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@article{201303,
        author = {D. Sreenivas Rao and Ennam Govinda and R.Vamsi and P.Sowjanya and A.Divakar},
        title = {BRAIN STROKE DETECTION AND PREDICTION USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {3817-3821},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201303},
        abstract = {Stroke is one of the leading causes of death and long-term disability worldwide. Early detection and accurate prediction of brain stroke conditions using medical imaging can significantly improve patient survival rates. The proposed Brain stroke detection and prediction using machine learning utilizes deep learning techniques to analyze MRI brain images and automatically detect abnormal conditions. The system integrates a trained Convolutional Neural Network (CNN) model stored in a serialized .pkl file and deployed using a Streamlit web interface in .py file. The uploaded MRI image undergoes preprocessing steps including resizing, normalization, and batch dimension expansion before being passed into the trained model. The application predicts whether a tumor/abnormality (which may indicate stroke-related brain damage) is present and displays the confidence score. Additionally, the system visualizes training and validation accuracy using stored training history data. [1] [2] This Al-based approach reduces dependency on manual inspection, enhances diagnostic speed, and supports healthcare professionals in early intervention planning.},
        keywords = {Brain Stroke Detection, Medical Image Analysis, Deep Learning, Convolutional Neural Network (CNN), MRI Image Processing, Healthcare AI.},
        month = {May},
        }