This project addresses in reducing pneumonia-related mortality by involving a development of an automatic screening system that analyzes X-ray images and distributed image data storage. This objective is achieved through a collaborative approach, employing Federated Learning to establish a platform that fosters cooperation of the hospitals. Each of them contributes to the advancement of diagnostics by constructing local models from their respective data. These local models are then transmitted to a central server that aggregate models to create a comprehensive global model. This process ensures that the resulting detection model remains unbiased. Importantly, patient data security is upheld, as the central server stores only global models but not sensitive patient information. Project also introduces an innovative system for archiving medical image data for a multifaceted purpose: it archives, anonymizes, and secures image, while also curating a dataset necessary for training a Computer Assisted Diagnosis systems. We are underlining this work to pushing the boundaries of machine learning especially in term of healthcare domain by a strong emphasis on patient privacy and anonymity.
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