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@article{162729,
        author = {AVINESH A and PUVANADEVI C and DINESH K and MADHANKUMAR A and YUVARAJ K},
        title = {ENHANCING BRAIN STROKE IDENTIFICATION USING DEEP LEARNING TECHNIQUES},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {10},
        pages = {894-897},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162729},
        abstract = {Introducing deep learning technology for real-time identification of brain stroke using MRI imaging and predicting early access to brain stroke with user medical details, our proposed system aims to enhance the reliability and efficiency of brain stroke identification and prediction. By harnessing powerful deep learning models, specialized algorithms, and advanced neural networks, our project explores the potential to transform stroke diagnosis and treatment. Specifically, we aim to rapidly analyze MRI scans to identify brain strokes and subtype classifications. This approach holds promise for improving diagnostic accuracy, expediting decision-making, and minimizing the long-term neurological impact of brain strokes. Despite challenges such as handling diverse medical image types and ensuring interpretability for medical professionals, our project endeavors to significantly enhance brain stroke diagnosis and treatment through innovative deep learning methods. Additionally, the system prioritizes patient privacy and data security while ensuring compliance with ethical standards. Through comprehensive documentation and maintenance activities, transparency and reliability are upheld throughout the project lifecycle, fostering trust and confidence in the system's capabilities.},
        keywords = {},
        month = {},
        }