Identification of Autism spectrum disorder based on a novel feature selection method and Variational Autoencoder. (arXiv:2204.03654v1 [eess.IV])

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 original 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.



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