基于YOLOv10的改进框架用于膝盖MRI病变检测,增强了小物体识别和低对比度特征提取功能
Hongwei Yang1,2, Wenqu Song1,2, Tiankai Jiang3
1Department of Orthopaedics, Affiliated Nantong Hospital 3 of Nantong University, Nantong, China.
Frontiers in artificial intelligence
|February 5, 2026
概括
这项研究引入了一个增强的YOLOv10框架,用于在膝盖MRI中改进前十字带 (ACL) 损伤的检测. 该模型在准确度上显示出显著的改进,并减少了小撕裂的错过诊断.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 整形外科手术 整形外科手术
背景情况:
- 在膝盖MRI中检测前十字带 (ACL) 病变是具有挑战性的,因为病变尺寸小,对比度低,形态不规则.
- 现有的方法在ACL损伤的准确和高效辅助诊断方面扎.
研究的目的:
- 开发一个增强的YOLOv10框架,用于在膝盖MRI中准确有效地检测ACL病变.
- 为临床实践提供精确的辅助诊断工具.
主要方法:
- 一个增强的YOLOv10框架,包含C2f-SimAM用于特征提取和空间注意.
- 一个适应空间融合 (ASF) 模块用于多尺度的特征融合.
- 一个混合损失函数 (Focal-EIoU和KPT损失) 和5倍交叉验证917个膝盖MRI病例.
主要成果:
- 拟议的模型比标准YOLOv10,更快的R-CNN和RT-DETR取得了统计学上显著的改进.
- 与标准YOLOv10.5相比,mAP@0.5增加了1.3% (p <0.05) 和mAP@0.5:0.95改善了2.5%
- 定性分析证实,小的,低对比度的ACL撕裂减少了虚假阴性.
结论:
- 增强的框架成功地将一般物体检测与医学成像需求相结合,用于ACL损伤诊断.
- 这种方法为在常规的临床工作流程中诊断ACL损伤提供了精确有效的解决方案.
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