Detection of Hard Exudates in Retinal Fundus Images using Deep Learning

TL;DR

This paper presents a deep learning-based method for detecting hard exudates in retinal fundus images to aid early diabetic retinopathy diagnosis, addressing the shortage of ophthalmologists and improving screening accuracy.

Contribution

It introduces a novel deep learning algorithm specifically designed for hard exudate detection in retinal images, advancing beyond traditional image processing and machine learning methods.

Findings

Deep learning algorithm effectively detects hard exudates

Improves early diagnosis of diabetic retinopathy

Addresses ophthalmologist shortage

Abstract

Diabetic Retinopathy (DR) is a retinal disorder that affects the people having diabetes mellitus for a long time (20 years). DR is one of the main reasons for the preventable blindness all over the world. If not detected early the patient may progress to severe stages of irreversible blindness. Lack of Ophthalmologists poses a serious problem for the growing diabetes patients. It is advised to develop an automated DR screening system to assist the Ophthalmologist in decision making. Hard exudates develop when DR is present. It is important to detect hard exudates in order to detect DR in an early stage. Research has been done to detect hard exudates using regular image processing techniques and Machine Learning techniques. Here, a deep learning algorithm has been presented in this paper that detects hard exudates in fundus images of the retina.