An Interpretable Deep Learning Model for Early Diabetic Retinopathy Detection and Grading

Abstract

Diabetic Retinopathy (DR) is a progressive eye disease that can lead to permanent vision impairment if not diagnosed at an early stage. This work presents an interpretable deep learning approach for automated DR detection and grading using retinal fundus images. The proposed framework applies Convolutional Neural Networks (CNNs) together with image preprocessing techniques to automatically identify retinal abnormalities and classify disease severity levels. Unlike traditional manual screening approaches, the model learns visual features directly from image data to improve efficiency and consistency.

The framework focuses on detecting clinically important indicators such as microaneurysms and exudates, supporting accurate grading across different stages of diabetic retinopathy. The system is designed to reduce dependency on manual diagnosis, improve reliability, and enhance screening scalability. Performance objectives include improving accuracy, sensitivity, and specificity compared with conventional screening workflows. The proposed approach demonstrates how AI-assisted ophthalmology systems can support large-scale healthcare screening and remote diagnostic environments where expert availability is limited.