Table 1.

Comparative study of literature related to COVID-19 detection and other chest-related diseases

Ref	Datasets	Objective	Models	Results
[1]	COVID-19 and SARS images [16], Normal images [10, 45].	To identify the COVID-19 using chest x-rays.	ResNet	Accuracy = 95.12%
[58]	COVID-19 images [16], Normal and Pneumonia images [50].	To classify the COVID-19 x-rays from the pneumonia-infected x-rays.	GoogleNet	Accuracy = 80.56%
[74]	COVID-19 images [16], Normal and Pneumonia images [51].	Using x-rays to automatically diagnose COVID-19 and pneumonia.	Xception + ResNet50V2	Accuracy = 91.40%
[71]	COVID-19 images [16], Normal and Pneumonia images [46].	To diagnose pneumonia and coronavirus infected images.	Patch based CNN	Accuracy = 88.90%
[105]	COVID-19 images [16], Normal images [97].	Classification of COVID-19 positive and healthy images.	18-layer residual CNN	Accuracy = 72.31%
[4]	COVID-19 images [16, 21], Normal and Pneumonia images [50]	Classification of COVID-19, pneumonia, and healthy images.	MobileNet-V2	Accuracy = 96.78%
[94]	COVID-19 images [16], Normal and Pneumonia images [50, 97].	To detect pneumonia, COVID-19, and normal images.	Inception V3	Accuracy = 76.0%
[83]	COVID-19 images [16, 21], Normal and Pneumonia images [50].	To classify COVID-19, pneumonia, and normal images using chest x-rays.	Resnet50 + SVM	Accuracy = 95.33%
[32]	Normal and Pneumonia images [97].	To detect pneumonia (normal, bacterial, and viral) cases from chest X-rays	CNN	Accuracy =95.72%
[25]	Normal and Pneumonia images [50, 97].	To detect and evaluate pneumonia (bacterial, viral, COVID-19 and normal)	CNN	Accuracy =94.84%