Table 5.
Performance measures of deep features
| Model | Explanation | Accuracy (%) | Sensitivity (%) | Specificity (%) | Precision (%) | F1 score | FPR (%) | FNR (%) |
|---|---|---|---|---|---|---|---|---|
| Model 13 | VGG16 + logistic regression | 58.36 | 63.24 | 55.4 | 46.24 | 0.534 | 44.6 | 36.76 |
| Model 14 | VGG16 + linear SVM | 62.24 | 0 | 100 | 0 | 0 | 0 | 100 |
| Model 15 | VGG16 + RBF-SVM | 82.985 | 83.79 | 82.49 | 74.39 | 0.788 | 17.5 | 16.2 |
| Model 16 | VGG16 + random forest | 77.84 | 82.21 | 75.18 | 66.77 | 0.737 | 24.8 | 17.78 |
| Model 17 | VGG19 + logistic regression | 74.22 | 80.04 | 70.69 | 62.31 | 0.701 | 29.3 | 19.96 |
| Model 18 | VGG19 + linear SVM | 76.42 | 76.68 | 76.26 | 66.21 | 0.711 | 23.74 | 23.32 |
| Model 19 | VGG19 + RBF-SVM | 83.06 | 90.12 | 78.78 | 72.04 | 0.8 | 21.22 | 9.88 |
| Model 20 | VGG19 + random forest | 80.15 | 84.58 | 77.46 | 69.48 | 0.763 | 22.54 | 15.4 |
| Model 21 | ResNet50 + logistic regression | 78.36 | 83 | 75.54 | 67.31 | 0.74 | 24.46 | 17 |
| Model 22 | ResNet50 + linear SVM | 79.03 | 87.15 | 74 | 67.12 | 0.758 | 25.89 | 12.85 |
| Model 23 | ResNet50 + RBF-SVM | 83.06 | 95.06 | 75.78 | 70.42 | 0.81 | 24.2 | 4.94 |
| Model 24 | ResNet50 + random forest | 80.3 | 87.15 | 79.14 | 68.9 | 0.77 | 23.86 | 12.85 |
FPR: False-positive rate, FNR: False-negative rate, RBF: Radial basis function, SVM: Support vector machine