尿液形成了基于YOLOv5n的元素实例细分
Shuqin Tu1, Hongxing Liu1, Liang Mao2
1College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China.
Scientific reports
|November 20, 2024
概括
这项研究引入了用于尿液分析的快速深度学习模型YOLOv5n. 它显著提高了检测和细分尿元素的速度和准确性,有助于临床诊断.
科学领域:
- 医学成像和诊断 医学成像和诊断
- 计算机视觉 计算机视觉
- 医疗保健中的人工智能
背景情况:
- 手动显微镜用于尿液分析是耗时且主观的.
- 目前的自动化方法在小,密集的尿元素中难以准确和快速.
- 精确检测和细分尿元素对于诊断尿路和脏疾病至关重要.
研究的目的:
- 开发一个快速而准确的实例细分模型,用于尿液形成的元素.
- 解决尿液分析现有算法的速度和精度的局限性.
- 为了利用深度学习进行自动化尿液颗粒检测.
主要方法:
- 提出了一个基于YOLOv5n的单阶段实例细分模型,用于尿液形成的元素.
- 利用骨干网络进行特征提取 (浅层图形和语义特征).
- 采用了用于多尺度特征融合的子网络和集成FCN用于检测和细分的头部网络.
主要成果:
- 在定制数据集上实现了91.8%的平均精度 (mAP50) 和每秒63.3 (FPS).
- 与Mask R-CNN和YOLOv8.6相比,显示了显著的速度改进 (60.9-62.6%更快).
- 与其他最先进的模型相比,它展示了卓越的精度 (1.4-3.6% mAP50增长) 和速度平衡.
结论:
- YOLOv5n模型为自动化尿液成形元素分析提供了高度准确和高效的解决方案.
- 这种深度学习方法通过更快,更精确的尿液分析,为临床疾病诊断提供技术支持.
- 开发的方法有望在自动化尿液分析系统中得到广泛采用.
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