Depression Diagnosis Algorithm Based on R2U-Net Using Facial Images

Depression is a chronic and common disease that not only causes social dysfunction depending on the severity but also accompanies social problems such as suicide. Therefore, it is very important to judge the severity of depression rather than to diagnose it. We propose an algorithm that combines Convolutional Neural Networks (CNN) and R2U-Net to analyze spatial features of facial images for depression diagnosis and to verify their performance using the AVEC 2014 database. The proposed algorithm leverages Recurrent Residual Convolution Units (RRCUs) to improve feature extraction with multiple iterations to help the model improve spatial information across multiple layers. Furthermore, CNN improves the model’s ability to analyze spatial features of facial images to extract useful information for depression diagnosis. The proposed model shows improved depression score prediction performance compared to other models including U-Net, R2U-Net, and ResNet50.