Table 4.

Performance of our method and state-of-the-art methods.

Study	Key aspects	Performance
Our method	-Lung segmentation	Accuracy = 0.971
	-Selection of slices with lesions	Sensitivity = 0.959
	-Slice-level prediction	Specificity = 0.981 AUC = 0.992
	-Patient-level prediction
	-157 patients (COVID-19: 57; CAP: 100)
	-Binary classification (COVID-19 or CAP)
Qi et al., 2021 [27]	-Deep features extracted by ResNet50	Accuracy = 0.959 Sensitivity = 0.972
	-241 patients (COVID-19: 141; CAP: 100)	Specificity = 0.941
	-Binary classification (COVID-19 or CAP)	AUC = 0.955
Javaheri et al., 2021 [41]	-Training a subset of the control dataset model	Accuracy = 0.933
	-Feed all the datasets into the trained model	Sensitivity = 0.909
	-Classifying the given CT images	Specificity = 1.00
	-335 CT images (COVID-19: 111; CAP: 115; Normal: 109)	AUC = 0.94
Song et al., 2020 [42]	-BigBiGAN framework is used for semantic feature extraction	Sensitivity = 0.92
	-Linear classifier is constructed using the semantic feature matrix	Specificity = 0.91
	-201 CT images (COVID-19: 98; non-COVID-19 pneumonia: 103)	AUC = 0.972
Basset et al., 2021 [43]	-Lung segmentation using Bi-convGRU	Accuracy = 0.968
	-Pretrained EfficientNet-b7 is used to obtain features	AUC = 0.988
	-Attention modules are used to learn multi-scale features for lesion localization
	-305 CT images (COVID-19: 169; CAP: 60; Normal: 76)
Ouyang et al., 2020 [26]	-VB-Net toolkit for lung segmentation	Accuracy = 0.875
	-Two 3D ResNet34 networks	Sensitivity = 0.869
	-Online attention module and ensemble learning	Specificity = 0.901
	-3645 CT images (COVID-19: 2565; CAP: 1080)	AUC = 0.944
	-Binary classification (COVID-19 or CAP)