Depression Detection And Notification System Using Deep Learning

Depression is a prevalent and severe mental health disorder that affects millions of individuals globally. Early detection and intervention are critical in preventing severe consequences such as self-harm and suicide. Traditional methods of diagnosing depression rely on self-reporting and clinical interviews, which may not always provide accurate or timely results. This paper presents an approach utilizing deep learning techniques to detect depression from various data sources, including textual inputs, facial expressions, and speech patterns. By leveraging Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and transformer-based models, our system can identify depression with high accuracy. The research highlights the significance of artificial intelligence in mental health diagnostics and the potential of deep learning to revolutionize depression detection and intervention strategies.