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Detection and Classification of Dental Caries in X-ray Images Using Deep Neural Networks

Authors:
Ramzi Ben Ali
Ridha Ejbali
Mourad Zaied

Keywords: dental X-ray; classification; Deep Neural Networks; Stacked sparse auto-encoder; Softmax

Abstract:
Dental caries, also known as dental cavities, is the most widespread pathology in the world. Up to a very recent period, almost all individuals had the experience of this pathology at least once in their life. Early detection of dental caries can help in a sharp decrease in the dental disease rate. Thanks to the growing accessibility to medical imaging, the clinical applications now have better impact on patient care. Recently, there has been interest in the application of machine learning strategies for classification and analysis of image data. In this paper, we propose a new method to detect and identify dental caries using X-ray images as dataset and deep neural network as technique. This technique is based on stacked sparse auto-encoder and a softmax classifier. Those techniques, sparse auto-encoder and softmax, are used to train a deep neural network. The novelty here is to apply deep neural network to diagnosis of dental caries. This approach was tested on a real dataset and has demonstrated a good performance of detection.

Pages: 223 to 227

Copyright: Copyright (c) IARIA, 2016

Publication date: August 21, 2016

Published in: conference

ISSN: 2308-4235

ISBN: 978-1-61208-498-5

Location: Rome, Italy

Dates: from August 21, 2016 to August 25, 2016