An Ensemble Based Machine Learning Classification for Automated Glaucoma Detection

Abstract

Glaucoma is an irredeemable eye disease that causes sight degeneration and is the fourth leading cause of vision impairment as per the World Report on Vision 2019. Several techniques exist for the screening, detection, treatment, and rehabilitation of glaucoma. But still, they are not sufficient to have control over this disease to prevent further vision loss. Studies done on the prevalence of glaucoma have reported a high proportion of undiagnosed patients. Late diagnosis is related to an increased risk of glaucoma associated with visual disability. For the effective management or prevention of blindness, the importance of early diagnosis of glaucoma cannot be underestimated. This paper has proposed an approach for effectively extracting the key features of colour retinal fundus images and categorizing them as normal or glaucomatous. The novel approach of an ensemble machine learning technique has been implemented with an Automated Weightage Based Voting (AWBV) algorithm. This paper has been designed to evaluate the performance of Probabilistic Neural Networks (PNN), K-Nearest Neighbour (KNN), SupportVector Machines (SVM), Naïve Bayes (NB) and Logistic Regression (LR) as individual and ensemble classifiers. It includes the extraction of fused features from various retinal fundus image datasets. The proposed Combined Features Fused Classifier (CF2C) model has had a remarkable performance with the IEEE DataPort image dataset, achieving an ensembled prediction accuracy of 96.25 %, a sensitivity of 95.83 % and a specificity of 96.67 % which are better results than those of the five classifiers individually.