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| Presentation | 2020-01-29 13:20 [Poster Presentation] Computerized Determination Method for Histological Classification of Breast Masses on Ultrasonographic Images Using CNN Features and Morphological Features Shinya Kunieda, Akiyoshi Hizukuri, Ryohei Nakayama (Ritsumeikan Univ.)  MI2019-76 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | The purpose of this study was to develop a computerized determination method for histological classifications of masses on breast ultrasonographic images using CNN (Convolutional Neural Network) features and morphologic features in order to assist clinicians in determining a treatment plan. Our database consisted of 585 breast ultrasonographic images obtained from 585 patients. In our proposed method, 1,024 CNN features and eight morphologic features were first determined from a mass. An SVM (Support Vector Machine) with those features was employed to classify among histological classifications of masses. Three-fold cross validation method was used for training and testing the SVM. The classification accuracies of the proposed method were 85.8% (187/218) for invasive carcinomas, 77.1% (54/70) for noninvasive carcinomas, 83.5% (152/182) for fibroadenomas, and 85.2% (98/115) for cysts, respectively. The proposed method yielding high classification accuracies would be useful in the differential diagnosis of masses on breast ultrasonographic images as diagnosis aid. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Histological Classification / Convolutional Neural Network / Mass / Ultrasonographic Image / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 399, MI2019-76, pp. 53-55, Jan. 2020. | |
| Paper # | MI2019-76 | |
| 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 | MI2019-76 |
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| 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 Determination Method for Histological Classification of Breast Masses on Ultrasonographic Images Using CNN Features and Morphological Features |
| Sub Title (in English) | |
| Keyword(1) | Histological Classification |
| Keyword(2) | Convolutional Neural Network |
| Keyword(3) | Mass |
| Keyword(4) | Ultrasonographic Image |
| Keyword(5) | |
| Keyword(6) | |
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| Keyword(8) | |
| 1st Author's Name | Shinya Kunieda |
| 1st Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
| 2nd Author's Name | Akiyoshi Hizukuri |
| 2nd Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
| 3rd Author's Name | Ryohei Nakayama |
| 3rd Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
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| Speaker | Author-1 |
| Date Time | 2020-01-29 13:20:00 |
| Presentation Time | 30 minutes |
| Registration for | MI |
| Paper # | MI2019-76 |
| Volume (vol) | vol.119 |
| Number (no) | no.399 |
| Page | pp.53-55 |
| #Pages | 3 |
| Date of Issue | 2020-01-22 (MI) |
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