Federated Learning Augmented with Convolutional Neural Networks for Brain Cancer Classification

Abstract

Leveraging MRI images for the categorization of brain tumors is a critical yet complex endeavor within the realm of medical imaging. Precise identification is crucial for prompt and effective treatment; however, the intricate nature of tumor morphology and variability in imaging often pose obstacles. Traditional practices largely rely on the manual examination of MRI images, supplemented by classic machine learning (ML) strategies. However, these techniques typically lack the robustness and scalability necessary for precise and automated tumor categorization. Key challenges include extensive manual processing, vulnerability to human error, limited ability to handle voluminous datasets, and insufficient adaptability to diverse tumor forms and imaging scenarios. To address these challenges, this paper introduces the use of federated learning, a ML-based model that integrates data from various entities, coupled with Convolutional Neural Networks (CNNs) for the classification of brain cancer. Federated learning facilitates the decentralized training of models across numerous clients while preserving the confidentiality of data, thus meeting the critical demand for privacy in the management of medical data. The model’s design incorporates transfer learning and a pre-trained CNN to refine its performance on the brain tumor dataset. The empirical evidence suggests that this integrated approach surpasses conventional techniques in the precise classification of brain cancer types. These insights underscore the promise of federated learning in conjunction with CNNs for medical image analysis, particularly in diagnosing brain cancer. This methodology could enhance the precision and efficiency of cancer classification, leading to improved treatment strategies and, ultimately, better patient care outcomes.