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Proceedings Paper

Convolutional network to detect exudates in eye fundus images of diabetic subjects
Author(s): Oscar Perdomo; John Arevalo; Fabio A. González
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Paper Abstract

Diabetic retinopathy has several clinical data sources for medical diagnosis, but the lack of tools to process the data generates a subjective and unclear diagnosis. The use of convolutional networks to analyze and extract features in eye fundus images may help with an automatic detection to support medical personnel in the grading of diabetic retinopathy. This paper presents a description of convolutional neural networks as a good methodology to detect and discriminate between exudate and healthy regions in eye fundus images.

Paper Details

Date Published: 26 January 2017
PDF: 6 pages
Proc. SPIE 10160, 12th International Symposium on Medical Information Processing and Analysis, 101600T (26 January 2017); doi: 10.1117/12.2256939
Show Author Affiliations
Oscar Perdomo, Univ. Nacional de Colombia (Colombia)
John Arevalo, Univ. Nacional de Colombia (Colombia)
Fabio A. González, Univ. Nacional de Colombia (Colombia)


Published in SPIE Proceedings Vol. 10160:
12th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva; Jorge Brieva; Ignacio Larrabide; , Editor(s)

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