Table 1.
Related literature on X-ray for the purpose of COVID-19 identification.
| Author (Month, Year) | Number of Cases and Image | Training Model | VisualizeUsing Gard CAM/Other | Accuracy (%) |
|---|---|---|---|---|
| Krishnamraju K [45] | 1000 COVID-19 and 1000 normal | VGG16+ MobileNet | No | 97 |
| Mousavi Z [46] | 939 healthy cases, 800 COVID-19 and 942 viral pneumonia | Developed LSTM network | No | 90 |
| Luz [47] | 1000 COVID-19, 1000 normal and 1000 pneumonia | Efficient deep learning model | Yes | 93.9 |
| Al-Waisy [48] | 400 COVID-19 and 400 normal | COVID-CheXNet | Yes | 99.99 |
| Aslan [59] | 1341 normal, 219 COVID-19 and 1345 viral pneumonia | mAlexNet +BiLSTM | No | 98.14, for first architecture and 98.70 |
| Chen [49] | 3 datasets total 513 COVID-19 and1984 non-COVID-19 | VGG16 | Yes | 98 |
| Wang [50] | 266 COVID-19, 8,066 normal and 5,538 pneumonia | COVID-Net | Yes | 93.30 |
| Gupta [51] | 361 COVID-19, 1345 pneumonia and 1341 normal | InstaCovNet-19 | Yes | 99.08 for 3 class and 99.53 for 2 class |
| Arsenovic [60] | 434 COVID-19, 1100 normal and 1100 bacterial pneumonia | ResNetCOVID-19 | No | 94.10 |
| Ammar [52] | 150 COVID-19, 150 normal, and 150 pneumonia | MobileNetV2, ResNet50V2, ResNet152V2, Xception, VGG16 and DenseNet12 | No | Highest accuracy 91.28 for MobileNetV2 |
| Jain [53] | 490 COVID-19, 1345 normal and 3632 pneumonia | Xception net, Inception net V3 and ResNeXt, | No | Highest accuracy 97.97 for Xception |
| Mohammadi R [54] | 181 COVID-19 and 364 normal | pre-trained VGG16, InceptionResNetV2, MobileNet and VGG19 | No | Highest accuracy 99.1 MobileNet |
| Chowdhury [61] | 1341 normal, 219 COVID-19 and 1345 viral pneumonia | PDCOVIDNet | Yes | 96.58 |
| Turkoglu [55] | 219 COVID-19, 1583 normal and 4290 pneumonia | COVIDetectioNet | No | 99.18 |
| Makris [62] | 112 COVID-19, 112 normal and 112 pneumonia | 9 well-known pre-trained CNN model | No | 95 for the best two model (Vgg16 and Vgg19) |
| Ouchicha [56] | 1341 normal, 219 COVID-19 and 1345 viral pneumonia | CVDNet | No | 96.69 |
| Civit-Masot [63] | 132 COVID-19, 132 healthy and 132 pneumonia | VGG16 | No | 86.00 |
| Mahmud [64] | 1583 normal, 305 COVID-19, 1493 viral pneumonia, 2780 bacterial pneumonia | CovXNet | Yes | 90.2 accuracy for four class |
| Khan [57] | 1203 normal, 290 COVID-19 931 viral pneumonia, 660 bacterial pneumonia | CoroNet | No | Overall accuracy of 89.6 |
| Ozturk [20] | 125 COVID-19 cases, 500 no findings, 500 pneumonia cases | DarkCovid-Net | Yes | 98.08 for two class and 87.02 |