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Stroke is a devastating and life-threatening medical condition that demands immediate intervention. Timely diagnosis and treatment are paramount in reducing mortality and mitigating long-term disabilities associated with stroke. This research aims to address these critical needs by proposing a real-time stroke detection system based on Deep Learning (DL) with the incorporation of Federated Learning (FL), which offers improved accuracy and privacy preservation. The purpose of this research is to develop an efficient and accurate model capable of distinguishing between stroke and non-stroke cases in real-time, assisting healthcare professionals in making rapid and informed decisions. Stroke detection has traditionally relied on manual interpretation of medical images, which is time-consuming and prone to human error. DL techniques have shown significant promise in automating this process, but the need for large and diverse datasets, as well as privacy concerns, remains challenging. To achieve this goal, our methodology involves training the DL model on extensive datasets containing both stroke and non-stroke medical images. This training process will enable the model to learn complex patterns and features associated with stroke, thereby improving its diagnostic accuracy. Furthermore, we will employ Federated Learning, a decentralized training approach, to enhance privacy while maintaining model performance. This approach allows the model to learn from data distributed across multiple healthcare institutions without sharing sensitive patient information. The proposed approach has been executed on NVIDIA platforms, taking advantage of their advanced GPU capabilities to enable real-time processing and analysis. This optimized model has the potential to revolutionize stroke diagnosis and patient care, ultimately saving lives and improving the quality of healthcare services in the field of neurology.
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