Enhancing Diabetic Retinopathy Diagnosis: A Lightweight CNN Architecture for Efficient Exudate Detection in Retinal Fundus Images

TL;DR

This paper presents a lightweight CNN designed for efficient and accurate detection of exudates in retinal images, aiding early diabetic retinopathy diagnosis with fewer parameters and high performance.

Contribution

The authors introduce a novel, compact CNN architecture with domain-specific data augmentation and regularization, reducing model size by 60% while maintaining high diagnostic accuracy.

Findings

Model contains only 4.73 million parameters.

Achieves an F1 score of 90% in exudate detection.

Reduces computational complexity compared to ResNet-18.

Abstract

Retinal fundus imaging plays an essential role in diagnosing various stages of diabetic retinopathy, where exudates are critical markers of early disease onset. Prompt detection of these exudates is pivotal for enabling optometrists to arrest or significantly decelerate the disease progression. This paper introduces a novel, lightweight convolutional neural network architecture tailored for automated exudate detection, designed to identify these markers efficiently and accurately. To address the challenge of limited training data, we have incorporated domain-specific data augmentations to enhance the model's generalizability. Furthermore, we applied a suite of regularization techniques within our custom architecture to boost diagnostic accuracy while optimizing computational efficiency. Remarkably, this streamlined model contains only 4.73 million parameters a reduction of nearly 60% compared to the standard ResNet-18 model, which has 11.69 million parameters. Despite its reduced complexity, our model achieves an impressive F1 score of 90%, demonstrating its efficacy in the early detection of diabetic retinopathy through fundus imaging.