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Image recognition of COVID-19 using DarkCovidNet architecture based on convolutional neural network

Pankaj Kumar (Department of Computer Science and Engineering, Noida Institute of Engineering and Technology, Greater Noida, India)
Bhavna Bajpai (Department of Information Technology, Dr CV Raman University, Khandwa, India)
Deepak Omprakash Gupta (Rajarambapu Institute of Technology, Sangli, India)
Dinesh C. Jain (Department of Information Technology, Acropolis Institute of Technology and Research Indore, Indore, India)
S. Vimal (Department of Information Technology, National Engineering College, Tuticorin, India)

World Journal of Engineering

ISSN: 1708-5284

Article publication date: 10 May 2021

Issue publication date: 22 February 2022

223

Abstract

Purpose

The purpose of this study/paper To focus on finding COVID-19 with the help of DarkCovidNet architecture on patient images.

Design/methodology/approach

We used machine learning techniques with convolutional neural network.

Findings

Detecting COVID-19 symptoms from patient CT scan images.

Originality/value

This paper contains a new architecture for detecting COVID-19 symptoms from patient computed tomography scan images.

Keywords

Citation

Kumar, P., Bajpai, B., Gupta, D.O., Jain, D.C. and Vimal, S. (2022), "Image recognition of COVID-19 using DarkCovidNet architecture based on convolutional neural network", World Journal of Engineering, Vol. 19 No. 1, pp. 90-97. https://doi.org/10.1108/WJE-12-2020-0655

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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