Table 5.
Comparison with the other state-of-the-art methods for COVID-19 binary and multi-classification
| Authors | Number of cases (COVID-19) | Data Format | Methodology | Two-class accuracy (%) | Three-class accuracy (%) | Sensitivity (%) |
|---|---|---|---|---|---|---|
| Narin et al. [20] |
Total cases = 100 COVID-19 = 50 |
X-ray | ResNet50 | 98 | – | 96 |
| Khan et al. [18] |
Total cases = 221 COVID-19 = 29 |
X-ray | Xception | 98.8 | 94.52 | 95 |
| Wang et al. [13] |
Total cases = 237 COVID-19 = 119 |
CT scan | Modified Inception | 82.9 | – | 81 |
| Wang et al. [172] |
Total cases = 300 COVID-19 = 100 |
X-ray | COVID-Net | 96.6 | 93.3 | 91 |
| Song et al. [173] |
Total cases = 57 COVID-19 = 30 |
CT scan | ResNet50 | 86 | – | 79 |
| Ozturk et al. [8] |
Total cases = 1127 COVID-19 = 127 |
X-ray | DarkCOVIDNet | 98.08 | 87.02 | 90.6 |
| Rahimzadeh and Attar [174] |
Total cases = 11,302 COVID-19 = 31 |
X-ray | ResNet50V2 + Xception | 99.5 | 91.4 | 80.53 |
| Apostolopoulos et al. [10] |
Total case = 1427 COVID-19 = 224 |
X-ray | MobileNetV2 | 96.7 | 93.5 | 98.6 |