Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence

Table 1

COVID-19/normal classification results. Class.: classification; bac. pneu.: bacterial pneumonia; Sens.: sensitivity; Spec.: specificity; Prec.: precision; Acc.: accuracy; AUC: area under the curve; Ref.: reference.

Class.	Subjects	Dataset	Method	Sens. (%); or recall	Spec. (%)	Prec. (%)	Acc. (%)	AUC (%)	F1-score	Ref.
COVID-19/normal	178 pneumonia; 247 normal	Private +; [21–23]	DL; IRRCNN	N/A	N/A	N/A	98.78	N/A	98.85	Alom et al. [9]; Preprint
COVID-19/normal	521 COVID-19; 397 normal; 76 bac. pneu.; 48 SARS	[24–26]	DL; ShuffleNet V2	90.52	91.58	N/A	91.21	96.89	N/A	Hu et al. [10]; Preprint
COVID-19/normal	106 COVID-19; 100 normal	Private +; [27, 28]	DL; ResNet50	98.2	92.2	N/A	N/A	99.6	N/A	Gozes et al. [12]; Preprint
COVID-19/normal	COVID-19: X-ray:117; CT:20; normal: X-ray:117; CT:20	[21, 22, 29]	DenseNet121; +; Bagging	99.00	N/A	99.00	99.00	N/A	99.00	Kassani et al. [13]; Preprint
COVID-19/normal	1,262 COVID-19; 1,230 normal	[23]	DenseNet201	96.29	96.21	96.29	96.25	97.0	96.29	Jaiswal et al. [20]; Peer-reviewed