Classification of Approximal Caries in Bitewing Radiographs Using Convolutional Neural Networks
Sensors 2021, 21(15), 5192; https://doi.org/10.3390/s21155192 (registering DOI)
Received: 11 June 2021 / Revised: 21 July 2021 / Accepted: 22 July 2021 / Published: 31 July 2021
(This article belongs to the Special Issue Artificial Intelligence & Robotics in Dental Medicine)
Dental caries is an extremely common problem in dentistry that affects a
significant part of the population. Approximal caries are especially
difficult to identify because their position makes clinical analysis
difficult. Radiographic evaluation—more specifically, bitewing
images—are mostly used in such cases. However, incorrect interpretations
may interfere with the diagnostic process. To aid dentists in caries
evaluation, computational methods and tools can be used. In this work,
we propose a new method that combines image processing techniques and
convolutional neural networks to identify approximal dental caries in
bitewing radiographic images and classify them according to lesion
severity. For this study, we acquired 112 bitewing radiographs. From
these exams, we extracted individual tooth images from each exam,
applied a data augmentation process, and used the resulting images to
train CNN classification models. The tooth images were previously
labeled by experts to denote the defined classes. We evaluated
classification models based on the Inception and ResNet architectures
using three different learning rates: 0.1, 0.01, and 0.001. The training
process included 2000 iterations, and the best results were achieved by
the Inception model with a 0.001 learning rate, whose accuracy on the
test set was 73.3%. The results can be considered promising and suggest
that the proposed method could be used to assist dentists in the
evaluation of bitewing images, and the definition of lesion severity and
appropriate treatments.
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