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Paper Abstract and Keywords
Presentation 2010-11-15 11:35
Choice method of classifier for computer aided diagnosis
Shunsuke Horie (Gifu Univ), Tomoko Matsubara (Nagoya Bunri Univ.), Satoshi Kasai (Konica Minolta Medical Imaging USA), Yoshikazu Uchiyama (Oita National College of Tec.), Chisako Muramatsu, Xiangrong Zhou, Takeshi Hara, Hiroshi Fujita (Gifu Univ) MI2010-71
Abstract (in Japanese) (See Japanese page) 
(in English) Various classifiers are used on the elimination of false positives or diagnosis of benign and malignant lesion. However, no classifier shows consistently superior performance regardless of the nature of the data. Therefore, it is necessary to clarify the selection criterion of the classifiers that shows optimal performance based on the nature of the feature used. Moreover, the available data for training is finite-sized in an experiment step of CAD development. Consequently, it is important to select the classifier that indicate steady classification performance applying to unknown large data set, based on one obtained by training. In this paper, we conducted the comparison study for diagnosis performance of five classifiers, namely, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Network, Support Vector Machine, and AdaBoost. Our dataset consists of benign and malignant lesions extracted clinical cases. The distribution analysis of features was performed. As a result, it becomes clear that the effects of nature of the features and number of training data on classifier performance are different each classifier. We propose the possibility of the selection method for classifier based on our results.
Keyword (in Japanese) (See Japanese page) 
(in English) Classifier / Clinical Data / nature of the data / AUC / Computer-aided diagnosis (CAD) / / /  
Reference Info. IEICE Tech. Rep., vol. 110, no. 280, MI2010-71, pp. 27-32, Nov. 2010.
Paper # MI2010-71 
Date of Issue 2010-11-08 (MI) 
ISSN Print edition: ISSN 0913-5685    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)
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Conference Information
Committee MI  
Conference Date 2010-11-15 - 2010-11-15 
Place (in Japanese) (See Japanese page) 
Place (in English) Shimadzu Corp. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Imaging 
Paper Information
Registration To MI 
Conference Code 2010-11-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Choice method of classifier for computer aided diagnosis 
Sub Title (in English)  
Keyword(1) Classifier  
Keyword(2) Clinical Data  
Keyword(3) nature of the data  
Keyword(4) AUC  
Keyword(5) Computer-aided diagnosis (CAD)  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Shunsuke Horie  
1st Author's Affiliation Gifu University (Gifu Univ)
2nd Author's Name Tomoko Matsubara  
2nd Author's Affiliation Nagoya Bunri University (Nagoya Bunri Univ.)
3rd Author's Name Satoshi Kasai  
3rd Author's Affiliation Konica Minolta Medical Imaging USA, INC. (Konica Minolta Medical Imaging USA)
4th Author's Name Yoshikazu Uchiyama  
4th Author's Affiliation Oita National College of Technology (Oita National College of Tec.)
5th Author's Name Chisako Muramatsu  
5th Author's Affiliation Gifu University (Gifu Univ)
6th Author's Name Xiangrong Zhou  
6th Author's Affiliation Gifu University (Gifu Univ)
7th Author's Name Takeshi Hara  
7th Author's Affiliation Gifu University (Gifu Univ)
8th Author's Name Hiroshi Fujita  
8th Author's Affiliation Gifu University (Gifu Univ)
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Speaker Author-1 
Date Time 2010-11-15 11:35:00 
Presentation Time 25 minutes 
Registration for MI 
Paper # MI2010-71 
Volume (vol) vol.110 
Number (no) no.280 
Page pp.27-32 
#Pages 6 
Date of Issue 2010-11-08 (MI) 

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