Salem, NemaAlharbi, WafaMateen, KulsoomSarfaraz, Safiya2025-02-242025-02-242025-02-20http://hdl.handle.net/20.500.14131/2039This study presents an automated diagnostic system for early glaucoma detection using fundus imaging. The methodology integrates wavelet-based noise reduction, vessel removal, and watershed segmentation to enhance diagnostic accuracy. Validated on G1020 and HRF datasets, the system achieved an 85.75% accuracy, 89% precision, 92% recall, and 90.5% F1-score using SVM with Linear Discriminant Analysis (LDA). Designed for scalability, the system leverages open-source tools, making it cost-effective and applicable in resource-limited settings. Potential applications include telemedicine platforms and portable diagnostic kits, enabling early glaucoma screening and supporting Sustainable Development Goal 3. While challenges such as dataset variability remain, this work lays a foundation for advancing AIassisted ophthalmological diagnostics.enGlaucoma detection, fundus imaging, machine learning, segmentation, feature extraction, classification, AI-assisted diagnosis.Capstone