3D-CA Net: a 3D-CNN with composite multidimensional attention for the assessment of lung aging

Tangbin Tian; Jingshan Pan; Jing Ge; Na Li; Dexin Yu; Liping Zuo; Tao Hu; Shengyu Liu

doi:10.1117/12.3050616

24 October 2024 3D-CA Net: a 3D-CNN with composite multidimensional attention for the assessment of lung aging

Tangbin Tian, Jingshan Pan, Jing Ge, Na Li, Dexin Yu, Liping Zuo, Tao Hu, Shengyu Liu

Proceedings Volume 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024); 1339605 (2024) https://doi.org/10.1117/12.3050616
Event: 3rd International Conference on Image Processing, Object Detection and Tracking (IPODT24), 2024, Nanjing, China

Abstract

As the population ages, early diagnosis and treatment of lung diseases become increasingly important. Accurate assessment of aging-related changes in lung CT images is crucial for the prevention and treatment of related diseases. Traditional methods for lung aging assessment from CT images are time-consuming, subjective, and heavily reliant on the clinical experience of doctors. To address these issues, this paper proposes a lung aging assessment method with 3D-CA Net. The feature extraction part of the proposed network consists of four main 3D Convolutional and Composite Multidimensional Attention Modules. By introducing the Composite Multidimensional Attention Module, the advantages of spatial attention and self-attention are both utilized. Additionally, an improved E-cross-entropy loss function is employed to reduce overfitting and enhance generalization. Experimental results demonstrate that the 3D-CA Net significantly outperforms existing methods in terms of accuracy, macro-averaged precision, macro-averaged recall and macro-averaged F1 score. This work provides a comprehensive solution for lung CT image aging assessment and offers insights for future advancements in medical imaging analysis.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tangbin Tian, Jingshan Pan, Jing Ge, Na Li, Dexin Yu, Liping Zuo, Tao Hu, and Shengyu Liu "3D-CA Net: a 3D-CNN with composite multidimensional attention for the assessment of lung aging", Proc. SPIE 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024), 1339605 (24 October 2024); https://doi.org/10.1117/12.3050616

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Lung

3D modeling

Computed tomography

3D image processing

Feature extraction

Education and training

Matrices

Show All Keywords

Keywords/Phrases

Search In:

Publication Years