Automatic system for diabetic retinopathy screening based on AM-FM, partial least squares, and support vector machines

E. Simon Barriga, Victor Murray, Carla Agurto, Marios Pattichis, Wendall Bauman, Gilberto Zamora, Peter Soliz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Scopus citations

Abstract

Diabetic retinopathy (DR) is a disease that affects over 170 million people worldwide. In the United States, it is estimated that over 10 million diabetics do not receive the recommended annual eye examinations, significantly increasing their risk of vision loss. In this paper we present an automatic system to detect the presence of DR by analyzing a photograph of the central field of the retina. The system applies Amplitude Modulation-Frequency Modulation (AM-FM) for feature extraction, and partial least squares (PLS) and support vector machines (SVM) for classification. We tested the system on a total of 400 images, and obtained an area under the ROC curve (AUC) of 0.86, and corresponding sensitivity/specificity of 98%/68%. We also tested the accuracy of the system for patients needing immediate referral to a specialist, obtaining an AUC of 0.98.

Original languageEnglish
Title of host publication2010 7th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2010 - Proceedings
Pages1349-1352
Number of pages4
DOIs
StatePublished - 2010
Externally publishedYes
Event7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Rotterdam, Netherlands
Duration: 14 Apr 201017 Apr 2010

Publication series

Name2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings

Conference

Conference7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010
Country/TerritoryNetherlands
CityRotterdam
Period14/04/1017/04/10

Keywords

  • AM-FM
  • Amplitude modulation-frequency modulation
  • Diabetic retinopathy
  • Partial least squares
  • Support vector machines

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