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Auto-classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model

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Computer Vision – ACCV 2018 Workshops (ACCV 2018)

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Abstract

Automatic clinical diagnosis of retinal diseases has emerged as a promising approach to facilitate discovery in areas with limited access to specialists. Based on the fact that fundus structure and vascular disorders are the main characteristics of retinal diseases, we propose a novel visual-assisted diagnosis hybrid model mixing the support vector machine (SVM) and deep neural networks (DNNs). Furthermore, we present a new clinical retina labels collection sorted by the professional ophthalmologist from the educational project Retina Image Bank, called EyeNet, for ophthalmology incorporating 52 retina diseases classes. Using EyeNet, our model achieves 90.40% diagnosis accuracy, and the model performance is comparable to the professional ophthalmologists (https://github.com/huckiyang/EyeNet2).

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Author information

Authors and Affiliations

  1. Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    C.-H. Huck Yang, Jia-Hong Huang, Hiromasa Morikawa & Jesper Tegnèr

  2. Georgia Institute of Technology, Atlanta, GA, USA

    C.-H. Huck Yang

  3. University of Waterloo, Waterloo, Canada

    Fangyu Liu

  4. Department of Ophthalmology, Bern University Hospital, Bern, Switzerland

    Meng Tian

  5. Department of Ophthalmology, Tri-Service General Hospital, Taipei, Taiwan

    M. D. I-Hung Lin

  6. National Taiwan University, Taipei, Taiwan

    Jia-Hong Huang, Yi Chieh Liu & Hao-Hsiang Yang

  7. Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Solna, Sweden

    Jesper Tegnèr

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  1. C.-H. Huck Yang
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Correspondence to C.-H. Huck Yang or Fangyu Liu .

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Editors and Affiliations

  1. School of Computer Science, University of Adelaide, Adelaide, Australia

    Gustavo Carneiro

  2. Data61, Commonwealth Scientific and Industrial Research Organization, Canberra, Australia

    Shaodi You

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Huck Yang, CH. et al. (2019). Auto-classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model. In: Carneiro, G., You, S. (eds) Computer Vision – ACCV 2018 Workshops. ACCV 2018. Lecture Notes in Computer Science(), vol 11367. Springer, Cham. https://doi.org/10.1007/978-3-030-21074-8_28

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  • DOI: https://doi.org/10.1007/978-3-030-21074-8_28

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