Table 3.
Discriminative performance of case-based DL models according to 5-fold cross-validation
| Model | Dataset | Accuracy [Variance, (95%CI)] | Sensitivity [Variance, (95%CI)] | Specificity [Variance, (95%CI)] | AUC [Variance, (95%CI)] |
|---|---|---|---|---|---|
| ResNet50 | Training | 1.000 (0.000, [1.000–1.000]) | 1.000 (0.000, [1.000–1.000]) | 1.000 (0.000, [1.000–1.000]) | 1.000 (0.000, [1.000–1.000]) |
| ResNet50 | Validation | 0.772 (0.005, [0.534–0.988]) | 0.808 (0.030, [0.631–0.960]) | 0.709 (0.041, [0.505–0.905]) | 0.842 (0.004, [0.639–0.997]) |
| ConvNeXt-B | Training | 1.000 (0.000, [1.000–1.000]) | 1.000 (0.000, [1.000–1.000]) | 1.000 (0.000, [1.000–1.000]) | 1.000 (0.000, [1.000–1.000]) |
| ConvNeXt-B | Validation | 0.817 (0.004, [0.598–0.994]) | 0.817 (0.016, [0.632–0.984]) | 0.811 (0.017, [0.622–0.979]) | 0.897 (0.004, [0.734–1.000]) |
| ViT_B/16 | Training | 1.000 (0.000, [1.000–1.000]) | 1.000 (0.000, [1.000–1.000]) | 1.000 (0.000, [1.000–1.000]) | 1.000 (0.000, [1.000–1.000]) |
| ViT_B/16 | Validation | 0.668 (0.014, [0.407–0.920]) | 0.725 (0.040, [0.522–0.925]) | 0.572 (0.024, [0.304–0.831]) | 0.681 (0.030, [0.434–0.908]) |
Abbreviation: DL deep learning, 95%CI 95% confidence interval, AUC area under the curve