基于DC-YOLOv5的部脊椎成熟的目标检测算法
Man Jiang1,2,3, Yun Hu1,2,3, Jianxia Li1,2,3
1Department of Stomatological Hospital, Chongqing Medical University, No.426 Songshibei Road, Yubei District, Chongqing, 401147, China.
Physical and engineering sciences in medicine
|August 12, 2024
概括
一个新的DC-YOLOv5模型自动化了从放射图片的椎脊椎成熟 (CVM) 阶段. 这种人工智能方法提高了正义牙科和骨科治疗规划的准确性和速度.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 矯正牙科 矯正牙科是一種矯正牙科.
背景情况:
- 椎脊椎成熟 (CVM) 对于定时进行正统牙科和骨科治疗至关重要.
- 准确的CVM分期有助于有效的治疗计划.
- 目前的方法可能缺乏完全的自动化和最佳准确性.
研究的目的:
- 开发一个完全自动化的目标检测模型,用于CVM分阶段.
- 通过深度学习提高CVM识别的准确性和效率.
- 引入一个优化的YOLOv5模型,用于分析头脑测量放射图.
主要方法:
- 使用了一套数据集,其中包括1800张标记的头脑测量放射图.
- 开发了DC-YOLOv5模型,优化了YOLOv5与Wise-IOU和Res-dcn-head的优化.
- 整合了卷积块注意模块 (CBAM) 以改善功能聚焦.
- 使用F1分数,mAP,精度和回忆来评估性能.
主要成果:
- DC-YOLOv5实现了高性能指标:F1得分为0.993,mAP0.5为0.994,mAP0.5:0.95为0.943. 这两项指标均为高性能指标.
- 与其他四种模型相比,该模型表现出更快的收,更高的准确性和更大的稳定性.
- 梯度类激活映射 (CAM) 用于分析预测区域.
结论:
- 该DC-YOLOv5算法提供高度准确和强大的CVM识别.
- 这种自动化方法支持更快,更精确的诊断在牙矯正.
- 该模型为医学成像和临床决策的进步做出了积极贡献.
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