Table 4. Classification results on VGG19 DCNN features. Best values are shown in bold.
Classifier | Performance measures | ||||
---|---|---|---|---|---|
Sensitivity (%) | Specificity (%) | FNR (%) | Accuracy (%) | F-score | |
Quadratic SVM | 92.2 | 90.8 | 7.7 | 91.5 | 0.916 |
Linear SVM | 90.8 | 87.6 | 9.1 | 89.2 | 0.894 |
Cubic SVM | 93.4 | 91.6 | 6.6 | 92.5 | 0.926 |
Fine KNN | 88.1 | 90.6 | 11.8 | 89.4 | 0.892 |
Medium KNN | 92.2 | 89.2 | 7.7 | 90.7 | 0.908 |
Cubic KNN | 92.4 | 87.7 | 7.5 | 90.1 | 0.903 |
Weighted KNN | 90.8 | 91.3 | 9.1 | 91.1 | 0.910 |
Ensemble subspace discriminant | 89.4 | 89.3 | 10.5 | 89.4 | 0.894 |