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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
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根据年龄和性别对脑MRI自闭症谱系障碍的多重分类使用深度学习.

Hidir Selcuk Nogay1, Hojjat Adeli2

  • 1Electrical and Energy Department, Bursa Uludag University, Bursa, Turkey.

Journal of medical systems
|January 22, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了用于诊断自闭症谱系障碍 (ASD) 的深度学习方法,表明年龄和性别在新型多分类模型中显著影响诊断准确性.

关键词:
在ASD中,使用的是ASD.在CED CED CED中.在美国,CNN是CNN.数据增强数据增强总服务总局 总服务总局多重分类是一种多重分类.这种sMRI是sMRI.

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科学领域:

  • 神经科学是一个神经科学.
  • 人工智能的人工智能
  • 医疗成像医学成像

背景情况:

  • 目前在自闭症谱系障碍 (ASD) 的快速和最终诊断和治疗方面存在局限性.
  • 需要新的技术解决方案来改善ASD诊断.
  • 研究年龄和性别等人口因素对自闭症诊断的影响至关重要.

研究的目的:

  • 开发和评估深度学习 (DL) 系统,以根据年龄和性别对ASD进行多重分类.
  • 确定年龄和性别因素对ASD诊断的贡献.
  • 用转移学习来比较自定义DL模型与预训练模型的性能.

主要方法:

  • 从ASD和典型发育 (TD) 患者的结构MRI (sMRI) 扫描进行了预处理.
  • 对于数据准备,采用了边检测 (CED) 和数据增强 (DA) 技术.
  • 使用网格搜索优化 (GSO) 开发了三个卷积神经网络 (CNN) 模型:基于性别的四重分类,基于年龄的四重分类,以及结合年龄和性别八位分类.
  • 这些模型使用五倍交叉验证进行了验证,并通过转移学习与预训练模型进行了比较.

主要成果:

  • 基于性别的模型实现了80.94%的准确性.
  • 基于年龄的模型实现了85.42%的准确性.
  • 结合年龄和性别的模型达到67.94%的准确性,在考虑这些因素时,在ASD的诊断准确性方面表现优于预训练模型.

结论:

  • 年龄和性别是影响ASD诊断的有效因素.
  • 开发的DL系统证明了通过多重分类策略改进ASD诊断的潜力.
  • 结合人口因素的定制设计DL模型与标准预训练模型相比,显示出有希望的结果.