  Dental caries is one of the most chronic diseases involving the majority of
the population during their lifetime. Caries lesions are typically diagnosed by
radiologists relying only on their visual inspection to detect via dental
x-rays. In many cases, dental caries is hard to identify using x-rays and can
be misinterpreted as shadows due to different reasons such as low image
quality. Hence, developing a decision support system for caries detection has
been a topic of interest in recent years. Here, we propose an automatic
diagnosis system to detect dental caries in Panoramic images for the first
time, to the best of authors' knowledge. The proposed model benefits from
various pretrained deep learning models through transfer learning to extract
relevant features from x-rays and uses a capsule network to draw prediction
results. On a dataset of 470 Panoramic images used for features extraction,
including 240 labeled images for classification, our model achieved an accuracy
score of 86.05\% on the test set. The obtained score demonstrates acceptable
detection performance and an increase in caries detection speed, as long as the
challenges of using Panoramic x-rays of real patients are taken into account.
Among images with caries lesions in the test set, our model acquired recall
scores of 69.44\% and 90.52\% for mild and severe ones, confirming the fact
that severe caries spots are more straightforward to detect and efficient mild
caries detection needs a more robust and larger dataset. Considering the
novelty of current research study as using Panoramic images, this work is a
step towards developing a fully automated efficient decision support system to
assist domain experts.
