# Advanced Segmentation of Diabetic Retinopathy Lesions Using DeepLabv3+

arXiv: https://arxiv.org/abs/2504.17306 · HTML: https://mlsanity.com/paper/2504.17306/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 24 Apr 2025.

## Builds on (most cited first, 2 of 2)

- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration](https://mlsanity.com/paper/1903.09555/) (2019)

## Built on by (most cited first, 1 of 1)

- [Explainable Fundus Image Curation and Lesion Detection in Diabetic Retinopathy](https://mlsanity.com/paper/2512.08986/) (2025)

## Abstract (arXiv)

To improve the segmentation of diabetic retinopathy lesions (microaneurysms, hemorrhages, exudates, and soft exudates), we implemented a binary segmentation method specific to each type of lesion. As post-segmentation, we combined the individual model outputs into a single image to better analyze the lesion types. This approach facilitated parameter optimization and improved accuracy, effectively overcoming challenges related to dataset limitations and annotation complexity. Specific preprocessing steps included cropping and applying contrast-limited adaptive histogram equalization to the L channel of the LAB image. Additionally, we employed targeted data augmentation techniques to further refine the model's efficacy. Our methodology utilized the DeepLabv3+ model, achieving a segmentation accuracy of 99%. These findings highlight the efficacy of innovative strategies in advancing medical image analysis, particularly in the precise segmentation of diabetic retinopathy lesions. The IDRID dataset was utilized to validate and demonstrate the robustness of our approach.