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Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions.
Alafif, Tarik; Tehame, Abdul Muneeim; Bajaba, Saleh; Barnawi, Ahmed; Zia, Saad.
  • Alafif T; Computer Science Department, Jamoum University College, Umm Al-Qura University, Jamoum 25375, Saudi Arabia.
  • Tehame AM; Department of Software Engineering, Sir Syed University of Engineering and Technology, Karachi 75300, Pakistan.
  • Bajaba S; Business Administration Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
  • Barnawi A; Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
  • Zia S; IT Department, Jeddah Cable Company, Jeddah 31248, Saudi Arabia.
Int J Environ Res Public Health ; 18(3)2021 01 27.
Article in English | MEDLINE | ID: covidwho-1050613
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Machine Learning / Deep Learning / COVID-19 Type of study: Diagnostic study / Observational study Limits: Humans Language: English Year: 2021 Document Type: Article Affiliation country: Ijerph18031117

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Machine Learning / Deep Learning / COVID-19 Type of study: Diagnostic study / Observational study Limits: Humans Language: English Year: 2021 Document Type: Article Affiliation country: Ijerph18031117