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@article{178522,
        author = {TANISHQ DHAWAN and YASH KUMAR},
        title = {Enhancing the detection efficiency of Glaucoma using machine learning algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4292-4296},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178522},
        abstract = {Elevated intraocular pressure is a key indicator of glaucoma, a condition that can lead to vision loss and damage to the optic nerve if not diagnosed early, often due to its subtle symptoms. To effectively manage and prevent vision impairment, early detection is crucial. Traditional diagnostic methods, such as optical coherence tomography and fundus imaging, rely heavily on the accurate segmentation of the optic cup-to-disk ratio. This study employs an ensemble approach combining ResNet101, MobileNet, and a refined VGGNet-19 model to enhance the learning of subtle structures from RGB fundus images and their spatial coordinates. The proposed method tackles the challenges associated with glaucoma classification and the segmentation of the optic cup and disk. The results demonstrated an overall accuracy of 94% on the test dataset. The VGG19 model achieved F1-scores of 0.92, 0.95, and 0.93, while ResNet101 recorded scores of 0.93, 0.94, and 0.935, and MobileNet delivered a competitive F1-score of 0.915. This research underscores the clinical importance of reliable early screening for managing this vision-threatening condition, illustrating how segmentation and deep learning techniques can facilitate effective automated diagnosis of glaucoma.},
        keywords = {Open Computer Vision (OpenCV), convolutional neural networks(CNN), artificial intelligence (AI), image segmentation.},
        month = {May},
        }