Convolutional network to detect exudates in eye fundus images of diabetic subjects

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...

Full description

Saved in:
Bibliographic Details
Main Authors Perdomo, Oscar, Arevalo, John, González, Fabio A
Format Conference Proceeding
LanguageEnglish
Published SPIE 26.01.2017
Online AccessGet full text

Cover

Loading…
More Information
Summary: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.
Bibliography:Conference Date: 2016-12-05|2016-12-07
Conference Location: Tandil, Argentina
ISBN:9781510607781
1510607781
ISSN:0277-786X
DOI:10.1117/12.2256939