Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet

被引：366

作者：

Panwar, Harsh ^{[1
]}

Gupta, P. K. ^{[1
]}

Siddiqui, Mohammad Khubeb ^{[2
]}

Morales-Menendez, Ruben ^{[2
]}

Singh, Vaishnavi ^{[1
]}

机构：

[1] Jaypee Univ Informat Technol, Dept Comp Sci & Engn, Solan 173234, HP, India

[2] Tecnol Monterrey, Sch Engn & Sci, Monterrey, NL, Mexico

来源：

CHAOS SOLITONS & FRACTALS | 2020年 / 138卷

关键词：

COVID-19; Detection; X-Rays; Deep learning; Convolutional neural network (CNN); nCOVnet;

D O I：

10.1016/j.chaos.2020.109944

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

Presently, COVID-19 has posed a serious threat to researchers, scientists, health professionals, and administrations around the globe from its detection to its treatment. The whole world is witnessing a lockdown like situation because of COVID-19 pandemic. Persistent efforts are being made by the researchers to obtain the possible solutions to control this pandemic in their respective areas. One of the most common and effective methods applied by the researchers is the use of CT-Scans and X-rays to analyze the images of lungs for COVID-19. However, it requires several radiology specialists and time to manually inspect each report which is one of the challenging tasks in a pandemic. In this paper, we have proposed a deep learning neural network-based method nCOVnet, an alternative fast screening method that can be used for detecting the COVID-19 by analyzing the X-rays of patients which will look for visual indicators found in the chest radiography imaging of COVID-19 patients. (c) 2020 Elsevier Ltd. All rights reserved.

引用

页数：8

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