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通过卷积神经网络对激活的微质进行分类

Chao-Hsiung Hsu¹, Artur Agaronyan¹, Raffensperger Katherine²

¹Molecular Imaging Laboratory, Department of Radiology, Howard University, Washington, DC, USA.

IEEE Biomedical Circuits and Systems Conference : healthcare technology : [proceedings]. IEEE Biomedical Circuits and Systems Conference

|March 28, 2024

概括

卷积神经网络 (CNN) 可以在脑图像中准确检测激活的微质细胞. 这种方法为微质形态的定量分析提供了潜力,有助于理解脑损伤反应.

科学领域:

神经科学是一个神经科学.
免疫学免疫学免疫学
计算生物学计算生物学

背景情况:

微质,大脑的免疫细胞,在受伤时激活和改变形态.
评估微质激活对于理解中枢神经系统疾病至关重要.
目前分析微质形态的方法可能是劳动密集型的.

研究的目的:

开发和评估一个卷积神经网络 (CNN) 模型,用于检测免疫组织化学图像中的激活微质.
为了比较不同CNN架构和支持矢量机 (SVM) 分类器的性能,用于此任务.

主要方法:

从经过心脏骤停的老鼠大脑中获取2D Iba1 免疫组织化学图像.
在超过54,000张单细胞图像上训练和测试CNN模型 (Resnet18,Resnet50,Resnet101) 和SVM分类器.
基于分类准确性的模型性能评估.

主要成果:

在120个训练时代后,Resnet18 CNN架构实现了最高的性能,分类准确率为95.5%,达到120个训练时代.
与SVM分类器相比,CNN模型表现出优越的性能.
在对照和受伤的大脑区域之间观察到微质形态的显著差异.

结论:

CNNs提供了一个高度准确和高效的工具,用于自动检测和定量分析激活的微质细胞.
这种方法对大规模分析不同大脑区域和损伤条件的微质形态有希望.
这些发现支持深度学习在神经炎症研究中的应用.

关键词:

在美国,CNN是CNN.心脏骤停是因为心脏停止了.细胞形态细胞形态学微质细胞中的微质细胞

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