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使用深度初始转移学习检测膝关节骨关节炎的严重程度.

Muhammad Sohail1, Muhammad Muzammil Azad1, Heung Soo Kim1

  • 1Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul, 04620, Republic of Korea.

Computers in biology and medicine
|January 1, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的AI模型,用于使用转移学习检测骨关节炎 (OA) 严重程度. 微调的InceptionV3模型显著提高了中度和重度OA等级的诊断准确性.

关键词:
深度学习是一种深度学习.开始模式的模型.膝关节关节炎 膝关节关节炎膝盖退化退化 膝盖退化骨关节炎是一种骨关节炎.转移学习转移学习

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 整形外科 整形外科 整形外科

背景情况:

  • 骨关节炎 (OA) 诊断依赖于放射学解释,这对于早期阶段可能具有挑战性.
  • 凯尔格伦和劳伦斯 (KL) 评分系统是标准的,但解释的准确性各不相同.
  • 目前用于OA检测的AI模型表现不一致.

研究的目的:

  • 为准确的骨关节炎 (OA) 严重程度分类开发一个改进的人工智能 (AI) 模型.
  • 通过转移学习方法,加强对OA严重程度水平的识别.
  • 改进现有的人工智能模型用于OA诊断.

主要方法:

  • 使用InceptionV3 (IV3) 模型的转移学习方法在骨关节炎倡议数据集上进行了微调.
  • 双阶段预处理和卷积神经网络被用于特征提取.
  • 微调的IV3 (FT-IV3) 模型的性能与原始的IV3模型进行了比较.

主要成果:

  • FT-IV3模型的准确度更高:96.33% (培训),93.82% (验证) 和92.25% (测试).
  • 最初的IV3模型的准确度为91.64% (培训),82.04% (验证) 和86.20% (测试).
  • 科恩对FT-IV3的卡帕值 (90.69%) 优于IV3 (83.15%),表明更好地诊断了OA的严重程度.

结论:

  • 微调的InceptionV3 (FT-IV3) 模型在分类关节炎严重程度方面表现出卓越的性能.
  • 这种人工智能方法显示了提高OA诊断准确性的巨大潜力,特别是在中度和严重等级.
  • 这项研究强调了转移学习在增强骨科疾病医疗图像分析方面的有效性.