Neural networks for exudate detection in retinal images

Gerald Schaefer; Edmond Leung

Neural networks for exudate detection in retinal images

Gerald Schaefer^*
, Edmond Leung

^*Corresponding author for this work

Aston University
School of Engineering and Applied Science

Research output: Chapter in Book/Published conference output › Conference publication

4 Citations (Scopus)

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 language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer
Pages	298-306
Number of pages	9
Volume	4842
Edition	PART 2
ISBN (Print)	9783540768555
Publication status	Published - 1 Dec 2007
Event	3rd International Symposium on Visual Computing, ISVC 2007 - Lake Tahoe, NV, United Kingdom Duration: 26 Nov 2007 → 28 Nov 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	4842 LNCS
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Conference

Conference	3rd International Symposium on Visual Computing, ISVC 2007
Country/Territory	United Kingdom
City	Lake Tahoe, NV
Period	26/11/07 → 28/11/07

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Cite this

Schaefer, G., & Leung, E. (2007). Neural networks for exudate detection in retinal images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 2 ed., Vol. 4842 , pp. 298-306). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4842 LNCS, No. PART 2). Springer.

Schaefer, Gerald ; Leung, Edmond. / Neural networks for exudate detection in retinal images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4842 PART 2. ed. Springer, 2007. pp. 298-306 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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title = "Neural networks for exudate detection in retinal images",

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\%.",

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Schaefer, G & Leung, E 2007, Neural networks for exudate detection in retinal images. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 edn, vol. 4842 , Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 4842 LNCS, Springer, pp. 298-306, 3rd International Symposium on Visual Computing, ISVC 2007, Lake Tahoe, NV, United Kingdom, 26/11/07.

Neural networks for exudate detection in retinal images. / Schaefer, Gerald; Leung, Edmond.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4842 PART 2. ed. Springer, 2007. p. 298-306 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4842 LNCS, No. PART 2).

Research output: Chapter in Book/Published conference output › Conference publication

TY - GEN

T1 - Neural networks for exudate detection in retinal images

AU - Schaefer, Gerald

AU - Leung, Edmond

PY - 2007/12/1

Y1 - 2007/12/1

N2 - 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%.

AB - 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%.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 306

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T2 - 3rd International Symposium on Visual Computing, ISVC 2007

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Schaefer G, Leung E. Neural networks for exudate detection in retinal images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 ed. Vol. 4842 . Springer. 2007. p. 298-306. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

Neural networks for exudate detection in retinal images

Abstract

Publication series

Conference

UN SDGs

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