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SAE-based classification of school-aged children with autism spectrum disorders using functional magnetic resonance imaging

机译：功能性磁共振成像基于SAE的学龄儿童自闭症谱系障碍分类

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This paper employs a novel-deep learning method and brain frequencies to discriminate school-aged children with autism spectrum disorders (ASD) from typically developing (TD) school-aged children with functional magnetic resonance imaging (fMRI) data of 84 subjects from the ABIDE (Autism Brain Imaging Data Exchange) database. Firstly, the fMRI data were preprocessed, and then each subject’s dataset was decomposed into 30 independent components (IC). Secondly, some key ICs were selected and inputted into a stacked autoencoder (SAE). The SAE was adopted for features subtraction and dimensionality reduction. Finally, a softmax classifier was used to discriminate the school-aged children with ASD from TD school-aged children. The average accuracy of the work was as high as 87.21% (average sensitivity = 92.86%, average specificity = 84.32%). The results of classification demonstrated that the proposed method may have the potential to automatically discriminate school-aged children with ASD from TD school-aged children. Attempts to use deep learning-based algorithms and brain frequencies to discriminate school-aged children with ASD from TD school-aged children should likely be a key step forward in auxiliary clinical utility.
机译：本文采用一种新型的深度学习方法和脑部频率来区分自闭症谱系障碍（ASD）的学龄儿童与典型发育（TD）的具有功能磁共振成像（fMRI）数据的84名受试者的学龄儿童（自闭症脑成像数据交换）数据库。首先，对fMRI数据进行预处理，然后将每个受试者的数据集分解为30个独立成分（IC）。其次，选择了一些关键IC，并将其输入到堆叠式自动编码器（SAE）中。 SAE用于特征减法和降维。最后，使用softmax分类器将ASD的学龄儿童与TD学龄儿童区分开。这项工作的平均准确率高达87.21％（平均灵敏度= 92.86％，平均特异性= 84.32％）。分类结果表明，该方法可能具有自动区分ASD学龄儿童和TD学龄儿童的潜力。尝试使用基于深度学习的算法和大脑频率来将学龄儿童与ASD区分为TD学龄儿童，这很可能是辅助临床应用中的关键一步。

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