Diabetic Retinopathy Diagnosis based on Convolutional Neural Network

TL;DR

This paper presents a CNN-based computer-aided diagnosis tool for diabetic retinopathy using image preprocessing and testing on three public datasets, achieving high accuracy in classifying healthy and unhealthy retinas.

Contribution

It introduces a CNN model with visual enhancement preprocessing for diabetic retinopathy detection, validated on multiple datasets with high accuracy.

Findings

Achieved up to 100% accuracy on DiaretDB0

High accuracy on DiaretDB1 and DrimDB datasets

Effective CNN architecture for retina image classification

Abstract

Diabetic Retinopathy DR is a popular disease for many people as a result of age or the diabetic, as a result, it can cause blindness. therefore, diagnosis of this disease especially in the early time can prevent its effect for a lot of patients. To achieve this diagnosis, eye retina must be examined continuously. Therefore, computer-aided tools can be used in the field based on computer vision techniques. Different works have been performed using various machine learning techniques. Convolutional Neural Network is one of the promise methods, so it was for Diabetic Retinopathy detection in this paper. Also, the proposed work contains visual enhancement in the pre-processing phase, then the CNN model is trained to be able for recognition and classification phase, to diagnosis the healthy and unhealthy retina image. Three public dataset DiaretDB0, DiaretDB1 and DrimDB were used in practical testing. The implementation of this work based on Matlab- R2019a, deep learning toolbox and deep network designer to design the architecture of the convolutional neural network and train it. The results were evaluated to different metrics; accuracy is one of them. The best accuracy that was achieved: for DiaretDB0 is 100%, DiaretDB1 is 99.495% and DrimDB is 97.55%.