Multi-layer feature attention enhanced network for diabetic retinopathy staging
10.3969/j.issn.1005-202X.2025.09.008
- VernacularTitle:面向糖尿病视网膜病变分级的多层特征关注增强网络
- Author:
Bingxue LIANG
1
;
Wenjing WANG
;
Haoqi WANG
;
Quan GUAN
;
Yuhua QIN
Author Information
1. 青岛科技大学信息科学技术学院,山东 青岛 266061
- Publication Type:Journal Article
- Keywords:
diabetes retinopathy;
image classification;
feature fusion;
computer-aided diagnosis
- From:
Chinese Journal of Medical Physics
2025;42(9):1174-1183
- CountryChina
- Language:Chinese
-
Abstract:
A multi-layer feature attention enhanced network is proposed to further improve the diagnostic accuracy of the severity of diabetic retinopathy.To address the inconsistent expression of global and local features when processing diabetic retinopathy images,a dual-branch parallel model combining ResNet-50 and DeiT-S is employed as the backbone architecture,and a feature fusion module is designed at the end of the network.Concurrently,a multi-scale location awareness enhancement module is developed to extract multi-scale information through dilated convolution with positional attention mechanism for enhancing the feature representation of lesions in fundus images,and a local feature enhancement module is constructed to strengthen the model's capability in extracting local information,thus improving model's capability to identify small lesions and minor changes.The experimental results show that the proposed multi-layer feature attention enhanced network achieves an accuracy of 87.61%,exhibiting excellent classification performance.This advancement provides a strong support for further development of diabetic retinopathy detection technology.
