Machine Learning and Artifact CNN grounded Methodology for Premature Glaucoma Ailment Identification

Abstract

Early detection of glaucoma is vital to prevent irreversible vision loss; however, conventional diagnostic techniques often struggle with limitations in accuracy, speed, and scalability. This study presents a novel methodology that integrates Machine Learning with an Artifact-Convolutional Neural Network (Artifact-CNN) framework to enhance the early prediction of glaucoma. By leveraging deep learning’s ability to extract intricate features from retinal fundus images, the proposed model aims to improve diagnostic accuracy while minimizing both false positives and false negatives. A structured dataset was utilized to train and evaluate the model, and performance was assessed using standard metrics such as accuracy, precision, recall, and F1-score. The results reveal that the proposed hybrid approach surpasses traditional classification models in performance and consistency. A detailed confusion matrix analysis confirms the model’s reliability in distinguishing between glaucomatous and non-glaucomatous conditions, reinforcing its practical relevance in clinical diagnostics. The integration of artifact-based enhancement within the CNN architecture allows the system to better interpret subtle patterns often missed in early-stage glaucoma. This research underscores the potential of AI-assisted ophthalmic tools to facilitate early, automated, and accurate glaucoma detection. Looking ahead, the study opens avenues for future enhancements such as expanding dataset diversity, improving cross-domain generalization, and deploying the model in real-time clinical settings for broader impact.