# An ensemble-based system for automatic screening of diabetic retinopathy

arXiv: https://arxiv.org/abs/1410.8576 · HTML: https://mlsanity.com/paper/1410.8576/

## Key facts

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

## Built on by (most cited first, 14 of 14)

- [Unmasking the Chameleons: A Benchmark for Out-of-Distribution Detection in Medical Tabular Data](https://mlsanity.com/paper/2309.16220/) (2023)
- [Curvature-based Feature Selection with Application in Classifying Electronic Health Records](https://mlsanity.com/paper/2101.03581/) (2021)
- [Diabetic Retinopathy Detection using Ensemble Machine Learning](https://mlsanity.com/paper/2106.12545/) (2021)
- [A deep learning model for classification of diabetic retinopathy in eye fundus images based on retinal lesion detection](https://mlsanity.com/paper/2110.07745/) (2021)
- [On the Robustness of Random Forest Against Untargeted Data Poisoning: An Ensemble-Based Approach](https://mlsanity.com/paper/2209.14013/) (2022)
- [Robust Regularized Locality Preserving Indexing for Fiedler Vector Estimation](https://mlsanity.com/paper/2107.12070/) (2021)
- [Generalization Properties of Decision Trees on Real-valued and Categorical Features](https://mlsanity.com/paper/2210.10781/) (2022)
- [Mitigating Overconfidence in Out-of-Distribution Detection by Capturing Extreme Activations](https://mlsanity.com/paper/2405.12658/) (2024)
- [Enhancing Eye Disease Diagnosis with Deep Learning and Synthetic Data Augmentation](https://mlsanity.com/paper/2407.17755/) (2024)
- [O2PF: Oversampling via Optimum-Path Forest for Breast Cancer Detection](https://mlsanity.com/paper/2101.05775/) (2021)
- [Targeted Deep Learning: Framework, Methods, and Applications](https://mlsanity.com/paper/2105.14052/) (2021)
- [Enhanced Nearest Neighbor Classification for Crowdsourcing](https://mlsanity.com/paper/2203.00781/) (2022)
- [A Meta-heuristic Approach to Estimate and Explain Classifier Uncertainty](https://mlsanity.com/paper/2304.10284/) (2023)
- [Salutary Labeling with Zero Human Annotation](https://mlsanity.com/paper/2405.17627/) (2024)

## Abstract (arXiv)

In this paper, an ensemble-based method for the screening of diabetic retinopathy (DR) is proposed. This approach is based on features extracted from the output of several retinal image processing algorithms, such as image-level (quality assessment, pre-screening, AM/FM), lesion-specific (microaneurysms, exudates) and anatomical (macula, optic disc) components. The actual decision about the presence of the disease is then made by an ensemble of machine learning classifiers. We have tested our approach on the publicly available Messidor database, where 90% sensitivity, 91% specificity and 90% accuracy and 0.989 AUC are achieved in a disease/no-disease setting. These results are highly competitive in this field and suggest that retinal image processing is a valid approach for automatic DR screening.