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

Oscar Perdomo; John Arevalo; Fabio A. Gonzalez

doi:10.1117/12.2256939

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 proceeding › Conference 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 language	English (US)
Title of host publication	12th International Symposium on Medical Information Processing and Analysis
Editors	Eduardo Romero, Natasha Lepore, Jorge Brieva, Ignacio Larrabide
Publisher	SPIE
ISBN (Electronic)	9781510607781
DOIs	https://doi.org/10.1117/12.2256939
State	Published - 2017
Externally published	Yes
Event	12th International Symposium on Medical Information Processing and Analysis, SIPAIM 2016 - Tandil, Argentina Duration: Dec 5 2016 → Dec 7 2016

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	10160
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	12th International Symposium on Medical Information Processing and Analysis, SIPAIM 2016
Country	Argentina
City	Tandil
Period	12/5/16 → 12/7/16

All Science Journal Classification (ASJC) codes

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

UN SDGs

This output contributes to the following Sustainable Development Goal(s)

Access to Document

10.1117/12.2256939

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

Perdomo, O., Arevalo, J., & Gonzalez, F. A. (2017). Convolutional network to detect exudates in eye fundus images of diabetic subjects. In E. Romero, N. Lepore, J. Brieva, & I. Larrabide (Eds.), 12th International Symposium on Medical Information Processing and Analysis [101600T] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10160). SPIE. https://doi.org/10.1117/12.2256939

Perdomo, Oscar ; Arevalo, John ; Gonzalez, Fabio A. / Convolutional network to detect exudates in eye fundus images of diabetic subjects. 12th International Symposium on Medical Information Processing and Analysis. editor / Eduardo Romero ; Natasha Lepore ; Jorge Brieva ; Ignacio Larrabide. SPIE, 2017. (Proceedings of SPIE - The International Society for Optical Engineering).

@inproceedings{c7941533a9a24d6e9a0b34bda9263fd4,

title = "Convolutional network to detect exudates in eye fundus images of diabetic subjects",

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.",

author = "Oscar Perdomo and John Arevalo and Gonzalez, {Fabio A.}",

note = "Publisher Copyright: {\textcopyright} 2017 SPIE. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.; 12th International Symposium on Medical Information Processing and Analysis, SIPAIM 2016 ; Conference date: 05-12-2016 Through 07-12-2016",

year = "2017",

doi = "10.1117/12.2256939",

language = "English (US)",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

publisher = "SPIE",

editor = "Eduardo Romero and Natasha Lepore and Jorge Brieva and Ignacio Larrabide",

booktitle = "12th International Symposium on Medical Information Processing and Analysis",

address = "United States",

}

Perdomo, O, Arevalo, J & Gonzalez, FA 2017, Convolutional network to detect exudates in eye fundus images of diabetic subjects. in E Romero, N Lepore, J Brieva & I Larrabide (eds), 12th International Symposium on Medical Information Processing and Analysis., 101600T, Proceedings of SPIE - The International Society for Optical Engineering, vol. 10160, SPIE, 12th International Symposium on Medical Information Processing and Analysis, SIPAIM 2016, Tandil, Argentina, 12/5/16. https://doi.org/10.1117/12.2256939

Convolutional network to detect exudates in eye fundus images of diabetic subjects. / Perdomo, Oscar; Arevalo, John; Gonzalez, Fabio A.

12th International Symposium on Medical Information Processing and Analysis. ed. / Eduardo Romero; Natasha Lepore; Jorge Brieva; Ignacio Larrabide. SPIE, 2017. 101600T (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10160).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Convolutional network to detect exudates in eye fundus images of diabetic subjects

AU - Perdomo, Oscar

AU - Arevalo, John

AU - Gonzalez, Fabio A.

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85014594327&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85014594327&partnerID=8YFLogxK

U2 - 10.1117/12.2256939

DO - 10.1117/12.2256939

M3 - Conference contribution

AN - SCOPUS:85014594327

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - 12th International Symposium on Medical Information Processing and Analysis

A2 - Romero, Eduardo

A2 - Lepore, Natasha

A2 - Brieva, Jorge

A2 - Larrabide, Ignacio

PB - SPIE

T2 - 12th International Symposium on Medical Information Processing and Analysis, SIPAIM 2016

Y2 - 5 December 2016 through 7 December 2016

ER -

Perdomo O, Arevalo J, Gonzalez FA. Convolutional network to detect exudates in eye fundus images of diabetic subjects. In Romero E, Lepore N, Brieva J, Larrabide I, editors, 12th International Symposium on Medical Information Processing and Analysis. SPIE. 2017. 101600T. (Proceedings of SPIE - The International Society for Optical Engineering). https://doi.org/10.1117/12.2256939

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

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

UN SDGs

Access to Document

Other files and links

Cite this