Table 5.

Techniques, attributes, and characteristics of imaging-COVID-19 applications

Authors	The basic objective	Pros	Limitations in study	Security method?	Simulation environments	Dataset and Size of Dataset	Using TL?	Mechanism	Application?
Basu, Sheikh [93]	Detecting COVID-19 from CT images utilizing an end-to-end architecture	-The accuracy is 98.87%	-High delay	No	Python	SARS-COV-2 CT Scan dataset (Small size)	Yes	CNN	Detection in chest X-ray
Scarpiniti, Ahrabi [94]	Learning features with pre-trained models	-High accuracy, precision, recall, F-measure, and AUC	-Low security -Low robustness	No	Python	COVIDx CT-2 dataset, include 3700 images (Small size)	No	CNN + Autencoder	Detection in chest CT
Hu, Shen [95]	Offering three modules for deep collaborative supervision and attention fusion based on ResUnet	-High segmentation performance -High generalization ability	-Low scalability	No	Python	COVID-19 segmentation dataset (Small size)	No	Encoder-decoder	Detection in chest CT
Muhammad, Hoque [96]	Presenting a deep feature augmentation system to enhance COVID-19 detection	-The achieved accuracy is 98%	-Lack of volumetric data representation	No	Not mentioned	Cohen JP (Small size)	Yes	CNN + LSTM	Detection in chest x-ray
Gayathri, Abraham [97]	Using DNN to extract features, reduce dimensionality, and classify data	-High accuracy -Low energy consumption	-Low robustness	No	MATLAB	The dataset consists of 783 X-ray images (Small size)	Yes	CNN + Autoencoder	Detection in chest X-ray