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The development of noninvasive brain imaging such as resting-state functionalmagnetic resonance imaging (rs-fMRI) and its combination with AI algorithmprovides a promising solution for the early diagnosis of Autism spectrumdisorder (ASD). However, the performance of the current ASD classificationbased on rs-fMRI still needs to be improved. This paper introduces aclassification framework to aid ASD diagnosis based on rs-fMRI. In theframework, we proposed a novel filter feature selection method based on thedifference between step distribution curves (DSDC) to select remarkablefunctional connectivities (FCs) and utilized a multilayer perceptron (MLP)which was pretrained by a simplified Variational Autoencoder (VAE) forclassification. We also designed a pipeline consisting of a normalizationprocedure and a modified hyperbolic tangent (tanh) activation function toreplace the original tanh function, further improving the model accuracy. Ourmodel was evaluated by 10 times 10-fold cross-validation and achieved anaverage accuracy of 78.12%, outperforming the state-of-the-art methods reportedon the same dataset. Given the importance of sensitivity and specificity indisease diagnosis, two constraints were designed in our model which can improvethe model's sensitivity and specificity by up to 9.32% and 10.21%,respectively. The added constraints allow our model to handle differentapplication scenarios and can be used broadly.

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