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.

Table 2 Comparison of sensitivity, specificity, and accuracy of physician and model-assisted reading for different types of image recognition

Type of CE	Mode of reading	Sensitivity, %	Specificity, %	Accuracy, %
I	P	95.96	98.10	97.39
	M	99.85	99.57	99.66
N	P	94.96	94.25	94.52
	M	98.84	99.77	99.43
P0Lk	P	84.31	99.89	99.75
	M	96.92	99.97	99.94
P0Lz	P	78.66	99.71	99.23
	M	97.30	99.90	99.83
P0X	P	65.45	99.87	99.62
	M	93.82	99.98	99.94
P1E	P	67.33	99.55	99.28
	M	91.75	99.96	99.90
P1U	P	74.88	99.68	98.57
	M	98.39	99.94	99.87
P1P	P	64.71	99.78	99.61
	M	94.12	99.94	99.91
P2U	P	98.35	99.94	99.76
	M	99.69	99.99	99.95
P2P	P	88.14	99.71	99.65
	M	100	99.96	99.96
P2V	P	52.38	99.92	99.62
	M	97.40	99.97	99.96
B	P	100	100	100
	M	100	100	100

The models in this table refer to the improved models (with modules added). B: Blood; CE: Capsule endoscopy; I: Invalid pictures; M: Model-assisted reading; N: Normal pictures; P: Physician reading; 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.

Table 3 Effect of stage 1 multimodal module ablation experiments on the performance metrics of the algorithm

Method	Color channel module				Accuracy, %	Sensitivity, %	Specificity, %
	R	G	B	RGB
Method 1	√	×	×	×	98.32	98.29	98.36
Method 2	×	√	×	×	96.97	96.99	96.93
Method 3	×	×	√	×	99.04	99.02	99.08
Method 4	×	×	×	√	99.08	99.05	99.12

Table 4 Effect of stage 1 attention module ablation experiments on the performance metrics of the algorithm

Method	Attention module			Accuracy, %	Sensitivity, %	Specificity, %
	SA	CA	MHSA
Method 1	√	×	×	98.79	98.75	98.86
Method 2	×	√	×	98.82	98.66	99.06
Method 3	×	×	√	99.08	99.05	99.12

CA: Channel attention module; MHSA: Multi-head self-attention; SA: Spatial attention module.

Table 5 Effect of ablation experiments in stage 2 on algorithm performance metrics

Module	Accuracy, %	EER, %	AUC, %
PN, √	98.96	0.24	98.86
PN, ×	96.38	0.29	95.02
ASPP, √	98.96	0.24	98.86
ASPP, ×	96.01	0.28	96.47
MHSA, √	98.96	0.24	98.86
MHSA, ×	96.22	0.29	95.68

ASPP: Atrous spatial pyramid pooling; AUC: Area under the curve; EER: Equal error rate; MHSA: Multi-head self-attention mechanism; PN: Parallel network.

Table 6 Effect of random number experiment on algorithm performance index in stage 2 feature erase module

Random number	Accuracy, %	EER, %	AUC, %
001	97.91	0.29	98.49
010	97.92	0.28	98.47
100	97.91	0.29	98.50
011	98.58	0.24	98.63
101	98.37	0.25	98.66
110	98.27	0.25	98.67
111	98.96	0.24	98.86

AUC: Area under the curve; EER: Equal error rate.

Table 7 Comparison of available models

Ref.	Year of publication	Application	Algorithm	Sensitivity, %	Specificity, %	Accuracy, %
Aoki et al[29]	2019	Erosion/ulcer	CNN system based on SSD	88.2	90.9	90.8
Ding et al[22]	2019	Ulcer	ResNet-152	99.7	99.9	99.8
		Bleeding		99.5	99.9	99.9
		Vascular lesion		98.9	99.9	99.2
Aoki et al[30]	2020	Protruding lesion	ResNet-50	100	99.9	99.9
		Bleeding		96.6	99.9	99.9
Current study	2023	Ulcer( P1U + P2U )	Improved ResNet-50 + YOLO-V5	99.7	99.9	99.9
		Vascular lesion		97.4	99.9	99.9
		Protruding lesion (P1P + P2P)		98.1	99.9	99.9
		Bleeding		100	100	100

CNN: Convolution neural network; P1U: Ulcer smaller than 2 cm; P1P: Protruding lesion smaller than 1 cm; P2P: Protruding lesion larger than 1 cm; P2U: Ulcer larger than 2 cm; SSD: Single shot multibox detector.