Table 13 Fold − 3 Cross-validation performance of DAC-GAN with CKPF images.
Model | Accuracy (%) | Sensitivity (%) | Specificity (%) | Precision (%) | Recall (%) | F1-Score (%) | IoU (%) |
|---|---|---|---|---|---|---|---|
DenseNet121 | 78.40 | 76.61 | 79.25 | 77.03 | 76.61 | 76.82 | 60.51 |
VGG19 | 79.61 | 77.94 | 80.33 | 78.27 | 77.94 | 78.10 | 61.69 |
Inception | 80.57 | 78.83 | 81.27 | 79.33 | 78.83 | 79.08 | 63.30 |
XCeption | 81.58 | 79.59 | 82.41 | 80.32 | 79.59 | 79.95 | 64.38 |
MobileNet | 82.74 | 80.41 | 83.33 | 81.24 | 80.41 | 80.82 | 66.26 |
ResNet-50 | 83.90 | 81.39 | 84.67 | 82.10 | 81.39 | 81.74 | 67.88 |
EfficientNet-B0 | 85.59 | 83.17 | 86.29 | 84.05 | 83.17 | 83.59 | 70.11 |
EfficientNet-B4 | 87.31 | 84.81 | 88.04 | 85.67 | 84.81 | 85.23 | 72.49 |
ConvNeXt | 88.95 | 86.36 | 89.70 | 87.44 | 86.36 | 86.89 | 74.52 |
ViT CNN | 89.82 | 87.30 | 90.10 | 88.15 | 87.30 | 87.72 | 76.28 |
SwinT | 90.05 | 87.96 | 90.42 | 88.80 | 87.96 | 88.38 | 77.46 |
WGAN | 93.80 | 92.81 | 92.12 | 92.80 | 92.88 | 92.88 | 92.91 |
CGAN | 94.55 | 93.12 | 93.19 | 92.31 | 93.99 | 93.55 | 93.51 |
DAC-GAN | 99.79 | 99.85 | 99.89 | 99.81 | 99.85 | 99.83 | 99.63 |
