Table 4.
Performance measures of handcrafted features
| Model | Explanation | Accuracy (%) | Sensitivity (%) | Specificity (%) | Precision (%) | F1 score | FPR (%) | FNR (%) |
|---|---|---|---|---|---|---|---|---|
| Model 1 | GLCM + logistic regression | 57.31 | 85.18 | 40 | 46.44 | 0.6 | 60 | 14.8 |
| Model 2 | GLCM + linear SVM | 56.86 | 83.79 | 40.53 | 46 | 0.59 | 59 | 16 |
| Model 3 | GLCM + RBF SVM | 62.24 | 0 | 100 | 0 | 0 | 0 | 100 |
| Model 4 | GLCM + random forest | 62.24 | 0 | 100 | 0 | 0 | 0 | 100 |
| Model 5 | LBP + logistic regression | 62.24 | 0 | 100 | 0 | 0 | 0 | 100 |
| Model 6 | LBP + linear SVM | 62.24 | 0 | 100 | 0 | 0 | 0 | 100 |
| Model 7 | LBP + RBF SVM | 62.24 | 0 | 100 | 0 | 0 | 0 | 100 |
| Model 8 | LBP + random forest | 62.24 | 0 | 100 | 0 | 0 | 0 | 100 |
| Model 9 | HOG + logistic regression | 74 | 93.47 | 62 | 60 | 0.73 | 38 | 6.52 |
| Model 10 | HOG + linear SVM | 62.24 | 0 | 100 | 0 | 0 | 0 | 100 |
| Model 11 | HOG + RBF SVM | 78 | 95.45 | 67 | 64 | 0.77 | 32.6 | 4.5 |
| Model 12 | HOG + random forest | 62.24 | 0 | 100 | 0 | 0 | 0 | 100 |
FPR: False-positive rate, FNR: False-negative rate, GLCM: Gray level co-occurrence matrix, RBF: Radial basis function, SVM: Support vector machine, LBF: Local binary pattern, HOG: Histogram of oriented gradients