DRetNet: A Novel Deep Learning Framework for Diabetic Retinopathy Diagnosis

2025-08-31

AI Summary

To address critical challenges in automated diabetic retinopathy (DR) diagnosis—including poor image quality, limited model interpretability, and insufficient integration of domain knowledge—this paper proposes a clinically deployable, physics-informed framework. Methodologically: (i) physics-informed neural networks (PINNs) enable adaptive image enhancement; (ii) a hybrid feature fusion network (HFFN) jointly leverages handcrafted ophthalmic features and deep representations; and (iii) a multi-stage classifier integrates uncertainty quantification with joint Grad-CAM and uncertainty heatmap visualization. Under resource-constrained conditions, the framework achieves robust and trustworthy performance: 92.7% accuracy, 97.8% AUC, 0.96 mAP, 0.85 Matthews correlation coefficient (MCC), and expert-rated clinical relevance of 4.8/5. It further demonstrates strong generalization to low-quality and out-of-distribution data. The core contribution lies in the first integrated incorporation of PINNs, quantitative interpretability, and ophthalmic priors—advancing DR intelligent diagnosis toward clinical trustworthiness.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Diabetic retinopathy (DR) is a leading cause of blindness worldwide, necessitating early detection to prevent vision loss. Current automated DR detection systems often struggle with poor-quality images, lack interpretability, and insufficient integration of domain-specific knowledge. To address these challenges, we introduce a novel framework that integrates three innovative contributions: (1) Adaptive Retinal Image Enhancement Using Physics-Informed Neural Networks (PINNs): this technique dynamically enhances retinal images by incorporating physical constraints, improving the visibility of critical features such as microaneurysms, hemorrhages, and exudates; (2) Hybrid Feature Fusion Network (HFFN): by combining deep learning embeddings with handcrafted features, HFFN leverages both learned representations and domain-specific knowledge to enhance generalization and accuracy; (3) Multi-Stage Classifier with Uncertainty Quantification: this method breaks down the classification process into logical stages, providing interpretable predictions and confidence scores, thereby improving clinical trust. The proposed framework achieves an accuracy of 92.7%, a precision of 92.5%, a recall of 92.6%, an F1-score of 92.5%, an AUC of 97.8%, a mAP of 0.96, and an MCC of 0.85. Ophthalmologists rated the framework's predictions as highly clinically relevant (4.8/5), highlighting its alignment with real-world diagnostic needs. Qualitative analyses, including Grad-CAM visualizations and uncertainty heatmaps, further enhance the interpretability and trustworthiness of the system. The framework demonstrates robust performance across diverse conditions, including low-quality images, noisy data, and unseen datasets. These features make the proposed framework a promising tool for clinical adoption, enabling more accurate and reliable DR detection in resource-limited settings.

Problem

• Addressing poor-quality image challenges in diabetic retinopathy diagnosis
• Enhancing interpretability and clinical trust in automated detection systems
• Integrating domain knowledge with deep learning for improved accuracy

Innovation

• Physics-informed neural networks for retinal image enhancement
• Hybrid feature fusion combining deep and handcrafted features
• Multi-stage classifier with uncertainty quantification for interpretability