Table 7.
Distinct mechanisms applied to identify Covid-19 individuals via chest X-ray and CT scan images.
| Reference | Category of Chest Images | Mechanism | Accuracy (%) |
|---|---|---|---|
| Oh et al. [28] | X-ray | Patch-based CNN | 88.9 |
| S. Wang et al. [29] | CT Scan | Modified Inception Transfer Learning | 92.4 |
| Loey et al. [30] | X-ray | Googlenet | 80.56 |
| Hemdan et al. [31] | X-ray | CovidX- Net | 90 |
| Rahimzadeh et al. [32] | X-ray | Xception+ ResNet-50V2 |
91.4 |
| Ali et al. [33] | CT scan | Alexnet | 82.6 |
| Wang et al. [34] | X-ray | COVID-Net | ‘93.3 |
| Zhang et al. [35] | X-ray | CNN | 96 |
| Horry et al. [36] | X-ray | VGG 19 | 81 |
| Tsiknakiset al. [37] | X-ray | Inception V3 | 92 |
| Vinod et al. [38] | X-ray CT Scan |
Decision Tree | 87 82 |
| Mei et al. [39] | CT Scan | CNN MLP Joint |
79.6 74.2 83.5 |
| Mizuho et al. [40] | X-ray | Computer-aided diagnosis | 83.6 |
| Deep Covix-net model (Proposed Method) |
X-ray CT Scan |
FFT + DWT + GLCM+ GLDM+ Texture |
96.8 97 |