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Fusion-Extracted Features by Deep Networks for Improved COVID-19 Classification with Chest X-ray Radiography
Convolutional neural networks (CNNs) have shown promise in accurately diagnosing coronavirus disease 2019 (COVID-19) and bacterial pneumonia using chest X-ray images. However, determining the optimal feature extraction approach is challenging. This study investigates the use of fusion-extracted feat...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10218019/ https://www.ncbi.nlm.nih.gov/pubmed/37239653 http://dx.doi.org/10.3390/healthcare11101367 |
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author | Lin, Kuo-Hsuan Lu, Nan-Han Okamoto, Takahide Huang, Yung-Hui Liu, Kuo-Ying Matsushima, Akari Chang, Che-Cheng Chen, Tai-Been |
author_facet | Lin, Kuo-Hsuan Lu, Nan-Han Okamoto, Takahide Huang, Yung-Hui Liu, Kuo-Ying Matsushima, Akari Chang, Che-Cheng Chen, Tai-Been |
author_sort | Lin, Kuo-Hsuan |
collection | PubMed |
description | Convolutional neural networks (CNNs) have shown promise in accurately diagnosing coronavirus disease 2019 (COVID-19) and bacterial pneumonia using chest X-ray images. However, determining the optimal feature extraction approach is challenging. This study investigates the use of fusion-extracted features by deep networks to improve the accuracy of COVID-19 and bacterial pneumonia classification with chest X-ray radiography. A Fusion CNN method was developed using five different deep learning models after transferred learning to extract image features (Fusion CNN). The combined features were used to build a support vector machine (SVM) classifier with a RBF kernel. The performance of the model was evaluated using accuracy, Kappa values, recall rate, and precision scores. The Fusion CNN model achieved an accuracy and Kappa value of 0.994 and 0.991, with precision scores for normal, COVID-19, and bacterial groups of 0.991, 0.998, and 0.994, respectively. The results indicate that the Fusion CNN models with the SVM classifier provided reliable and accurate classification performance, with Kappa values no less than 0.990. Using a Fusion CNN approach could be a possible solution to enhance accuracy further. Therefore, the study demonstrates the potential of deep learning and fusion-extracted features for accurate COVID-19 and bacterial pneumonia classification with chest X-ray radiography. |
format | Online Article Text |
id | pubmed-10218019 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102180192023-05-27 Fusion-Extracted Features by Deep Networks for Improved COVID-19 Classification with Chest X-ray Radiography Lin, Kuo-Hsuan Lu, Nan-Han Okamoto, Takahide Huang, Yung-Hui Liu, Kuo-Ying Matsushima, Akari Chang, Che-Cheng Chen, Tai-Been Healthcare (Basel) Article Convolutional neural networks (CNNs) have shown promise in accurately diagnosing coronavirus disease 2019 (COVID-19) and bacterial pneumonia using chest X-ray images. However, determining the optimal feature extraction approach is challenging. This study investigates the use of fusion-extracted features by deep networks to improve the accuracy of COVID-19 and bacterial pneumonia classification with chest X-ray radiography. A Fusion CNN method was developed using five different deep learning models after transferred learning to extract image features (Fusion CNN). The combined features were used to build a support vector machine (SVM) classifier with a RBF kernel. The performance of the model was evaluated using accuracy, Kappa values, recall rate, and precision scores. The Fusion CNN model achieved an accuracy and Kappa value of 0.994 and 0.991, with precision scores for normal, COVID-19, and bacterial groups of 0.991, 0.998, and 0.994, respectively. The results indicate that the Fusion CNN models with the SVM classifier provided reliable and accurate classification performance, with Kappa values no less than 0.990. Using a Fusion CNN approach could be a possible solution to enhance accuracy further. Therefore, the study demonstrates the potential of deep learning and fusion-extracted features for accurate COVID-19 and bacterial pneumonia classification with chest X-ray radiography. MDPI 2023-05-10 /pmc/articles/PMC10218019/ /pubmed/37239653 http://dx.doi.org/10.3390/healthcare11101367 Text en © 2023 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 Lin, Kuo-Hsuan Lu, Nan-Han Okamoto, Takahide Huang, Yung-Hui Liu, Kuo-Ying Matsushima, Akari Chang, Che-Cheng Chen, Tai-Been Fusion-Extracted Features by Deep Networks for Improved COVID-19 Classification with Chest X-ray Radiography |
title | Fusion-Extracted Features by Deep Networks for Improved COVID-19 Classification with Chest X-ray Radiography |
title_full | Fusion-Extracted Features by Deep Networks for Improved COVID-19 Classification with Chest X-ray Radiography |
title_fullStr | Fusion-Extracted Features by Deep Networks for Improved COVID-19 Classification with Chest X-ray Radiography |
title_full_unstemmed | Fusion-Extracted Features by Deep Networks for Improved COVID-19 Classification with Chest X-ray Radiography |
title_short | Fusion-Extracted Features by Deep Networks for Improved COVID-19 Classification with Chest X-ray Radiography |
title_sort | fusion-extracted features by deep networks for improved covid-19 classification with chest x-ray radiography |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10218019/ https://www.ncbi.nlm.nih.gov/pubmed/37239653 http://dx.doi.org/10.3390/healthcare11101367 |
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