AUTOMATIC IDENTIFICATION AND CLASSIFICATION OF MICROANEURYSMS FOR DETECTION OF DIABETIC RETINOPATHY

Gowthaman R

Abstract: Diabetic Retinopathy is major cause for visual loss and visual impaired vision worldwide. A proper detection and treatment of this disease in needed in time. Microaneurysms detection is difficult process because they appeared as a first sign of diabetic retinopathy disease. In past few years, many approaches raised for the identification and detection of this diseases using some features extraction techniques, mathematical algorithms and artificial neural network classifiers which lacks in some drawbacks in preprocessing, extraction of appropriate features, blood vessels extraction and in chosing classification techniques. This paper is developed to prefectly detect the candidate regions by using Gabor filter bank and separation of blood vessels from the retina image. Then for each candidate region different feature vectors are extracted. These features are given to multi class classifier for training and testing. Performance of this proposed work is evaluated with performance metrics such as accuracy, sensitivity, specificity and execution time and proved as a successful method for automatic early detection of diabetic retinopathy.

Keywords: Microaneurysms (MAs), Diabetic Retinopathy (DR), Support Vector Machine (SVM), Extreme Learning Machine (ELM).

DOI: https://doi.org/10.15623/ijret.2014.0302081