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Paper Abstract and Keywords
Presentation 2020-01-29 13:20
[Poster Presentation] Computerized Classification Method of Benign and Malignant Masses in Multiple MRI Sequences using Convolutional Neural Network
Yuichi Mima, Akiyoshi Hizukuri, Ryohei Nakayama (Ritsumeikan Univer) MI2019-77
Abstract (in Japanese) (See Japanese page) 
(in English) Breast magnetic resonance imaging (MRI) has a higher sensitivity of early breast cancer than mammography and ultrasonography, but the specificity is lower. The purpose of this study was to develop a computerized classification method for distinguishing between benign and malignant masses by analyzing multiple MRI sequences with convolutional neural networks (CNNs). Our database consisted of multiple MRI sequences for 43 patients with masses. In our proposed method, the CNNs were first trained independently for each MRI sequence. The outputs of the middle layers in the trained CNNs were then inputted to a support vector machine (SVM) for distinguishing between benign and malignant masses. With the proposed method, the classification accuracy, the sensitivity, the specificity, the positive predictive value, and the negative predictive value were 88.4% (38/43), 90.0% (27/30), 84.6% (11/13), 76.9% (10/13), and 93.3% (28/30), respectively. The proposed method achieved high classification performance and would be useful in differential diagnoses of masses as diagnostic aid.
Keyword (in Japanese) (See Japanese page) 
(in English) Breast magnetic resonance imaging / Multiple sequences, / Mass / Convolutional neural network / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 399, MI2019-77, pp. 57-59, Jan. 2020.
Paper # MI2019-77 
Date of Issue 2020-01-22 (MI) 
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 MI2019-77

Conference Information
Committee MI  
Conference Date 2020-01-29 - 2020-01-30 
Place (in Japanese) (See Japanese page) 
Place (in English) OKINAWAKEN SEINENKAIKAN 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Image Engineering, Analysis, Recognition, etc. 
Paper Information
Registration To MI 
Conference Code 2020-01-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Computerized Classification Method of Benign and Malignant Masses in Multiple MRI Sequences using Convolutional Neural Network 
Sub Title (in English)  
Keyword(1) Breast magnetic resonance imaging  
Keyword(2) Multiple sequences,  
Keyword(3) Mass  
Keyword(4) Convolutional neural network  
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1st Author's Name Yuichi Mima  
1st Author's Affiliation Ritsumeikan University (Ritsumeikan Univer)
2nd Author's Name Akiyoshi Hizukuri  
2nd Author's Affiliation Ritsumeikan University (Ritsumeikan Univer)
3rd Author's Name Ryohei Nakayama  
3rd Author's Affiliation Ritsumeikan University (Ritsumeikan Univer)
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Speaker Author-1 
Date Time 2020-01-29 13:20:00 
Presentation Time 30 minutes 
Registration for MI 
Paper # MI2019-77 
Volume (vol) vol.119 
Number (no) no.399 
Page pp.57-59 
#Pages 3 
Date of Issue 2020-01-22 (MI) 

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