【摘要】 提出一种基于改进的一维卷积神经网络(1D-ICNN)的阿尔茨海默病与异常型额颞叶痴呆诊断模型，对卷积层的输出进行下采样的最大池化操作和特征压缩的全局平均池化操作。该模型在47例阿尔茨海默病和39例行为异常型额颞叶痴呆患者脑结构磁共振数据上的分类精度为86.63%,优于传统机器学习模型和一般深度学习模型。此外，采用SHAP可解释方法对模型的预测结果进行解释，并对解释结果进行可视化。更多还原

【Abstract】 An improved one-dimensional convolutional neural network(1D-ICNN) model is proposed to diagnose Alzheimer’s disease and abnormal frontotemporal dementia. In the model, the output of convolution layer was down sampled and the global average pooling of feature compression was performed. The classification accuracy of this model on brain structure MRI data of 47 patients with Alzheimer’s disease and 39 patients with behavioral disorder frontotemporal dementia was 86.63%, which was better than traditional machine learning model and traditional deep learning model. In addition, the SHAP interpretable method was used to interpret the prediction results of the model, and the interpretation results were visualized.更多还原