Glaucoma detection using hybrid machine learning techniques

Abstract

An eye disease called glaucoma can cause irreversible blindness if left undetected and not properly managed. The major challenge is that glaucoma often has no symptoms in its early stages, making it hard to detect using traditional testing methods like eye pressure measurements and eye exams. Several techniques for Glaucoma detection encountered difficulties due to a short training dataset, resulting in overfitting and under fitting problems. A hybrid machine learning approach based on CNN-SVM for detection of Glaucoma was proposed. Initially images taken from glaucoma dataset were preprocessed using standard scalar, then the preprocessed images are fed into CNN for transformation into high level features, the extracted features are subsequently passed onto an SVM classifier to distinguish between normal and glaucomatous conditions. Experimental results for the proposed CNN-SVM offers an accuracy,  precision, recall and F1-score of 100% demonstrating its superiority over other existing techniques such as SVM which has accuracy, precision, recall and F1-score of 93%, 92%, 90% and 94% respectively and CNN with the accuracy, precision, recall and F1- score of 95%, 99%, 88% and 90% respectively. The integration of CNN and SVM presents a promising framework for automated Glaucoma detection, offering significant potential for real-world clinical applications.