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| Brain functional connections classification method based on significant sparse strong correlation |
Ming LI1,2,3( ),Li-juan DUAN1,2,3,*(),Wen-jian WANG1,2,3,Qing EN4 |
1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2. Beijing Key Laboratory of Trusted Computing, Beijing 100124, China
3. National Engineering Laboratory for Critical Technologies of Information Security Classified Protection, Beijing 100124, China
4. Artificial Intelligence and Machine Learning (AIML) Lab, School of Computer Science, Carlton University, Ottawa K1S 5B6, Canada |
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Abstract A brain functional connectivity classification method based on significant sparse strong correlation was proposed to solve problems that data dimension of brain functional connectivity is too high and many redundant features affect the accuracy of neural network classification. In this method, the salient features sparse module was used to filter and enhance the original features. The sparse strong correlation feature context fusion module was used to aggregate the salient feature information in different receptive fields. The fully connected neural network was used for classification prediction. The results on ABIDE and ADHD-200 datasets showed that the accuracy of brain functional connection classification algorithm was improved by 10.41% and 12.5% respectively compared with the existing brain functional connection classification algorithm. The visualization results of the important features show that the proposed method can accurately locate the brain regions related to disease, which has a certain practical value.
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Received: 25 March 2022
Published: 02 December 2022
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| Fund: 国家自然科学基金资助项目(62176009, 62106065)；北京市教委重点项目(KZ201910005008) |
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Corresponding Authors:
Li-juan DUAN
E-mail: Liming@emails.bjut.edu.cn;ljduan@bjut.edu.cn
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基于显著稀疏强关联的脑功能连接分类方法
针对脑功能连接数据维度过高、冗余特征过多影响神经网络分类准确率的问题，提出一种基于显著稀疏强关联的脑功能连接分类方法. 该方法利用显著特征稀疏模块对原始特征进行筛选增强；采用稀疏强关联特征上下文融合模块对不同感受野内的显著特征信息进行聚合；使用全连接神经网络进行分类预测. 在ABIDE以及ADHD-200数据集上的实验结果表明，所提方法相较于现有的脑功能连接分类算法在准确率上分别提升了10.41%和12.50%. 重要特征的可视化结果表明所提方法能准确定位与疾病相关的脑区，具有一定实际应用价值.
关键词：
显著特征识别,
特征筛选,
特征增强,
特征融合,
脑功能连接分类
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