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| Author | Images type | AI methods | Task | Results |
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| Nayak et al. [5] | Chest X-ray (CXR) images | AlexNet, GoogLeNet, MobileNet-V2, SqueezeNet, VGG-16, ResNet-50, ResNet-34, and Inception-V3 | Classification of COVID-19 from normal cases | Accuracy of ResNet-34 is 98.33% |
| Shorfuzzaman and Hossain [6] | CXR images | VGG-16 network | Classification of COVID-19 cases | Accuracy is 95.6% and AUC is 0.97 |
| Linda [7] | CXR images | A deep CNN, namely, COVID-Net | Detection of COVID-19 cases | Accuracy is 92.4% |
| Rahman et al. [8] | CXR images | Novel U-Net model | Automatic detection of COVID-19 | Accuracy is 95.11% |
| Jin et al. [9] | CT images | 2D deep CNN | Rapid COVID-19 detection | Accuracy is 94.98% and AUC is 97.91% |
| Narin et al. [10] | CXR images | Pretrained ResNet-50 | Detection of coronavirus pneumonia-infected patient | Accuracy is 98% |
| Chowdhury et al. [11] | CXR images | AlexNet, ResNet-18, DenseNet201, and SqueezeNet | Automatic detection of COVID-19 pneumonia | Accuracy is 98.3% |
| Maghdid et al. [12] | CXR images and CT images | A new CNN and pretrained AlexNet with transfer learning | Effective COVID-19 detection technique | Accuracy is 98% on X-ray images and 94.1% on CT images |
| Gour and Jain [13] | CXR images | Multiple CNN models | Classification CT samples with COVID-19, influenza viral pneumonia, and no infection. | Accuracy is 96%, sensitivity is 98.2% and specificity is 92.2%. |
| Kang et al. [14] | CT images | KNN as well as NB | Automatic analysis pipeline for COVID-19 | Accuracy 95%, sensitivity 93.2%, specificity 96.6% |
| Khanday et al. [15] | Clinical data | Multinomial naive bayes and logistic regression | Identifying pandemic with clinical text information | Accuracy is 96.2% |
| Sethy et al. [16] | CXR images | CNNs with the help of support vector machine (SVM) | Detecting the COVID-19 disease | Accuracy is 95.38% |
| Alakus and Turkoglu [17] | CXR images | CNN based LSTM, CNN-RNN | Analyzing the COVID-19 | Accuracy 86.66%, precision 86.75%, recall 99.42% |
| Rasheed et al. [18] | CXR images | CNN and logistic regression | Diagnosis of COVID-19 | Accuracy is 97.6 for CNN and 100% for LR |
| Gao et al. [19] | CT images | Dual-branch combination network (DCN) | Accurate diagnosis and lesion segmentation of COVID-19 | Accuracy is 96.74% on the internal dataset and 92.87% on the external dataset |
| Goel et al. [20] | CXR images | Optimized convolutional neural network (OptCoNet) | Automatic diagnosis of COVID-19 | Accuracy is 97.78% |
| Nour et al. [21] | CXR images | SVM | Detection of COVID-19 infection | Accuracy 98.97% sensitivity 89.39% specificity 99.75% |
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