优化的Yolov8功能融合算法用于牙科疾病检测.
Qimeng Wang1, Xingfei Zhu2, Zhaofei Sun1
1Jiangnan University, Wuxi, Jiangsu, 214122, China; The Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Wuxi, Jiangsu, 214122, China.
Computers in biology and medicine
|February 9, 2025
概括
本研究引入了一种改进的YEM-SAFN模型,用于从全景电影中增强牙科疾病检测. 该模型显著提高了识别牙疾病的准确性,克服了像图像扭曲这样的挑战.
科学领域:
- 牙科 牙科是指牙科的专业.
- 计算机视觉 计算机视觉
- 医疗成像医学成像
背景情况:
- 全景牙科膜遭受放大扭曲和低对比度,妨碍精确的疾病检测.
- 由于这些图像质量问题,现有的物体检测算法在识别牙疾病方面表现不佳.
研究的目的:
- 开发一个改进的YEM-SAFN模型,以便在口腔全景电影中更好地识别牙疾病.
- 在牙科成像中应对小目标,多尺度变异和组织重叠的挑战.
主要方法:
- 整合了一种新的小目标网络结构和多尺度检测头.
- 整合了HCSA关注机制,以便在特定疾病地区集中提取特征.
- 实施了重新设计的加权聚变模块,以提高多尺度特征利用率.
主要成果:
- 改进的YEM-SAFN算法实现了平均平均精度 (mAP) 86.7%,比原来的YOLOv8s增加了3.2%.
- 与其他主流物体检测算法相比,其表现优越.
- 有效地解决了多个规模的目标和特征提取挑战.
结论:
- 增强的YEM-SAFN模型为牙科疾病的识别和诊断提供了高效和准确的解决方案.
- 这种方法提高了牙科疾病自动检测系统的可靠性.
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