Table 12.

Literature review of the state–of–the-art deep models using X-ray images on associated datasets (PNA stands for pneumonia).

Study	No. of cases	Method	Accuracy (%)
Ozturk et al. [5]	125 COVID-19	DarkCovidNet
	500 No-finding		98.08
	125 COVID-19
	500 Pneumonia
	500 No-finding		87.02
Tabik et al. [6]	426 COVID-19	COVID-SD Net	97.72
Tabik et al. [6]	426 Normal	COVID-SD Net	97.72
Rahman et al. [8]	3619 COVID-19	ChexNet	96.29
	8851 Normal
	6012 Pneumonia
Toğaçar et al. [10]	295 COVID-19	MobileNetV, SVM	96.28
	65 Normal
	98 Pneumonia
Ioannis et al. [14]	224 COVID-19	VGG-19	93.48
	700 Pneumonia
	504 Healthy
Karakanis et al. [12]	275 COVID-19	ResNet, CGAN
	275 Normal		98.7
	275 COVID-19
	275 Normal
	275 Bacterial PNA		98.3
Wang and Wong [13]	53 COVID-19	COVID-Net	92.4
Wang and Wong [13]	8066 Healthy	COVID-Net	92.4
Sethy and Behra [17]	25 COVID-19	ResNet 50, SVM	95.38
Sethy and Behra [17]	25 No-finding	ResNet 50, SVM	95.38
Jain et al. [9]	COVID-19	Xception	97.97
	Normal
	Pneumonia
Jin et al. [15]	543 COVID-19	AlexNet	98.64
	600 Normal
	600 Viral PNA
Hemdan et al. [11]	25 COVID-19	COVIDX-Net	90.00
Hemdan et al. [11]	25 No-finding	COVIDX-Net	90.00
Narin et al. [16]	50 COVID-19	Res-Net 50	98.00
Narin et al. [16]	50 No-finding	Res-Net 50	98.00
Mahmud et al. [7]	305 COVID-19	CovXNet
	305 Normal		97.4
	305 COVID-19
	305 Normal
	305 Viral PNA
	305 Bacterial PNA		90.3
Minaee et al. [18]	184 COVID-19	SqueezeNet	92.30
	5000 Non-COVID
	6054 Pneumonia
Abbaas et al. [28]	80 Covid-19	DeTraC	93.10
	105 Normal
	11 SARS
Tahir et al. [27]	423 COVID-19	InceptionV3	97.73
	134 SARS
	144 MERS
Proposed method (Transferred deep features of VGG-Net, deep feature fusion, the LMPL classifier)	423 COVID-19	VGG-Net19
	1341 Normal
	1345 Viral PNA		99.39
	423 COVID-19
	134 SARS
	144 MERS		98.86