Early Diagnosis of Diabetic Retinopathy using Random Forest Algorithm

Abstract

The diabetic retinopathy is one of the most frequent causes of visual damage and vision loss. It can cause blindness in the absence of the diagnosis and the treatment. The automatic detection of the hard exudate in color fundus retinal images is an important task to early diagnosis the diabetic retinopathy. In this paper, a hard exudate detection algorithm is proposed. It is based on the application of a learning method to retinal image with removed optic disk. This paper proposes the use of Random Forest algorithm with a specific parameter from which a binary mask of exudate is obtained after intensity thresholding. It achieves 91.40% for sensitivity and 94.38% for the accuracy.