Ophthalmic diagnosis using deep learning with fundus images - A critical review.

Journal: Artificial intelligence in medicine
Published Date: Nov 22, 2019

Abstract

An overview of the applications of deep learning for ophthalmic diagnosis using retinal fundus images is presented. We describe various retinal image datasets that can be used for deep learning purposes. Applications of deep learning for segmentation of optic disk, optic cup, blood vessels as well as detection of lesions are reviewed. Recent deep learning models for classification of diseases such as age-related macular degeneration, glaucoma, and diabetic retinopathy are also discussed. Important critical insights and future research directions are given.

Authors

  • Sourya Sengupta
    Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Ontario, Canada; Department of Systems Design Engineering, University of Waterloo, Ontario, Canada. Electronic address: s28sengu@uwaterloo.ca.
  • Amitojdeep Singh
    Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Ontario, Canada; Department of Systems Design Engineering, University of Waterloo, Ontario, Canada.
  • Henry A Leopold
    Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Ontario, Canada; Department of Systems Design Engineering, University of Waterloo, Ontario, Canada.
  • Tanmay Gulati
    Department of Computer Science and Engineering, Manipal Institute of Technology, India.
  • Vasudevan Lakshminarayanan
    Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Ontario, Canada; Department of Systems Design Engineering, University of Waterloo, Ontario, Canada.

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