Table 6.
CNN Architectures on COVIDx and COVIDcxr. We trained the popular CNN architectures on both datasets for 30 epochs using the optimized data augmentation pipeline.
| CNN | Dataset | Acc (%) | AUC (%) | MCC (%) | Precision (%) | Recall (%) | F1 (%) |
|---|---|---|---|---|---|---|---|
| VGG-16 | COVIDcxr | 80.73 | 94.68 | 72.29 | 82.03 | 81.35 | 80.53 |
| VGG-19 | 84.90 | 95.67 | 77.74 | 85.31 | 85.26 | 84.92 | |
| ResNet-18 | 85.94 | 96.72 | 79.40 | 86.84 | 86.31 | 86.14 | |
| ResNet-34 | 79.69 | 94.91 | 70.02 | 80.26 | 80.03 | 79.70 | |
| ResNet-50 | 82.81 | 95.90 | 75.31 | 84.90 | 83.29 | 83.12 | |
| EfficientNet-B0 | 88.02 | _ | 82.01 | 87.98 | 88.03 | 88.00 | |
| VGG-16 | COVIDx | 93.41 | 98.70 | 87.74 | 94.40 | 89.41 | 91.61 |
| VGG-19 | 93.60 | 98.55 | 88.06 | 95.29 | 85.53 | 89.24 | |
| ResNet-18 | 93.29 | 98.86 | 87.48 | 95.03 | 86.73 | 90.05 | |
| ResNet-34 | 94.74 | 99.10 | 90.19 | 95.85 | 89.95 | 92.53 | |
| ResNet50 | 95.12 | 99.22 | 90.92 | 96.08 | 91.76 | 93.72 | |
| EfficientNet-B0 | 95.69 | _ | 92.01 | 96.24 | 94.76 | 95.48 |