Tooth Numbering and Condition Recognition on Dental Panoramic Radiograph Images Using CNNs

Abstract

Dentists and medical personnel strive to provide patients with prompt medical services. In the past, Dental Panoramic Radiograph (DPR) was often used to diagnose and understand the dental condition of patients. In recent years, many machine learning and deep learning methods have been applied to medical image recognition problems. Moreover, when combined with deep learning methods, data augmentation and image pre-processing methods can also give positive feedback. This study aims to combine data augmentation and data pre-processing methods with advanced deep learning methods to build an innovative and practical two-phase DPR recognition and classification method to assist dentists in diagnosis. It will help to improve the medical quality of dental services by speeding up and saving valuable physician manpower cost and time. Prior to the two-phase recognition based on several effective Convolutional Neural Networks (CNNs), the data augmentation and data pre-processing are processed. In the first phase of this method, the position and numbering of the tooth is automatically classified as one of 32 tooth positions from the DPR tooth images. In the second phase, the dental conditions are automatically recognized from the 6 dental conditions, including orthodontics, endodontic therapy, dental restoration, impaction, implant, and dental prosthesis. The experimental results showed that the trained network, without image pre-processing and augmentation, identified the dental position numbering with an accuracy of 90.93%, and the dental condition with an accuracy of 93.33%. After data augmentation, the accuracy of tooth numbering can be increased to 95.62%, and the accuracy of dental condition can be increased to 98.33%. This is a significant improvement when compared with past research.