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https://hdl.handle.net/20.500.14094/90007658
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2024-04-30
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90007658 (fulltext)
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メタデータID
90007658
アクセス権
open access
出版タイプ
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タイトル
Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods
著者
Nishio, Mizuho ; Noguchi, Shunjiro ; Matsuo, Hidetoshi ; Murakami, Takamichi
著者ID
A2632
研究者ID
1000050581998
KUID
https://kuid-rm-web.ofc.kobe-u.ac.jp/search/detail?systemId=bf541f7b44992589520e17560c007669
著者名
Nishio, Mizuho
西尾, 瑞穂
ニシオ, ミズホ
所属機関名
医学部附属病院
著者名
Noguchi, Shunjiro
著者名
Matsuo, Hidetoshi
著者ID
A2302
研究者ID
1000020252653
KUID
https://kuid-rm-web.ofc.kobe-u.ac.jp/search/detail?systemId=4d5c2d2510f6d3a2520e17560c007669
著者名
Murakami, Takamichi
村上, 卓道
ムラカミ, タカミチ
所属機関名
医学研究科
収録物名
Scientific Reports
巻(号)
10(1)
ページ
17532-17532
出版者
Nature Research
刊行日
2020-10-16
公開日
2020-12-08
抄録
This study aimed to develop and validate computer-aided diagnosis (CXDx) system for classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray (CXR) images. From two public datasets, 1248 CXR images were obtained, which included 215, 533, and 500 CXR images of COVID-19 pneumonia patients, non-COVID-19 pneumonia patients, and the healthy samples, respectively. The proposed CADx system utilized VGG16 as a pre-trained model and combination of conventional method and mixup as data augmentation methods. Other types of pre-trained models were compared with the VGG16-based model. Single type or no data augmentation methods were also evaluated. Splitting of training/validation/test sets was used when building and evaluating the CADx system. Three-category accuracy was evaluated for test set with 125 CXR images. The three-category accuracy of the CAD system was 83.6% between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy. Sensitivity for COVID-19 pneumonia was more than 90%. The combination of conventional method and mixup was more useful than single type or no data augmentation method. In conclusion, this study was able to create an accurate CADx system for the 3-category classification. Source code of our CADx system is available as open source for COVID-19 research.
カテゴリ
医学研究科
医学部附属病院
学術雑誌論文
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© The Author(s) 2020.
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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資源タイプ
journal article
言語
English (英語)
eISSN
2045-2322
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関連情報
DOI
https://doi.org/10.1038/s41598-020-74539-2
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