在使用YOLOv7的咬伤X射线图中增强虫检测.
Wannakamon Panyarak1, Kittichai Wantanajittikul2, Arnon Charuakkra1
1Division of Oral and Maxillofacial Radiology, Department of Oral Biology and Diagnostic Sciences, Faculty of Dentistry, Chiang Mai University, Suthep Road, Suthep, Mueang Chiang Mai District, Chiang Mai, 50200, Thailand.
Journal of digital imaging
|August 28, 2023
概括
YOLOv7在咬牙翼X射线图上检测牙损伤方面明显优于YOLOv3. 增加图像大小并没有提高性能,而较高的IOU和置信值降低了整体准确度,尽管精度增加了.
科学领域:
- 人工智能在牙科中的应用
- 放射学和成像技术的技术.
- 机器学习用于医学诊断.
背景情况:
- 通过咬翼放射检测牙损伤,对于及时干预至关重要.
- 传统的方法可能是主观和耗时的.
- 使用深度学习模型进行自动检测,有可能提高效率和准确性.
研究的目的:
- 为了评估图像大小的影响,交叉超过联盟 (IoU) 值,以及对牙损伤检测YOLO模型性能的置信值.
- 为了比较YOLOv3和YOLOv7模型在咬翼放射图上识别各种病变的有效性.
主要方法:
- 使用了2575张咬翼放射图,使用ICCMSTM放射评分系统进行了注释.
- 采用了各种配置的YOLOv3和YOLOv7模型 (图像大小,IOU,置信值).
- 使用精度,回忆,F1得分和平均平均精度 (mAP) 评估模型性能.
主要成果:
- YOLOv7在YOLOv3上表现出更高的性能,实现了更高的精度,F1得分和mAP.
- 将图像大小从640x640增加到1280x1280像素并没有显著改变YOLOv7的mAP.
- 将IOU从50%提高到75%和置信值从0.001提高到0.5,提高了精度,但减少了回忆和整体mAP.
结论:
- YOLOv7是一个比YOLOv3更有效的模型,用于自动检测咬牙的牙损伤.
- 在这种情况下,图像大小优化不是性能增强的主要驱动因素.
- 调整IOU和置信值需要仔细平衡,以优化虫检测的精度和回忆.
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