Abstract
The project mainly focuses to detect a Glaucoma percentage in a person’s eye. Glaucoma is an eye disease which is mainly responsible for vision impairment. So it is necessary to detect the presence of Glaucoma in the early stages. If detected in the early stage we can control our intra ocular pressure by adopting lifestyle changes and by using some other medications. The lifestyle changes may include exercising regularly and reducing stress. Hence the early detection of Glaucoma can help people from losing their vision. A machine learning model has been proposed which tells the Glaucoma percentage of the eye. In this we are giving a fundus image as the input to the model and it tells the Glaucoma percentage. This Glaucoma percentage can be detected using two things that are optic cup and optic disk. To measure the Glaucoma percentage it is necessary to find the optic cup ratio to optic disk ratio. Generally health eye optic cup ratio to optic disk ratio is less than 0.5. In Glaucoma eye the optic cup ratio to optic disk ratio is greater than 0.5. By using this concept we are going to develop the model.
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Maram, B., Sahukari, J., Lokesh, T. (2023). A Framework for Glaucoma Diagnosis Prediction Using Retinal Thickness Using Machine Learning. In: Dash, R.N., Rathore, A.K., Khadkikar, V., Patel, R., Debnath, M. (eds) Smart Technologies for Power and Green Energy. Lecture Notes in Networks and Systems, vol 443. Springer, Singapore. https://doi.org/10.1007/978-981-19-2764-5_6
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DOI: https://doi.org/10.1007/978-981-19-2764-5_6
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