Autism Classification Using Topological Features and Deep Learning: A Cautionary Tale.

ABSTRACT: The identification of autistic individuals using resting state functional connectivity networks can provide an objective diagnostic method for autism spectrum disorder (ASD). The present state-of-the-art machine learning model using deep learning has a classification accuracy of 70.2% on the ABIDE (Autism Brain Imaging Data Exchange) data set. In this paper, we explore the utility of topological features in the classification of ASD versus typically developing control subjects. These topological features have been shown to provide a complementary source of discriminative information in applications such as 2D object classification and social network analysis. We evaluate the performance of three different representations of topological features - persistence diagrams, persistence images, a

SUBMITTER: Rathore A 

PROVIDER: S-EPMC7390646 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature