Table 5.
The evaluation metrics obtained by using the classical network model for the three- and four-classification tasks.
| Model | Three-classification task | Four-classification task | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Acc (%) | Pre (%) | Re (%) | F1 (%) | Param (M) | Acc (%) | Pre (%) | Re (%) | F1 (%) | Param (M) | |
| VGG16 | 93.80 | 94.05 | 93.79 | 93.79 | 134.27 | 93.15 | 92.97 | 93.21 | 93.08 | 134.28 |
| AlexNet | 95.59 | 95.02 | 95.61 | 95.18 | 57.02 | 93.64 | 93.34 | 93.73 | 93.50 | 57.02 |
| ResNet50 | 97.10 | 96.68 | 96.92 | 96.79 | 23.51 | 95.23 | 95.15 | 95.37 | 95.22 | 23.52 |
| DenseNet | 97.87 | 97.54 | 97.93 | 97.73 | 6.96 | 95.63 | 95.50 | 95.87 | 95.67 | 6.96 |
| EfficientNetV2 | 96.9 | 96.23 | 97.23 | 96.68 | 20.18 | 96.47 | 96.34 | 96.66 | 96.49 | 20.18 |
| Inception_V3 | 92.32 | 91.86 | 91.90 | 91.80 | 21.79 | 90.64 | 90.29 | 91.45 | 90.77 | 21.79 |
| VisionTransformer | 72.20 | 71.15 | 67.36 | 68.20 | 87.46 | 49.79 | 51.87 | 48.17 | 49.12 | 87.46 |
| SwinTransformer | 88.4 | 89.22 | 85.41 | 86.88 | 48.84 | 78.07 | 77.78 | 78.21 | 77.97 | 48.84 |
| Proposed | 99.51 | 99.47 | 99.53 | 99.50 | 16.41 | 98.01 | 98.13 | 98.02 | 98.07 | 16.41 |