Automatic Diabetic Retinopathy Recognition Method based on GLDM Features and Feed Forward Neural Network Classifier.

Abstract

Detection and recognition of DR at the early phase can prevent the risk of gradual damage in the retina and vision loss. Many works have been introduced for automatic DR recognition and diagnosis in recent years. To date, there are still some issues that are required to work on to improve the quality and the performance of automatic DR recognition systems. Therefore, this paper introduces a machine learning based approach for DR diagnosis and recognition by proposing texture analysis features of GLDM technique and feed-forward neural network classifier. The proposed method has achieved a recognition accuracy of 95% according to undertaken experiments and performance analysis.