# Early Blindness Detection Based on Retinal Images Using Ensemble Learning

arXiv: https://arxiv.org/abs/2006.07475 · HTML: https://mlsanity.com/paper/2006.07475/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2020.

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

- [Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study](https://mlsanity.com/paper/2412.20235/) (2024)
- [Designing the Architecture of a Convolutional Neural Network Automatically for Diabetic Retinopathy Diagnosis](https://mlsanity.com/paper/2110.03877/) (2021)

## Abstract (arXiv)

Diabetic retinopathy (DR) is the primary cause of vision loss among grownup people around the world. In four out of five cases having diabetes for a prolonged period leads to DR. If detected early, more than 90 percent of the new DR occurrences can be prevented from turning into blindness through proper treatment. Despite having multiple treatment procedures available that are well capable to deal with DR, the negligence and failure of early detection cost most of the DR patients their precious eyesight. The recent developments in the field of Digital Image Processing (DIP) and Machine Learning (ML) have paved the way to use machines in this regard. The contemporary technologies allow us to develop devices capable of automatically detecting the condition of a persons eyes based on their retinal images. However, in practice, several factors hinder the quality of the captured images and impede the detection outcome. In this study, a novel early blind detection method has been proposed based on the color information extracted from retinal images using an ensemble learning algorithm. The method has been tested on a set of retinal images collected from people living in the rural areas of South Asia, which resulted in a 91 percent classification accuracy.