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| Authors | Database used | Obtained results | Highlights |
|
| Tang et al. [23] | 247 COVID-19 patients and 152 other pneumonia patients | In the algorithm model, the average diagnosis time per person has been reduced to 0.4 s. | It has high application value. |
| Jiang and Xu [24] | CT images of patients diagnosed with COVID-19 in Zhongnan hospital | The sensitivity of the intelligence-assisted diagnosis model is 96%. | Comprehensive diagnosis accuracy is high. |
| Umri et al. [25] | GitHub and Kaggle website | The accuracy is 98%. | Compared with VGG-16, the effect of CNN is better and significant. |
| Gomes et al. [26] | Kaggle website | The average accuracy is 89.78%; the average sensitivity is 89.79%. | Computing costs are lower than those using deep learning techniques. |
| Narin [27] | Kaggle website | The highest sensitivity value is 96.35%. | It is beneficial to reduce the doctors’ misdiagnosis rate. |
| Singh and Singh [28] | 6,500 chest X-rays | The overall accuracy is 95.83%. | It is used to diagnose COVID-19 from chest X-ray images. |
| Sivaramakrishnan et al. [29] | CXR images of children aged 1 to 5 years collected at Guangzhou Medical Center | The highest accuracy is 99.01%. | Weighted average performance significantly improves performance. |
| Hernandez et al. [30] | https://www.sirm.org/category/senza-categoria/COVID-19/ | The accuracy rate is about 90%. | It provides a completely new way of thinking. |
| Wang et al. [31] | Chest CT scans of 251 patients with corresponding voxel-grade lobes | The proposed method has an accuracy of 93.3%. | It detects the most accurate location of the lesion area. |
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