DETECTION OF GLAUCOMA USING MACHINE LEARNING

ABSTRACT

Glaucoma is a leading cause of blindness worldwide, and early detection and treatment can significantly reduce the risk of vision loss. Machine learning has emerged as a powerful tool for the detection and diagnosis of glaucoma, using features extracted from optical coherence
tomography (OCT) scans of the retina. In this study, we develop a machine learning algorithm for the detection of glaucoma using OCT
scans. We use a dataset of OCT scans from both healthy individuals and patients with glaucoma, and extract features using state-of-the-art image processing techniques. We then train a machine learning model using these features, and evaluate its performance on a separate test set. Our results show that the machine learning algorithm achieves high accuracy in detecting glaucoma, with a model accuracy of 96%, our model was able to achieve a high accuracy. The algorithm also exhibits high sensitivity and specificity, indicating its potential as a screening tool for glaucoma.

Overall, our study demonstrates the potential of machine learning for the early detection of glaucoma, which could lead to improved patient outcomes and reduced healthcare costs.