Table 1. The summary of various deep learning algorithms for COVID-19 lung CT scans and the segmentation results.
RVE, ACC, DC, Sen, Sp and F1 represent relative volume error, accuracy, Dice coefficient, sensitivity, specificity and F1 score, respectively.
| Literature | Data Type | Dataset | Technique | Segmentation results |
|---|---|---|---|---|
| Fan et al. (2020) | CT Scan | 100 CT images | Semi supervised CNN | 73.9% (DC) |
| FCN8s network | 96.0% (Sp) | |||
| Wang et al. (2020b) | CT Scan | 558 CT images | Residual connection | 80.7% (DC) |
| CNN | 16.0% (RVE) | |||
| Yan et al. (2020) | CT Scan | 21,658 CT images | Deep CNN | 72.6% (DC) |
| 75.1% (Sen) | ||||
| Zhou, Canu & Ruan (2020) | CT Scan | 100 CT images | Attention mechanism | 69.1% (DC) |
| Res-Net, dilation convolution | 81.1% (Sen) | |||
| Elharrouss et al. (2020) | CT Scan | 100 CT images | Encoder-decoder-based CNN | 78.6% (Dice) |
| 71.1% (Sen) | ||||
| Chen, Yao & Zhang (2020) | CT Scan | 110 CT images | Encoder-decoder-based CNN | 83.0% (DC) |
| 89.0% (ACC) | ||||
| Xu et al. (2020) | CT Scan | 110 CT images | CNN | 86.7% (ACC) |
| 83.9% (F1) | ||||
| Shuai et al. (2020) | CT Scan | 670 CT images | CNN | 73.1% (ACC) |
| 67.0% (Sp) |