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A large margin piecewise linear classifier with fusion of deep features in the diagnosis of COVID-19
The world has experienced epidemics of coronavirus infections several times over the last two decades. Recent studies have shown that using medical imaging techniques can be useful in developing an automatic computer-aided diagnosis system to detect pandemic diseases with high accuracy at an early s...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8504027/ https://www.ncbi.nlm.nih.gov/pubmed/34688172 http://dx.doi.org/10.1016/j.compbiomed.2021.104927 |
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author | Azouji, Neda Sami, Ashkan Taheri, Mohammad Müller, Henning |
author_facet | Azouji, Neda Sami, Ashkan Taheri, Mohammad Müller, Henning |
author_sort | Azouji, Neda |
collection | PubMed |
description | The world has experienced epidemics of coronavirus infections several times over the last two decades. Recent studies have shown that using medical imaging techniques can be useful in developing an automatic computer-aided diagnosis system to detect pandemic diseases with high accuracy at an early stage. In this study, a large margin piecewise linear classifier was developed to diagnose COVID-19 compared to a wide range of viral pneumonia, including SARS and MERS, using chest x-ray images. In the proposed method, a preprocessing pipeline was employed. Moreover, deep pre- and post-rectified linear unit (ReLU) features were extracted using the well-known VGG-Net19, which was fine-tuned to optimize transfer learning. Afterward, the canonical correlation analysis was performed for feature fusion, and fused deep features were passed into the LMPL classifier. The introduced method reached the highest performance in comparison with related state-of-the-art methods for two different schemes (normal, COVID-19, and typical viral pneumonia) and (COVID-19, SARS, and MERS pneumonia) with 99.39% and 98.86% classification accuracy, respectively. |
format | Online Article Text |
id | pubmed-8504027 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85040272021-10-12 A large margin piecewise linear classifier with fusion of deep features in the diagnosis of COVID-19 Azouji, Neda Sami, Ashkan Taheri, Mohammad Müller, Henning Comput Biol Med Article The world has experienced epidemics of coronavirus infections several times over the last two decades. Recent studies have shown that using medical imaging techniques can be useful in developing an automatic computer-aided diagnosis system to detect pandemic diseases with high accuracy at an early stage. In this study, a large margin piecewise linear classifier was developed to diagnose COVID-19 compared to a wide range of viral pneumonia, including SARS and MERS, using chest x-ray images. In the proposed method, a preprocessing pipeline was employed. Moreover, deep pre- and post-rectified linear unit (ReLU) features were extracted using the well-known VGG-Net19, which was fine-tuned to optimize transfer learning. Afterward, the canonical correlation analysis was performed for feature fusion, and fused deep features were passed into the LMPL classifier. The introduced method reached the highest performance in comparison with related state-of-the-art methods for two different schemes (normal, COVID-19, and typical viral pneumonia) and (COVID-19, SARS, and MERS pneumonia) with 99.39% and 98.86% classification accuracy, respectively. Elsevier Ltd. 2021-12 2021-10-11 /pmc/articles/PMC8504027/ /pubmed/34688172 http://dx.doi.org/10.1016/j.compbiomed.2021.104927 Text en © 2021 Elsevier Ltd. All rights reserved. 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 Azouji, Neda Sami, Ashkan Taheri, Mohammad Müller, Henning A large margin piecewise linear classifier with fusion of deep features in the diagnosis of COVID-19 |
title | A large margin piecewise linear classifier with fusion of deep features in the diagnosis of COVID-19 |
title_full | A large margin piecewise linear classifier with fusion of deep features in the diagnosis of COVID-19 |
title_fullStr | A large margin piecewise linear classifier with fusion of deep features in the diagnosis of COVID-19 |
title_full_unstemmed | A large margin piecewise linear classifier with fusion of deep features in the diagnosis of COVID-19 |
title_short | A large margin piecewise linear classifier with fusion of deep features in the diagnosis of COVID-19 |
title_sort | large margin piecewise linear classifier with fusion of deep features in the diagnosis of covid-19 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8504027/ https://www.ncbi.nlm.nih.gov/pubmed/34688172 http://dx.doi.org/10.1016/j.compbiomed.2021.104927 |
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