IEICE Technical Committee Submission System
Conference Paper's Information
Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2021-03-16 13:30
Effect of Improving Versatility of Lung Nodules Classification Model by Fine-Tuning
Taku Ri, Tatsuya Yamazaki (Niigata Univ.) MI2020-72
Abstract (in Japanese) (See Japanese page) 
(in English) In order to improve the survival rate of lung cancer patients, it is important to detect nodules at an early stage, but the number of radiologists is insufficient for recent increase of CT (Computer Tomography) examinations. Meanwhile, a diagnosis aiding system using a computer for supporting radiologist diagnoses is drawing attention. In this study, we construct a nodule detection system to support the diagnosis using deep learning methods, that automatically classify positiveness or negativeness of a lesion in CT images. A nodule classification model in the system is based on a three-dimensional convolutional neural network which is applied to the classification phase to determine positiveness or negativeness of nodules. We also apply the Fine-Tuning method as versatility improvement for the constructed nodule classification model for accuracy verification.
Keyword (in Japanese) (See Japanese page) 
(in English) CT Image / nodules / detection system / three-dimensional convolutional neural network / Fine-Tuning / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 431, MI2020-72, pp. 102-107, March 2021.
Paper # MI2020-72 
Date of Issue 2021-03-08 (MI) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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 MI2020-72

Conference Information
Committee MI  
Conference Date 2021-03-15 - 2021-03-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Imaging 
Paper Information
Registration To MI 
Conference Code 2021-03-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Effect of Improving Versatility of Lung Nodules Classification Model by Fine-Tuning 
Sub Title (in English)  
Keyword(1) CT Image  
Keyword(2) nodules  
Keyword(3) detection system  
Keyword(4) three-dimensional convolutional neural network  
Keyword(5) Fine-Tuning  
1st Author's Name Taku Ri  
1st Author's Affiliation Niigata University (Niigata Univ.)
2nd Author's Name Tatsuya Yamazaki  
2nd Author's Affiliation Niigata University (Niigata Univ.)
3rd Author's Name  
3rd Author's Affiliation ()
4th Author's Name  
4th Author's Affiliation ()
5th Author's Name  
5th Author's Affiliation ()
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
Date Time 2021-03-16 13:30:00 
Presentation Time 15 
Registration for MI 
Paper # IEICE-MI2020-72 
Volume (vol) IEICE-120 
Number (no) no.431 
Page pp.102-107 
#Pages IEICE-6 
Date of Issue IEICE-MI-2021-03-08 

[Return to Top Page]

[Return to IEICE Web Page]

The Institute of Electronics, Information and Communication Engineers (IEICE), Japan