Abstract:
Dental cavity diseases are very common diseases and half of the world population
suffers from it. Dental caries has been a common health issue throughout the world, which can
even lead to dental pulp and root apical inflammation eventually and the most frequent dental
health issue in the general population. Therefore, timely and effective treatment of dental caries
is vital for patients to reduce pain. To this end, Deep learning techniques have demonstrated
remarkable diagnostic capabilities within the radiology field. The aim of this study is investigate
a CNN pre-trained model for classifying the stages of tooth cavity diseases and additionally, we
sought to compare the classification results achieved by deep learning models with those of
expert dentists.
Methods: In this study, two dental experts participated in preparing and evaluating collected
4725 dental X-ray images. The combination of these images formed our reference dataset. From
this dataset, we established training and validation set consisting of 4269 images, as well as a
separate test set comprising 456 images. To achieve our objectives, we employed a convolutional
neural network and utilized two pre-trained models, VGG16 and InceptionResNetV2. All These
models were developed to detect the stages of the cavity and classify them according to their
labels: Enamel, Dentin, Pulp, and Healthy tooth.
Results: Based on trained models, the CNN model has achieved a Base accuracy of 0.899% and
a validation rate of 0.910%, VGG16 scored an accuracy of 0.9243% and a validation rate of
0.9557%, while InceptionResNetV2 scored an accuracy of 0.977% and a validation of 0.978%.
The expert and the neural network demonstrated comparable results across the metrics (F1 score,
precision, and recall). In terms of cavity stage classification, InceptionResNetV2 exhibited an
impressive best accuracy of 97.7% than the other two models. Furthermore, the recall results for
InceptionResNetV2 in the ED/DD/PD/HT stages were 0.96%, 1.00%, 0.94%, and 1.00%,
respectively.
Conclusions: This paper concludes that the deep learning methods we implemented are
comparable performance to experts in determining tooth cavity stages using dental panoramic
radiographs. The application of these techniques could have significant implications in dental
diagnostics to determine the stages of cavities in the right way.
