Blockchain Based Federated Learning for Medical Imaging Using Computer Vision: A State-of-the-Art Review and Future Research Directions

Computer vision advances in artificial intelligence have revolutionized medical imaging significantly by making more accurate and efficient diagnoses. Federated Learning (FL) is one promising solution that allows model cooperation during training without centralizing patient data and making it publicly accessible. Blockchain technology provides a transparent and secure framework for trust management, auditability, and decentralized consensus. This paper reviews the intersection of FL and blockchain in medical imaging, with special focus given to the use of computer vision algorithms like Convolutional Neural Networks (CNNs). It presents an overview of the state-of-the-art frameworks, architectures, and methodologies that combine blockchain-based FL for medical imaging. It also performs an experimental study to compare common federated learning approaches for medical image analysis. This survey can facilitate better comprehension of the existing research status, issues, and potential research directions in this promising research area.