Table 6.
Comparison of proposed CNN-SVM + Sobel method using private database with other methods in detecting COVID-19 using X-ray images from different private databases.
| Study | Number of Cases | Network | Train-Test | Evaluation Metrics |
|---|---|---|---|---|
| Hall et al. [11] | 455 images | VGG-16 and ResNet-50 | 10-fold | AUC: 0.997 |
| Hemdan et al. [14] | 50 images | DesnseNet, VGG16, MobileNet v2.0 etc. | 80–20% | F1 score: 91% |
| Abbas et al. [15] | 196 images | CNN with transfer learning | 70–30% | Accuracy: 95.12% |
| Sensitivity: 97.91% | ||||
| Specificity: 91.87% | ||||
| PPV: 93.36% | ||||
| Zhang et al. [17] | 213 images | ResNet, EfficientNet | 5-fold | Sensitivity: 71.70% |
| AUC: 0.8361 | ||||
| Narin et al. [20] | 100 images | ResNet50 | 10-fold | Accuracy: 98% |
| Ozturk et al. [34] | 625 images | Darknet-19 | 5-fold | Accuracy: 98.08% |
| Khan et al. [35] | 1251 images | CNN | 4-fold | Accuracy: 89.6% |
| Sensitivity: 98.2% | ||||
| PPV: 93% | ||||
| Iwendi et al. [59] | NA | Random Forest algorithm | NA | Accuracy: 94% |
| boosted by the AdaBoost algorithm | F1-score: 86% | |||
| Haghanifar et al. [37] | 780 images | DenseNet-121 | 75–25% | Accuracy: 87.21% |
| U-Net | ||||
| Oh et al. [38] | 502 images | DenseNet | 80–20% | Accuracy: 91.9% |
| U-Net | ||||
| Tartaglione et al. [39] | 137 images | ResNet | 70–30% | Accuracy: 85% |
| Proposed Method | 1332 images | CNN-SVM + Sobel | 10-fold | Accuracy: 99.02% |
| Sensitivity: 100% | ||||
| Specificity: 95.23% | ||||
| AUC: 0.9770 |