AN AUTOMATED DIABETIC RETINOPATHY CLASSIFICATION SYSTEM USING BAYESIAN LOGISTIC REGRESSION CLASSIFIER
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Abstract
Diabetic retinopathy (DR) is the damage to the retina due to elevated blood sugar levels that is the prime reason for loss of sight world-wide. The best solution to this problem is by controlling the blood sugar levels and also by regularly getting the eyes checked using an image capturing system to check for signs of any damage to the retina. This work proposes a method for identifying DR by the use of various image processing techniques. Textural attributes are extracted from the grey scale images and optimal features are selected. Bayesian Logistic Regression (BLR) classifier is used for classifying the images as DR-Present and DR-absent for the publically available database. The results are compared with various performance metrics like accuracy, sensitivity and specificity. An accuracy of 98.4% is obtained in the proposed method.
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