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Contemporary study on deep neural networks to diagnose Covid-19 using digital posteroanterior x-ray images

COVID-19 is a transferable disease inherited from the SARS-CoV-2 virus. A total of 594 million people have been infected, and 6.4 million human beings have died due to COVID-19. The fastest way to diagnose the disease is by radiography. Deep learning has been the most popular technique for image cla...

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Main Authors: Saad Akbar, Humera Tariq, Muhammad Fahad, Ghufran Ahmed, Hassan Jamil Syed
Format: Article
Language:English
Published: Multidisciplinary Digital Publishing Institute (MDPI) 2022
Subjects:
QA71-90 Instruments and machines
RC705-779 Diseases of the respiratory system
Online Access:https://eprints.ums.edu.my/id/eprint/42305/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/42305/
https://doi.org/10.3390/electronics11193113
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spelling my.ums.eprints.423052024-12-16T04:02:27Z https://eprints.ums.edu.my/id/eprint/42305/ Contemporary study on deep neural networks to diagnose Covid-19 using digital posteroanterior x-ray images Saad Akbar Humera Tariq Muhammad Fahad Ghufran Ahmed Hassan Jamil Syed QA71-90 Instruments and machines RC705-779 Diseases of the respiratory system COVID-19 is a transferable disease inherited from the SARS-CoV-2 virus. A total of 594 million people have been infected, and 6.4 million human beings have died due to COVID-19. The fastest way to diagnose the disease is by radiography. Deep learning has been the most popular technique for image classification during the last decade. This paper aims to examine the contributions of machine learning for the detection of COVID-19 using Deep Learning and explores the overall application of convolutional neural networks of some famous state-of-the-art deep learning pretrained models. In this research, our objective is to explore the various image classification strategies for CXIs and the application of deep learning models for optimization and feature selection. The study presented in this article shows that the accuracy of deep learning models when detecting COVID-19 on the basis of chest X-ray images ranges from 93 percent to above 99 percent. Multidisciplinary Digital Publishing Institute (MDPI) 2022 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/42305/2/FULL%20TEXT.pdf Saad Akbar and Humera Tariq and Muhammad Fahad and Ghufran Ahmed and Hassan Jamil Syed (2022) Contemporary study on deep neural networks to diagnose Covid-19 using digital posteroanterior x-ray images. Electronics, 11. pp. 1-16. https://doi.org/10.3390/electronics11193113
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
topic QA71-90 Instruments and machines
RC705-779 Diseases of the respiratory system
spellingShingle QA71-90 Instruments and machines
RC705-779 Diseases of the respiratory system
Saad Akbar
Humera Tariq
Muhammad Fahad
Ghufran Ahmed
Hassan Jamil Syed
Contemporary study on deep neural networks to diagnose Covid-19 using digital posteroanterior x-ray images
description COVID-19 is a transferable disease inherited from the SARS-CoV-2 virus. A total of 594 million people have been infected, and 6.4 million human beings have died due to COVID-19. The fastest way to diagnose the disease is by radiography. Deep learning has been the most popular technique for image classification during the last decade. This paper aims to examine the contributions of machine learning for the detection of COVID-19 using Deep Learning and explores the overall application of convolutional neural networks of some famous state-of-the-art deep learning pretrained models. In this research, our objective is to explore the various image classification strategies for CXIs and the application of deep learning models for optimization and feature selection. The study presented in this article shows that the accuracy of deep learning models when detecting COVID-19 on the basis of chest X-ray images ranges from 93 percent to above 99 percent.
format Article
author Saad Akbar
Humera Tariq
Muhammad Fahad
Ghufran Ahmed
Hassan Jamil Syed
author_facet Saad Akbar
Humera Tariq
Muhammad Fahad
Ghufran Ahmed
Hassan Jamil Syed
author_sort Saad Akbar
title Contemporary study on deep neural networks to diagnose Covid-19 using digital posteroanterior x-ray images
title_short Contemporary study on deep neural networks to diagnose Covid-19 using digital posteroanterior x-ray images
title_full Contemporary study on deep neural networks to diagnose Covid-19 using digital posteroanterior x-ray images
title_fullStr Contemporary study on deep neural networks to diagnose Covid-19 using digital posteroanterior x-ray images
title_full_unstemmed Contemporary study on deep neural networks to diagnose Covid-19 using digital posteroanterior x-ray images
title_sort contemporary study on deep neural networks to diagnose covid-19 using digital posteroanterior x-ray images
publisher Multidisciplinary Digital Publishing Institute (MDPI)
publishDate 2022
url https://eprints.ums.edu.my/id/eprint/42305/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/42305/
https://doi.org/10.3390/electronics11193113
_version_ 1818835203743809536
score 13.223943

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