Table 9.
Benchmarking of the proposed CADx study against the existing work in COVID classification and characterization
| R# | C1 | C2 | C3 | C4 | C5 | C6 |
|---|---|---|---|---|---|---|
| Author | AI-Model ML vs. DL | Data Size | Accuracy | COVID Severity |
COVID Disease Characterization |
|
| R1 | Zhang et al. [21] (2020) | TL-based 3-D ResNet 18 | 617,775 CT lung images from 4154 patients | 92.49% (AUC: 0.9813) | X | X |
| R2 | Wang et al. [22] (2020) | TL-based DenseNet-like structure | 5372 patients with CT images | 80.12% (AUC:0.90) | X | X |
| R3 | Oh et al. [23] (2020) | TL-based FC-DenseNet103 | 13,645 patients | 88.9% | X | X |
| R4 | Yang et al. [24] (2020) | TL-based DenseNet | healthy person: 149; COVID-19 patients: 146 | 92% (AUC: 0.98) | X | X |
| R5 | Wu et al. [25] (2020) | TL-based ResNet50 | 1018 patients (375,590 CT images) | 98.23% (AUC: 0.9971) | X | X |
| R6 | Proposed Study | ML-based classifier: RF | 990 Controls and 705 COVID | 99.41 ± 0.62% (AUC: 0.988) (p < 0.0001) | Block Imaging | Block Imaging, Bispectrum, Entropy |
| R7 | Proposed Study | DL-based systems: CNN | 990 Controls and 705 COVID | 99.41 ± 5.12% (AUC: 0.991) (p < 0.0001) | Block Imaging | Block Imaging, Bispectrum, Entropy |
TL Transfer learning, ML machine learning, RF Random Forest, DT Decision Tree, k-NN k Nearest Neighbor, AUC area under the curve.