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EDNC: Ensemble Deep Neural Network for COVID-19 Recognition
The automatic recognition of COVID-19 diseases is critical in the present pandemic since it relieves healthcare staff of the burden of screening for infection with COVID-19. Previous studies have proven that deep learning algorithms can be utilized to aid in the diagnosis of patients with potential...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8938826/ https://www.ncbi.nlm.nih.gov/pubmed/35314648 http://dx.doi.org/10.3390/tomography8020071 |
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author | Yang, Lin Wang, Shui-Hua Zhang, Yu-Dong |
author_facet | Yang, Lin Wang, Shui-Hua Zhang, Yu-Dong |
author_sort | Yang, Lin |
collection | PubMed |
description | The automatic recognition of COVID-19 diseases is critical in the present pandemic since it relieves healthcare staff of the burden of screening for infection with COVID-19. Previous studies have proven that deep learning algorithms can be utilized to aid in the diagnosis of patients with potential COVID-19 infection. However, the accuracy of current COVID-19 recognition models is relatively low. Motivated by this fact, we propose three deep learning architectures, F-EDNC, FC-EDNC, and O-EDNC, to quickly and accurately detect COVID-19 infections from chest computed tomography (CT) images. Sixteen deep learning neural networks have been modified and trained to recognize COVID-19 patients using transfer learning and 2458 CT chest images. The proposed EDNC has then been developed using three of sixteen modified pre-trained models to improve the performance of COVID-19 recognition. The results suggested that the F-EDNC method significantly enhanced the recognition of COVID-19 infections with 97.75% accuracy, followed by FC-EDNC and O-EDNC (97.55% and 96.12%, respectively), which is superior to most of the current COVID-19 recognition models. Furthermore, a localhost web application has been built that enables users to easily upload their chest CT scans and obtain their COVID-19 results automatically. This accurate, fast, and automatic COVID-19 recognition system will relieve the stress of medical professionals for screening COVID-19 infections. |
format | Online Article Text |
id | pubmed-8938826 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89388262022-03-23 EDNC: Ensemble Deep Neural Network for COVID-19 Recognition Yang, Lin Wang, Shui-Hua Zhang, Yu-Dong Tomography Article The automatic recognition of COVID-19 diseases is critical in the present pandemic since it relieves healthcare staff of the burden of screening for infection with COVID-19. Previous studies have proven that deep learning algorithms can be utilized to aid in the diagnosis of patients with potential COVID-19 infection. However, the accuracy of current COVID-19 recognition models is relatively low. Motivated by this fact, we propose three deep learning architectures, F-EDNC, FC-EDNC, and O-EDNC, to quickly and accurately detect COVID-19 infections from chest computed tomography (CT) images. Sixteen deep learning neural networks have been modified and trained to recognize COVID-19 patients using transfer learning and 2458 CT chest images. The proposed EDNC has then been developed using three of sixteen modified pre-trained models to improve the performance of COVID-19 recognition. The results suggested that the F-EDNC method significantly enhanced the recognition of COVID-19 infections with 97.75% accuracy, followed by FC-EDNC and O-EDNC (97.55% and 96.12%, respectively), which is superior to most of the current COVID-19 recognition models. Furthermore, a localhost web application has been built that enables users to easily upload their chest CT scans and obtain their COVID-19 results automatically. This accurate, fast, and automatic COVID-19 recognition system will relieve the stress of medical professionals for screening COVID-19 infections. MDPI 2022-03-21 /pmc/articles/PMC8938826/ /pubmed/35314648 http://dx.doi.org/10.3390/tomography8020071 Text en © 2022 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 Yang, Lin Wang, Shui-Hua Zhang, Yu-Dong EDNC: Ensemble Deep Neural Network for COVID-19 Recognition |
title | EDNC: Ensemble Deep Neural Network for COVID-19 Recognition |
title_full | EDNC: Ensemble Deep Neural Network for COVID-19 Recognition |
title_fullStr | EDNC: Ensemble Deep Neural Network for COVID-19 Recognition |
title_full_unstemmed | EDNC: Ensemble Deep Neural Network for COVID-19 Recognition |
title_short | EDNC: Ensemble Deep Neural Network for COVID-19 Recognition |
title_sort | ednc: ensemble deep neural network for covid-19 recognition |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8938826/ https://www.ncbi.nlm.nih.gov/pubmed/35314648 http://dx.doi.org/10.3390/tomography8020071 |
work_keys_str_mv | AT yanglin edncensembledeepneuralnetworkforcovid19recognition AT wangshuihua edncensembledeepneuralnetworkforcovid19recognition AT zhangyudong edncensembledeepneuralnetworkforcovid19recognition |