DRetNet: A Novel Deep Learning Framework for Diabetic Retinopathy Diagnosis

• URL: http://arxiv.org/abs/2509.01072v1
• Date: Mon, 01 Sep 2025 02:27:16 GMT
• Title: DRetNet: A Novel Deep Learning Framework for Diabetic Retinopathy Diagnosis
• Authors: Idowu Paul Okuwobi, Jingyuan Liu, Jifeng Wan, Jiaojiao Jiang,
• Abstract summary: Current DR detection systems struggle with poor-quality images, lack interpretability, and insufficient integration of domain-specific knowledge.<n>We introduce a novel framework that integrates three innovative contributions.<n>The 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.
• Score: 8.234135343778993
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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