Hybrid Deep Learning Framework for Enhanced Diabetic Retinopathy Detection: Integrating Traditional Features with AI-driven Insights

TL;DR

This paper presents a hybrid deep learning framework that combines traditional clinical features with AI-driven insights to improve the accuracy and early detection of Diabetic Retinopathy from fundus images.

Contribution

It introduces a novel multimodal diagnostic framework that integrates handcrafted features with deep learning for enhanced DR detection accuracy.

Findings

Outperforms standalone deep learning models in classification accuracy.

Reduces false negatives in diabetic retinopathy detection.

Enables scalable and interpretable screening in high-burden regions.

Abstract

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.