A Comparative Study of Learning Methods for Diabetic Retinopathy Classification

Abstract

AbstractDiabetic retinopathy refers to a state of the human eye that affects retina blood vessels, causing vision impairment or even complete vision loss. In this paper, we classify DR fundus images into the presence and absence of the disease by using a combination of deep learning layers and machine learning algorithms. Machine learning (e.g. random forest and support vector machine (SVM)) and deep learning (e.g. convolutional neural networks (CNNs)) are the most well-known approaches for small and big data, respectively, in image classification tasks. The results are compromised when there is a lack of data. Furthermore, a machine learning algorithm takes less time to train than a deep learning method. As a result, we attempted to develop models that combined machine learning and deep learning approaches. So, three models are proposed in this paper to comparatively study their results. The first model utilises dense layers to classify the data, while the second and third models use SVM and random forest classifiers, respectively. Hence, the model employs CNN and machine learning algorithms to increase the accuracy and efficacy of limited data sets. Usually, it is considered that deep learning algorithms perform better on images, while our results show that random forest outperformed the other approaches. We discovered that combining two learning approaches allows us to get superior outcomes on a short data set without sacrificing accuracy or other measures.KeywordsDiabetic retinopathyDeep learningConvolutional neural networkMachine learning