Convolutional Neural Networks Based Classifier for Diabetic Retinopathy

Abstract

Diabetic Retinopathy (DR) is a consequence of diabetes which causes damage to the retinal blood vessel networks. In most diabetics, this is a major vision-threatening problem. Color fundus pictures are used to diagnose DR, which requires competent doctors to determine lesions presence. The job of detecting DR in an automated manner is difficult. In terms of automated illness identification, feature extraction is quite useful. In the current setting, Convolutional Neural Networks (CNN) outperforms prior handcrafted feature-based image classification approaches in terms of image classification efficiency. This paper introduces CNN structure for extracting characteristics from retinal fundus pictures in order to develop the accuracy of classification. This proposed method, the output features of CNN are employed as input to many classifiers of machine learning. Using images from the MESSIDOR datasets, this method is tested under Random Tree, Hoeffiding Tree and Random Forest classifiers. Accuracy, False Positive Rate (FPR), Precision, Recall, F-1 score, specificity and Kappa-score for used classifiers are compared to find out the efficiency of the classifier. For the MESSIDOR datasets, the suggested feature extraction approach combined with the Random forest classifier surpasses all other classifiers which gains 88% and 0.7288 of average accuracy and Kappa-score (k-score) respectively.