Table 3.

Summary of performance of ml models applied for COVID‐19 detection using CT images

Reference	Number of samples	Segmentation technique	Model's name (ML techniques)	Purpose	ACC/AUC	Sen (%)	Spec (%)
71	4352 scans from 3322 patients (1292 COVID‐19, 1735 CAP, and 1325 non‐PNA)	U‐Net	COVNet (3D RestNet50)	COVID‐19 detection	0.96	90	96
				CAP detection	0.95	87	92
				Non‐PNA detection	0.98	94	96
72	419 cases positive COVID‐19	CNN	CNN, SVM, random forest and MLP	Prediction of COVID‐19 probability	0.92	84.3	82.8
	486 cases negative COVID‐19
51	COVID‐19 data set: 924 COVID‐19, and 342 other PNA	DenseNet121‐FPN	COVID‐19Net (DenseNet‐like structure)	Prediction of COVID‐19 probability	80.12%/0.88	79.35	81.16
	CT‐EGFR data set: 4106 lung cancer patients			COVID‐19 vs. other PNA	85%/0.86	79.35	71.43
73	1029 scans of 922 COVID‐19 patients	AH‐Net architecture	Densnet‐121	COVID‐19 detection from other clinical entities	89.6%/0.941	84.5	91.6
	1695 scans of 1695 cancer, CP, and any clinical indication patients
74	1194 CTs of 80 COVID‐19 patients	Not performed	FCONet (pre‐trained DL models)	Multi‐class classifier COVID‐19 vs. other PNA vs. no PNA	99.87%	99.58	100
	1357 CTs of 100 other PNA patients
	1442 CTs of 126 normal and lung cancer patients
75	150 3D CT scans of COVID‐19	multi‐view U‐Net	VGG architecture	Binary classification:
	150 scans CAP			COVID‐19 vs. normal	96.2%/0.970	94.5	95.3
	150 scans normal
				COVID‐19 vs. CAP	89.1%/0.906	87.0	86.2
17	835 COVID‐19 patients	DeepLabv3	3D ResNet‐18	Binary classification:
	888 other PNA patients			COVID‐19 vs. No‐COVID‐19	92.49%/0.9797	94.93	91.13
	783 normal cases			Multi‐class classification:		–	–
				COVID‐19 vs. normal vs. other PNA	92.49%/0.9813
44	230 CT scans from 79 patients with COVID‐19	Not performed	AD3D‐MIL	Binary classification: COVID‐19 vs. normal & CP	97.9%	–	–
	100 CT scans from 100 CP patients			Multi‐class classification: COVID‐19 vs. CP vs. normal	94.3%	–	–
	130 CT scans from 130 normal cont
47	1495 COVID‐19 patients	VB‐Net	AFS‐DF	Binary classification:
	1027 CAP patients			COVID‐19 vs. CAP	91.79%/0.963	93.05	89.95
46	1495 scans with COVID‐19	V‐Net	multi‐view ML technique	Binary classification:
	1027 scans with CAP			COVID‐19 vs. CAP	95.5%	96.6	93.2
76	3389 scans with COVID‐19 from 2565 patients	VB‐Net toolkit	Attention 3D ResNet34 + sampling strategy	Binary classification:
	1593 scans with CAP from 1080 patients			COVID‐19 vs. CAP	87.5%/0.944	86.9	90.1
77	132 583 CT slices from 1186 patients:	Manually and three‐dimensional Slicer software	EfficientNet B4	Binary classification: COVID‐19 vs. other PNA
	521 COVID‐19 patients			Test data set of 119 patients	96%/0.95	95	96
	665 non‐COVID‐19 patients			Test data set of 395 patients	91%/0.95	94	87
78	313 positive COVID‐19	U‐Net	DeCoVNet (AlexNet, ResNet)	Predicting COVID‐19 probability	0.959	90.7	91.1
	229 negative COVID‐19
79	219 scans from 110 COVID‐19 patients	3D CNN (V‐Net, IR, RPN)	ResNet‐based	Multi‐class classification:			–
	224 IAVP patients. 175 healthy			COVID‐19 vs. IAVP vs. healthy	86.7%	–	–
37	416 CT scans from 206 COVID‐19 patients	Not performed	MSCNN	Binary classification:
	412 CP patients			COVID‐19 vs. CP at slice level	97.7%/0.962	99.5	95.6
				COVID‐19 vs. CP at scan level	87.1%/0.934	89.1	85.7

Abbreviations: ACC, accuracy; AD3D‐MIL, attention‐based deep 3D multiple instance learning; AFS‐DF, adaptive feature selection guided deep forest; AUC, area under the receiver‐operating characteristics curve; CAP, community‐acquired pneumonia; CP, common pneumonia; IAVP, influenza‐A viral pneumonia; MLP, multilayer perceptron; MSCNN, multiscale convolutional neural network; PNA, pneumonia; Sen, sensitivity; Spec, specificity; SVM, support vector machine.