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A Survey on Deep Learning and Machine Learning for COVID-19 Detection

Published: 13 April 2022 Publication History

Abstract

Coronaviruses are a type of virus that can cause a variety of disorders and exist in different types. COVID-19 is derived from a special type of a respiratory illness caused by the SARS-CoV-2 virus, discovered in 2019. Approximately two years ago, COVID-19 was discovered in the Chinese city of Wuhan, and it has since become a worldwide source of concern. A COVID-19 confirmed patient is experiencing symptoms such as fever, fatigue, and a dry cough. Based on the results of laboratory tests and/or chest X-rays, the COVID-19 diagnosis is established. When it comes to research using chest CT scans /X-ray for the diagnosis of COVID-19, which is based on medical imaging, Artificial Intelligence (AI) approaches are increasingly being applied in a variety of ways. Machine learning and deep learning are fields of artificial intelligence that can be used to analyze the data that was acquired in order to better understand the origins of COVID-19. The outcomes of applying such an approach will aid in a better understanding of the nature of the threat and how it might be mitigated. For this reason, this work gives an overview of deep learning and machine learning approaches for the detection of COVID-19. Several COVID-19 detection methods are discussed in detail, as well as the issues, current challenges associated with artificial intelligence and medical researchers' approaches to providing a comprehensive assessment of detecting COVID-19.

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ICFNDS '21: Proceedings of the 5th International Conference on Future Networks and Distributed Systems
December 2021
847 pages
ISBN:9781450387347
DOI:10.1145/3508072
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 13 April 2022

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