Reliable image classification based on evidence and GCN

Zhonghai He; Xiang Xu; Zhuming Lian; Yu Luo

doi:10.1117/12.3052688

11 December 2024 Reliable image classification based on evidence and GCN

Zhonghai He, Xiang Xu, Zhuming Lian, Yu Luo

Proceedings Volume 13445, International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024); 134452M (2024) https://doi.org/10.1117/12.3052688
Event: International Conference on Electronics. Electrical and Information Engineering (ICEEIE 2024), 2024, Haikou, China

Abstract

With good classification performance, digital medical image methods based on deep learning have received more and more attention. However, the problem of these methods is that the black-box characteristic of deep learning makes it difficult to explain the model and prediction results. In order to improve the interpretability and reliability of medical image classification tasks, this paper designs a network structure that introduces eliminating uncertainty in GCN feedback by using evidence theory and subjective logic. The diagnosis experiments of COVID-19 images show that the model can effectively improve the recognition accuracy of COVID-19 patients, and has good reliability.

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

Citation Download Citation

Zhonghai He, Xiang Xu, Zhuming Lian, and Yu Luo "Reliable image classification based on evidence and GCN", Proc. SPIE 13445, International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024), 134452M (11 December 2024); https://doi.org/10.1117/12.3052688

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