Table 5.

The methods, properties, and features of CNN-COVID-19 mechanisms.

Authors	Main idea	Advantages	Research challenges	Security mechanism?	Dataset	Using TL?	Method	Usage?
Kedia and Katarya [57]	Using deep CNN model ‘‘CoVNet-19” is being used to find COVID-19 patients.	-The combined classification accuracy of Pneumonia and Normal is 98.28%, with average precision and recall of 98.33 for both.	-The high complexity of network	No	There are 6214 chest X-rays in five separate datasets.	Yes	CNN	Detection in chest X-ray
Wang, Nayak [58]	Learning features with pre-trained models.	-Quicker diagnoses time	-It cannot handle heterogeneous data, such as CCT and CXR mixed data, patient history, and other information.	No	284 COVID-19, 281 pneumonia, 293 secondary pulmonary tuberculosis, and 306 normal images were included in the dataset.	Yes	CNN	Detection in chest CT
		-High accuracy	-This study's dataset is both size and category restricted.
Ismael and Şengür [59]	Detecting COVID-19 using three deep CNN based on chest X-ray images.	-High accuracy	-Data is not enough for to TL method	No	COVID-19 images (180) and normal chest X-ray images (200).	Yes	CNN	Detection in chest X-ray
		-Low response time
Abdel-Basset, Chang [60]	Proposing a semi-supervised few-shot segmentation model.	-The FSS-2019-nCov's generalization efficiency improves as a result of the semi-supervised learning	-Lack of volumetric data representation	No	The Italian Society of Medical and Interventional Radiology dataset.	No	CNN	Detection in chest CT
			-Owing to a lack of supervision, it was impossible to achieve a very accurate segmentation.
Ezzat, Hassanien [61]	Using a pre-trained CNN that combined with an optimization algorithm.	-High accuracy	-High complexity	No	The University of Montreal has made the COVID-19 Chest X-ray dataset available.	Yes	CNN + GSA	Detection in chest X-ray
		-Low delay	-High energy usage