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Paper Abstract and Keywords
Presentation 2023-11-14 13:00
Brain Disease Classification Based on Brain MRI Images Using 3D-CNN
Daisuke Hayashi, Akio Nagasaka, Yuji Mochizuki, Takayuki Hayashi (Hitachi), Takefumi Ueno (NHO Hizen Psychiatric Center) MICT2023-29 MI2023-22
Abstract (in Japanese) (See Japanese page) 
(in English) Schizophrenia and Alzheimer’s disease are brain diseases that cause structural changes in the brain. In this paper, we classify such disorders on the basis of the above-mentioned structural brain changes through T1-weighted images of short-time imaging, which is less burdensome for patients. We propose a method of detecting the micro, complex and widely distributed anatomical structures characteristic of schizophrenia with high accuracy from a small amount of data of MRI images by using a three-dimensional (3D) reconstruction smoothing process with bicubic interpolation and a 3D-CNN model with 3D efficient channel attention. Our proposed method classified Alzheimer’s disease, schizophrenia, and normal control with 96.5% accuracy. In a regional analysis of the above classification model using 3D Gradient-weighted Class Activation Mapping, we identified the frontal lobe, left superior temporal gyrus, and subcortical regions as important for classifying schizophrenia, and the temporal lobe, parietal lobe, occipital lobe, and subcortical regions for classifying Alzheimer’s disease, which is consistent with the salient network dysfunction hypothesis.
Keyword (in Japanese) (See Japanese page) 
(in English) MRI / Brain / T1-weighted images / ECA / 3D-CNN / 3D-Grad-CAM / Brain disease / Mental illness  
Reference Info. IEICE Tech. Rep., vol. 123, no. 257, MI2023-22, pp. 15-20, Nov. 2023.
Paper # MI2023-22 
Date of Issue 2023-11-07 (MICT, 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 MICT2023-29 MI2023-22

Conference Information
Committee MI MICT  
Conference Date 2023-11-14 - 2023-11-14 
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 2023-11-MI-MICT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Brain Disease Classification Based on Brain MRI Images Using 3D-CNN 
Sub Title (in English)  
Keyword(1) MRI  
Keyword(2) Brain  
Keyword(3) T1-weighted images  
Keyword(4) ECA  
Keyword(5) 3D-CNN  
Keyword(6) 3D-Grad-CAM  
Keyword(7) Brain disease  
Keyword(8) Mental illness  
1st Author's Name Daisuke Hayashi  
1st Author's Affiliation Hitachi, Ltd. Research & Development Group (Hitachi)
2nd Author's Name Akio Nagasaka  
2nd Author's Affiliation Hitachi, Ltd. Research & Development Group (Hitachi)
3rd Author's Name Yuji Mochizuki  
3rd Author's Affiliation Hitachi, Ltd. Social Infrastructure Systems Business Unit (Hitachi)
4th Author's Name Takayuki Hayashi  
4th Author's Affiliation Hitachi, Ltd. Social Infrastructure Systems Business Unit (Hitachi)
5th Author's Name Takefumi Ueno  
5th Author's Affiliation NHO Hizen Psychiatric Center (NHO Hizen Psychiatric Center)
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Speaker Author-1 
Date Time 2023-11-14 13:00:00 
Presentation Time 20 minutes 
Registration for MI 
Paper # MICT2023-29, MI2023-22 
Volume (vol) vol.123 
Number (no) no.256(MICT), no.257(MI) 
Page pp.15-20 
#Pages 6 
Date of Issue 2023-11-07 (MICT, MI) 

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