Beta
ManageSearchChatDiscover
169739

An Automated Early Detection and Classification Method for COVID-19 Stages Based on Deep Learning Technique Using Chest CT Images.

Article

Last updated: 22 Jan 2023

OverviewSimilar Items

Subjects

-

Tags

Computer Engineering and Systems

Abstract

Coronavirus Disease 2019 (COVID-19) has widely spread all over the world since the ending of 2019. Until now, the death toll and injuries counted by the world's newest has not stopped. It is better to find an automatic classification technique to find out the extent of pneumonia, and be helpful tools for faster decisions in clinical practice. The Early detection of COVID-19 is an important and urgent need for stopping the spread of the disease. The aim of this study is to develop an automated early detection and classification method for COVID-19 patients based on deep learning technique using chest CT images. The proposed technique classifies the five COVID-19 infection grades with accuracy 98%. It based on using a deep convolutional neural network (CNN) with the ResNet50 model. It detects the early infection grades with a precision 98.3%. Several statistical performance measures had been evaluated for multi-classification categories.  The proposed technique helps the physicians to make faster decisions and treatments for different COVID-19 grades.

DOI

10.21608/bfemu.2021.169739

Keywords

COVID-19, Chest CT, CO-RADS Grade, Deep neural network

Authors

First Name

Mohamed

Last Name

Abdelsalam

MiddleName

Moawad

Affiliation

Assistant Professor., Computers and Systems Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.

Email

mohmoawed@yahoo.com

City

Mansoura

Orcid

-

First Name

Mervat

Last Name

El-Seddek

MiddleName

-

Affiliation

Assistant Professor., Department of Electronics and Communications ., Misr Higher Institute for Engineering and Technology., El-Mansoura University., Mansoura., Egypt.

Email

mervat.elseddek@ieee.org

City

Dakahlia

Orcid

-

Volume

46

Article Issue

1

Related Issue

20965

Issue Date

2021-03-01

Receive Date

2020-11-30

Publish Date

2021-05-09

Page Start

31

Page End

40

Print ISSN

1110-0923

Online ISSN

2735-4202

Article File

BFEMU_Volume 46_Issue 1_Pages 31-40.pdf

PDF . 1.7MB

Link

https://bfemu.journals.ekb.eg/article_169739.html

Detail API

https://bfemu.journals.ekb.eg/service?article_code=169739

Order

26

Type

Research Studies

Type Code

1,205

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