Table 4.

Comparison with state of the art methods

Study	Dataset	Model description	Classification accuracy (%)
Narin et al. (2020)	2-class: 50 COVID-19/50 normal	Transfer learning with Resnet50 and InceptionV3	91.13
Panwar et al. (2020)	2-class 142 COVID-19/142 normal	nCOVnet CNN	88
Altan et al. (Altan & Karasu, 2020)	3-class: 219 COVID-19 1341 normal, 1345 pneumonia viral	2D curvelet transform, chaotic salp swarm algorithm (CSSA), EfficientNet-B0	91
Chowdhury et al. (2020)	3-class, 423 COVID-19, 1579 normal, 1485 pneu monia viral	transfer learning with ChexNet	92.70
Wang and Wong (Wang et al., 2020b)	3-class, 358 COVID-19/5538 normal/8066 pneumonia	COVID-Net	93.30
Das et al. (2020)	3-class: 62 COVID-19/1341 normal/1345 pneumonia	ResNet features and XGBoost classifier	90
Sethy and Behera (2020)	3-class: 127 COVID-19/127 normal/127 pneumonia	Resnet50 features and SVM	92.33
Ozturk et al. (2020)	3-class: 125 COVID-19/500 normal 500 pneumonia	DarkCovidNet CNN	87.20
Khan et al. (2020)	4-class: 284 COVID-19/310 normal/330 pneumonia bacterial/327 pneumonia viral	CoroNet CNN	89.60
Mahmud et al. (2020)	4-class: 305 COVID-19 + 305 normal + 305 viral, pneumonia + 305 bacterial pneumonia	Stacked multi-resolution CovXNet	90.30
Al-Timemy et al. (2020)	5-class, 435 COVID-19/439 normal/439 pneumonia bacterial/439 pneumonia viral/434 tuberculosis	Resnet50 features and ensemble of subspace discriminant classifier	91.6
Proposed framework	5-class, 435 COVID-19/439 normal/439 pneumonia bacterial/439 pneumonia viral/434 tuberculosis	Multi-scale features CoVIRNet	93.28
Proposed frame- work + random forest	5-class, 435 COVID-19/439 normal/439 pneumonia bacterial/439 pneumonia viral/434 tuberculosis	Multi-scale features CoVIRNet	96.17