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@article{183794,
        author = {Sarumathi S and Minal Khandare and Shibindas K and Shivakumar and Sudeep.D.C and Sidharth.K},
        title = {A Comprehensive Analysis on Deep Learning Framework for Mental Disorder Identification using Facial Emotions},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {3401-3409},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183794},
        abstract = {The review explores recent advancements in mental disorder detection through facial emotion recognition using deep learning techniques. It highlights the effectiveness of hybrid models that integrate Convolutional Neural Networks (CNNs), ResNet50, and Vision Transformer (ViT) for robust emotion classification. The study examines various approaches, including real-time face detection with YOLOv8 and interpretability enhancements through Grad-CAM and Saliency Maps, demonstrating improved accuracy and transparency in identifying mental health conditions. Key challenges identified include data diversity, cultural variability in facial expressions, and real-time deployment in clinical environments. The proposed study explains about the comparative analysis of methodologies for the identification of mental disorder and paves the way for finaling the best methodology for implementation.},
        keywords = {Mental Disorder Detection, Facial Emotion Recognition, Deep Learning, CNN, ResNet50, Vision Trans- former, YOLOv8, Grad-CAM, Saliency Maps},
        month = {August},
        }