Table 5.
Performance comparison of the proposed method with others for identification of COVID-19 using chest x-ray image database.
| Small Dataset | |||||
|---|---|---|---|---|---|
| Ref. | Models | Accuracy (%) | Precision (%) | Specificity %) | Sensitivity (%) |
| [46] | AlexNet | 99.00 | 98.00 | 99.00 | 99.00 |
| [43] | Covid-Net | 93.30 | 98.90 | – | 91.00 |
| [47] | Modified MobileNet | 95.00 | 99.00 | – | 96.00 |
| Our Work | ResNet50 | 98.82 | 98.65 | 98.66 | 98.98 |
| AlexNet | 98.82 | 99.16 | 99.15 | 98.50 | |
| Large Dataset | |||||
| Ref |
Models |
Accuracy (%) |
Precision (%) |
Specificity %) |
Sensitivity (%) |
| [44] | COVID-Net | 90.10 | 84.00 | – | 98.20 |
| DenseNet-201 | 91.75 | 94.24 | 78.00 | – | |
| [48] | ResNet50+SVM | 95.38 | – | 93.47 | 97.29 |
| [49] | ResNet-101 | 71.90 | – | 71.80 | 77.30 |
| [50] | XCOVNet | 98.44 | 99.29 | – | 99.48 |
| [51] | Xception | 91.00 | 92.00 | – | 87.00 |
| [52] | ResNet-50 | 98.00 | 94.81 | 98.44 | 87.29 |
| [53] | DenseNet-121 | 88.00 | – | 90.00 | 87.00 |
| [43] | Modified ResNet | 99.30 | – | – | 99.10 |
| [54] | XCOVNet | 88.90 | 83.40 | 96.40 | 85.90 |
| Our Work | ResNet50 | 95.67 | 95.37 | 95.40 | 95.94 |
| AlexNet | 93.62 | 93.95 | 93.91 | 93.34 | |