Cargando…
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...
Autores principales: | , , , |
---|---|
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 |
_version_ | 1783751522831564800 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8434119 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT okologabrieliluebe ontheuseofdeeplearningforimagingbasedcovid19detectionusingchestxrays AT katsigiannisstamos ontheuseofdeeplearningforimagingbasedcovid19detectionusingchestxrays AT althobaititurke ontheuseofdeeplearningforimagingbasedcovid19detectionusingchestxrays AT ramzannaeem ontheuseofdeeplearningforimagingbasedcovid19detectionusingchestxrays |