Problem-Oriented Strategy for Diabetic Retinopathy Identification

Abstract

Diabetic retinopathy is a prevalent and sight-threatening complication of diabetes that affects individuals worldwide. Effectively addressing this condition requires adapting approaches to the specific characteristics of retinal images. Existing works often tackle the diagnostic challenge without focusing on a specific aspect. In contrast, our study introduces a new problem-oriented strategy that addresses key gaps in diabetic retinopathy using three novel, tailored approaches. First, to address the underexploitation of high-resolution retinal images, we propose a resolution-preserving, data-based approach that employs patch-based analysis without downscaling while also mitigating data scarcity and imbalance. Second, inspired by real-world clinical practice, we develop a symptoms-based approach that explicitly segments multiple key pathological indicators (blood vessels, exudates, and microaneurysms) and then uses them to guide the classification network. Third, we propose a hierarchical approach that decomposes the multi-stage classification task into multiple hierarchical binary classifications, enabling more specialized feature learning and informed decision-making across different severity levels. Evaluations on both EyePACS and APTOS benchmark datasets showcased superior performance, surpassing or matching contemporary state-of-the-art results. These outcomes demonstrate the effectiveness of our proposed approaches and underscore the strategy's potential to improve diabetic retinopathy diagnosis.