Autism spectrum disorder analysis by using a 3D-ResNet-based approach

Abstract

Autism spectrum disorder is a heterogeneous neurological disorder. The early diagnosis of autism is critical to apply effective treatment. Presently, most diagnoses are based on behavioral observations of symptoms. There has been an increasing number of approaches using magnetic resonance imaging with the development of deep learning in recent years. However, the interfering elements and insignificant differentiation between positive and negative samples have seriously affected the classification performance. In this paper, a multi-scale information fusion mechanism is proposed to combine with attention sub-nets to establish an end-to-end classification model, which selects appropriate fusion strategies for the outputs of different layers of the convolutional neural network to make comprehensive use of the information at different levels of the image. Experiments are conducted by using the dataset of Autistic Brain Imaging Data Exchange. The results show that the proposal achieves better performance than the models in comparison.