A Deep Learning Technique for Automatic Teeth Recognition in Dental Panoramic X-Ray Images Using Modified Palmer Notation System

Dental healthcare providers need to examine a large number of panoramic X-ray images every day. It is quite time consuming, tedious, and error-prone job. The examination quality is also directly related to the experience and the personal factors, i.e., stress, fatigue, etc., of the dental care providers. To assist them handling this problem, a residual network-based deep learning technique, i.e., faster R-CNN technique, is proposed in this study. Two kinds of residual networks, i.e., ResNet-50 and ResNet-101, are used as the base network of faster R-CNN separately. A modified version of Palmer notation (PN) system is proposed in this research for numbering the teeth. The modified Palmer notation (MPN) system does not use any notation like PN system. The MPN system is proposed for mainly three reasons: (i) teeth are divided into total eight categories, and to keep this similarity, a new numbering system is proposed that has the same number of category, (ii) 8-category MPN system is less complex to implement than 32-category universal tooth numbering (UTN) system, and with some preprocessing steps, MPN system can be converted into 32-category UTN system, and finally (iii) for the convenience of the dentist, i.e., it is more feasible to utilize 8-category MPN system than 32-category UTN system. Total 900 dental X-ray images were used as training data, while 100 images were used as test data. The method achieved 0.963 and 0.965 mean average precision (mAP) for ResNet-50 and ResNet-101, respectively. The obtained results demonstrate the effectiveness of the proposed method and satisfy the condition of clinical implementation. Therefore, the method can be considered as a useful and reliable tool to assist the dental care providers in dentistry.