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©The Author(s) 2025.
World J Gastroenterol. Jun 7, 2025; 31(21): 107601
Published online Jun 7, 2025. doi: 10.3748/wjg.v31.i21.107601
Published online Jun 7, 2025. doi: 10.3748/wjg.v31.i21.107601
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) |
- Citation: Huang YH, Lin Q, Jin XY, Chou CY, Wei JJ, Xing J, Guo HM, Liu ZF, Lu Y. Classification of pediatric video capsule endoscopy images for small bowel abnormalities using deep learning models. World J Gastroenterol 2025; 31(21): 107601
- URL: https://www.wjgnet.com/1007-9327/full/v31/i21/107601.htm
- DOI: https://dx.doi.org/10.3748/wjg.v31.i21.107601