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COV-VGX: An automated COVID-19 detection system using X-ray images and transfer learning
Coronavirus (COVID-19) has been one of the most dangerous and acute deadly diseases across the world recently. Researchers are trying to develop automated and feasible COVID-19 detection systems with the help of deep neural networks, machine learning techniques, etc. In this paper, a deep learning-b...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Authors. Published by Elsevier Ltd.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8445760/ https://www.ncbi.nlm.nih.gov/pubmed/34549079 http://dx.doi.org/10.1016/j.imu.2021.100741 |
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author | Saha, Prottoy Sadi, Muhammad Sheikh Aranya, O.F.M. Riaz Rahman Jahan, Sadia Islam, Ferdib-Al |
author_facet | Saha, Prottoy Sadi, Muhammad Sheikh Aranya, O.F.M. Riaz Rahman Jahan, Sadia Islam, Ferdib-Al |
author_sort | Saha, Prottoy |
collection | PubMed |
description | Coronavirus (COVID-19) has been one of the most dangerous and acute deadly diseases across the world recently. Researchers are trying to develop automated and feasible COVID-19 detection systems with the help of deep neural networks, machine learning techniques, etc. In this paper, a deep learning-based COVID-19 detection system called COV-VGX is proposed that contributes to detecting coronavirus disease automatically using chest X-ray images. The system introduces two types of classifiers, namely, a multiclass classifier that automatically predicts coronavirus, pneumonia, and normal classes and a binary classifier that predicts coronavirus and pneumonia classes. Using transfer learning, a deep CNN model is proposed to extract distinct and high-level features from X-ray images in collaboration with the pretrained model VGG-16. Despite the limitation of the COVID-19 dataset, the model is evaluated with sufficient COVID-19 images. Extensive experiments for multiclass classifier have achieved 98.91% accuracy, 97.31% precision, 99.50% recall, 98.39% F1-score, while 99.37% accuracy, 98.76% precision, 100% recall, 99.38% F1-score for binary classifier. The proposed system can contribute a lot in diagnosing COVID-19 effectively in the medical field. |
format | Online Article Text |
id | pubmed-8445760 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Authors. Published by Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84457602021-09-17 COV-VGX: An automated COVID-19 detection system using X-ray images and transfer learning Saha, Prottoy Sadi, Muhammad Sheikh Aranya, O.F.M. Riaz Rahman Jahan, Sadia Islam, Ferdib-Al Inform Med Unlocked Article Coronavirus (COVID-19) has been one of the most dangerous and acute deadly diseases across the world recently. Researchers are trying to develop automated and feasible COVID-19 detection systems with the help of deep neural networks, machine learning techniques, etc. In this paper, a deep learning-based COVID-19 detection system called COV-VGX is proposed that contributes to detecting coronavirus disease automatically using chest X-ray images. The system introduces two types of classifiers, namely, a multiclass classifier that automatically predicts coronavirus, pneumonia, and normal classes and a binary classifier that predicts coronavirus and pneumonia classes. Using transfer learning, a deep CNN model is proposed to extract distinct and high-level features from X-ray images in collaboration with the pretrained model VGG-16. Despite the limitation of the COVID-19 dataset, the model is evaluated with sufficient COVID-19 images. Extensive experiments for multiclass classifier have achieved 98.91% accuracy, 97.31% precision, 99.50% recall, 98.39% F1-score, while 99.37% accuracy, 98.76% precision, 100% recall, 99.38% F1-score for binary classifier. The proposed system can contribute a lot in diagnosing COVID-19 effectively in the medical field. The Authors. Published by Elsevier Ltd. 2021 2021-09-17 /pmc/articles/PMC8445760/ /pubmed/34549079 http://dx.doi.org/10.1016/j.imu.2021.100741 Text en © 2021 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Saha, Prottoy Sadi, Muhammad Sheikh Aranya, O.F.M. Riaz Rahman Jahan, Sadia Islam, Ferdib-Al COV-VGX: An automated COVID-19 detection system using X-ray images and transfer learning |
title | COV-VGX: An automated COVID-19 detection system using X-ray images and transfer learning |
title_full | COV-VGX: An automated COVID-19 detection system using X-ray images and transfer learning |
title_fullStr | COV-VGX: An automated COVID-19 detection system using X-ray images and transfer learning |
title_full_unstemmed | COV-VGX: An automated COVID-19 detection system using X-ray images and transfer learning |
title_short | COV-VGX: An automated COVID-19 detection system using X-ray images and transfer learning |
title_sort | cov-vgx: an automated covid-19 detection system using x-ray images and transfer learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8445760/ https://www.ncbi.nlm.nih.gov/pubmed/34549079 http://dx.doi.org/10.1016/j.imu.2021.100741 |
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