Table 7 Proposed features fusion and selection results on ISIC-MSK dataset
Method | Performance measures | Â | Â | |||
|---|---|---|---|---|---|---|
| Â | Sensitivity (%) | Precision (%) | Specificity (%) | FNR (%) | FPR | Accuracy (%) |
Decision tree | 92.95 | 93.1 | 94.30 | 6.9 | 0.07 | 93.1 |
Quadratic discriminant analysis | 95.95 | 95.45 | 91.90 | 4.5 | 0.04 | 95.5 |
Quadratic SVM | 96.25 | 96.10 | 95.60 | 3.8 | 0.03 | 96.2 |
Logistic regression | 95.10 | 95.10 | 95.60 | 4.8 | 0.04 | 95.2 |
Naive bayes | 92.80 | 93.30 | 95.60 | 6.9 | 0.07 | 93.1 |
Weighted KNN | 95.10 | 95.10 | 95.60 | 4.8 | 0.04 | 95.2 |
Ensemble boosted tree | 95.10 | 95.10 | 95.60 | 4.80 | 0.04 | 95.2 |
Ensemble subspace discriminant | 95.10 | 95.10 | 95.60 | 4.8 | 0.04 | 95.2 |
Cubic KNN | 89.35 | 90.65 | 95.60 | 10.0 | 0.10 | 90.0 |
Proposed | 96.60 | 97.0 | 98.30 | 2.8 | 0.01 | 97.2 |