Tooth Localization using YOLOv3 for Dental Diagnosis on Panoramic Radiographs

作     者：Bui, Toan Huy Hamamoto, Kazuhiko Paing, May Phu 

作者机构：Course of Science and Technology Graduate School of Science and Technology Tokai University Tokyo108-8619 Japan School of Information and Telecommunication Engineering Tokai University Tokyo108-8619 Japan School of Engineering King Mongkut’s Institute of Technology Ladkrabang Bangkok10520 Thailand 

出 版 物：《IEEJ Transactions on Electronics, Information and Systems》 (IEEJ Trans. Electron. Inf. Syst.)

年 卷 期：2022年第142卷第5期

页      面：557-562页

核心收录：

基　　金：The objective of the research has been successfully accomplished. The tooth has been well detected from the oral. The value of precision shows a high detection accuracy therefore, the method is valuable and practical for the doctor. All the aspects of the problem seem to be explored. However, we believe that there is a way to improve the method even better. Therefore, in future work, we would like to try more methods to improve a better result for this objective. A deeper model should be tried to build as a base network of Yolo. Also, there is a competition between Yolov3 and single-shot detection (SSD)(21) which also should be considered. Acknowledgment This research is financially supported by Japan International Co-operation Agent (JICA). The author also would like to thank Dr. Makoto Kumon for his contribution to the dataset in this research 

主　　题：Object detection 

摘      要：Oral health is one of most major concerns that affect the life quality of billions of people around the world. Diagnosis treatment usually takes time due to the lack of doctors compared to a huge number of patients. Many researchers proposed methods to make an early disease detection for patients to assist doctors using computer aid diagnosis (CAD). However, most previous methods are not end-to-end methods and still require human involvement. The biggest challenge is that most researchers do not provide a good tooth detection technique before diagnosis. Therefore, the main objective, that builds a system to assist doctors, remains unaccomplished or just fairly successful. This paper proposed a detection method to localize the tooth using the Yolov3 model as a base network in the dental panoramic radiograph. The method consists of two main parts: image preprocessing and tooth localization. Firstly, because deep learning requires a big dataset, the original image is applied augmentation technique to improve the size of the dataset as well as diversity. Then, each image is resized to fit the input layer of the network;however, to prevent the information loss and boost the performance, we keep the original ratio of the images and change the ratio of the input layer in the model that can fit the image ratio. Next, we feed images into Yolov3, which is specially modified to fit the problem, for training. We add more detection heads into the backbone and concatenate the previous head detection’s result with a proper layer to produce a more preeminent result. The final assessment shows an impressive result that the method reaches 95.58% and 94.90% for precision and recall, respectively. As a result, our proposed method is more reliable and practical in the tooth localization field, as well as helpful to reduce the doctor s effort. © 2022 The Institute of Electrical Engineers of Japan.