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 …

arxiv autism autoencoder feature selection identification