在膝盖X射线中基于深度学习的骨质疏松症分类,使用转移学习方法
Muhammad Bilal Qureshi1, Muhammad Sani1, Ali Raza2
1Department of Computer Science & IT, University of Lakki Marwat, Lakki Marwat, 28420, KPK, Pakistan.
Scientific reports
|November 3, 2025
概括
这项研究引入了一个深度学习模型ResNet-50,用于在膝盖X射线中准确检测骨质疏松症. ResNet-50模型实现了90%的准确性,为早期骨折预防提供了负担得起和有效的诊断工具.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 骨质疏松症研究 骨质疏松症研究
背景情况:
- 骨质疏松症会导致骨折,特别是在绝经后的妇女和老年人中.
- 目前的骨质疏松症诊断方法昂贵而复杂.
- 现有的骨放射学深度学习模型在架构,数据集适应和特征提取方面存在局限性.
研究的目的:
- 利用深度学习开发一种可负担得起和准确的方法,用于早期检测骨质疏松症.
- 解决医疗成像中当前诊断系统和深度学习模型的局限性.
主要方法:
- 利用ResNet-50模型与转移学习用于在膝盖X射线中检测骨质疏松症.
- 在372张X射线图像的数据集上对ResNet-50模型进行了训练和微调,具有经过验证的T分数等级.
- 将ResNet-50的性能与其他模型进行比较,包括VGG-16和CNNs.
主要成果:
- 微调的ResNet-50模型实现了90%的准确性,超过了VGG-16 (88%),非微调的ResNet-50 (83%),ResNet-18 (79%) 和3层CNN (66%).
- ResNet-50显示出高度的灵敏度和特异性.
- 该模型通过深度转移学习来检测骨质疏松症的可靠性.
结论:
- 使用ResNet-50的深度转移学习为医学成像系统提供了显著的增强.
- ResNet-50模型为医疗保健从业者提供了一种有效的诊断工具,用于早期发现骨质疏松症和预防骨折.
- 这种方法可以通过增强诊断能力来改善患者的治疗结果.
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