Table 4.

Summary of performance of ML models applied for detection of COVID‐19 using Chest X‐ray

Reference	Number of samples	Pre‐processing data	Model's name (ML techniques)	Purpose	ACC/AUC	Sen (%)	Spec (%)
39	127 COVID‐19 images	Not provided	DarkCovidNet (Darknet‐19)	Binary classification:
	500 PNA images			COVID‐19 vs. no‐findings	98.08%	95.13	95.3
	500 no‐findings images			Multiclass classification:
				COVID‐19 vs. healthy vs. other PNA	87.02%	85.35	92.18
98	250 COVID‐19	Resize to 224 × 224 pixels	VGGNet (VGG‐16)	Model 1: healthy vs. COVID‐19 and pulmonary	96%	96	98
	2753 other pulmonary diseases			Model 2: COVID‐19 vs. other pulmonary	98%	87	94
	3520 healthy controls
54	295 COVID‐19 images	Not provided	MobileNetV2,	Multiclass classification:
	98 PNA images		SqueeaNet,	COVID‐19 vs. healthy vs. other PNA	99.27%	100	99.07
	65 normal class images		SVM.
99	162 COVID‐19 images	Horizontal flip, width and heigh shift range (0.2), rotation angle	DL‐based decision‐tree	Binary classification:
	492 TB images			COVID‐19 vs. TB	100%/1.00	100	100
	585 Normal images			COVID‐19 vs. non‐ TB	89%/0.89	93	86
55	219 COVID‐19	Rotate and flip augmentation approaches on training data	CNN, SVM, KNN, Decision Tree.	Multiclass classification:
	1341 normal			COVID‐19 vs. other viral PNA vs. normal	98.97%	89.39	99.75
	1345 viral PNA.
100	180 COVID‐19; 20 viral PNA; 57 TB; 54 bacterial PNA; 191 normal	Normalization	FC ‐DenseNet103	Multiclass classification:
		Segmentation	ResNet‐18	Viral PNA including COVID‐19 vs. bacterial PNA vs. TB vs. normal	88.9%	89.5	96.4
101	231 COVID‐19 images	Resize to 128 × 128 width and height shift, shift range (0.2), and horizontal flip	CapsNet (5 fully convolutional layers)	Binary classification:
	1050 no‐findings images			COVID‐19 vs. healthy	97.24%	97.42	97.04
	1050 PNA images			Multiclass classification:
				COVID‐19 vs. other PNA vs. healthy	84.22%	84.22	91.79
102	219 COVID‐19; 1345 viral PNA;1341 normal		CVDNet (CNN)	Multiclass classification:
				COVID‐19 vs. other PNA vs. healthy	96.69%	96.84	–
92	284 COVID‐19 images	Resize to 224 × 224 pixels with a resolution of 72 dpi	CoroNet (Xception CNN)	Binary classification: COVID‐19 vs. normal	99%	99.3	98.6
	330 bacterial PNA images			Multiclass classification:	89.6%	89.92	96.4
	327 viral PNA images			COVID‐19 vs. bacterial PNA vs. viral PNA vs. healthy controls
	310 normal images			COVID‐19 vs. healthy vs. other PNA	95%	96.9	97.5
103	142 COVID‐19 images	Resize to 224 × 224 pixels, horizontal and vertical flipping	nCOVnet (CNN)	Binary classification:
	142 normal images			COVID‐19 vs. normal	88.10%	97.62	78.57
104	105 samples COVID‐19	Flipping, translation and rotation, a histogram modification technique	DeTraC (pretrained CNN)	Multiclass classification:
	11 samples SARS			COVID‐19 vs. normal vs. SARS	93.1%	100	85.18
	80 samples normal
105	364 images COVID‐19	The image contrast enhancement algorithm (ICEA)	MH‐CovidNet (DL, BPSO and BGWO meta‐heuristic algorithms, SVM)	Multiclass classification:
	364 images PNA			COVID‐19 vs. healthy vs. other PNA	99.38%	–	–
	364 images normal.
21	423 images COVID‐19	Resize, rotation, and translation	pre‐trained CNN algorithms	Binary classification: COVID‐19 vs. normal	99.70%	99.70	99.55
	1485 images viral PNA			Multiclass classification:	97.94%	97.94	98.80
	1579 images normal			COVID‐19 vs. normal vs. other viral PNA
53	184 images COVID‐19	Flipping and rotation	ResNet18,	Binary classification:
	5000 images Non‐COVID‐19		ResNet50, SqueezeNet, DenseNet‐161	COVID‐19 vs. No‐COVID‐19	0.992	98	92.9

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.