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Paper Abstract and Keywords
Presentation 2017-05-26 08:50
Classification of optic disc shape in glaucoma using machine learning based on quantified ocular parameters from ophthalmic examination instruments
Guangzhou An (TOPCON/RIKEN), Kazuko Omodaka, Satoru Tsuda, Yukihiro Shiga, Naoko Takada (Tohoku Univ.), Tsutomu Kikawa (TOPCON), Toru Nakazawa (Tohoku Univ./RIKEN), Hideo Yokota (RIKEN), Masahiro Akiba (TOPCON/RIKEN) SIP2017-12 IE2017-12 PRMU2017-12 MI2017-12
Abstract (in Japanese) (See Japanese page) 
(in English) This study aims to classify OAG patients’ optic disc shape objectively. This study enrolled 163 eyes of 105 OAG patients. Four machine learning classifiers, such as Support Vector Machine, Neural Network, Naive Bayes, Gradient Boosting Decision Tree, were applied with a variety of quantified data from ophthalmic examination equipment. As a result of comparing the prediction models’ performance, the accuracy of neural network was the highest, 85.6% validated with test data. It is expected that, the confidence level of the predicted disc types should be very useful for the medical care of patients with OAG.
Keyword (in Japanese) (See Japanese page) 
(in English) glaucoma / optic disc shape / OCT / machine learning / feature selection / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 50, MI2017-12, pp. 63-66, May 2017.
Paper # MI2017-12 
Date of Issue 2017-05-18 (SIP, IE, PRMU, 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)
Download PDF SIP2017-12 IE2017-12 PRMU2017-12 MI2017-12

Conference Information
Committee PRMU IE MI SIP  
Conference Date 2017-05-25 - 2017-05-26 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MI 
Conference Code 2017-05-PRMU-IE-MI-SIP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Classification of optic disc shape in glaucoma using machine learning based on quantified ocular parameters from ophthalmic examination instruments 
Sub Title (in English)  
Keyword(1) glaucoma  
Keyword(2) optic disc shape  
Keyword(3) OCT  
Keyword(4) machine learning  
Keyword(5) feature selection  
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1st Author's Name Guangzhou An  
1st Author's Affiliation TOPCON Corporation/RIKEN (TOPCON/RIKEN)
2nd Author's Name Kazuko Omodaka  
2nd Author's Affiliation Tohoku University (Tohoku Univ.)
3rd Author's Name Satoru Tsuda  
3rd Author's Affiliation Tohoku University (Tohoku Univ.)
4th Author's Name Yukihiro Shiga  
4th Author's Affiliation Tohoku University (Tohoku Univ.)
5th Author's Name Naoko Takada  
5th Author's Affiliation Tohoku University (Tohoku Univ.)
6th Author's Name Tsutomu Kikawa  
6th Author's Affiliation TOPCON Corporation (TOPCON)
7th Author's Name Toru Nakazawa  
7th Author's Affiliation Tohoku University/RIKEN (Tohoku Univ./RIKEN)
8th Author's Name Hideo Yokota  
8th Author's Affiliation RIKEN (RIKEN)
9th Author's Name Masahiro Akiba  
9th Author's Affiliation TOPCON Corporation/RIKEN (TOPCON/RIKEN)
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Speaker Author-1 
Date Time 2017-05-26 08:50:00 
Presentation Time 30 minutes 
Registration for MI 
Paper # SIP2017-12, IE2017-12, PRMU2017-12, MI2017-12 
Volume (vol) vol.117 
Number (no) no.47(SIP), no.48(IE), no.49(PRMU), no.50(MI) 
Page pp.63-66 
#Pages 4 
Date of Issue 2017-05-18 (SIP, IE, PRMU, MI) 

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