| نویسندگان | marzieh razaghi,زهرا شهیدی |
| همایش | 13th Iranian/3rd International Machine Vision and Image Processing Conference (MVIP) 2024 |
| تاریخ برگزاری همایش | 2024-03-06 - 2024-03-07 |
| محل برگزاری همایش | 1 - تهران |
| ارائه به نام دانشگاه | دانشگاه خوارزمی |
| نوع ارائه | سخنرانی |
| سطح همایش | بین المللی |
چکیده مقاله
pose significant challenges. Traditionally, dental disease diagnosis
was a manual and time-consuming process, requiring dentists to
meticulously examine and evaluate the condition. The integration
of artificial intelligence (AI) represents a transformative approach
to aid in medical imaging diagnostics. Specifically, leveraging AI
for diagnosing dental issues entails the automatic localization of
lesions. In this study, the Yolo V8 deep learning model is employed
to develop an innovative method for the detection and
categorization of common dental problems. The primary
objective of this approach is to establish a comprehensive database
comprising two distinct categories of dental X-ray images:
BiteWing X-ray Images and Orthopantomography X-ray (OPG).
These categories aim to facilitate the diagnosis and classification
of various dental diseases. The results of the experiments showed
that the best performance in training YOLOv8m was achieved
with mAP of 71.6%, recall of 90%, and precision of 90%.