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@article{189847,
        author = {Sonali R and Kavitha C R and Dhanalakshmi M H and Harshitha B N and Likitha B D},
        title = {Detection and Classification of Brain Stroke Using Federated Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {4663-4667},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189847},
        abstract = {Brain stroke is a significant contributing factor to death and disability as it requires accurate detection and diagnosis. Centralized deep learning models have the following setbacks: dataPrivacy, amountof Data Shared by Hospitals. To address these challenges, federated learning for brain stroke image classification is proposed within this work. The work utilizes convolutional neural networks to classify brain stroke into ischemic, hemorrhage, and normal stroke types. In our experiment, federated learning results demonstrated a high level of efficiency and reliability while addressing dataPrivacy issues. The work was done using a web interface that enables real-time brain stroke detection and report generation to enable various institutions to benefit from the model for various purposes.},
        keywords = {Brain Srtoke Detection & Classification, Convolution Neural Networks, Deep Learning, FedAvg, Federated learning, Medical Image Analysis},
        month = {January},
        }