Glaucotrack: Intelligent Eye Health Screening Using Machine Learning

Glaucoma is generally a chronic and cumulative optic nerve impairment characterized by the deterioration of the Retinal Ganglionic and their nerve fibers. It is the second most significant cause of eye blindness. The Fund-us Camera is a kind of Modern Visualization Tool used to scrutinize the Anatomy of an eye to determine theglaucoma impairment. The appearance of the optic Cup occupies a prominent role. The cup augments with theextension of glaucoma occupying most of the disc space. The manual examination of the Optic disc and Optic Cup is time-consuming. Also, the disease is often asymptomatic in its early stages, making it complicated to detect glaucoma through Ophthalmic examination. However, the Schema Objects apply a distinct basis for the binary classification of Glaucomatous and Non - Glaucomatous images. To diagnose the same, the proposed model in this scheme algorithm is sufficient to identify glaucoma by achieving a validation accuracy of 80%.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-14
DOI
https://doi.org/10.5281/zenodo.15214832
Primary Topic
Retinal Imaging and Analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

Glaucotrack: Intelligent Eye Health Screening Using Machine Learning

Zenodo (CERN European Organization for Nuclear Research)
Retinal Imaging and Analysis
article

Glaucotrack: Intelligent Eye Health Screening Using Machine Learning

article en

Abstract

Glaucoma is generally a chronic and cumulative optic nerve impairment characterized by the deterioration of the Retinal Ganglionic and their nerve fibers. It is the second most significant cause of eye blindness. The Fund-us Camera is a kind of Modern Visualization Tool used to scrutinize the Anatomy of an eye to determine theglaucoma impairment. The appearance of the optic Cup occupies a prominent role. The cup augments with theextension of glaucoma occupying most of the disc space. The manual examination of the Optic disc and Optic Cup is time-consuming. Also, the disease is often asymptomatic in its early stages, making it complicated to detect glaucoma through Ophthalmic examination. However, the Schema Objects apply a distinct basis for the binary classification of Glaucomatous and Non - Glaucomatous images. To diagnose the same, the proposed model in this scheme algorithm is sufficient to identify glaucoma by achieving a validation accuracy of 80%.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 39%
Retinal Imaging and Analysis
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