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Title:
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AN APPROACH TO ANALYZE VISUAL FIELD AND DIAGNOSE GLAUCOMA PROGRESSION USING ARTIFICIAL NEURAL NETWORK TECHNIQUES |
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Author(s):
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Sakthiaseelan Karthigasoo , Selvakumar Manickam , Yu-n Cheah |
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ISBN:
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972-98947-1-X |
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Editors:
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Pedro Isaías and Nitya Karmakar |
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Year:
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2003 |
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Edition:
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2 |
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Keywords:
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Glaucoma, visual field, machine learning, per location differences, neural network ensemble, rough sets . |
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Type:
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Short Paper |
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First Page:
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861 |
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Last Page:
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864 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Glaucoma is the name for a group of diseases that can destroy the optic nerve, the main nerve of the eye. The word glaucoma means "hard eyeball." Any one of the conditions classified as glaucoma can lead to irreversible blindness by damaging the optic nerve. Glaucoma is a progressive optic neuropathy with changes in the optic nerve head reflected in the visual field. The visual field sensitivity test is commonly used in a clinical setting to evaluate glaucoma. Standard automated perimetry (SAP) is a common computerized visual field test whose output is amendable to machine learning. Visual field measurement is a test to diagnose glaucoma. In this paper, we present a proposed system architecture to diagnose glaucoma progression using artificial neural network techniques. We also present an approach of analyzing visual field measurements using per location differences. |
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