Detection of Glaucoma using Machine Learning Approach

Abstract: Conventional clinical diagnosis is often expensive, time-consuming, dependent on expert interpretation, and not readily available in primary healthcare settings. This study proposes an automated computer-aided diagnosis system for detecting and analysing retinal defects using fundus images. The proposed hybrid framework focuses on multiple anatomical and pathological structures, including retinal blood vessels, optic disc, and exudate lesions. Image pre- processing techniques are employed to enhance retinal images and extract relevant regions of interest. Retinal vessels are segmented using a Fully Connected Conditional Random Field (FC-CRF), followed by supervised classification using SVM and KNN. Optic disc and exudate lesions are segmented using mathematical morphology, fuzzy-set theory, and adaptive thresholding. The proposed system aims to support efficient, automated, and reliable screening of glaucoma and diabetic retinopathy (DR), particularly where limited training data and clinical resources are available.

Keywords: Glaucoma, Fuzzy Logic, Machine Learning, Eyes, Diabetes.