Table 3.
Performance of the proposed deep learning framework under different convolution neural networks and histogram of oriented gradient (HOG).
| Feature extractor | Sensitivity, mean (SD) | Specificity, mean (SD) | Precision, mean (SD) | F1-score, mean (SD) | AUCa, mean (SD) |
|---|---|---|---|---|---|
| HOG | 90.77 (2.62) | 27.35 (8.98) | 85.03 (1.86) | 87.77 (1.41) | 0.65 (0.06) |
| Inception-v4 | 92.54 (3.53) | 43.70 (8.64) | 87.91 (1.95) | 90.12 (1.90) | 0.80 (0.05) |
| 3D ResNet | 94.57b (2.61) | 54.57 (6.46) | 90.20 (1.81) | 92.30 (1.44) | 0.87 (0.04) |
| 3D ResNext | 94.17 (2.67) | 51.74 (7.33) | 89.62 (2.21) | 91.81 (1.82) | 0.85 (0.05) |
| Inflated 3D | 92.94 (3.47) | 49.78 (8.00) | 89.08 (1.85) | 90.94 (2.24) | 0.82 (0.06) |
a
AUC: area under the curve.
b
Italicized numbers represent the best result under each metric.