Table 7.

Summary of Deep learning models from the literature used for COVID-19 classification on similar dataset.

Selected Work	Model	Number of Images	Data source	Application
Ozturk et al. [56]	DarkNet	1125, 11.1% SARS-CoV-2, 44.4% Pneumonia, 44.4% No finding	[55,57]	Binary Classification (SARS-CoV-2, No finding) and Multiclass Classification (SARS-CoV-2, No finding, Pneumonia
Abbas et al. [58]	CNN	1764	[55]	Detection of SARS-CoV-2 infection
Das et al. [59]	Xception	1125, 11.1% SARS-CoV-2, 44.4% Pneumonia, 44.4% No finding	[55, 57]]	Automatic detection of COVID-19 infection
Wang et al. [54]	DNN	13800, 2.56% COVID-19, 58% Pneumonia, 40% healthy %	[55,60,61,62,63]	Classification of the lung into three categories: No infection, SARS-CoV-2-Viral/bacterial infection
Panwar et al. [64]	nCOVnet	337, 192 COVID Positive	[55]	Detection of SARS-CoV-2 infection
Apostolopoulos et al. [65]	VGG-19	224 Covid-19, 504 healthy instances, 400 bacteria and 314 viral	[55,66]	Automatic detection of COVID-19 disease
Marques et al. [67]	EfficientNet	404 Normal, Pneumonia and COVID-19	[55]	Binary classification (COVID-19, normal patients) and multi-class (COVID-19, pneumonia, normal patients)