Paper Abstract and Keywords |
Presentation |
2020-09-03 14:55
Performance Improvement of Alzheimer's Disease Classification Using Convolutional Neural Network Daiki Endo, Koichi Ito, Takafumi Aoki (Tohoku Univ.) MI2020-31 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Alzheimer's disease (AD) is a progressive brain disease that causes a different pattern of brain atrophy from normal aging.
Early identification of AD is crucial since the progression of the disease can be slowed down by medication.
In the field of image recognition, their accuracy have been significantly improved by using convolutional neural networks (CNNs).
Similarly, in the field of medical image processing, researches on the diagnostic support using CNN have been studied.
On the other hand, the number of medical images provided for CNN training is extremely small, which may cause over-fitting in training.
In this paper, we propose an AD identification method using pre-training with an autoencoder to suppress over-fitting.
Through experiments using a large-scale database, we demonstrate the effectiveness of our proposed method. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
computer aided diagnosis / brain MRI image / Alzheimer's disease / convolutional neural network / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 120, no. 156, MI2020-31, pp. 63-67, Sept. 2020. |
Paper # |
MI2020-31 |
Date of Issue |
2020-08-27 (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) |
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MI2020-31 |
Conference Information |
Committee |
MI |
Conference Date |
2020-09-03 - 2020-09-03 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Medical Image Analysis |
Paper Information |
Registration To |
MI |
Conference Code |
2020-09-MI |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Performance Improvement of Alzheimer's Disease Classification Using Convolutional Neural Network |
Sub Title (in English) |
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computer aided diagnosis |
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brain MRI image |
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Alzheimer's disease |
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convolutional neural network |
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1st Author's Name |
Daiki Endo |
1st Author's Affiliation |
Tohoku University (Tohoku Univ.) |
2nd Author's Name |
Koichi Ito |
2nd Author's Affiliation |
Tohoku University (Tohoku Univ.) |
3rd Author's Name |
Takafumi Aoki |
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Tohoku University (Tohoku Univ.) |
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Speaker |
Author-1 |
Date Time |
2020-09-03 14:55:00 |
Presentation Time |
15 minutes |
Registration for |
MI |
Paper # |
MI2020-31 |
Volume (vol) |
vol.120 |
Number (no) |
no.156 |
Page |
pp.63-67 |
#Pages |
5 |
Date of Issue |
2020-08-27 (MI) |
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