Neural networks for exudate detection in retinal images

Gerald Schaefer*, Edmond Leung

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

Diabetic retinopathy is a common eye disease directly associated with diabetes and one of the leading causes for blindness. One of its early indicators is the presence of exudates on the retina. In this paper we present a neural network-based approach to automatically detect exudates in retina images. A sliding windowing technique is used to extract parts of the image which are then passed to the neural net to classify whether the area is part of an exudate region or not. Principal component analysis and histogram specification are used to reduce training times and complexity of the network, and to improve the classification rate. Experimental results on an image data set with known exudate locations show good performance with a sensitivity of 94.78% and a specificity of 94.29%.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Pages298-306
Number of pages9
Volume4842
EditionPART 2
ISBN (Print)9783540768555
Publication statusPublished - 1 Dec 2007
Event3rd International Symposium on Visual Computing, ISVC 2007 - Lake Tahoe, NV, United Kingdom
Duration: 26 Nov 200728 Nov 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4842 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference3rd International Symposium on Visual Computing, ISVC 2007
CountryUnited Kingdom
CityLake Tahoe, NV
Period26/11/0728/11/07

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