Abstract
This project introduces a novel approach to detecting depression using multimodal deep learning, integrating convolutional neural networks (CNNs) for image analysis and long short-term memory (LSTM) networks for textual understanding. Implemented in MATLAB, the system leverages emotional intelligence from social media content to capture nuanced indicators of depression. By analyzing both images and text, the model aims to provide a comprehensive understanding of user’s emotional states, offering a promising avenue for early intervention and support. Two methods are employed: live face-based stress level detection and text-based stress level detection. This approach addresses the limitations of traditional detection methods by harnessing the rich emotional cues present in social media, thereby contributing to the mitigation of depression's escalating burden on individuals and society.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Research Journal on Advanced Engineering Hub (IRJAEH)
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
