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

Oscar Perdomo, John Arevalo, Fabio A. Gonzalez

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

9 Scopus citations

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.

Original languageEnglish (US)
Title of host publication12th International Symposium on Medical Information Processing and Analysis
EditorsEduardo Romero, Natasha Lepore, Jorge Brieva, Ignacio Larrabide
PublisherSPIE
ISBN (Electronic)9781510607781
DOIs
StatePublished - 2017
Externally publishedYes
Event12th International Symposium on Medical Information Processing and Analysis, SIPAIM 2016 - Tandil, Argentina
Duration: Dec 5 2016Dec 7 2016

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10160
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference12th International Symposium on Medical Information Processing and Analysis, SIPAIM 2016
CountryArgentina
CityTandil
Period12/5/1612/7/16

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Convolutional network to detect exudates in eye fundus images of diabetic subjects'. Together they form a unique fingerprint.

Cite this