Automatic detection of Diabetic eye disease through deep learning using fundus images: A Survey

Rubina Sarki, Khandakar Ahmed, Hua Wang, Yanchun Zhang

Research output: Contribution to journalReview ArticleOtherpeer-review

78 Citations (Scopus)

Abstract

Diabetes Mellitus, or Diabetes, is a disease in which a person's body fails to respond to insulin released by their pancreas, or it does not produce sufficient insulin. People suffering from diabetes are at high risk of developing various eye diseases over time. As a result of advances in machine learning techniques, early detection of diabetic eye disease using an automated system brings substantial benefits over manual detection. A variety of advanced studies relating to the detection of diabetic eye disease have recently been published. This article presents a systematic survey of automated approaches to diabetic eye disease detection from several aspects, namely: i) available datasets, ii) image preprocessing techniques, iii) deep learning models and iv) performance evaluation metrics. The survey provides a comprehensive synopsis of diabetic eye disease detection approaches, including state of the art field approaches, which aim to provide valuable insight into research communities, healthcare professionals and patients with diabetes.

Original languageEnglish
Pages (from-to)151133-151149
Number of pages17
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 10 Aug 2020
Externally publishedYes

Keywords

  • deep leaning
  • Diabetic eye disease
  • diabetic retinopathy
  • glaucoma
  • image processing
  • macular edema
  • transfer learning

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