卷积神经网络用于分类儿科中手腕骨折的红外热图像
Olamilekan Shobayo1, Reza Saatchi2, Shammi Ramlakhan3
1Department of Computing, Sheffield Hallam University, Sheffield S1 2NU, UK.
Healthcare (Basel, Switzerland)
|May 24, 2024
概括
卷积神经网络模型准确地使用红外热成像识别了儿科手腕骨折. 虽然有效,但建议增加样本大小以提高诊断准确度.
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:
- 儿童手腕骨折需要准确和及时的诊断.
- 放射性X射线是标准的诊断工具,但正在探索替代的成像方法.
- 红外热成像 (IRT) 提供了一种非侵入性方法,可视化与受伤相关的生理变化.
研究的目的:
- 开发和评估卷积神经网络 (CNN) 模型,使用IRT图像对儿科手腕骨折进行分类.
- 评估使用FFT处理的IRT图像用于骨折检测的可行性.
- 调查图像增强和放弃对CNN业绩的影响.
主要方法:
- 收集了儿科手腕的IRT图像 (骨折与扭伤) (n=40).
- 感兴趣的区域使用快速里埃转换 (FFT) 进行处理并重新调整大小.
- 在70%的数据上训练了一个34层的CNN模型,并在30%的数据上进行了测试,并应用了数据增强.
主要成果:
- CNN模型在识别手腕骨折时实现了88%的灵敏度和76%的准确性.
- 图像增强技术在训练期间改善了CNN的概括性.
- 该模型展示了CNN在基于IRT的骨折检测中的潜力.
结论:
- CNN模型可以有效地从IRT图像中对儿科手腕骨折进行分类.
- 需要对更大的数据集进行进一步的研究,以提高诊断准确度.
- IRT与CNN结合,为儿科骨折评估提供了一个有希望的非侵入性方法.
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