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104387

Accurate Diagnosis of COVID-19 Based on Deep Neural Networks and Chest X-Ray Images.

Article

Last updated: 22 Jan 2023

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Subjects

-

Tags

Electronics and Communications Engineering

Abstract

The present study aims at preventing spread out of COVID-19 by early detection of infected cases using chest X-ray images and convolutional neural networks. Covid-19 chest X-ray dataset were collected from public sources as well as through agreements with hospitals and physicians with the consent of their patients. A deep learning algorithm based on convolutional neural networks (CNN) was implemented utilizing X-ray images to diagnose COVID-19. ResNet50, short for Residual Networks, is a classic neural network that was used as a backbone for the classification task. It accelerates the speed of training of the deep networks and reduces the effect of vanishing gradient problems. Images were first resized and then pre-processed to increase sharpness, contrast, and clarity. Images were fed into a deep neural network to predict the probability of COVID-19 infectious. The deep learning calculation acquired an area under the curve (AUC) of the receiver operating characteristics (ROC) of 0.9888, 96.2% sensitivity, 98% accuracy, and 100% specificity. Moreover, the algorithm can be easily modified to add extra images (normal and COVID-19) to improve performance. The proposed system introduces a great help to all nations to screen and diagnose COVID-19 as a faster alternative compared with conventional method that uses PCR

DOI

10.21608/bfemu.2020.104387

Keywords

COVID-19, Chest X-ray Images, Deep Learning, CNN

Authors

First Name

Hossam El-Din

Last Name

Moustafa

MiddleName

Salah

Affiliation

Mansoura University Faculty of Engineering Electronics and Communications Department

Email

hossam_moustafa@ieee.org

City

Mansoura

Orcid

0000-0002-8242-942X

First Name

Mervat

Last Name

El-Seddek

MiddleName

-

Affiliation

Department of Electronics and Communications - Misr Higher Institute for Engineering and Technology - Mansoura

Email

mervat.elseddek@ieee.org

City

Mansoura

Orcid

-

Volume

45

Article Issue

3

Related Issue

15732

Issue Date

2020-09-01

Receive Date

2020-05-21

Publish Date

2020-07-22

Page Start

11

Page End

15

Print ISSN

1110-0923

Online ISSN

2735-4202

Article File

BFEMU_Volume 45_Issue 3_Pages 11-15.pdf

PDF . 1.2MB

Link

https://bfemu.journals.ekb.eg/article_104387.html

Detail API

https://bfemu.journals.ekb.eg/service?article_code=104387

Order

7

Type

Review articles

Type Code

1,206

Publication Type

Journal

Publication Title

MEJ. Mansoura Engineering Journal

Publication Link

https://bfemu.journals.ekb.eg/

MainTitle

-

Details

Type

Article

Created At

22 Jan 2023