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

Zhang，Fangyu1,2; Wei，Yanjie2; Liu，Jin4; Wang，Yanlin2; Xi，Wenhui2; Pan，Yi2,3

2022-09-01

10.1016/j.compbiomed.2022.105854

COMPUTERS IN BIOLOGY AND MEDICINE   Impact Factor & Quartile

0010-4825

1879-0534

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.

ABIDE ASD Classification Filter feature selection fMRI VAE

SCI ; EI

English

First

National Key Research and Development Program of China[2018YFB0204403];National Natural Science Foundation of China[U1813203];Youth Innovation Promotion Association[Y2021101];

Life Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology

Biology ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Mathematical & Computational Biology

WOS:000863562600007

PERGAMON-ELSEVIER SCIENCE LTD

20223012396669

Brain mapping ; Classification (of information) ; Computer aided diagnosis ; Diseases ; Hyperbolic functions ; Learning systems ; Magnetic resonance imaging ; Radial basis function networks

Biomedical Engineering:461.1 ; Magnetism: Basic Concepts and Phenomena:701.2 ; Information Theory and Signal Processing:716.1 ; Computer Applications:723.5 ; Imaging Techniques:746 ; Information Sources and Analysis:903.1 ; Mathematics:921

COMPUTER SCIENCE

2-s2.0-85134432500

Scopus

1.College of Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.Centre for High Performance Computing,Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences,Shenzhen,518055,China
3.College of Computer Science and Control Engineering,Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences,Shenzhen,518055,China
4.School of Computer Science and Engineering,Central South University,Changsha,410083,China

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.

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.

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).