  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.
