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@article{188226,
        author = {Shravni Navin Mastud and Sarthak Markad and Om Matere and Nayan Nagendra and Dattatray Takale},
        title = {Brain Tumor Detection Using Hugging Face},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {2351-2355},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188226},
        abstract = {Brain tumors demand early, precise diagnosis for optimal treatment outcomes. This study presents an AI-based brain tumor detection system using Hugging Face's Vision Transformer (ViT) model integrated with a web frontend built in HTML, CSS, and JavaScript for user-friendly MRI image classification. The system enables users to upload MRI scans via a responsive web interface, processes them through the fine-tuned ViT model on Google Colab backend, and displays tumor classifications (glioma, meningioma, pituitary, no tumor) with confidence scores. Experimental results show 98-99% accuracy, validating its effectiveness for accessible medical screening.},
        keywords = {Brain MRI Classification, Deep Learning, Hugging Face, Vision Transformer, Web-Based Diagnosis.},
        month = {December},
        }