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

被引:84
作者
Alafif, Tarik [1 ]
Tehame, Abdul Muneeim [2 ]
Bajaba, Saleh [3 ]
Barnawi, Ahmed [4 ]
Zia, Saad [5 ]
机构
[1] Umm Al Qura Univ, Jamoum Univ Coll, Comp Sci Dept, Jamoum 25375, Saudi Arabia
[2] Sir Syed Univ Engn & Technol, Dept Software Engn, Karachi 75300, Pakistan
[3] King Abdulaziz Univ, Business Adm Dept, Jeddah 21589, Saudi Arabia
[4] King Abdulaziz Univ, Fac Comp & Informat Technol, Jeddah 21589, Saudi Arabia
[5] Jeddah Cable Co, IT Dept, Jeddah 31248, Saudi Arabia
关键词
COVID-19; diagnosis; treatment; artificial intelligence; machine learning; deep learning; EBOLA;
D O I
10.3390/ijerph18031117
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页码:1 / 24
页数:24
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