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Paper Abstract and Keywords
Presentation 2023-05-18 15:30
Grad-CAM approach for Multiclass Magnetic Resonance Imaging Tumor detection and Classification
Tahir Hussain, Shouno Hayaru (UEC) MI2023-4
Abstract (in Japanese) (See Japanese page) 
(in English) The growth of abnormal cells in the human brain causes brain tumors (BT). Early diagnosis becomes essential for timely treatment for patient survival. A radiologist examines magnetic resonance imaging (MRI) to diagnose and identify tumors through manual evaluation. This process is time-consuming and requires expertise for a complete understanding of tumor type and location. Existing methods suffer from unsatisfactory performance and lack of model explainability, especially in multiclass BT for clinical translation. However, physicians perceive the model results to be unsatisfactory due to Blackbox. Our study addresses these issues for multiclass classification of brain MRI tumor images and proposed a pre-train visual geometry group (VGG-19) that runs a new form of gradient-weighted class activation mapping (Grad-CAM) algorithm for model explainability. The Grad-CAM was used within the developed convolutional neural network (CNN) model, for the model explainability for BT diagnosis. The experimental findings show that the pre-train-VGG-19-Grad-CAM gives better classification and visualization results as compared to stat-off-art deep learning (DL) models with improved accuracy. The heatmap results can help radiologists to explain and validate the classification results by indicating the tumor region on the brain MRI and reducing misclassification.
Keyword (in Japanese) (See Japanese page) 
(in English) Grad-CAM / model explainability / pre-train-VGG-19 / / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 37, MI2023-4, pp. 10-13, May 2023.
Paper # MI2023-4 
Date of Issue 2023-05-11 (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 MI2023-4

Conference Information
Committee MI  
Conference Date 2023-05-18 - 2023-05-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Nagoya Congress Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Image Processing 
Paper Information
Registration To MI 
Conference Code 2023-05-MI 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Grad-CAM approach for Multiclass Magnetic Resonance Imaging Tumor detection and Classification 
Sub Title (in English)  
Keyword(1) Grad-CAM  
Keyword(2) model explainability  
Keyword(3) pre-train-VGG-19  
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1st Author's Name Tahir Hussain  
1st Author's Affiliation University of Electro-Communication (UEC)
2nd Author's Name Shouno Hayaru  
2nd Author's Affiliation University of Electro-Communication (UEC)
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Speaker Author-1 
Date Time 2023-05-18 15:30:00 
Presentation Time 30 minutes 
Registration for MI 
Paper # MI2023-4 
Volume (vol) vol.123 
Number (no) no.37 
Page pp.10-13 
#Pages 4 
Date of Issue 2023-05-11 (MI) 

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