Table 4.
Benchmarking table.
| C0 | C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | C11 | C12 | C13 | C14 | C15 | C16 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SN | Author | Year | TP | TS | IS2 | TM | DL Model | Modality | XAI | Heatmap Models | AUC | SEN | SPE | PRE | F1 | ACC |
| 1 | Lu et al. [140] | 2021 | 2482 | 100 to 500 | 5 | CGENet | CT | ✗ | Grad-CAM | ✗ | 97.9 | 97.7 | 97.7 | 97.8 | 97.8 | |
| 2 | Lahsaini et al. [141] | 2021 | 177 | 4968 | ✗ | 6 | Transferred DenseNet201 | CT | ✗ | Grad-CAM | 0.988 | 99.5 | 98.2 | 97.8 | 98 | 98.2 |
| 3 | Zhang et al. [143] | 2021 | 86 | 5504 | 1024(CT) 2048(X-Ray) | 8 | MIDCAN | CT, X-ray | ✗ | Grad-CAM | 0.98 | 98.1 | 98 | 97.9 | 98 | 98 |
| 4 | Monta et al. [144] | 2021 | 9208 | 299 | 7 | Fused-DenseNet-Tiny | X-ray | ✗ | Grad-CAM | ✗ | ✗ | ✗ | 98.4 | 98.3 | 98 | |
| 5 | Proposed Suri et al. |
2022 | 80 | 5000 | 512 | 3 | DenseNet-121 DenseNet-169 DenseNet-201 |
CT | ✓ | Grad-CAM Grad-CAM++ Score-CAM FasterScore-CAM |
0.99 0.99 0.99 |
0.96 0.97 0.98 |
0.975 0.98 0.985 |
0.96 0.97 0.98 |
0.96 0.97 0.98 |
98 98.5 99 |
TP: total patients; TS: total slice; IS: image size; TM: total models; AUC: area under the curve; SEN (%): sensitivity (or recall); SPE (%): specificity; PRE (%): precision; ACC (%): accuracy.