Automatic Diagnosis of Depression Based on Facial Expression Information and Deep Convolutional Neural Network

Depression is a complex mental disease, which involves many factors such as psychology, physiology, and society, and which causes harm to society. Up to now, there are no valuable biomarkers for clinical diagnosis. This research constructed a dataset, which includes calm, sad, and happy facial expressions from both patients with depression and normal people, and classification and visualization of depression. The network includes a dual-scale convolution module, adaptive channel attentional mechanism, and gradient class activation mapping technique. In which, dual-scale convolution captures features of the facial region at different scales and the adaptive channel attention captures the facial region with the most significant features. The results show that we improve the performance of depression classification based on facial information, and recruit gradient class activation mapping technique obtaining a specific visual face pattern of depression that is different from that of normal people, which provides a potential interpretable and discriminant evidence for the clinical diagnosis. Thereby, promoting the development and application of artificial intelligence in the field of psychiatry.