Diabetic Retinopathy Grade and Macular Edema Risk Classification Using Convolutional Neural Networks

Jia He, Linlin Shen, Xingfang Ai, Xuechen Li

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

8 Citations (Scopus)

Abstract

Diabetic retinopathy (DR) is one of the major causes of blindness in the western world. Effective treatment of DR is available, when detected early enough, which makes this a vital process. Computers are able to obtain much quicker classifications once trained, giving the ability to aid clinicians in real-time classification. This work employed a deep convolutional neural network (CNN) based method for diabetic retinopathy classification. Three independent CNNs were employed for the classification of DR grade, macular edema risk and multi-label, which included the combination of both grade and risk classes. A fusion method was used to combine all features extracted by the CNNs and make the final classification result. The classification accuracy of the grade and risk were 0.65 and 0.72, respectively. The classification results showed the proposed network fusion method can improve the performances on both task - DR grading and macular edema risk.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages463-466
Number of pages4
ISBN (Electronic)9781728137209
DOIs
Publication statusPublished - Jul 2019
Externally publishedYes
Event2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019 - Shenyang, China
Duration: 12 Jul 201914 Jul 2019

Publication series

Name2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019

Conference

Conference2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019
Country/TerritoryChina
CityShenyang
Period12/07/1914/07/19

Keywords

  • deep learning
  • Diabetic Retinopathy Severity
  • fundus image
  • multi-CNNs
  • Risk of Diabetic Macular Edema

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment

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