OCT-NET: A convolutional network for automatic classification of normal and diabetic macular edema using sd-oct volumes

Oscar Perdomo, Sebastian Otalora, Fabio A. Gonzalez, Fabrice Meriaudeau, Henning Muller

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

23 Scopus citations

Abstract

Diabetic macular edema (DME) is one of the most common eye complication caused by diabetes mellitus, resulting in partial or total loss of vision. Optical Coherence Tomography (OCT) volumes have been widely used to diagnose different eye diseases, thanks to their sensitivity to represent small amounts of fluid, thickness between layers and swelling. However, the lack of tools for automatic image analysis for supporting disease diagnosis is still a problem. Convolutional neural networks (CNNs) have shown outstanding performance when applied to several medical images analysis tasks. This paper presents a model, OCT-NET, based on a CNN for the automatic classification of OCT volumes. The model was evaluated on a dataset of OCT volumes for DME diagnosis using a leave-one-out cross-validation strategy obtaining an accuracy, sensitivity, and specificity of 93.75%.

Original languageEnglish (US)
Title of host publication2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PublisherIEEE Computer Society
Pages1423-1426
Number of pages4
ISBN (Electronic)9781538636367
DOIs
StatePublished - May 23 2018
Externally publishedYes
Event15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States
Duration: Apr 4 2018Apr 7 2018

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2018-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Country/TerritoryUnited States
CityWashington
Period4/4/184/7/18

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Fingerprint

Dive into the research topics of 'OCT-NET: A convolutional network for automatic classification of normal and diabetic macular edema using sd-oct volumes'. Together they form a unique fingerprint.

Cite this