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Computer Aided Diagnosis in Ophthalmology: Deep Learning Applications

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Classification in BioApps

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 26))

Abstract

The automated diagnosis of ophthalmologic diseases to assist the medical ophthalmologist in their daily practice is the subject of much research. Recently, image processing based on very deep and complex processing structures became the focus of renewed interest, mostly as a result of excellent performance in a wide range of problems. One of the main drivers of this interest in these structures, convolutional neural networks (CNNs), is the availability of fast and highly parallel hardware, enabling the fast training and efficient use of these structures. In this chapter, we briefly describe the major characteristics of CNNs and discuss the anatomy and physiology of the eye and common ocular diseases. A review of the state-of-the-art use of CNNs in the diagnosis of common eye diseases is then presented. The selection of the works reviewed followed the criteria of utility, recency and quality in order to assemble a set of representative works. An original contribution reporting the use of CNNs to quantify some corneal endothelial morphometric parameters is then presented in a separate section. Finally, some considerations are made on possible developments of the techniques described, as made possible by the evolution of computing technology.

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Author information

Authors and Affiliations

  1. Centro Cirúrgico de Coimbra and Department of Physics, University of Coimbra, Coimbra, Portugal

    José N. Galveia

  2. Centro Cirúrgico de Coimbra, Coimbra, Portugal

    António Travassos

  3. Department of Electrical and Computer Engineering, University of Coimbra, Coimbra, Portugal

    Francisca A. Quadros

  4. Instituto de Telecomunicações and the Department of Electrical and Computer Engineering, University of Coimbra, Coimbra, Portugal

    Luís A. da Silva Cruz

Authors
  1. José N. Galveia
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  2. António Travassos
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  3. Francisca A. Quadros
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  4. Luís A. da Silva Cruz
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Corresponding author

Correspondence to José N. Galveia .

Editor information

Editors and Affiliations

  1. Department of Information Technology, Techno India College of Technology, Kolkata, India

    Nilanjan Dey

  2. Faculty of Engineering, Tanta University, Tanta, Egypt

    Amira S. Ashour

  3. K.S. Institute of Technology, Bangalore, Karnataka, India

    Surekha Borra

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Galveia, J.N., Travassos, A., Quadros, F.A., da Silva Cruz, L.A. (2018). Computer Aided Diagnosis in Ophthalmology: Deep Learning Applications. In: Dey, N., Ashour, A., Borra, S. (eds) Classification in BioApps. Lecture Notes in Computational Vision and Biomechanics, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-65981-7_10

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  • DOI: https://doi.org/10.1007/978-3-319-65981-7_10

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  • Online ISBN: 978-3-319-65981-7

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