Classification of pediatric video capsule endoscopy images for small bowel abnormalities using deep learning models

Table 1 The data distribution for the classification of normal mucosa, erosions/erythema, ulcers, and polyps

	Training set	Testing set	Total
Normal mucosa	916	228	1144
Erosions/erythema	272	68	340
Ulcers	151	37	188
Polyps	501	125	626
Total images	1840	458	2298

Table 2 The hyperparameter values of the deep learning models

Type of hyper-parameter	DenseNet121	VGG16	ResNet50	ViT
Number of epochs	100	100	100	300
Batch size	32	32	32	16
Learning rate	1 × 10-3	1 × 10-3	1 × 10-3	6 × 10-3
Weight decay	4 × 10-4	4 × 10-4	4 × 10-4	4 × 10-4
momentum	0.8	0.8	0.8	0.8
Optimizer	SGD	SGD	SGD	SGD

VGG: Visual geometry group; ViT: Vision transformer; SGD: Stochastic gradient descent.

Table 3 Baseline demographic and clinical characteristics of enrolled patients (n = 162), n (%)

Patient characteristics	Value
Gender
Female	56 (35)
Male	106 (65)
Age (year), median (IQR)	11.00 (9.00, 13.00)
Chief complaint
Abdominal pain	101 (62)
Diarrhea	31 (19)
Anemia	2 (1.2)
Hematochezia	18 (11)
Vomiting	13 (8.0)
Fever	8 (4.9)
Oral ulceration	4 (2.5)
Mucocutaneous hyperpigmentation of the mouth and lips	5 (3.1)
Perianal abscess	10 (6.2)
Swallowed VCE by the patients	137 (85)
Placed VCE by endoscopy	25 (15)
Stomach transit time (minute), median (IQR)	22 (5, 55)
Small bowel transit time (minute), median (IQR)	282 (217, 377)
Number of lesions per patient
None	69 (43)
Single	33 (20)
Multiple	60 (37)
Diagnosis
Crohn disease	36 (22)
Ulcerative colitis	2 (1.2)
Suspected inflammatory bowel disease	30 (19)
Behcet disease	2 (1.2)
Disorder of gut-brain interaction	31 (19)
Polyps	12 (7.4)
Gastroenteritis	35 (22)
Others	14 (8.6)

IQR: Interquartile range; VCE: Video capsule endoscopy.

Table 4 Accuracy of different deep learning models in detecting lesions of normal mucosa, ulcers, erosions/erythema, and polyps

Model	Overall accuracy (%) (95%CI)	Normal mucosa (%) (95%CI)	Ulcers (%) (95%CI)	Erosions/erythema (%) (95%CI)	Polyps (%) (95%CI)
DenseNet121	90.6 (89.2-92.0)	98.6 (96.0-100)	83.3 (75.6-91.1)	81.9 (74.2-89.6)	100 (100-100)
VGG16	88.3 (87.9-88.8)	92.2 (89.7-94.6)	91.6 (89.9-93.3)	72.1 (63.6-80.6)	75.0 (66.8-83.2)
ResNet50	90.5 (89.9-91.1)	98.1 (96.8-99.4)	87.0 (82.3-91.7)	77.3 (72.4-82.2)	100 (100-100)
ViT	88.1 (86.7-89.6)	93.2 (88.5-97.9)	87.4 (77.6-97.3)	73.0 (61.3-84.8)	87.9 (76.3-99.5)

VGG: Visual geometry group; ViT: Vision transformer; CI: Confidence interval.

Table 5 The overall performances of the deep learning models in the training set

Model	Accuracy (%) (95%CI)	Precision (%) (95%CI)	Recall (%) (95%CI)	F1-score (%) (95%CI)	AU-ROC (%) (95%CI)
DenseNet121	90.6 (89.2-92.0)	91.8 (89.6-94.0)	91.0 (89.8-92.1)	91.2 (89.4-92.9)	93.7 (92.9-94.5)
VGG16	88.3 (87.9-88.8)	83.0 (80.7-85.3)	82.8 (81.0-84.6)	82.6 (81.9-83.3)	89.2 (88.1-90.3)
ResNet50	90.5 (89.9-91.2)	92.5 (91.6-93.3)	90.7 (90.0-91.3)	91.3 (90.7-91.9)	93.4 (93.1-93.8)
ViT	88.1 (86.7-89.6)	84.4 (78.5-90.3)	85.4 (81.0-89.7)	84.6 (80.0-89.2)	90.4 (88.1-92.7)

VGG: Visual geometry group; ViT: Vision transformer; AU-ROC: Area under the receiver operating curve; CI: Confidence interval.

Table 6 Pairwise comparisons of the models’ overall performances in the training set (P value)

Evaluated metrics	DenseNet121 vs VGG16	DenseNet121 vs ResNet50	DenseNet121 vs ViT	VGG16 vs ResNet50	VGG16 vs ViT	ResNet50 vs ViT
Accuracy	0.004	0.999	0.002	0.006	0.978	0.003
Precision	0.001	0.981	0.003	0.001	0.85	0.001
Recall	0.001	0.995	0.002	0.001	0.212	0.003
F1-score	0.001	0.999	0.001	0.001	0.421	0.001
AU-ROC	0.001	0.984	0.001	0.001	0.304	0.002

VGG: Visual geometry group; ViT: Vision transformer; AU-ROC: Area under the receiver operating curve.

Table 7 The overall performances of the deep learning models in the testing set

Model	Accuracy (%)	Precision (%)	Recall (%)	F1-score (%)	AU-ROC (%)
DenseNet121	88.6	87.5	79.0	82.5	87.1
VGG16	85.5	87.0	73.3	77.3	83.6
ResNet50	89.7	87.8	81.0	83.8	88.5
ViT	86.6	81.3	80.0	80.5	87.5

VGG: Visual geometry group; ViT: Vision transformer; AU-ROC: Area under the receiver operating curve.