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Analysing visual field and diagnosing glaucoma progression using a hybrid of per location differences and artificial neural network ensembles

Visual function test results for glaucoma diagnosis is perceived to be subjective and problematic.In this paper, we aim to address the issues and problems associated with these current approaches.We present (a) a system architecture for analyzing visual field and diagnosing glaucoma progression; (b)...

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Main Authors: Karthigasoo, Sakthiaseelan, Manickam, Selvakumar, Cheah, Yu-N
Format: Conference or Workshop Item
Language:English
Published: 2004
Subjects:
QA76 Computer software
R Medicine (General)
Online Access:http://repo.uum.edu.my/13849/1/KM119.pdf
http://repo.uum.edu.my/13849/
http://www.kmice.cms.net.my
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spelling my.uum.repo.138492015-05-10T05:05:42Z http://repo.uum.edu.my/13849/ Analysing visual field and diagnosing glaucoma progression using a hybrid of per location differences and artificial neural network ensembles Karthigasoo, Sakthiaseelan Manickam, Selvakumar Cheah, Yu-N QA76 Computer software R Medicine (General) Visual function test results for glaucoma diagnosis is perceived to be subjective and problematic.In this paper, we aim to address the issues and problems associated with these current approaches.We present (a) a system architecture for analyzing visual field and diagnosing glaucoma progression; (b) a per location differences approach for analyzing visual field to obtain measurements of glaucoma progression; and (c) a neural network ensemble approach where several artifial neural network are jointly used to diagnose glaucoma progression.It is hoped that it would be possible to diagnose glaucoma progression with just one reading of a patient’s visual field. 2004-02-14 Conference or Workshop Item PeerReviewed application/pdf en http://repo.uum.edu.my/13849/1/KM119.pdf Karthigasoo, Sakthiaseelan and Manickam, Selvakumar and Cheah, Yu-N (2004) Analysing visual field and diagnosing glaucoma progression using a hybrid of per location differences and artificial neural network ensembles. In: Knowledge Management International Conference and Exhibition 2004 (KMICE 2004), 14-15 February 2004, Evergreen Laurel Hotel, Penang. http://www.kmice.cms.net.my
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutionali Repository
url_provider http://repo.uum.edu.my/
language English
topic QA76 Computer software
R Medicine (General)
spellingShingle QA76 Computer software
R Medicine (General)
Karthigasoo, Sakthiaseelan
Manickam, Selvakumar
Cheah, Yu-N
Analysing visual field and diagnosing glaucoma progression using a hybrid of per location differences and artificial neural network ensembles
description Visual function test results for glaucoma diagnosis is perceived to be subjective and problematic.In this paper, we aim to address the issues and problems associated with these current approaches.We present (a) a system architecture for analyzing visual field and diagnosing glaucoma progression; (b) a per location differences approach for analyzing visual field to obtain measurements of glaucoma progression; and (c) a neural network ensemble approach where several artifial neural network are jointly used to diagnose glaucoma progression.It is hoped that it would be possible to diagnose glaucoma progression with just one reading of a patient’s visual field.
format Conference or Workshop Item
author Karthigasoo, Sakthiaseelan
Manickam, Selvakumar
Cheah, Yu-N
author_facet Karthigasoo, Sakthiaseelan
Manickam, Selvakumar
Cheah, Yu-N
author_sort Karthigasoo, Sakthiaseelan
title Analysing visual field and diagnosing glaucoma progression using a hybrid of per location differences and artificial neural network ensembles
title_short Analysing visual field and diagnosing glaucoma progression using a hybrid of per location differences and artificial neural network ensembles
title_full Analysing visual field and diagnosing glaucoma progression using a hybrid of per location differences and artificial neural network ensembles
title_fullStr Analysing visual field and diagnosing glaucoma progression using a hybrid of per location differences and artificial neural network ensembles
title_full_unstemmed Analysing visual field and diagnosing glaucoma progression using a hybrid of per location differences and artificial neural network ensembles
title_sort analysing visual field and diagnosing glaucoma progression using a hybrid of per location differences and artificial neural network ensembles
publishDate 2004
url http://repo.uum.edu.my/13849/1/KM119.pdf
http://repo.uum.edu.my/13849/
http://www.kmice.cms.net.my
_version_ 1644281298803490816
score 13.211869

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