探索基于卷积集团核随机网络的精神分裂症的白质干扰
S A Varaprasad1, Tripti Goel1, M Tanveer2
1Biomedical Imaging Lab, National Institute of Technology Silchar, Silchar, 788010, Assam, India.
概括
精神分裂症 (SZ) 与显著的白质 (WM) 干扰有关. 一个新的深度学习模型准确地识别了这些WM变化,有助于SZ诊断.
科学领域:
- 神经成像是一种神经成像.
- 人工智能在医学中的应用
- 计算神经科学是一种神经科学.
背景情况:
- 精神分裂症 (SZ) 呈现出认知缺陷和结构性大脑异常.
- 卷积神经网络 (CNN) 提供了识别复杂大脑变化的潜力.
- 结构磁共振成像 (sMRI) 检测白质 (WM),灰质 (GM) 和脑脊液 (CSF) 的破坏.
研究的目的:
- 开发和评估CNN组合KRR-RVFL模型,用于检测SZ中的WM中断.
- 为了比较模型在不同类型的大脑组织 (WM,GM,CSF) 的性能.
- 为了研究在SZ.组织体积和症状严重程度之间的关系.
主要方法:
- 一个八层CNN与五个基于Kernel Ridge回归的随机向量功能链接 (KRR-RVFL) 分类器集成.
- 将分类器输出的集成平均值用于最终分类.
- 在组织体积和症状尺度之间进行了相关性分析.
主要成果:
- 拟议的CNN合奏KRR-RVFL在识别WM中断时实现了97.33%的准确性.
- 在患有SZ的个体中,WM组织体积的减少比GM更大.
- 在组织体积和症状严重程度之间发现了显著的相关性.
结论:
- 开发的深度学习模型有效地识别了与SZ相关的WM中断.
- 在SZ,WM的完整性受到显著损害,比GM更严重.
- 这种方法有助于临床医生通过突出WM变化的作用来诊断SZ.
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