Machine Learning and Artifact Convolutional Neural Network based Approach for Early-Stage Glaucoma Prediction

Abstract

Early detection of glaucoma is crucial for preventing irreversible vision loss, yet traditional diagnostic methods often face challenges in accuracy and efficiency. This research proposes a Machine Learning and Artifact Convolutional Neural Network-based approach for early-stage glaucoma prediction, leveraging deep learning techniques to enhance diagnostic precision. The model is trained and evaluated using a well-structured dataset, ensuring robust performance through metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that the proposed approach outperforms conventional classification methods, achieving superior accuracy while minimizing false positives and false negatives. A comprehensive analysis using a confusion matrix further validates its reliability in distinguishing between glaucoma and non-glaucoma cases. The study highlights the potential of AI-driven solutions in ophthalmology, offering a promising tool for automated, efficient, and early glaucoma detection. Future work may focus on expanding datasets, improving model generalization, and integrating real-world clinical applications to enhance diagnostic reliability