Table 1.
Related surveys on AI techniques for detecting COVID-19 from radiography images
| Study | Key topics | No. of reviewed papers1 | No. of discussed datasets2 | Benchmarking deep models3 |
|---|---|---|---|---|
| Ulhaq et al. [12] | Vision-based diagnosis, control and treatment | 21 | 6 | – |
| Pham et al. [14] | AI and big data-based diagnosis, outbreak prediction, and biomedicine | 32 | 2 | – |
| Shi et al. [13] | AI-based image acquisition, segmentation, and diagnosis | 14 | 4 | – |
| Kalkreuth et al. [17] | COVID-19 dataset listing | 4 | 12 | – |
| Latif et al. [16] | AI-based COVID-19 diagnosis, pandemic modeling, dataset description, and bibliometric analysis | 25 | 5 | – |
| Nguyen [18] | AI-based COVID-19 diagnosis, modeling, text mining, and dataset description | 12 | 10 | - |
| Mohamadou et al. [15] | Mathematical modeling of pandemic and COVID-19 diagnosis | 20 | 6 | – |
| Our study | Deep learning–based COVID-19 diagnosis | 38 | 16 | Benchmarked 315 deep models that comprises the combinations of 15 CNNs and 21 classifiers |
1Diagnosis-related papers, 2Radiography-based datasets, 3“-” means not applicable for the paper
The Italic entries signify our contributions