Table 7.
Evaluation of the proposed severity detection method with state-of-the-art methods.
| Author | Class | Subject | Method | Performance metric | Value |
|---|---|---|---|---|---|
| Changati et al. | Lung infection percentage | 9749 chest CT volume | Deep learning, deep reinforcement learning Ref. [2] | Pearson correlation coefficient | 0.92 for percentage of opacity (P < 0.001) |
| Shen et al. | Severe/non severe | CT images from 44 patients | Thresholding and adaptive region growing Ref. [26] | Pearson correlation coefficient | r ranged from 0.7679 to 0.837, P < 0.05 |
| Xiao et al. | Severe/non severe | 23,812 Covid-19 images | ResNet-34 Ref. [24] | Precision AUC | 81.3% 98.7% |
| Shan et al. | Segment and quantify infection regions | 549 CT volumes | VBNet Ref. [27] | Dice similarity coefficient | 91.6%10.0 |
| Pu et al. | COVID-19 severity and progression | 72 COVID-19 and 120 others volumes | UNetBER Algorithm Ref. [25] | Sensitivity Specificity |
95%84% |
| Tang et al. | Severe/non severe | Chest CT images of 176 patients | Random forest algorithm Ref. [28] | Accuracy | 87.5% |
| Proposed method | Covid 19 severity (High, Moderate, Low) | COVID-CT data set (public) (349 images with metadata) | ResNet-50 and Densenet-201 algorithm | Sensitivity SpecificityPrecision Accuracy |
95.23%97.41%95.3%97.84% |