A Review of COVID-19 Diagnosis and Detection Using Artificial Intelligence
DOI:
https://doi.org/10.59746/jfes.v1i1.9Keywords:
Neural network, Coronavirus, COVID-19Abstract
Coronavirus has received widespread attention from the community of researchers and
medical scientists in the past year. Deploying based on Artificial Intelligence (AI) networks and
models in real world to learn about and diagnose COVID-19 is a critical mission for medical
personnel to help preventing the rapid spread of this virus. This article is a brief review of recent
papers concerning about detection of the virus; most of the schemes used to detect and diagnose
COVID-19 rely on chest X-Ray, some on sounds of breathing, and by using electrocardiogram (ECG)
trace images, all these schemes based on artificial neural network for early screening of COVID-19
and estimating human mobility to limit its spread. In some studies, an accuracy rate that was obtained
exceeded 95%, which is an acceptable value and that can be relied upon in the diagnosis. Therefore,
currently screening tests are better in terms accuracy and reliability for diagnosing patients with severe
and acute respiratory syndrome coronavirus, frequently the most used test is the (RT-PCR).
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