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On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays

The global COVID-19 pandemic that started in 2019 and created major disruptions around the world demonstrated the imperative need for quick, inexpensive, accessible and reliable diagnostic methods that would allow the detection of infected individuals with minimal resources. Radiography, and more sp...

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Autores principales: Okolo, Gabriel Iluebe, Katsigiannis, Stamos, Althobaiti, Turke, Ramzan, Naeem
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434119/
https://www.ncbi.nlm.nih.gov/pubmed/34502591
http://dx.doi.org/10.3390/s21175702
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author Okolo, Gabriel Iluebe
Katsigiannis, Stamos
Althobaiti, Turke
Ramzan, Naeem
author_facet Okolo, Gabriel Iluebe
Katsigiannis, Stamos
Althobaiti, Turke
Ramzan, Naeem
author_sort Okolo, Gabriel Iluebe
collection PubMed
description The global COVID-19 pandemic that started in 2019 and created major disruptions around the world demonstrated the imperative need for quick, inexpensive, accessible and reliable diagnostic methods that would allow the detection of infected individuals with minimal resources. Radiography, and more specifically, chest radiography, is a relatively inexpensive medical imaging modality that can potentially offer a solution for the diagnosis of COVID-19 cases. In this work, we examined eleven deep convolutional neural network architectures for the task of classifying chest X-ray images as belonging to healthy individuals, individuals with COVID-19 or individuals with viral pneumonia. All the examined networks are established architectures that have been proven to be efficient in image classification tasks, and we evaluated three different adjustments to modify the architectures for the task at hand by expanding them with additional layers. The proposed approaches were evaluated for all the examined architectures on a dataset with real chest X-ray images, reaching the highest classification accuracy of 98.04% and the highest F1-score of 98.22% for the best-performing setting.
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spelling pubmed-84341192021-09-12 On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays Okolo, Gabriel Iluebe Katsigiannis, Stamos Althobaiti, Turke Ramzan, Naeem Sensors (Basel) Article The global COVID-19 pandemic that started in 2019 and created major disruptions around the world demonstrated the imperative need for quick, inexpensive, accessible and reliable diagnostic methods that would allow the detection of infected individuals with minimal resources. Radiography, and more specifically, chest radiography, is a relatively inexpensive medical imaging modality that can potentially offer a solution for the diagnosis of COVID-19 cases. In this work, we examined eleven deep convolutional neural network architectures for the task of classifying chest X-ray images as belonging to healthy individuals, individuals with COVID-19 or individuals with viral pneumonia. All the examined networks are established architectures that have been proven to be efficient in image classification tasks, and we evaluated three different adjustments to modify the architectures for the task at hand by expanding them with additional layers. The proposed approaches were evaluated for all the examined architectures on a dataset with real chest X-ray images, reaching the highest classification accuracy of 98.04% and the highest F1-score of 98.22% for the best-performing setting. MDPI 2021-08-24 /pmc/articles/PMC8434119/ /pubmed/34502591 http://dx.doi.org/10.3390/s21175702 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Okolo, Gabriel Iluebe
Katsigiannis, Stamos
Althobaiti, Turke
Ramzan, Naeem
On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays
title On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays
title_full On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays
title_fullStr On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays
title_full_unstemmed On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays
title_short On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays
title_sort on the use of deep learning for imaging-based covid-19 detection using chest x-rays
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434119/
https://www.ncbi.nlm.nih.gov/pubmed/34502591
http://dx.doi.org/10.3390/s21175702
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