Deep Learning Applications to Combat Novel Coronavirus (COVID-19) Pandemic

被引：55
作者：
Asraf A. [1 ]
Islam M.Z. [1 ]
Haque M.R. [1 ]
Islam M.M. [1 ]
机构：
[1] Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna
来源：
SN Computer Science | 2020年 / 1卷 / 6期
关键词：
COVID-19; Deep learning; Diagnosis; Novel coronavirus; Pandemic;
D O I：
10.1007/s42979-020-00383-w
中图分类号：
学科分类号：
摘要：
During this global pandemic, researchers around the world are trying to find out innovative technology for a smart healthcare system to combat coronavirus. The evidence of deep learning applications on the past epidemic inspires the experts by giving a new direction to control this outbreak. The aim of this paper is to discuss the contributions of deep learning at several scales including medical imaging, disease tracing, analysis of protein structure, drug discovery, and virus severity and infectivity to control the ongoing outbreak. A progressive search of the database related to the applications of deep learning was executed on COVID-19. Further, a comprehensive review is done using selective information by assessing the different perspectives of deep learning. This paper attempts to explore and discuss the overall applications of deep learning on multiple dimensions to control novel coronavirus (COVID-19). Though various studies are conducted using deep learning algorithms, there are still some constraints and challenges while applying for real-world problems. The ongoing progress in deep learning contributes to handle coronavirus infection and plays an effective role to develop appropriate solutions. It is expected that this paper would be a great help for the researchers who would like to contribute to the development of remedies for this current pandemic in this area. © 2020, Springer Nature Singapore Pte Ltd.
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