# An Ensemble-based System for Microaneurysm Detection and Diabetic Retinopathy Grading

arXiv: https://arxiv.org/abs/1410.8577 · HTML: https://mlsanity.com/paper/1410.8577/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2014.

## Built on by (most cited first, 5 of 5)

- [DRG-Net: Interactive Joint Learning of Multi-lesion Segmentation and Classification for Diabetic Retinopathy Grading](https://mlsanity.com/paper/2212.14615/) (2022)
- [Multitasking Deep Learning Model for Detection of Five Stages of Diabetic Retinopathy](https://mlsanity.com/paper/2103.04207/) (2021)
- [Diabetic Retinopathy Detection using Ensemble Machine Learning](https://mlsanity.com/paper/2106.12545/) (2021)
- [A comprehensive survey on computer-aided diagnostic systems in diabetic retinopathy screening](https://mlsanity.com/paper/2208.01810/) (2022)
- [AdvBlur: Adversarial Blur for Robust Diabetic Retinopathy Classification and Cross-Domain Generalization](https://mlsanity.com/paper/2510.24000/) (2025)

## Abstract (arXiv)

Reliable microaneurysm detection in digital fundus images is still an open issue in medical image processing. We propose an ensemble-based framework to improve microaneurysm detection. Unlike the well-known approach of considering the output of multiple classifiers, we propose a combination of internal components of microaneurysm detectors, namely preprocessing methods and candidate extractors. We have evaluated our approach for microaneurysm detection in an online competition, where this algorithm is currently ranked as first and also on two other databases. Since microaneurysm detection is decisive in diabetic retinopathy grading, we also tested the proposed method for this task on the publicly available Messidor database, where a promising AUC 0.90 with 0.01 uncertainty is achieved in a 'DR/non-DR'-type classification based on the presence or absence of the microaneurysms.