Enhanced Brain Tumor Detection and Privacy Preserving Using Federated Learning

Abstract

Brain cancers pose significant difficulties for both diagnosis and treatment, underscoring the necessity for precise and private-protecting detection techniques. Federated learning is used to solve this, allowing several healthcare facilities to work together to train detection models without jeopardizing patient privacy. This paper presents an approach called federated learning that may be used to improve brain tumor identification while protecting patient privacy. Brain tumors are dangerous medical disorders that need to be accurately diagnosed in order to be effectively treated. However, sharing private patient information is a common practice in traditional medical data analysis methodologies, which raises privacy issues. Federated learning helps with this by enabling cooperative training of a common model amongst several hospitals or institutions without requiring the exchange of raw data. This method protects patient privacy by having each institution train the model using its own local data and only sharing model updates. We show through trials that our method is efficient in reliably identifying brain tumors while upholding privacy norms, presenting a viable option for improving medical diagnosis without jeopardizing patient privacy.