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.
Abstract (translated)
糖尿病视网膜病变（DR）是全球导致失明的主要原因之一，因此早期检测以防止视力丧失至关重要。目前的自动化DR检测系统在处理质量较差的图像时往往表现不佳，并且缺乏可解释性以及特定领域的知识整合不足。为了应对这些挑战，我们提出了一种新颖的框架，集成了三个创新贡献： 1. **基于物理信息神经网络（PINNs）的自适应视网膜图像增强**：该技术通过引入物理约束动态地增强了视网膜图像，提高了微动脉瘤、出血和渗出等关键特征的可见性。 2. **混合特征融合网络（HFFN）**：通过结合深度学习嵌入与手工设计的特征，HFFN 利用已学得的表示以及特定领域的知识来提升泛化能力和准确性。 3. **多阶段分类器及不确定性量化**：此方法将分类过程分解为逻辑步骤，提供可解释的预测和置信度评分，从而增强临床信任。 所提出的框架达到了92.7%的准确率、92.5%的精确率、92.6%的召回率、92.5%的F1分数、97.8%的AUC值、0.96的mAP和0.85的MCC。眼科医生将该框架的预测评为高度临床相关（4.8/5分），突显了它与实际诊断需求的高度一致。通过Grad-CAM可视化分析以及不确定性热图等定性分析，进一步增强了系统的可解释性和可信度。 此框架在各种条件下表现出色，包括质量较差的图像、噪音数据和未见过的数据集。这些特性使得所提出的框架成为临床应用中的有力工具，在资源有限的情况下实现更准确且可靠的DR检测。
URL
https://arxiv.org/abs/2509.01072
