An ASD Classification Based on a Pseudo 4D ResNet: Utilizing Spatial and Temporal Convolution

Abstract

The psychiatric condition known as autism spectrum disorder (ASD) affects children and adults alike. As a medical imaging technology, functional magnetic resonance imaging (fMRI) is widely used to study the brains of persons with ASD. This study introduces a novel technique: a pseudo 4D ResNet (P4D ResNet) to simultaneously extract and classify the brain activity of ASD patients. A P4D ResNet can extract both temporal and spatial information from fMRI data, which mainly consists of two different residual blocks stacked together. In a P4D ResNet, to reduce computational and parametric quantities, each residual block is combined with a 3D spatial filter and a 1D temporal filter instead of a 4D spatiotemporal convolution, which can perform parallel computation. Due to the high dimensionality of the complete data and the limited amount of data, in this article, each piece of fMRI data are sampled at equal intervals of a set length in the time dimension for data expansion. Compared with other existing models, the experiments show that the proposed model for ASD classification achieved better results.