基于图像的AI-Powered评估压力损伤使用你只看一次 (YOLO) 版本8模型
Mehedi Hasan Tusar1, Fateme Fayyazbakhsh1,2,3, Niloofar Zendehdel1
1Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, Missouri, USA.
Advances in wound care
|March 13, 2025
概括
本研究介绍了YOLOv8s深度学习模型,用于准确的压力损伤分阶段,实现高精度和回忆. 这些发现为压力损伤评估中的临床决策提供了强大的工具.
科学领域:
- 医学成像分析分析 医学成像分析
- 医疗保健中的人工智能
- 深度学习用于诊断.
背景情况:
- 压力损伤是一个重大的临床挑战.
- 准确的分期对于有效的治疗和患者的治疗结果至关重要.
- 目前用于压力损伤检测和分期的方法可能是主观的.
研究的目的:
- 通过机器学习增强压力损伤的检测和分期.
- 为了评估这个应用程序的You Only Look Once版本8 (YOLOv8) 深度学习模型.
- 为了确定压力损伤阶段的最佳YOLOv8变体和优化器.
主要方法:
- 使用了高质量的,公开可用的数据集.
- 评估了多个YOLOv8变体 (YOLOv8n,YOLOv8s,YOLOv8m,YOLOv8l,YOLOv8x) 的情况.
- 五个优化器 (Adam,AdamW,NAdam,RAdam,SGD) 经过超参数调整进行了测试.
主要成果:
- 带有AdamW优化器的YOLOv8s实现了最高的性能.
- 关键指标包括84.16%的平均精度 (IoU ≥0.5) 和82.31%的回忆.
- 合奏模型在未见的图像上表现出强的性能.
结论:
- YOLOv8s和合奏模型显著提高了压力损伤分期的准确性.
- 该模型在具有挑战性的阶段 (如第二阶段) 中表现出了显著的准确性.
- 这种方法为压力损伤管理中的临床决策提供了可靠的工具.
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