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Paper Abstract and Keywords
Presentation 2026-02-20 15:45
Classification of emergency diseases using an anomaly detection method in chest X-ray images
Arisa Masuzawa (Muroran Institute of Technology), Chiharu Kai, Satoshi Kasai (Fujita Health Univ.), Satoshi Kondo (Muroran Institute of Technology) ITS2025-79 IE2025-94
Abstract (in Japanese) (See Japanese page) 
(in English) Chest X-ray images are dangerous for emergency patients when diagnostic times are long, and they are difficult to interpret. Because they are commonly used in health checkups, doctors must interpret a large number of images. Therefore, there is a need to reduce the burden on doctors and enable immediate diagnoses by using machine learning to achieve practical interpretation and diagnosis. In this study, we apply an anomaly detection method to chest X-ray images containing normal and multiple diseases, and evaluate the improvement in classification performance by removing a large number of true negative images in the classifier, in order to accurately and immediately detect emergency diseases. We also report the results of combining three different anomaly detection methods to combine anomaly detection and classification methods.
Keyword (in Japanese) (See Japanese page) 
(in English) Chest X-ray / Anomaly Detection / Image classification / / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 356, IE2025-94, pp. 228-232, Feb. 2026.
Paper # IE2025-94 
Date of Issue 2026-02-12 (ITS, IE) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF ITS2025-79 IE2025-94

Conference Information
Committee IE ITS ITE-MMS ITE-ME ITE-AIT ITE-SIP  
Conference Date 2026-02-19 - 2026-02-20 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IE 
Conference Code 2026-02-IE-ITS-MMS-ME-AIT-SIP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Classification of emergency diseases using an anomaly detection method in chest X-ray images 
Sub Title (in English)  
Keyword(1) Chest X-ray  
Keyword(2) Anomaly Detection  
Keyword(3) Image classification  
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1st Author's Name Arisa Masuzawa  
1st Author's Affiliation Muroran Institute of Technology (Muroran Institute of Technology)
2nd Author's Name Chiharu Kai  
2nd Author's Affiliation Fujita Health University (Fujita Health Univ.)
3rd Author's Name Satoshi Kasai  
3rd Author's Affiliation Fujita Health University (Fujita Health Univ.)
4th Author's Name Satoshi Kondo  
4th Author's Affiliation Muroran Institute of Technology (Muroran Institute of Technology)
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Speaker Author-1 
Date Time 2026-02-20 15:45:00 
Presentation Time 15 minutes 
Registration for IE 
Paper # ITS2025-79, IE2025-94 
Volume (vol) vol.125 
Number (no) no.355(ITS), no.356(IE) 
Page pp.228-232 
#Pages 5 
Date of Issue 2026-02-12 (ITS, IE) 

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