使用深度学习模型来诊断腰椎间盘的方法
Ardha Ardea Prisilla1,2, Yue Leon Guo3,4,5, Yih-Kuen Jan6
1Department of Fashion Design, LaSalle College Jakarta, Jakarta, Indonesia.
Frontiers in bioengineering and biotechnology
|September 21, 2023
概括
这项研究表明,YOLOv5x在MRI扫描中有效检测腰椎间盘 (LDH),优于其他YOLO模型. 增强和非增强数据集都为早期LDH诊断带来了有希望的结果.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 在MRI中检测腰椎间盘 (LDH) 是复杂的,因为呈现的变化.
- 深度学习模型,特别是你只看一次 (YOLO) 系列,显示了在临床环境中自动检测LDH的潜力.
研究的目的:
- 评估和比较YOLOv5,YOLOv6和YOLOv7模型在不同腰部区域检测腰椎间盘 (LDH) 的性能.
- 通过使用MRI图像来确定最佳的YOLO模型,以准确有效地进行LDH诊断.
主要方法:
- 将DICOMMRI图像转换为JPEG,并使用ROBOFLOW选择,标记和增强相关切片.
- YOLOv5x,YOLOv6和YOLOv7模型在550张图像的数据集上进行了训练,有和没有增强.
- 基于平均平均精度 (mAP) 和跨腰椎间盘水平的检测精度来评估模型性能.
主要成果:
- YOLOv5x表现出最高的性能,达到89.30%的mAP,表现优于YOLOv6和YOLOv7.
- 增强 (AUG) 和非增强 (非AUG) 数据集都显示了YOLOv5x.的令人满意的训练性能.
- 使用YOLOv5x的非增强数据集在L2-L3,L3-L4,L4-L5和L5-S1区域检测LDH时取得了90%以上的准确性.
结论:
- YOLOv5x是MRI扫描中检测腰椎间盘 (LDH) 的高效模型,在增强和非增强数据集中提供了有希望的结果.
- 选择一个合适的YOLO模型,如YOLOv5x,可以显著帮助临床医生在早期LDH诊断和患者管理.
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