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

arXiv: https://arxiv.org/abs/2408.06784 · HTML: https://mlsanity.com/paper/2408.06784/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 40 references that are in the map.
- First posted on arXiv 13 Aug 2024.

## Builds on (most cited first, 3 of 3)

- [A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability](https://mlsanity.com/paper/2008.09772/) (2020)
- [RTNet: Relation Transformer Network for Diabetic Retinopathy Multi-lesion Segmentation](https://mlsanity.com/paper/2201.11037/) (2022)
- [A Deep Learning Interpretable Classifier for Diabetic Retinopathy Disease Grading](https://mlsanity.com/paper/1712.08107/) (2017)

## Built on by (most cited first, 2 of 2)

- [YOLOv11 for Vehicle Detection: Advancements, Performance, and Applications in Intelligent Transportation Systems](https://mlsanity.com/paper/2410.22898/) (2024)
- [Enhancing Construction Site Safety: A Lightweight Convolutional Network for Effective Helmet Detection](https://mlsanity.com/paper/2409.12669/) (2024)

## Abstract (arXiv)

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.