With the development of artificial intelligence technologies, it is possible to use computer to read digital medical images. Because Alzheimer’s disease (AD) has the characteristics of high incidence and high disability, it has attracted the attention of many scholars, and its diagnosis and treatment have gradually become a hot topic. In this paper, a multimodal diagnosis method for AD based on three-dimensional shufflenet (3DShuffleNet) and principal component analysis network (PCANet) is proposed. First, the data on structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) are preprocessed to remove the influence resulting from the differences in image size and shape of different individuals, head movement, noise, and so on. Then, the original two-dimensional (2D) ShuffleNet is developed three-dimensional (3D), which is more suitable for 3D sMRI data to extract the features. In addition, the PCANet network is applied to the brain function connection analysis, and the features on fMRI data are obtained. Next, kernel canonical correlation analysis (KCCA) is used to fuse the features coming from sMRI and fMRI, respectively. Finally, a good classification effect is obtained through the support vector machines (SVM) method classifier, which proves the feasibility and effectiveness of the proposed method.

中文翻译：

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基于深度学习和多模式特征融合的阿尔茨海默氏病辅助诊断

随着人工智能技术的发展，可以使用计算机读取数字医学图像。由于阿尔茨海默氏病（AD）具有高发病率和高残疾的特点，因此引起了许多学者的关注，其诊断和治疗逐渐成为热门话题。提出了一种基于三维shufflenet（3DShuffleNet）和主成分分析网络（PCANet）的AD多模态诊断方法。首先，对有关结构磁共振成像（sMRI）和功能磁共振成像（fMRI）的数据进行预处理，以消除由于不同个体的图像大小和形状，头部运动，噪声等差异而产生的影响。然后，将原始的二维（2D）ShuffleNet开发为三维（3D），这更适合3D sMRI数据提取特征。另外，将PCANet网络应用于脑功能连接分析，并获得fMRI数据的特征。接下来，使用内核规范相关分析（KCCA）融合来自sMRI和fMRI的特征。最后，通过支持向量机方法分类器获得了很好的分类效果，证明了该方法的可行性和有效性。