Table 4 Comparison of the efficacy of ResNet, DenseNet, Vision Transformer, Swin Transformer and SA in identifying benign and malignant ovarian tumors
From: Machine learning models in evaluating the malignancy risk of ovarian tumors: a comparative study
Model | AUC | Sensitivity | Specificity | NPV | PPV | Youden index | Cutoff | +LR | -LR | DOR | |
|---|---|---|---|---|---|---|---|---|---|---|---|
ResNet | 0.91 (0.85 - 0.95) | 82.1 (60.7 - 88.9) | 93.4 (87.5 - 97.1) | 99.6 (99.2 - 99.8) | 20.3 (7.2 - 45.7) | 0.75 | >0.58 | 11.73 | 0.25 | 46.92 | |
DenseNet | 0.91 (0.86 - 0.95) | 84.6 (69.5 - 94.1) | 92.6 (86.5 - 96.6) | 99.7 (99.3 - 99.8) | 26.0 (8.1 - 58.4) | 0.77 | >0.25 | 11.47 | 0.17 | 67.47 | |
Vision Transformer | 0.87 (0.81 - 0.92) | 84.6 (69.5 - 94.1) | 81.2 (73.1 - 87.7) | 99.6 (99.2 - 99.8) | 8.4 (4.5 - 15.1) | 0.66 | >0.17 | 4.49 | 0.19 | 23.63 | |
Swin Transformer | 0.92 (0.87 - 0.96) | 87.2 (72.6 - 95.7) | 94.3 (88.5 - 97.7) | 99.7 (99.4 - 99.9) | 23.7 (7.9 - 52.7) | 0.81 | >0.33 | 15.19 | 0.14 | 108.5 | |
SA | 0.97 (0.93 - 0.99) | 87.2 (72.6 - 95.7) | 98.4 (94.2 - 99.8) | 99.7 (99.4 - 99.9) | 52.0 (8.7 - 92.5) | 0.86 | >3 | 53.18 | 0.13 | 409.08 |