One of the major causes of vision loss is Diabetic Retinopathy (DR). Presence of Hard Exudates (HE) in retinal images is one of the prominent and most reliable symptoms of Diabetic Retinopathy. Thus, it is essential to clinically examine for HEs to perform an early diagnosis and monitoring of DR. In this paper, a classification-based approach using Functional Link Artificial Neural Network (FLANN) classifier to extract HEs in a retinal fundus image is illustrated.
Luminosity Contrast Normalization pre-processing step was employed. Classification performances were compared between Multi-Layered Perceptron (MLP), Radial Basis Function (RBF) and FLANN classifiers. Better classification performance was observed for FLANN classifier. GUI package with Region of Interest (ROI) selection tool was developed.