Table 3.
Performance comparison of different models for detecting COVID-19 on the source dataset (the best rates are bold-faced)
| Model/method | Evaluation metrics | |||
|---|---|---|---|---|
| Accuracy | Precision | Recall | F1 | |
| AdaBoost | 95.1 | 93.6 | 96.7 | 95.1 |
| AlexNet | 93.7 | 94.9 | 92.2 | 93.6 |
| Decision tree | 79.4 | 76.8 | 83.1 | 79.8 |
| EfficientNetB0 | 98.9 | 99.1 | 98.9 | 99.0 |
| GoogleNet | 91.7 | 90.2 | 93.5 | 91.8 |
| ResNet50 | 94.9 | 93.0 | 97.1 | 95.0 |
| ResNet50V2 | 94.2 | 92.8 | 96.7 | 94.1 |
| ShuffleNet | 97.5 | 96.1 | 99.0 | 97.5 |
| SqueezeNet | 95.1 | 94.2 | 96.2 | 95.2 |
| VGG-16 | 94.9 | 94.0 | 95.4 | 94.9 |
| Xception | 98.8 | 99.0 | 98.6 | 98.8 |
|
| ||||
| Contrastive learning [35] | 90.8 | 95.7 | 85.8 | 90.8 |
| COVID CT-Net [30] | 90.7 | 88.5 | 85.0 | 90.0 |
| DenseNet201-based [48] | 96.2 | 96.2 | 96.2 | 96.2 |
| Modified VGG19 [52] | 95.0 | 95.3 | 94.0 | 94.3 |
| xDNN [17] | 97.3 | 99.1 | 95.5 | 97.3 |
|
| ||||
| ADA-COVID | 99.9 | 99.9 | 99.8 | 99.9 |