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@article{168734,
        author = {Rishabh Saxena and Keshav Sharma and Sheenam Naaz},
        title = {Mental Illness Detection Using Deep Learning Models},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {2009-2013},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168734},
        abstract = {Because mental health illnesses are becoming more common and early diagnosis is crucial, detecting mental disease has become a key challenge in modern healthcare. In this study, the MOMDA dataset is used, which includes both voice recordings and EEG signals, to diagnose mental diseases using deep learning techniques. Since voice and speech patterns might offer valuable insights into an individual’s mental health, we are primarily focused on the audio dataset. We want to assess audio cues that correspond with different mental health disorders using cutting-edge deep learning models, providing a scalable, effective, and non-invasive method of mental health screening. Because it offers personalized treatment plans, ongoing monitoring, and early identification, this approach has the potential to completely transform mental health diagnostics.},
        keywords = {Deep Learning, Artificial Neural Network, Audio Pattern, Audio Processing},
        month = {October},
        }