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@article{176616,
        author = {Thati Kushvanth and Pabbathi Nikhil and A. Nitesh Reddy and Dr. Preet Kamal},
        title = {Emotion Recognition Systems: Diagnosing and Treating Mood Disorders like Anxiety and Depression.},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {6031-6036},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176616},
        abstract = {This study utilizes the accelerated progress of deep learning and computer vision to create AI-based models for emotion recognition from images. Mood disorders like depression and anxiety are usually diagnosed using subjective self-reports, which may result in inaccuracies and delayed interventions. This study suggests an AI-based emotion recognition system based on a pre-trained Mobile Net model to identify facial expressions into various emotional categories. Using transfer learning, the model is trained fine on a facial image dataset with data augmentation strategies to generalize better. The system is trained with an optimized deep-learning pipeline incorporating early stopping and model checkpoints for higher accuracy and protection against overfitting.
Performance is measured in terms of accuracy metrics, and predictions are verified using visualization methods. The model presented provides a data-driven method for mood disorder evaluation, allowing healthcare professionals to make more objective and timely diagnoses. This study identifies the potential of AI-based solutions in mental health applications while stressing ethical concerns, data privacy, and real-world applicability},
        keywords = {Deep learning, emotion recognition, mood disorders, Mobile Net, facial expression analysis, artificial intelligence, transfer learning, image classification, mental health diagnosis, data augmentation, computer vision, healthcare AI.},
        month = {April},
        }