Real Time Neural Style Transfer Using Deep Convolutional Networks

Author(s)	Mr. Edulakanti Sampath Goud, Mr. Vadlamudi Sai Bhargav, Mr. Meka Mounish Ram, Dr. A Naresh Kumar
Country	India
Abstract	This report describes the development of a full-stack web application that uses deep learning to assess depression severity through facial emotion analysis. The system analyzes uneven facial images using CNN-based models and real-time neural style transfer to identify visual cues linked to depression. Built with HTML, CSS, JavaScript (frontend) and Python Django (backend), it manages user data, appointments, and feedback, with MySQL for secure storage. The application enables early detection and continuous monitoring of mental health, providing a low-cost and scalable solution, especially useful in resource-limited settings.
Keywords	Depression, Fatigue, Uneven Facial Images, Deep Convolutional Neural Networks, Neural Style Transfer, Python, Django, HTML, CSS, JavaScript, MySQL, Web Application, Feedback System, Mental Health Monitoring, Image Processing, AI in Healthcare, Full-Stack Development, Version Control.
Field	Engineering
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-17

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International Journal For Multidisciplinary Research