Table 8.
Comparison of the results with state of the art DL networks with CT images
| Reference | Methods | Accuracy (%) | Sensitivity (%) | Specificity (%) | F1 score (%) | PPV (%) | NPV (%) |
|---|---|---|---|---|---|---|---|
| [8] | ResNet-50 | - | 90 | 96 | - | - | - |
| [12] | CGAN | 82.91 | - | - | - | - | - |
| [18] | Ensemble CNN | 86 | - | - | 86.7 | - | - |
| [19] | CNN-Resnet-18 | 86.7 | - | - | - | - | - |
| [22] | CNN-Ensemble | - | - | - | 92.2 | - | |
| [24] | DL | 90.8 | 84 | 93 | - | - | - |
| [27] | CNN | - | 98.2 | 92.2 | - | - | - |
| [38] | CNN | 94.98 | 94.06 | 95.47 | - | - | - |
| [39] | CNN-Resnet-50 | - | 93 | - | - | - | - |
| Proposed | Optimized GAN based InceptionV3 | 99.22 | 99.78 | 97.78 | 98.79 | 97.82 | 99.77 |