Deep learning for classification of dental plaque images

Sultan Imangaliyev*, Monique H. van der Veen, Catherine M.C. Volgenant, Bart J.F. Keijser, Wim Crielaard, Evgeni Levin

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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.

Original languageEnglish
Title of host publicationMachine Learning, Optimization, and Big Data - 2nd International Workshop, MOD 2016, Revised Selected Papers
EditorsGiuseppe Nicosia, Giovanni Giuffrida, Piero Conca, Panos M. Pardalos
PublisherSpringer Verlag,
Pages407-410
Number of pages4
ISBN (Print)9783319514680
DOIs
Publication statusPublished - 2016
Event2nd International Workshop on Machine Learning, Optimization and Big Data, MOD 2016 - Volterra, Italy
Duration: 26 Aug 201629 Aug 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10122 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Workshop on Machine Learning, Optimization and Big Data, MOD 2016
Country/TerritoryItaly
CityVolterra
Period26/08/1629/08/16

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2016.

Keywords

  • Bioinformatics
  • Computational biology
  • Computer vision
  • Convolutional neural networks
  • Deep learning
  • Dentistry
  • Quantitative lightinduced fluorescence

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