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Deep Learning in Multi-Class Lung Diseases’ Classification on Chest X-ray Images
Chest X-ray radiographic (CXR) imagery enables earlier and easier lung disease diagnosis. Therefore, in this paper, we propose a deep learning method using a transfer learning technique to classify lung diseases on CXR images to improve the efficiency and accuracy of computer-aided diagnostic system...
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/PMC9025806/ https://www.ncbi.nlm.nih.gov/pubmed/35453963 http://dx.doi.org/10.3390/diagnostics12040915 |
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author | Kim, Sungyeup Rim, Beanbonyka Choi, Seongjun Lee, Ahyoung Min, Sedong Hong, Min |
author_facet | Kim, Sungyeup Rim, Beanbonyka Choi, Seongjun Lee, Ahyoung Min, Sedong Hong, Min |
author_sort | Kim, Sungyeup |
collection | PubMed |
description | Chest X-ray radiographic (CXR) imagery enables earlier and easier lung disease diagnosis. Therefore, in this paper, we propose a deep learning method using a transfer learning technique to classify lung diseases on CXR images to improve the efficiency and accuracy of computer-aided diagnostic systems’ (CADs’) diagnostic performance. Our proposed method is a one-step, end-to-end learning, which means that raw CXR images are directly inputted into a deep learning model (EfficientNet v2-M) to extract their meaningful features in identifying disease categories. We experimented using our proposed method on three classes of normal, pneumonia, and pneumothorax of the U.S. National Institutes of Health (NIH) data set, and achieved validation performances of loss = 0.6933, accuracy = 82.15%, sensitivity = 81.40%, and specificity = 91.65%. We also experimented on the Cheonan Soonchunhyang University Hospital (SCH) data set on four classes of normal, pneumonia, pneumothorax, and tuberculosis, and achieved validation performances of loss = 0.7658, accuracy = 82.20%, sensitivity = 81.40%, and specificity = 94.48%; testing accuracy of normal, pneumonia, pneumothorax, and tuberculosis classes was 63.60%, 82.30%, 82.80%, and 89.90%, respectively. |
format | Online Article Text |
id | pubmed-9025806 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90258062022-04-23 Deep Learning in Multi-Class Lung Diseases’ Classification on Chest X-ray Images Kim, Sungyeup Rim, Beanbonyka Choi, Seongjun Lee, Ahyoung Min, Sedong Hong, Min Diagnostics (Basel) Article Chest X-ray radiographic (CXR) imagery enables earlier and easier lung disease diagnosis. Therefore, in this paper, we propose a deep learning method using a transfer learning technique to classify lung diseases on CXR images to improve the efficiency and accuracy of computer-aided diagnostic systems’ (CADs’) diagnostic performance. Our proposed method is a one-step, end-to-end learning, which means that raw CXR images are directly inputted into a deep learning model (EfficientNet v2-M) to extract their meaningful features in identifying disease categories. We experimented using our proposed method on three classes of normal, pneumonia, and pneumothorax of the U.S. National Institutes of Health (NIH) data set, and achieved validation performances of loss = 0.6933, accuracy = 82.15%, sensitivity = 81.40%, and specificity = 91.65%. We also experimented on the Cheonan Soonchunhyang University Hospital (SCH) data set on four classes of normal, pneumonia, pneumothorax, and tuberculosis, and achieved validation performances of loss = 0.7658, accuracy = 82.20%, sensitivity = 81.40%, and specificity = 94.48%; testing accuracy of normal, pneumonia, pneumothorax, and tuberculosis classes was 63.60%, 82.30%, 82.80%, and 89.90%, respectively. MDPI 2022-04-06 /pmc/articles/PMC9025806/ /pubmed/35453963 http://dx.doi.org/10.3390/diagnostics12040915 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 Kim, Sungyeup Rim, Beanbonyka Choi, Seongjun Lee, Ahyoung Min, Sedong Hong, Min Deep Learning in Multi-Class Lung Diseases’ Classification on Chest X-ray Images |
title | Deep Learning in Multi-Class Lung Diseases’ Classification on Chest X-ray Images |
title_full | Deep Learning in Multi-Class Lung Diseases’ Classification on Chest X-ray Images |
title_fullStr | Deep Learning in Multi-Class Lung Diseases’ Classification on Chest X-ray Images |
title_full_unstemmed | Deep Learning in Multi-Class Lung Diseases’ Classification on Chest X-ray Images |
title_short | Deep Learning in Multi-Class Lung Diseases’ Classification on Chest X-ray Images |
title_sort | deep learning in multi-class lung diseases’ classification on chest x-ray images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025806/ https://www.ncbi.nlm.nih.gov/pubmed/35453963 http://dx.doi.org/10.3390/diagnostics12040915 |
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