Two stream Non-Local CNN-LSTM network for the auxiliary assessment of mental retardation

At present, the assessment of mental retardation is mainly based on clinical interview, which requires the participation of experienced psychiatrist and is laborious. Studies have shown that there are correlations between mental retardation and abnormal behaviors (such as, hyperkinetic, tics, stereotypes, etc.). On the basis of this fact, a two stream Non-Local CNN-LSTM network has been proposed to learn the features of upper body behavior and facial expression of patients, thus, to achieve the preliminary screening of mental retardation. Specifically, RGB and optical flow are extracted separately from interview videos, and a two stream network based on contribution mechanism is designed to effectively fuse the information of two kinds of images, which may update the network in a new approach of alternating iteration training to find the optimal model. Besides, by introducing non-local mechanism and adopting it to the network, the global feature sensing can be established more effectively to reduce the background interference for video clip in a short time zone. Experiments on clinical video dataset show that the performance of proposed model is better than other prevalent deep learning methods of behavioral feature learning, the accuracy reaches 89.15% in basic experiment, and is further improved to 89.52% in the supplementary experiment. Furthermore, the experimental results show that this method still has a lot of room for improvement. In general, our work indicates that the proposed model has potential value for the clinical diagnosis and screening of mental retardation.