Glaucoma Detection with Retinal Fundus Images
- Authors: Shreshtha Mehta1, Amit Gupta2, Deepti Sahu3, Pawan Kumar Singh4, Satya Prakash Yadav5
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View Affiliations Hide Affiliations1 Department of Biotechnology, Graphic Era Deemed to be University, Dehradun, India 2 Department of Computer Science and Engineering, Graphic Era Hill University, Dehradun, India 3 Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, India 4 Department of Computer Science and Engineering, G.L. Bajaj Institute of Technology and Management (GLBITM), Greater Noida, India 5 School of Computer Science Engineering and Technology (SCSET), Bennett University, Greater Noida, Uttar Pradesh, India
- Source: A Practitioner's Approach to Problem-Solving using AI , pp 72-87
- Publication Date: October 2024
- Language: English
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This paper discusses numerous methods for glaucoma detection. Because of its impact on the optic nerve and the loss of ganglion cells, which eventually results in vision loss, glaucoma has emerged as the leading cause of blindness worldwide. In this article, we provide a few methods for recognizing glaucoma in its earliest stages, which can prevent irreversible damage to a person's vision. We explore ROI (region of interest), optic cup and disc ratio, LSACM, and LSACM-SP techniques in this research, all of which help us achieve significant segmentation results. The development of diagnostic methods for several eye illnesses began with the discovery of the "optical disc (OD)". To produce circular OD milestones, this methodology rounds extrinsic morphology and detecting methodologies. The OD's pixels must be provided as raw data. To achieve this, a methodology based on the chosen voting method is devised.
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