使用YOLOv8模型检测膝关节关节炎的机器学习和深度学习算法的比较分析
1Department of Computer Engineering, Selcuk University, 42250 Selcuklu, Konya, Türkiye.
Journal of X-ray science and technology
|February 27, 2025
概括
这项研究表明,与传统的机器学习和深度学习方法相比,YOLOv8模型在检测膝关节关节炎方面优越. YOLOv8x-cls实现了最高的准确性,为早期膝关节关节炎诊断提供了一个有前途的工具.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 膝关节关节炎是一种广泛影响全球健康的疾病.
- 早期检测和治疗对于控制膝关节关节炎进展和改善患者结果至关重要.
- 为了更准确的诊断工具,正在探索先进的计算方法.
研究的目的:
- 评估和比较各种机器学习 (ML),深度学习 (DL) 和YOLOv8分类模型在检测膝关节关节炎方面的性能.
- 从医学图像中识别最有效的算法来准确地分类膝关节关节炎.
- 评估YOLOv8模型在临床背景下用于膝关节关节炎诊断的实用性.
主要方法:
- 使用了"膝关节关节炎检测注释数据集",包括五个类别 (正常,可疑,轻度,中度,严重) 的1650张图像.
- 使用了传统的ML模型 (k-NN,SVM,GBM),DL模型 (DenseNet,EfficientNet,InceptionV3) 和YOLOv8分类模型 (YOLOv8n-cls到YOLOv8x-cls).
- 数据是使用保留方法 (80%的培训,10%的验证,10%的测试) 分割的.
主要成果:
- 在膝关节关节炎检测方面,YOLOv8模型显著优于ML和DL算法.
- ML模型的准确度在63.61% (k-NN) 到67.36% (GBM) 之间.
- DL模型显示出不同程度的成功,InceptionV3达到79.41%,而YOLOv8x-cls达到86.96%的最高精度.
结论:
- YOLOv8分类模型,特别是YOLOv8x-cls,在膝关节关节炎检测方面表现出卓越的性能.
- 这些发现表明,YOLOv8模型为自动膝关节关节炎诊断提供了有希望的,高度准确的方法.
- 该研究强调了先进的人工智能在提高膝关节关节炎的早期检测和管理方面的潜力.
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