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

doi:10.1016/j.compbiomed.2022.105854

	Identification of Autism spectrum disorder based on a novel feature selection method and Variational Autoencoder
	Zhang, Fangyu 1,2; Wei, Yanjie 2; Liu, Jin 4; Wang, Yanlin 2; Xi, Wenhui 2; Pan, Yi 2,3
	2022-09-01
发表期刊	COMPUTERS IN BIOLOGY AND MEDICINE
ISSN	0010-4825
卷号	148 页码:11
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.
关键词	ASD fMRI Filter feature selection VAE ABIDE Classification
DOI	10.1016/j.compbiomed.2022.105854
收录类别	SCI
语种	英语
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
WOS类目	Biology ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Mathematical & Computational Biology
WOS记录号	WOS:000863562600007
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：9[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19787
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.Southern Univ Sci & Technol, Coll Engn, Shenzhen 518055, Peoples R China 2.Chinese Acad Sci, Shenzhen Inst Adv Technol, Ctr High Performance Comp, Shenzhen 518055, Peoples R China 3.Chinese Acad Sci, Shenzhen Inst Adv Technol, Coll Comp Sci & Control Engn, Shenzhen 518055, Peoples R China 4.Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, 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.