INTELLIGENT GLAUCOMA DIAGNOSIS AND SEGMENTATION USING DEEP LEARNING TECHNIQUES

This project presents a deep learning-based framework for intelligent glaucoma diagnosis and segmentation using retinal fundus images. By integrating U-Net for segmentation and CNN models for classification, the system can automatically identify glaucoma and determine its severity level. The proposed approach minimizes manual effort, accelerates the diagnostic process, and improves screening efficiency. Automated analysis of retinal structures enables reliable detection of disease-related abnormalities and supports timely medical intervention. The developed system has the potential to become a valuable decision-support tool in ophthalmology, contributing to better eye-care services and helping prevent vision loss through early diagnosis.

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