Table 5.

Success rates of models according to different metrics in detecting Covid-19 on the Covid-CT and Sars-Cov-2 datasets containing CT images.

Model	Accuracy	Precision	Recall	F1-Score	Specificity	AUC (%)
DenseNet	92.09	0.92	0.92	0.92	92.09	92.09
AlexNet	86.51	0.87	0.86	0.87	86.49	86.49
ResNet	87.44	0.88	0.97	0.87	87.47	87.47
CspNet [98]	85.58	0.86	0.86	0.86	85.53	85.53
VGG16	50.39	0.25	0.50	0.34	50.00	50.00
VGG19	50.39	0.25	0.50	0.34	50.00	50.00
CovXNet [42]	88.99	0.89	0.89	0.89	89.03	89.03
CoroNet [40]	92.25	0.92	0.92	0.92	92.26	92.26
CovidXrayNet [41]	91.16	0.92	0.91	0.91	91.12	91.12
DarkCovidNet [39]	88.92	0.88	0.87	0.87	88.92	88.92
Proposed (No DDC)	91.63	0.92	0.92	0.92	91.62	91.62
Proposed+ DataAug.	86.36	0.86	0.86	0.86	86.33	86.33
Proposed (No GB)	93.33	0.93	0.93	0.93	93.31	93.31
Proposed(CovidDWNet+GB)	99.84	1.0	1.00	1.00	99.85	99.85