Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions

With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COV...

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Bibliographic Details
Published in:	International journal of environmental research and public health 2021-01, Vol.18 (3), p.1117
Main Authors:	Alafif, Tarik, Tehame, Abdul Muneeim, Bajaba, Saleh, Barnawi, Ahmed, Zia, Saad
Format:	Article
Language:	eng
Subjects:	Artificial intelligence Coronaviruses COVID-19 COVID-19 - diagnosis COVID-19 - therapy Deep learning Deep Learning - trends Diagnosis Epidemics Humans Learning algorithms Machine Learning - trends Neural networks Outbreaks Polls & surveys Research facilities Research institutions Review Reviews Severe acute respiratory syndrome coronavirus 2 Viral diseases Viruses
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Summary:	With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COVID-19 epidemic has spread rapidly across the world, leading to international outbreaks. The COVID-19 fight to curb the spread of the disease involves most states, companies, and scientific research institutions. In this research, we look at the Artificial Intelligence (AI)-based ML and DL methods for COVID-19 diagnosis and treatment. Furthermore, in the battle against COVID-19, we summarize the AI-based ML and DL methods and the available datasets, tools, and performance. This survey offers a detailed overview of the existing state-of-the-art methodologies for ML and DL researchers and the wider health community with descriptions of how ML and DL and data can improve the status of COVID-19, and more studies in order to avoid the outbreak of COVID-19. Details of challenges and future directions are also provided.
ISSN:	1660-4601 1661-7827 1660-4601