Table 13.
Comparative study between proposed classifier and well-known classifiers on ISIB 2017 dataset.
| Classifier | Method | Acc (%) | Sen (%) | Spec (%) | Prec | AUC | Time (in sec) |
|---|---|---|---|---|---|---|---|
| TREE | CT | 92.83 | 90.60 | 93.37 | 76.81 | 0.96 | 8.12 |
| ST | 88.00 | 88.89 | 87.78 | 63.80 | 0.92 | 12.67 | |
| SVM | LSVM | 96.67 | 93.16 | 97.52 | 90.08 | 0.95 | 11.42 |
| CSVM | 86.83 | 85.47 | 87.16 | 61.73 | 0.92 | 140.54 | |
| QSVM | 97.50 | 94.87 | 98.14 | 92.50 | 0.97 | 21.47 | |
| MGSVM | 96.67 | 94.87 | 97.10 | 88.80 | 0.98 | 13.45 | |
| KNN | FKNN | 94.00 | 92.31 | 94.41 | 80.00 | 0.90 | 9.04 |
| MKNN | 97.83 | 94.02 | 98.76 | 94.83 | 0.96 | 10.01 | |
| Cosine | 97.50 | 93.16 | 98.55 | 93.97 | 0.97 | 10.78 | |
| Cubic | 97.50 | 94.02 | 98.34 | 93.22 | 0.97 | 102.01 | |
| WKNN | 98.50 | 96.58 | 98.96 | 95.76 | 0.98 | 12.75 | |
| YOLO | Proposed Method | 99.00 | 97.44 | 99.38 | 97.44 | 0.99 | 7.14 |