利用多部位功能磁共振成像识别自闭症谱系障碍

Shabeena Lylath, Laxmi B. Rananavare
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摘要

自闭症谱系障碍(ASD)是一种神经发育性疾病,其特点是在社会交往和沟通方面存在持久的困难。经分析,自闭症患者可能会表现出重复行为和有限的兴趣。自闭症被归类为一种谱系障碍,这意味着症状的严重程度可能因人而异,从轻微到严重不等。为了检测自闭症,本文设计了一种属性特征图方法,利用静态依赖特征来完成自闭症的诊断。在第一阶段,根据功能磁共振成像(fMRI)数据设计提取的特征;在下一阶段,属性特征图层通过 ASD 分类学习各节点信息的特征。此外,在第三步中,它还用于从 fMRI 导出的大脑功能连接矩阵中独立提取区别特征。本研究中使用的定制卷积神经网络(CNN)是在一个综合数据集上进行训练的,该数据集包括被诊断为 ASD 的个体和发育正常的个体。在第四阶段,开发了一个学习原型,以提高定制卷积神经网络的分类性能。进一步进行的实验分析表明,与现有系统相比,所提出的模型工作效率更高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Autism spectrum disorder identification with multi-site functional magnetic resonance imaging
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by enduring difficulties in social interaction and communication. People analyzed with ASD may display repetitive behaviors and limited interests. Autism is classified as a spectrum disorder, implying that the symptom intensity might range from mild to severe depending on the individual. To detect ASD in this paper an attribute feature graph approach is designed by using the stastical dependencies features that necessarily accomplish the diagnosis of ASD. In the first phase the features extracted are designed based on the functional magnetic resonance imaging (fMRI) data, in the next-step the attribute feature graph layer learns the features of the node information of various nodes by ASD classification. Further, in the third step, it is employed to independently extract distinguishing features from the functional connectivity matrices of the brain that are derived from fMRI. The custom convolutional neural network (CNN) used in this study is trained on a comprehensive dataset comprising individuals diagnosed with ASD and typically developing individuals. In the fourth stage, a prototype learning is developed to augment the classification performance of the custom-CNN. The experimental analysis further carried out states that the proposed model works efficiently in comparison with the existing system.
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