	689	930	1529	17935	47220
	MiroCam	13452	612	817	494	1091	2062	180	341	205	421	782	6897	27354
Test set	PillCam	9221	245	435	122	235	1261	119	2608	109	139	1703	8446	24643
	MiroCam	4518	112	418	153	68	415	68	1581	68	92	1181	3970	12644

B: Blood; CE: Capsule endoscopy; I: Invalid pictures; N: Normal pictures; P0Lk: Lymphangiectasia; P0Lz: Lymphoid follicular hyperplasia; P0X: Xanthoma; P1E: Erosion; P1P: Protruding lesion smaller than 1 cm; P1U: Ulcer smaller than 2 cm; P2P: Protruding lesion larger than 1 cm; P2U: Ulcer larger than 2 cm; P2V: Vasculopathy.