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Deep Learning-based diagnosis of COVID-19 using Chest X-Ray Images

by Salah Zaher
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 48
Year of Publication: 2023
Authors: Salah Zaher
10.5120/ijca2023923315

Salah Zaher . Deep Learning-based diagnosis of COVID-19 using Chest X-Ray Images. International Journal of Computer Applications. 185, 48 ( Dec 2023), 41-49. DOI=10.5120/ijca2023923315

@article{ 10.5120/ijca2023923315,
author = { Salah Zaher },
title = { Deep Learning-based diagnosis of COVID-19 using Chest X-Ray Images },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2023 },
volume = { 185 },
number = { 48 },
month = { Dec },
year = { 2023 },
issn = { 0975-8887 },
pages = { 41-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number48/33018-2023923315/ },
doi = { 10.5120/ijca2023923315 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:29:10.326526+05:30
%A Salah Zaher
%T Deep Learning-based diagnosis of COVID-19 using Chest X-Ray Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 48
%P 41-49
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

COVID-19 is a highly contagious disease caused by SARS-CoV-2 and has tragically claimed over 6 million lives worldwide. Early detection is crucial for containing its spread and providing appropriate treatment. In this article, we present a system to help physicians diagnose COVID-19 pneumonia by analyzing chest radiographs (CXR). The proposed system uses a dataset of 7232 CXR images, where 80% were used for training and 20% for testing. The methodology used in the proposed system consists of four steps: preprocessing the images through image processing, extracting features from the images using a pre-trained VGG16 model as a deep learning technique for feature extraction, applying two machine learning algorithms, namely Random Forests and Support Vector Machines (SVMs), and finally comparing the performance of the two algorithms. The SVM classifier achieved a classification accuracy of 97.2% at Gamma = 0.001 and C = 5, whereas Random Forests achieved an accuracy of 87%. Thus, the SVM classifier proved to be more capable in this type of classification.

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Index Terms

Computer Science
Information Sciences

Keywords

Covid-19 SARS-CoV-2 X-ray (CXR) VGG16 deep learning machine learning support vector machines (SVMs) random forest.

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