一种改进的YOLOv7-Tiny方法用于医疗输液监测中的液体水平检测
Yonghu Chang1, Changwen Pu1, Shijie Li2
1School of Medical Information Engineering, Zunyi Medical University, Zunyi 563000, China.
Computers in biology and medicine
|July 5, 2025
概括
这项研究引入了一种增强的YOLOv7微型模型,用于准确,实时的医疗输液液体水平检测. 改进的系统减少了患者的风险和医疗保健提供者的工作量.
科学领域:
- 医疗技术 医疗技术 医学技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 静脉注射输液监测需要不断关注,增加了患者的焦虑和医疗保健工作人员的工作量.
- 现有的液体水平检测方法在复杂的医疗环境中缺乏准确性和适应性.
研究的目的:
- 开发一种改进的基于YOLOv7-tiny的方法,用于在医疗输液监测中准确和实时检测液体水平.
- 为了提高检测准确度和适应复杂条件的适应性,并优化边缘设备部署.
主要方法:
- 提出了一个改进的YOLOv7微型模型,包含了新型模块:ELAN-DS-ELAN,DCN-ELAN和P-ELAN.
- 在不同条件下对4296张注释注释的输液瓶图像进行了训练.
- 使用回忆,mAP和推断速度评估性能.
主要成果:
- 改进的YOLOv7-tiny方法实现了88.319%的回忆率和90.102%的mAP@[0.5:0.95].
- 与基线YOLOv7-tiny相比,表现出优越的性能,具有可比的计算复杂性.
- 在嵌入式设备上确认了强大的实时功能.
结论:
- 拟议的方法显著提高了智能输液监测系统的准确性和可用性.
- 允许可靠的实时液体水平检测,减少医疗保健提供者的工作量和患者的风险.
- 显示了在医疗监测中更广泛应用的潜力.
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