Depression Status Estimation by Deep Learning based Hybrid Multi-Modal Fusion Model
This research aims to improve the preliminary detection of mild depression for individuals, which is crucial for effective treatment given societal stigmas and individual variations.
The paper addresses the challenge of early detection of mild depression by proposing a hybrid deep learning model that fuses video, audio, and text modalities. This model achieved 96.3% accuracy and an AUC of 0.9682 on a dataset, demonstrating its ability to discriminate between classes in complex real-world scenarios.
Preliminary detection of mild depression could immensely help in effective treatment of the common mental health disorder. Due to the lack of proper awareness and the ample mix of stigmas and misconceptions present within the society, mental health status estimation has become a truly difficult task. Due to the immense variations in character level traits from person to person, traditional deep learning methods fail to generalize in a real world setting. In our study we aim to create a human allied AI workflow which could efficiently adapt to specific users and effectively perform in real world scenarios. We propose a Hybrid deep learning approach that combines the essence of one shot learning, classical supervised deep learning methods and human allied interactions for adaptation. In order to capture maximum information and make efficient diagnosis video, audio, and text modalities are utilized. Our Hybrid Fusion model achieved a high accuracy of 96.3% on the Dataset; and attained an AUC of 0.9682 which proves its robustness in discriminating classes in complex real-world scenarios making sure that no cases of mild depression are missed during diagnosis. The proposed method is deployed in a cloud-based smartphone application for robust testing. With user-specific adaptations and state of the art methodologies, we present a state-of-the-art model with user friendly experience.