An Improved Global–Local Fusion Network for Depression Detection Telemedicine Framework

Abstract

In recent years, remote depression detection is becoming a new favorite of the Internet of Things because of its promise. However, deployable and sensitive information security has not been properly addressed. Thus, a novel depression detection framework is proposed to successfully solve the above problems. This article concentrates solely on the development and execution of our proposed core algorithm, LKCT. Further details concerning the system’s construction will be disclosed separately as future work. The contribution of this article can be summarized as follows: 1) proposing a depression detection framework that is easy to promote and can effectively solve the security of sensitive information; 2) proposing a novel two-stream deep network LKCT based on the global–local concept; 3) designing a novel network called purified vision transformer (PIT) to enhance the model’s ability to capture local details in images; and 4) introducing decoupled knowledge distillation to distill LKCT into the lightweight network MobileNet for deployability. To evaluate the true performance of our proposed method, we conducted sufficient experiments on the Chinese Academy of Sciences Institute of Automation (CASIA), facial expression recognition challenge (FERC), AVEC2014, and private depression data sets. LKCT achieved an accuracy of 93.4% on CASIA, demonstrating its strong ability to distinguish facial features. The accuracy on FERC reached 99%, proving its ability to accurately distinguish simple emotions. On AVEC2014, the model achieved state-of-the-art results with an RMSE of 7.41 and MAE of 5.48, validating its ability to identify depression. The distilled model obtained an average accuracy of 83% on the private data set, indicating good generalization ability.