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

Author

Listed:
  • Tarik Alafif

    (Computer Science Department, Jamoum University College, Umm Al-Qura University, Jamoum 25375, Saudi Arabia
    These authors contributed equally to this work.)

  • Abdul Muneeim Tehame

    (Department of Software Engineering, Sir Syed University of Engineering and Technology, Karachi 75300, Pakistan
    These authors contributed equally to this work.)

  • 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)

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.

Suggested Citation

  • Tarik Alafif & Abdul Muneeim Tehame & Saleh Bajaba & Ahmed Barnawi & Saad Zia, 2021. "Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions," IJERPH, MDPI, vol. 18(3), pages 1-24, January.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:3:p:1117-:d:488108
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    Citations

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    Cited by:

    1. Zhencheng Fan & Zheng Yan & Shiping Wen, 2023. "Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
    2. Maria Vasiliki Sanida & Theodora Sanida & Argyrios Sideris & Minas Dasygenis, 2024. "An Advanced Deep Learning Framework for Multi-Class Diagnosis from Chest X-ray Images," J, MDPI, vol. 7(1), pages 1-24, January.

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