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

Tarik Alafif, Abdul Muneeim Tehame, Saleh Bajaba, Ahmed Barnawi and Saad Zia
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Tarik Alafif: Computer Science Department, Jamoum University College, Umm Al-Qura University, Jamoum 25375, Saudi Arabia
Abdul Muneeim Tehame: Department of Software Engineering, Sir Syed University of Engineering and Technology, Karachi 75300, Pakistan
Saleh Bajaba: Business Administration Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Ahmed Barnawi: Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Saad Zia: IT Department, Jeddah Cable Company, Jeddah 31248, Saudi Arabia

IJERPH, 2021, vol. 18, issue 3, 1-24

Abstract: 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.

Keywords: COVID-19; diagnosis; treatment; artificial intelligence; machine learning; deep learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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