An Automated Diabetic Retinopathy Classification System Using Bayesian Logistic Regression Classifier

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 worldwide. 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 publicly 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.

A Systematic Review of Current Scenario of Diabetic Retinopathy Severity Grading using Image Content

“…We are currently focusing on Bayesian Logistic Regression (BLR), a powerful method used for automated categorization of diabetic retinopathy. The following part thoroughly analyzes the detailed technique that forms the foundation of BLR, incorporating enlightening insights from the research conducted by Prasad et al, [7]. As we delve into the complex mechanisms of BLR, our investigation extends beyond mere theory.…”