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Identification of Autism spectrum disorder based on a novel feature selection method and Variational Autoencoder

文献类型：期刊论文

作者Zhang, Fangyu3,4; Wei, Yanjie3; Liu, Jin1; Wang, Yanlin3; Xi, Wenhui3; Pan, Yi2,3
刊名COMPUTERS IN BIOLOGY AND MEDICINE
出版日期2022-09-01
卷号148页码:11
关键词ASD fMRI Filter feature selection VAE ABIDE Classification
ISSN号0010-4825
DOI10.1016/j.compbiomed.2022.105854
英文摘要The development of noninvasive brain imaging such as resting-state functional magnetic resonance imaging (rs-fMRI) and its combination with AI algorithm provides a promising solution for the early diagnosis of Autism spectrum disorder (ASD). However, the performance of the current ASD classification based on rs-fMRI still needs to be improved. This paper introduces a classification framework to aid ASD diagnosis based on rs-fMRI. In the framework, we proposed a novel filter feature selection method based on the difference between step distribution curves (DSDC) to select remarkable functional connectivities (FCs) and utilized a multilayer perceptron (MLP) which was pretrained by a simplified Variational Autoencoder (VAE) for classification. We also designed a pipeline consisting of a normalization procedure and a modified hyperbolic tangent (tanh) activation function to replace the classical tanh function, further improving the model accuracy. Our model was evaluated by 10 times 10-fold cross-validation and achieved an average accuracy of 78.12%, outperforming the state-of-the-art methods reported on the same dataset. Given the importance of sensitivity and specificity in disease diagnosis, two constraints were designed in our model which can improve the model's sensitivity and specificity by up to 9.32% and 10.21%, respectively. The added constraints allow our model to handle different application scenarios and can be used broadly.
资助项目National Key Research and Development Program of China[2018YFB0204403] ; Shenzhen KQTD Project[KQTD20200820113106007] ; Strategic Priority CAS Project[XDB38050100] ; National Science Foundation of China[U1813203] ; Shenzhen Basic Research Fund[RCYX2020071411473419] ; Shenzhen Basic Research Fund[JSGG20201102163800001] ; CAS Key Lab[2011DP173015] ; Natural Science Foundation of Hunan Province[2022JJ30753] ; Youth Innovation Promotion Association, CAS[Y2021101]
WOS研究方向Life Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology
语种英语
WOS记录号WOS:000863562600007
出版者PERGAMON-ELSEVIER SCIENCE LTD
源URL[http://119.78.100.204/handle/2XEOYT63/19786]  
专题中国科学院计算技术研究所期刊论文
通讯作者Wei, Yanjie; Pan, Yi
作者单位1.Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Peoples R China
2.Chinese Acad Sci, Shenzhen Inst Adv Technol, Coll Comp Sci & Control Engn, Shenzhen 518055, Peoples R China
3.Chinese Acad Sci, Shenzhen Inst Adv Technol, Ctr High Performance Comp, Shenzhen 518055, Peoples R China
4.Southern Univ Sci & Technol, Coll Engn, Shenzhen 518055, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Fangyu,Wei, Yanjie,Liu, Jin,et al. Identification of Autism spectrum disorder based on a novel feature selection method and Variational Autoencoder[J]. COMPUTERS IN BIOLOGY AND MEDICINE,2022,148:11.
APA Zhang, Fangyu,Wei, Yanjie,Liu, Jin,Wang, Yanlin,Xi, Wenhui,&Pan, Yi.(2022).Identification of Autism spectrum disorder based on a novel feature selection method and Variational Autoencoder.COMPUTERS IN BIOLOGY AND MEDICINE,148,11.
MLA Zhang, Fangyu,et al."Identification of Autism spectrum disorder based on a novel feature selection method and Variational Autoencoder".COMPUTERS IN BIOLOGY AND MEDICINE 148(2022):11.

入库方式： OAI收割

来源：计算技术研究所

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