Table 5. Classification results after performing the fusion of the AlexNet, VGG19, Inceptionv3 DCNN features. Best values are shown in bold.
| Classifier | Performance measures | ||||
|---|---|---|---|---|---|
| Sensitivity (%) | Specificity (%) | FNR (%) | Accuracy (%) | F-score | |
| Cubic SVM | 97.12 | 95.99 | 3.0 | 96.6 | 0.966 |
| Linear SVM | 95.53 | 91.48 | 4.56 | 93.5 | 0.936 |
| Quadratic SVM | 96.68 | 95.51 | 3.3 | 96.1 | 0.961 |
| Fine KNN | 93.81 | 92.4 | 6.1 | 93.1 | 0.932 |
| Medium KNN | 97.40 | 89.15 | 2.59 | 93.3 | 0.935 |
| Cubic KNN | 96.86 | 88.35 | 3.13 | 92.6 | 0.929 |
| Weighted KNN | 96.23 | 91.48 | 3.76 | 93.9 | 0.940 |