A Comparative Study on Autism Spectrum Disorder Detection via 3D Convolutional Neural Networks.

The prevalence of Autism Spectrum Disorder (ASD) in the United States has increased by 178% from 2000 to 2016. However, due to the lack of well-trained specialists and the time-consuming diagnostic process, many children are not able to be promptly diagnosed. Recently, several research have taken steps to explore automatic video-based ASD detection systems with the help of machine learning and deep learning models, such as support vector machine (SVM) and long short-term memory (LSTM) model. However, the models mentioned above could not extract effective features directly from raw videos. In this study, we aim to take advantages of 3D convolution-based deep learning models to aid video-based ASD detection. We explore three representative 3D convolutional neural networks (CNNs), including C3D, I3D and 3D ResNet. In addition, a new 3D convolutional model, called 3D ResNeSt, is also proposed based on ResNeSt. We evaluate these models on an ASD detection dataset. The experimental results show that, on average, all of the four 3D convolutional models can obtain competitive results when compared to the baseline using LSTM model. Our proposed 3D ResNeSt model achieves the best performance, which improves the average detection accuracy from 0.72 to 0.85.