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Deep Learning for Classification of Dental Plaque Images

  • Sultan ImangaliyevEmail author
  • Monique H. van der Veen
  • Catherine M. C. Volgenant
  • Bart J. F. Keijser
  • Wim Crielaard
  • Evgeni Levin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10122)

Abstract

Dental diseases such as caries or gum disease are caused by prolonged exposure to pathogenic plaque. Assessment of such plaque accumulation can be used to identify individuals at risk. In this work we present an automated dental red autofluorescence plaque image classification model based on application of Convolutional Neural Networks (CNN) on Quantitative Light-induced Fluorescence (QLF) images. CNN model outperforms other state of the art classification models providing a 0.75 ± 0.05 F1-score on test dataset. The model directly benefits from multi-channel representation of the images resulting in improved performance when all three colour channels were used.

Keywords

Deep learning Convolutional neural networks Computer vision Bioinformatics Computational biology Quantitative light-induced fluorescence Dentistry 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Sultan Imangaliyev
    • 1
    • 2
    Email author
  • Monique H. van der Veen
    • 2
  • Catherine M. C. Volgenant
    • 2
  • Bart J. F. Keijser
    • 1
    • 2
  • Wim Crielaard
    • 2
  • Evgeni Levin
    • 1
  1. 1.Netherlands Organisation for Applied Scientific Research, TNO Earth, Life and Social SciencesZeistThe Netherlands
  2. 2.Academic Centre for Dentistry AmsterdamUniversity of Amsterdam and Free University AmsterdamAmsterdamThe Netherlands

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