Pioneering Early Glaucoma Identification Through Deep Learning Insights
Nalini Sampath, A. Siva Ramakrishna Praneeth, Ch.Bayapa Reddy, Ch.Vijaya Durga Reddy, G. Revanth Sai · 2024
Glaucoma, a degenerative eye condition causing irreversible vision loss, often goes undiagnosed until it is advanced due to its asymptomatic early stages. Timely intervention and the prevention of visual impairment depend heavily on early detection. The innovative use of deep learning methods, in particular convolutional neural networks (CNNs), to transform the early detection of glaucoma is examined in this research. Fundus scans of the retina can be used to identify distinctive morphological changes that are suggestive of glaucoma, especially in the Optic Nerve Head (ONH). This research attempts to create very precise and effective algorithms that can identify glaucoma in its early stages by utilizing insights from deep learning. These developments might revolutionize the diagnosis of glaucoma, allowing for early intervention and greatly enhancing patient outcomes in the fight against visual loss.