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

Szu-Yin Lin,Hao-Yun Chang

doi:10.1109/access.2021.3136026

Abstract

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

Highlights

Dental Panoramic Radiograph (DPR) is a scanned wideangle X-ray radiograph taken from the upper and lower jaw section of the patient
The goal of this study is to design and construct a DPR classification system based on Convolutional Neural Networks (CNNs), image pre-process and data augmentation for tooth position numbering and condition classification, which can improve the efficiency of tooth position numbering classification in DPR and to reduce the workload of dental medical personnel
We proposed a novel two-phase dental panoramic radiograph classification method based on deep learning

Summary

Introduction

Dental Panoramic Radiograph (DPR) is a scanned wideangle X-ray radiograph taken from the upper and lower jaw section of the patient. DPR was often used by medical personnel as an important reference diagnostic basis for understanding the dental condition of patients. It assisted dentists to provide patients with the most immediate medical services. When the number of patients is much larger than the number of medical professionals, the quality of dental treatment decreases. If the image recognition method of deep learning can automatically analyze the patient's panoramic X-ray images, it will help to speed up and save valuable physician manpower cost and time, and improve the medical quality of dental services, especially when determining the content of a large number of panoramic Xray images. The purpose of this research is to propose an effective method for recognition and classification of tooth position numbering and tooth condition by combining image pre-processing and data augmentation with several advanced deep learning methods, to accurately and effectively deal with the problems of automatic recognition and classification on DPR. Deep learning has performed prominently in the field of machine learning and feature learning, and is VOLUME XX, 2017

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