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Hybrid Statistical Framework for Diabetic Retinopathy Detection

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Book cover Image Analysis and Recognition (ICIAR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10882))

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Abstract

We present in this paper a novel hybrid statistical framework for retinal image classification and diabetic retinopathy detection. Our purpose here is to develop a probabilistic SVM-based kernel combined with a finite mixture of Scaled Dirichlet distributions. The developed method offers more flexibility in data modeling and classification since it takes advantage of both generative and discriminative models. Quantitative results obtained from a large dataset of real retinal images confirm the effectiveness of the proposed framework.

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Author information

Authors and Affiliations

  1. Université de Tunis El Manar, ENIT, Laboratoire: LR-SITI Signal Image et Technologies de l’Information, 1002, Tunis, Tunisie

    Sami Bourouis

  2. College of Computers and Information Technology, Taif University, Taif, Kingdom of Saudi Arabia

    Sami Bourouis & Atef Zaguia

  3. The Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC, H3G 1T7, Canada

    Nizar Bouguila

Authors
  1. Sami Bourouis
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  2. Atef Zaguia
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  3. Nizar Bouguila
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Corresponding author

Correspondence to Sami Bourouis .

Editor information

Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Aurélio Campilho

  2. University of Waterloo, Waterloo, Ontario, Canada

    Fakhri Karray

  3. Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

    Bart ter Haar Romeny

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Bourouis, S., Zaguia, A., Bouguila, N. (2018). Hybrid Statistical Framework for Diabetic Retinopathy Detection. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_78

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  • DOI: https://doi.org/10.1007/978-3-319-93000-8_78

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92999-6

  • Online ISBN: 978-3-319-93000-8

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