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 language | English |
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Title of host publication | Machine Learning, Optimization, and Big Data - 2nd International Workshop, MOD 2016, Revised Selected Papers |
Editors | Giuseppe Nicosia, Giovanni Giuffrida, Piero Conca, Panos M. Pardalos |
Publisher | Springer Verlag, |
Pages | 407-410 |
Number of pages | 4 |
ISBN (Print) | 9783319514680 |
DOIs | |
Publication status | Published - 2016 |
Event | 2nd International Workshop on Machine Learning, Optimization and Big Data, MOD 2016 - Volterra, Italy Duration: 26 Aug 2016 → 29 Aug 2016 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 10122 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 2nd International Workshop on Machine Learning, Optimization and Big Data, MOD 2016 |
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Country/Territory | Italy |
City | Volterra |
Period | 26/08/16 → 29/08/16 |
Bibliographical note
Publisher Copyright:© Springer International Publishing AG 2016.
Keywords
- Bioinformatics
- Computational biology
- Computer vision
- Convolutional neural networks
- Deep learning
- Dentistry
- Quantitative lightinduced fluorescence