Earticle

초록

영어

As the importance of artificial intelligence (AI) as a diagnostic aid in the medical field is gradually increasing, our study constructed an AI model that detects root canal treatment areas using oral and maxillofacial data. We constructed models using three types (v5s, v5l, v5x) of the real-time object detection algorithm YOLO (You Only Look Once) version 5 to meet the medical field's requirement for more precise, faster, and accurate performance. Each model was trained for 300 epochs using an SGD optimizer and as a result of the experiment, all versions of YOLO v5 algorithms showed high mAP@.5 performance over 0.93. However, for mAP@ .5:.95 performance which corresponds to more precise detection performance evaluation, it was confirmed that there is a difference in performance depending on the network size of the model. Thus, we suggest that YOLO v5x model with the largest network size is most suitable for detecting root canal treatment areas. Through this research, we suggest future research directions in fields related to development of diagnostic aids based on AI and look forward to developing more advanced object detection algorithms.

목차

Abstract
I.INTRODUCTION
II.RELATED WORK
A.YOLO
III.ROOT CANAL DETECTION
A.Oral and Maxillofacial Data
B.Root Canal Detection Based on YOLO v5
IV.EXPERIMENTAL EVALUATION AND DISCUSSION
V.CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Inpyo Hong [ Department of Computer Engineering Gachon University ]
• Kiho Lim [ Department of Computer Science William Paterson University New Jersey, USA ]
• Chang Choi [ Department of Computer Engineering Gachon University Seongnam-si, Republic of Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004