使用YOLOv12,YOLOv11和YOLO-NAS进行急性缺血性中风的高级实时检测:用于多类分类的比较研究
Marwa El-Geneedy1,2, Hossam El-Din Moustafa3,4, Hatem Khater5
1Electronics and Communications Engineering Department, Mansoura University, Mansoura, 35516, Egypt. melgeneedy@std.mans.edu.eg.
Scientific reports
|September 15, 2025
概括
在MRI扫描中,YOLOv11和YOLOv12模型在检测急性缺血性中风 (AIS) 中具有很高的准确性. YOLOv11提供了最好的整体性能,平衡精度和回忆,可靠的AIS诊断.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
- 神经学 神经学
背景情况:
- 急性缺血性中风 (AIS) 是一个主要的全球健康问题,需要快速准确的诊断方法.
- 磁共振成像 (MRI) 对AIS检测至关重要,但及时解释仍然具有挑战性.
研究的目的:
- 为了比较评估YOLOv12,YOLOv11和YOLO-NAS对象检测模型在MRI中多类AIS检测的性能.
- 评估检测准确性和临床适用性推断速度之间的权衡.
主要方法:
- 预先处理了四个MRI类别 (正常,PD患者,AIS,控制) 的数据集,并将其分为培训,验证和测试集.
- 通过使用精度,回忆,mAP@50和推断速度指标训练和评估YOLOv12,YOLOv11和YOLO-NAS模型.
主要成果:
- YOLOv11实现了最高的mAP@50 (98.5%) 精度 (95.4%) 和回忆 (96.6%).
- YOLOv12显示了类似的结果 (mAP@50 98.3%),但推断速度稍慢一些.
- YOLO-NAS提供了最快的推断 (154 FPS),但精度明显低 (76.3%).
结论:
- YOLOv11和YOLOv12对于MRI中精确的AIS检测具有高度可靠性,其中YOLOv11是表现最好的.
- 选择YOLO模型取决于临床工作流程,在紧急中风护理中平衡速度与准确性的需求.
- 通过Roboflow等平台实时部署是可行的,用于自动化AIS检测.
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