Table 3.
Comparative analysis of existing with proposed methods
| Methods | Sensitivity | Specificity | Accuracy | F-score |
|---|---|---|---|---|
| DWS-CNN (Binary Class) | 98.37 | 98.59 | 98.54 | 98.63 |
| DWS-CNN (Multi Class) | 99.13 | 99.20 | 99.06 | 98.97 |
| FR-CNN | 97.65 | 95.48 | 97.36 | 98.46 |
| ResNet-50 | 93.00 | 67.74 | 89.61 | 93.94 |
| Inception V3 | 91.00 | 74.19 | 88.74 | 93.33 |
| AlexNet | 92.50 | 71.43 | 90.50 | 94.63 |
| CovxNet | 90.50 | 95.80 | 91.70 | 91.10 |
| CapsNet | 84.22 | 91.79 | 89.19 | 84.21 |
| VGG19 | 97.05 | 96.00 | 96.33 | 94.24 |
| Deep Transfer Learning | 89.61 | 92.03 | 90.75 | 90.43 |
| Multi-Layer Perceptron | 93.00 | 87.23 | 93.13 | 93.00 |
| Logistic Regression | 93.00 | 90.34 | 92.12 | 92.00 |
| K-Nearest Neighbour | 89.00 | 90.65 | 88.91 | 89.00 |
| Decision Tree | 87.00 | 88.93 | 86.71 | 87.00 |