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基于神经网络级联异常纹理信息传播,对精神分裂症和抑郁症患者的分类进行了分类

Linfeng Gan¹, Linfeng Wang¹, Hu Liu²

¹School of Railway Transportation, Shanghai Institute of Technology, Shanghai 201418, China.

|February 25, 2024

概括

磁共振成像 (MRI) 纹理分析成功地将精神分裂症 (SCZ) 和严重抑郁症 (MD) 与健康对照 (HC) 区分开来. 这种新的方法为诊断这些大脑疾病提供了有希望的生物标志物.

科学领域:

神经成像是一种神经成像.
计算精神病学是一种计算精神病学.
生物标志物发现发现

背景情况:

区分精神分裂症 (SCZ),严重抑郁症 (MD) 和健康对照 (HC) 对于有效治疗至关重要.
当前的诊断方法可能缺乏客观的生物标志物,需要先进的分析技术.
磁共振成像 (MRI) 提供了一个对大脑结构和功能的非侵入性窗口.

研究的目的:

确定基于MRI的新型生物标志物,以区分SCZ,MD和HC.
评估纹理分析和深度学习在分类这些组的有效性.
探索纹理特征作为精神疾病定量标记物的潜力.

主要方法:

来自141名SCZ患者,103名MD患者和238名HC患者的大脑MRI扫描被细分.
纹理测量 (度,对比度) 通过使用灰色水平共发生矩阵 (GLCM) 从细分区域提取.
深度学习模型用于二进制分类 (SCZ与HC,MD与HC,SCZ与MD) 并进行交叉验证.

主要成果:

纹理分析成功地分类了SCZ,MD和HC组.
分类准确率为84.6% (SCZ与HC),86.4% (MD与HC) 和76.21% (SCZ与MD).
纹理特征显示出高灵敏度和特异性,特别是在SCZ与HC分类方面.

结论:

磁力共振成像纹理分析是一种有效的特征提取方法,用于区分SCZ,MD和HC.
这种方法捕获了丰富的图像信息,可能会比其他方法提高分类准确度.
基于纹理的生物标志物显示出对精神疾病的客观诊断和分层有很大的希望.

关键词:

诊断的分类是诊断的分类.大型抑郁症主要是抑郁症.精神分裂症患者的精神分裂症患者.质地特征是质地特征.

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