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Preliminary detection of mild depression could immensely help in effectivetreatment of the common mental health disorder. Due to the lack of properawareness and the ample mix of stigmas and misconceptions present within thesociety, mental health status estimation has become a truly difficult task. Dueto 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 couldefficiently adapt to specific users and effectively perform in real worldscenarios. We propose a Hybrid deep learning approach that combines the essenceof one shot learning, classical supervised deep learning methods and humanallied interactions for adaptation. In order to capture maximum information andmake efficient diagnosis video, audio, and text modalities are utilized. OurHybrid Fusion model achieved a high accuracy of 96.3% on the Dataset; andattained an AUC of 0.9682 which proves its robustness in discriminating classesin complex real-world scenarios making sure that no cases of mild depressionare missed during diagnosis. The proposed method is deployed in a cloud-basedsmartphone application for robust testing. With user-specific adaptations andstate of the art methodologies, we present a state-of-the-art model with userfriendly experience.

Depression Status Estimation by Deep Learning based Hybrid Multi-Modal Fusion Model