Fundamentals and Methods of Machine and Deep Learning

7Detection of Diabetic Retinopathy Using Ensemble Learning Techniques

Anirban Dutta, Parul Agarwal*, Anushka Mittal, Shishir Khandelwal and Shikha Mehta

Jaypee Institute of Information Technology, Noida, India

Abstract

Reliable detection of Diabetic Retinopathy (DR) in digital fundus images still remains a challenging task in medical imaging. Early detection of DR is essential to save a patient’s loss as it is an irreversible loss. Premature symptoms of DR include the growth of features like hemorrhages, exudates, and microaneurysms on the retinal surface of the eye. The primary focus of this paper is on the detection of the severity of DR by considering the individual and the combined features. Image processing techniques are applied for the automated extraction of the retinal features. Machine learning algorithms along with ensemble learning techniques are used on these features for further detection of DR. In medical imaging, where the available data is highly imbalanced, specifically designed ensemble learning techniques are proved to be very helpful. In this paper, three ensemble learning algorithms—AdaNaive, AdaSVM, and Adaforest—are developed and compared with machine learning algorithms for binary and multi-classification of DR. Experimental results reveal that proposed algorithms outperform existing algorithms.

Keywords: Diabetic Retinopathy, machine learning, ensemble learning techniques, binary classification, multiclass classification

7.1 Introduction

As reported ...