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@article{186754,
        author = {Dr.Nilesh N Thorat},
        title = {Automated Glaucoma Detection System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {3050-3056},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186754},
        abstract = {Glaucoma is a progressive eye disease that remains one of the leading causes of irreversible blindness globally. While early detection is crucial, conventional diagnostic methods require specialized equipment and expert ophthalmologists, creating challenges for timely screening in many areas. This paper introduces an automated glaucoma detection system that utilizes machine learning on retinal fundus images. The proposed approach integrates image enhancement, optic disc and cup segmentation, and feature-based classification to identify glaucomatous changes from normal cases. By combining deep learning techniques with traditional image processing, the system is designed to offer high accuracy at a low computational cost. Such a tool can serve as an effective decision-support system in clinical settings, enhancing early diagnosis and reducing the risk of preventable blindness.},
        keywords = {Glaucoma detection, fundus image analysis, deep learning, convolutional neural networks (CNN), medical image processing.},
        month = {November},
        }