. 2021 Dec 14;141:105141. doi: 10.1016/j.compbiomed.2021.105141

Table 10.

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

Authors	Main idea	Advantages	Research challenges	Security mechanism?	Dataset	Using TL?	Method	Usage?
Chen, Zhang [78]	Introducing the autoencoder's hash addressing memory module.	-High robustness	-Need to test on more difficult datasets	No	COVID-19 CT images, X-Ray Images, and reference image database	No	Autoencoder	Detect the anomaly, especially in the case of COVID-19 detection
		-Low delay	-High complexity
		-High accuracy
Li, Fu [79]	Using autoencoders to extract deeper information from CT images.	-Low response time	-The data set is not very big.	No	COVID-CT dataset includes 275 positive images and 195 negative images.	No	Autoencoder	Detection in chest CT
Li, Fu [79]		-High accuracy	-Low robustness	No		No	Autoencoder	Detection in chest CT
Atlam, Torkey [80]	Presenting a Cox regression-based autoencoder technique.	-High accuracy	-High delay	No	There are 1085 patients in this dataset.	No	Autoencode + Cox regression	COVID-19 survival analysis
Atlam, Torkey [80]	Presenting a Cox regression-based autoencoder technique.	-High scalability	-High energy consumption	No	There are 1085 patients in this dataset.	No	Autoencode + Cox regression	COVID-19 survival analysis
Wen, Wang [81]	Using an autoencoder-based anomaly detection technique for COVID-19 surveillance.	-High robustness	-Data is not available because the results of the symptom extraction procedure are considered confidential health information	No	The dataset was compiled from clinical documentation created between January 1st, 2011, and May 1st, 2020.	No	Autoencoder	COVID-19 syndromic surveillance
Wen, Wang [81]		-High accuracy	-Low scalability	No		No	Autoencoder	COVID-19 syndromic surveillance