Autism disorder, generally known as Autism Spectrum Disorder (ASD) is a brain disorder characterized by lack of communication skills, social aloofness and repetitions in the actions in the patients, which is affecting millions of the people across the globe. Accurate identification of autistic patients is considered a challenging task in the domain of brain disorder science. To address this problem, we have proposed a three-stage feature selection approach for the classification of ASD on the preprocessed Autism Brain Imaging Data Exchange (ABIDE) rs-fMRI Dataset. In the first stage, a large neural network which we call a “Teacher ” was trained on the correlation-based connectivity matrix to learn the latent representation of the input. In the second stage an autoencoder which we call a “Student” autoencoder was given the task to learn those trained “Teacher” embeddings using the connectivity matrix input. Lastly, an SFFS-based algorithm was employed to select the subset of most discriminating features between the autistic and healthy controls. On the combined site data across 17 sites, we achieved the maximum 10-fold accuracy of 82% and for the individual site-wise data, based on 5-fold accuracy, our results outperformed other state of the art methods in 13 out of the total 17 site-wise comparisons.

中文翻译：

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三阶段教师，学生神经网络和基于顺序前馈选择的自闭症谱系分类特征选择方法

自闭症，通常称为自闭症谱系障碍（ASD），是一种脑部疾病，其特征在于缺乏沟通技能，社交超然性和患者行为重复，这正在影响全球数以百万计的人。正确识别自闭症患者被认为是脑疾病科学领域的一项艰巨任务。为了解决这个问题，我们提出了一种用于预处理自闭症脑成像数据交换（ABIDE）rs-fMRI数据集的ASD分类的三阶段特征选择方法。在第一阶段，我们在基于相关性的连通性矩阵上训练了一个大型神经网络（我们称为“教师”），以学习输入的潜在表示形式。在第二阶段，我们给自动编码器（我们称为“学生”自动编码器）赋予了使用连通性矩阵输入来学习那些经过训练的“教师”嵌入的任务。最后，采用基于SFFS的算法来选择自闭症患者和健康对照者之间最具区别性特征的子集。在17个站点的组合站点数据上，我们获得了82％的最高10倍精度，对于单个站点数据，基于5倍精度，我们的结果在13个站点中超过了其他现有方法总共17项现场比较。