融合传统特征与深度学习，提升糖尿病视网膜病变早期检测准确率
Hybrid Deep Learning Framework for Enhanced Diabetic Retinopathy Detection: Integrating Traditional Features with AI-driven Insights
- 结合手工提取临床特征与深度学习自动识别模式
- 分类性能优于纯深度学习模型，降低漏诊率
- 适合资源有限地区大规模筛查，兼具可解释性与效率
糖尿病视网膜病变（DR）是糖尿病（DM）引发的严重致盲并发症，尤其在糖尿病患者众多的印度尤为突出。长期高血糖会损伤视网膜微血管，导致微动脉瘤、出血和渗出等病变，若未及时发现将造成不可逆视力丧失。由于早期无症状，早期筛查至关重要。眼底成像可通过检测细微视网膜病灶实现精准诊断。本文提出一种混合诊断框架，整合传统特征提取与深度学习（DL），以增强DR检测能力。手工特征可捕捉关键临床标志物，而深度学习则实现分层模式识别，提升早期诊断能力。该模型融合可解释的临床数据与学习到的特征，在分类性能上超越单一深度学习方法，显著降低假阴性率。此多模态AI驱动方案为糖尿病负担较重地区提供了可扩展、高精度的DR筛查路径。
原文摘要 · Abstract (English)
Diabetic Retinopathy (DR), a vision-threatening complication of Dia-betes Mellitus (DM), is a major global concern, particularly in India, which has one of the highest diabetic populations. Prolonged hyperglycemia damages reti-nal microvasculature, leading to DR symptoms like microaneurysms, hemor-rhages, and fluid leakage, which, if undetected, cause irreversible vision loss. Therefore, early screening is crucial as DR is asymptomatic in its initial stages. Fundus imaging aids precise diagnosis by detecting subtle retinal lesions. This paper introduces a hybrid diagnostic framework combining traditional feature extraction and deep learning (DL) to enhance DR detection. While handcrafted features capture key clinical markers, DL automates hierarchical pattern recog-nition, improving early diagnosis. The model synergizes interpretable clinical data with learned features, surpassing standalone DL approaches that demon-strate superior classification and reduce false negatives. This multimodal AI-driven approach enables scalable, accurate DR screening, crucial for diabetes-burdened regions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。