Abstract
Glaucoma, a progressive neurodegenerative disease, presents a formidable challenge as it affects the optic nerve due to increased pressure within the eye. This impairment results in abnormalities in the visual field called as the "silent thief of sight", because it frequently eludes early detection. This makes regular screenings crucial for timely intervention. In this research, an innovative approach for automating the glaucoma detection is introduced. Leveraging advanced deep learning techniques, including DenseNet201 and NASNet, the research focusses on developing a system capable of detecting glaucoma from fundus images. This novel method shows promise in improving the efficiency and precision of glaucoma diagnosis, potentially transforming patient care in this field.
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