Jiang Wenjing, Liu Shuaiqi, Zhang Hong, Sun Xiuming, Wang Shui-Hua, Zhao Jie, Yan Jingwen
College of Electronic and Information Engineering, Hebei University, Baoding, China.
Machine Vision Technological Innovation Center of Hebei, Baoding, China.
Front Aging Neurosci. 2022 Jul 5;14:948704. doi: 10.3389/fnagi.2022.948704. eCollection 2022.
As a neurodevelopmental disorder, autism spectrum disorder (ASD) severely affects the living conditions of patients and their families. Early diagnosis of ASD can enable the disease to be effectively intervened in the early stage of development. In this paper, we present an ASD classification network defined as CNNG by combining of convolutional neural network (CNN) and gate recurrent unit (GRU). First, CNNG extracts the 3D spatial features of functional magnetic resonance imaging (fMRI) data by using the convolutional layer of the 3D CNN. Second, CNNG extracts the temporal features by using the GRU and finally classifies them by using the Sigmoid function. The performance of CNNG was validated on the international public data-autism brain imaging data exchange (ABIDE) dataset. According to the experiments, CNNG can be highly effective in extracting the spatio-temporal features of fMRI and achieving a classification accuracy of 72.46%.
作为一种神经发育障碍，自闭症谱系障碍（ASD）严重影响患者及其家庭的生活状况。ASD的早期诊断能够在疾病发展的早期阶段对其进行有效干预。在本文中，我们提出了一种通过结合卷积神经网络（CNN）和门控循环单元（GRU）定义为CNNG的ASD分类网络。首先，CNNG利用三维CNN的卷积层提取功能磁共振成像（fMRI）数据的三维空间特征。其次，CNNG利用GRU提取时间特征，最后通过Sigmoid函数对其进行分类。CNNG的性能在国际公共数据——自闭症大脑成像数据交换（ABIDE）数据集上得到了验证。根据实验，CNNG在提取fMRI的时空特征以及实现72.46%的分类准确率方面非常有效。
