Yuan-Jin Lin
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Yuan-Jin Lin has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Ai-Yun Tseng
Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan
1 shared publication - Cheng-Hao Peng
Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 320317, Taiwan
1 shared publication - Chiung-An Chen
Department of Electrical Engineering, Ming Chi University of Technology, New Taipei, Taiwan
1 shared publication - He-Sheng Chou
Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 320317, Taiwan
1 shared publication - Kuo-Chen Li
Department of Information Management, Chung Yuan Christian University, Taoyuan City 320314, Taiwan
1 shared publication - Shih-Lun Chen
Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 320314, Taiwan
1 shared publication
Latest Publications
- Open Access
- 9 February 2024
- Published by MDPI AG in Electronics
- Vol. 13 (4) , 702
Classification of the Relative Position between the Third Molar and the Inferior Alveolar Nerve Using a Convolutional Neural Network Based on Transfer Learning
In recent years, there has been a significant increase in collaboration between medical imaging and artificial intelligence technology. The use of automated techniques for detecting medical symptoms has become increasingly prevalent. However, there has been a lack of research on the relationship between impacted teeth and the inferior alveolar nerve (IAN) in DPR images. The severe compression of teeth against the IAN may necessitate the requirement for nerve canal treatment. To reduce the occurrence of such events, this study aims to develop an auxiliary detection system capable of precisely locating the relative positions of the IAN and impacted teeth through object detection and image enhancement. This system is designed to shorten the duration of examinations for dentists while concurrently mitigating the chances of diagnostic errors. The innovations in this research are as follows: (1) using YOLO_v4 to identify impacted teeth and the IAN in DPR images achieves an accuracy of 88%. However, the developed algorithm in this study achieves an accuracy of 93%. (2) Image enhancement is utilized in this study to expand the dataset, with an accuracy of up to 2~3% enhancement in detecting diseases. (3) The segmentation technique proposed in this study surpasses previous methods by achieving 6% higher accuracy in dental diagnosis.
- 9 February 2024
- Published by MDPI AG in Electronics
- Vol. 13 (4) , 702
Open AccessClassification of the Relative Position between the Third Molar and the Inferior Alveolar Nerve Using a Convolutional Neural Network Based on Transfer Learning
In recent years, there has been a significant increase in collaboration between medical imaging and artificial intelligence technology. The use of automated techniques for detecting medical symptoms has become increasingly prevalent. However, there has been a lack of research on the relationship between impacted teeth and the inferior alveolar nerve (IAN) in DPR images. The severe compression of teeth against the IAN may necessitate the requirement for nerve canal treatment. To reduce the occurrence of such events, this study aims to develop an auxiliary detection system capable of precisely locating the relative positions of the IAN and impacted teeth through object detection and image enhancement. This system is designed to shorten the duration of examinations for dentists while concurrently mitigating the chances of diagnostic errors. The innovations in this research are as follows: (1) using YOLO_v4 to identify impacted teeth and the IAN in DPR images achieves an accuracy of 88%. However, the developed algorithm in this study achieves an accuracy of 93%. (2) Image enhancement is utilized in this study to expand the dataset, with an accuracy of up to 2~3% enhancement in detecting diseases. (3) The segmentation technique proposed in this study surpasses previous methods by achieving 6% higher accuracy in dental diagnosis.