Table 7.
Comparison of the proposed technique with other deep learning techniques for COVID-19 diagnostic using chest X-ray images
| Study | No. of cases | Architecture | Data set | Accuracy |
|---|---|---|---|---|
| Ioannis et al. [5] | 224 COVID-19, 700 Pneumonia, 504 Healthy | VGG-19 | [4, 11, 24] | 98.75 |
| Wang et al. [61] | 53 COVID(+), 5526 COVID(−), 8066 Healthy | COVID-Net | [11, 32, 54, 59] | 92.4 |
| Ozturk et al. [40] | 125 COVID-19, 500 Pneumonia, 500 No finding | Dark COVID-Net | [11] | 98 |
| Asif et al. [25] | 1300 images of COVID-19, normal, pneumonia | CoroNet | [11, 43] | 95 |
| Tougaccar et al. [55] | 295 COVID-19, 98 Pneumonia, 65 No findings | MobileNetV2 | [11, 54] | 99.27 |
| Narin et al. [38] | 50 COVID-19, 50 No findings | ResNet-50 | [11] | 98 |
| Hemaden et al. [18] | 25 COVID-19, 25 No findings | VGG-19, DenseNet-121 | [11] | 90 |
| Sethy et al. [48] | 25 COVID-19, 25 No findings | ResNet-50 | [11] | 95.38 |
| Toraman et al. [56] | 1050 COVID, 1050 No finding | CapsNet | [11, 62] | 97.24 |
| Panwar et al. [41] | 192 COVID-19, 145 No findings | nCOVnet | [11] | 97.62 |
| Ucar et al. [58] | 76 COVID-19, 1583 normal, 4290 pneumonia | Bayes-SqeezeNet | [11, 23] | 98.3 |
| Proposed | 219 COVID-19, 219 Viral Pneumonia, 219 Normal | Augmented | [11, 54] |