使用顺序特征嵌入和规范化的多核支向量机器来对阿尔茨海默病前期阶段进行多类分类
Oyekanmi O Olatunde1, Kehinde S Oyetunde2, Jihun Han3
1Department of Systems Science and Industrial Engineering, Binghamton University, NY 13902, USA.
NeuroImage
|November 21, 2024
概括
准确地分类神经退行阶段 (认知正常,轻度认知障碍,阿尔茨海默病) 是至关重要的. 结合成像和临床数据的新框架实现了最先进的多类分类准确性,优于以前的方法.
科学领域:
- 神经科学是一个神经科学.
- 医学成像分析 医学成像分析
- 机器学习用于医疗保健
背景情况:
- 早期发现神经退行性疾病,如阿尔茨海默氏症 (AD),对于及时干预至关重要.
- 轻度认知障碍 (MCI) 呈现异质特征,挑战认知正常 (CN),MCI和AD阶段的准确分类.
- 现有的分类方法通常依赖于对对二进制比较,使直接的多类评估和解释复杂化.
研究的目的:
- 开发一个强大的框架,用于直接多类分类神经退行性疾病阶段 (CN与MCI与MCI). AD).). 在此之前,我们已经知道了.
- 克服二元分类方法在处理MCI数据异质性的局限性.
- 在神经退行检测中建立一个新的最先进的 (SOTA) 性能基准,用于多类分类.
主要方法:
- 一个新的框架,集成无监督的集体多重组规范化的稀疏低级近似与规范化的多核支向量机器 (SVM).
- 从MRI和PET神经成像数据中提取关节特征嵌入.
- 将成像特征与临床数据 (Apoe4,Adas11,MPACC数字,内体积) 结合起来,使用规范化的多核SVM进行分类.
主要成果:
- 在CN与MCI与MCI之间取得的SOTA表现. AD多类分类,平均准确度为84.87±6.09,F1得分为84.83±6.12.
- 在二进制分类任务中表现出强大的泛化,在大多数类别中实现了SOTA结果.
- 与现有的最佳分数相比,CN与MCI二元分类显示了0.2%的最小绩效下降.
结论:
- 拟议的框架有效地解决了分类异质神经退行性疾病阶段的挑战.
- 这种综合方法为多类分类提供了一个比顺序二进制任务更准确,更易于解释的方法.
- 这些发现为在神经退行症早期检测和干预方面改进诊断工具铺平了道路.
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