Abstract
In dentistry, panoramic X-rays help the doctor seriously in diagnosing the patient's condition, and through them, he can see what he does not see directly. In this research, we applied popular deep learning architecture to dental panoramic X-ray images. The main aim of the study is to propose a teeth-based segmentation model. We used the Mask R-CNN, which is a prosperous deep learning model composed of a ResNet50 backbone and a feature pyramid. The proposal achieved excellent results using tooth-based annotation on 521 dental panoramic X-ray images. We reached average segmentation precision rates of 78.342% and 75.392% for the bounding box and segmentation, respectively.
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