Attention-Enhanced Deep Features with Heterogeneous Ensemble Learning for Glaucoma Detection

2026-09-06

AI Summary

本文提出了一种结合注意力增强的深度特征提取和异质集成学习的青光眼检测框架，以解决特征优化、类别不平衡和预测鲁棒性问题。

Abstract

Glaucoma is a progressive optic neuropathy characterized by irreversible damage to the optic nerve, making timely diagnosis critical to prevent permanent vision loss. Although deep learning has demonstrated promising performance in automated glaucoma detection, existing approaches often overlook feature refinement, suffer from class imbalance, and rely on individual classifiers that limit prediction robustness. To address these challenges, this paper proposes a hybrid glaucoma detection framework that integrates attention-enhanced deep feature extraction with heterogeneous ensemble learning. Specifically, deep representations are extracted using InceptionV3 and subsequently refined by incorporating the Convolutional Block Attention Module (CBAM) to enhance discriminative retinal features. To improve classification robustness, the extracted features are classified using multiple machine learning models together with Single-Level Ensemble (SLE) and Double-Level Ensemble (DLE) strategies, while SMOTE combined with Tomek Links (SMOTE+TL) is employed to alleviate class imbalance. Furthermore, a systematic comparison of handcrafted, deep, and attention-enhanced deep feature representations is conducted. Experimental evaluation on two public retinal fundus datasets demonstrates that deep feature-based methods consistently outperform handcrafted feature-based methods, while the proposed attention-enhanced framework achieves the best overall performance. Furthermore, Grad-CAM visualizations confirm that the proposed model focuses on clinically relevant retinal regions, providing interpretable evidence on the model's prediction process.

Problem

• Glaucoma Detection
• Feature Refinement
• Class Imbalance
• Prediction Robustness

Innovation

• Attention-Enhanced Deep Features
• Heterogeneous Ensemble Learning
• Convolutional Block Attention Module (CBAM)
• Class Imbalance
• Interpretable Evidence