Exploratory study on direct prediction of diabetes using deep residual networks

S. Abbasi-Sureshjani, B. Dashtbozorg, B.M. ter Haar Romeny, F. Fleuret

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

7 Citations (Scopus)

Abstract

Diabetes is threatening the health of many people in the world. People may be diagnosed with diabetes only when symptoms or complications such as diabetic retinopathy start to appear. Retinal images reflect the health of the circulatory system and they are considered as a cheap and patient-friendly source of information for diagnosis purposes. Convolutional neural networks have enhanced the performance of conventional image processing techniques significantly by neglecting inconsistent feature extraction pipelines and learning informative features automatically from data. In this work we explore the possibility of using the deep residual networks as one of the state-of-the-art convolutional networks to diagnose diabetes directly from retinal images, without using any blood glucose information. The results indicate that convolutional networks are able to capture informative differences between healthy and diabetic patients and it is possible to differentiate between these two groups using only the retinal images. The performance of the proposed method is significantly higher than human experts.

Original languageEnglish
Title of host publicationVipIMAGE2017
EditorsJ.M.R.S. Tavares, R.M.N. Jorge
PublisherSpringer
Pages797-802
Number of pages6
Volume27
ISBN (Print)978-3-319-68194-8
DOIs
Publication statusPublished - 2018
Event6th ECCOMAS European Congress on Computational Methods in Applied Sciences and Engineering, October 18-20 2017, Porto, Portugal - Porto, Portugal
Duration: 18 Oct 201720 Oct 2017
https://paginas.fe.up.pt/~vipimage/

Publication series

NameLecture notes in computational vision and biomechanics
Volume27

Conference

Conference6th ECCOMAS European Congress on Computational Methods in Applied Sciences and Engineering, October 18-20 2017, Porto, Portugal
Abbreviated titleECCOMAS2017
CountryPortugal
CityPorto
Period18/10/1720/10/17
Internet address

Keywords

  • Deep learning
  • Diabetes
  • Diabetic retinopathy
  • ResNet
  • Retinal images

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