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构建精神分裂症识别方法,采用多个大脑区域的GLCM特征和机器学习技术.

Şerife Gengeç Benli1, Merve Andaç1

  • 1Department of Biomedical Engineering, Faculty of Engineering, Erciyes University, Kayseri 38280, Turkey.

Diagnostics (Basel, Switzerland)
|July 14, 2023
PubMed
概括

磁共振成像揭示了特定大脑区域的独特纹理特征,有助于诊断精神分裂症. 左半球是左半球.

科学领域:

  • 神经成像是一种神经成像.
  • 精神疾病 精神疾病
  • 生物标志物发现发现

背景情况:

  • 精神分裂症的诊断是复杂的,对于有效的治疗至关重要.
  • 磁共振成像为精神分裂症提供了潜在的生物标志物.
  • 分析特定大脑区域的纹理特征可能会使精神分裂症患者与健康对照者区分开来.

研究的目的:

  • 用结构性MR图像对精神分裂症患者和健康对照人群之间的双边杏仁体,尾状体,状体,状体和状体区域的纹理差异进行数值分析.
  • 为了确定哪个大脑半球表现出更独特的纹理特征.
  • 为了比较各种机器学习方法的分类性能来检测精神分裂症.

主要方法:

  • 灰色水平共发生矩阵 (GLCM) 的特征是从两个半球的五个特定的大脑区域 (杏仁体,尾状体,状体,状体,状体,丘脑) 中提取的.
  • 机器学习算法包括Adaboost,梯度提升,极度梯度提升,随机森林,k-最近邻居,线性差异分析 (LDA) 和天真贝叶斯被用来进行分类.
  • 根据准确性,敏感性,特异性和曲线下的面积 (AUC) 评估了分类成功.

主要成果:

  • 来自五个特定大脑区域的纹理特征,特别是在左半球,与健康个体相比,对精神分裂症的分类性能更高.
  • 线性差异分析 (LDA) 算法取得了优异的分类结果.
关键词:
机器学习是机器学习.精神分裂症是一种精神分裂症.结构 MR 图像的结构 MR 图像.

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  • LDA显示了100%的AUC,94.4%的准确性,92.31%的灵敏度,100%的特异性和91.9%的F1得分.

    • 结论:

    • 特定大脑区域的纹理特征,而不是整个大脑,是识别精神分裂症的重要指标.
    • 左半球的纹理特征特别有望作为精神分裂症检测的生物标志物.
    • 机器学习,特别是LDA,可以有效地利用这些纹理特征来准确诊断精神分裂症.