Abstract: Glaucom Detection is devoted to advancing the diagnosis of glaucoma and its primary sub-conditions: Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), and Drusen, recognizing their significant impact on global vision loss. Glaucoma, often termed the "silent thief of sight," leads to progressive optic nerve damage and irreversible vision loss. While elevated intraocular pressure (IOP) is a primary cause, not all cases stem from high IOP, underscoring the multifactorial nature of its development involving genetics, age, race, and family history. This complexity necessitates a comprehensive approach for early detection and management to counteract the disease's silent progression.To meet the pressing need for early intervention, this project integrates cutting-edge machine learning and image analysis techniques alongside traditional diagnostic methods like tonometry and ophthalmoscopy. By harnessing these advanced technologies, the project aims to enhance early detection capabilities, facilitating tailored approaches to diagnose and manage glaucoma and its sub-conditions effectively. Early identification is paramount due to the insidious nature of glaucoma, which often advances unnoticed until irreversible damage occurs. Prioritizing early intervention not only decelerates disease progression but also safeguards the quality of life for affected individuals, underscoring the significance of personalized diagnostic and treatment strategies for various complications and subtypes of glaucoma. This holistic approach seeks to revolutionize glaucoma management by integrating state-of-the-art technology with established diagnostic methods, ultimately improving outcomes for patients worldwide..
Keywords: Glaucoma diagnosis Choroidal Neovascularization (CNV) Diabetic Macular Edema (DME) Drusen Early detection Machine learning Image analysis.
Cite:
Sharath Kumar, Athik Rehaman, Lathesh Kumar, Mayur S Karkera, Mohammed Muneef,"Glaucoma Detection using Machine Learning with OCT", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.133117.
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DOI:
10.17148/IJARCCE.2024.133117
