Review of Machine Learning Applications Using Retinal Fundus Images

Yeonwoo Jeong, Yu Jin Hong, Jae Ho Han

Research output: Contribution to journalReview articlepeer-review

16 Citations (Scopus)


Automating screening and diagnosis in the medical field saves time and reduces the chances of misdiagnosis while saving on labor and cost for physicians. With the feasibility and development of deep learning methods, machines are now able to interpret complex features in medical data, which leads to rapid advancements in automation. Such efforts have been made in ophthalmology to analyze retinal images and build frameworks based on analysis for the identification of retinopathy and the assessment of its severity. This paper reviews recent state-of-the-art works utilizing the color fundus image taken from one of the imaging modalities used in ophthalmology. Specifically, the deep learning methods of automated screening and diagnosis for diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are investigated. In addition, the machine learning techniques applied to the retinal vasculature extraction from the fundus image are covered. The challenges in developing these systems are also discussed.

Original languageEnglish
Article number134
Issue number1
Publication statusPublished - 2022 Jan

Bibliographical note

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.


  • Deep learning
  • Fundus image
  • Machine learning
  • Retinal image

ASJC Scopus subject areas

  • Clinical Biochemistry


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