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@article{201137,
        author = {Miriam Beryl Paul K and P Balasubramani and Lakshmi Priya K and M Rajalaxmi},
        title = {Deep Learning-Based Stress and Depression Detection System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {3303-3319},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201137},
        abstract = {This project presents a deep learning-based approach for the classification of mental health disorders, specifically stress and depression, using a Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN). The system utilizes Electroencephalogram (EEG) signals as input to capture brain activity patterns associated with different mental states. 
By integrating convolutional layers for spatial feature extraction and recurrent layers for temporal pattern learning, the ESRGAN model effectively analyzes complex EEG data. 
The implementation is carried out in MATLAB, incorporating image processing techniques to transform EEG signals into meaningful representations suitable for deep learning classification. The proposed model classifies stress levels into three stages: mild, moderate, and severe, and also detects the presence of depression. 
Following classification, the system provides personalized suggestions and recommendations, including lifestyle modifications, stress management techniques, and potential treatment options to help users manage their mental health condition. 
This intelligent framework aims to support early detection and intervention, thereby improving mental well-being and reducing the risk of severe psychological disorders. The results demonstrate that the ESRGAN-based approach achieves high accuracy and reliability, making it a promising tool for automated mental health assessment and decision support systems},
        keywords = {},
        month = {May},
        }