A Privacy-Preserving Healthcare Architecture using Blockchain and Advanced Deep Learning

The rapid advancements in cloud computing and deep learning technologies have paved the way for innovative solutions in healthcare systems, particularly in ensuring security and efficiency. This research explores a hybrid approach integrating Convolutional Neural Networks (CNNs) with blockchain technology to enhance cloud-based healthcare systems' security, scalability, and privacy. The study focuses on leveraging the strengths of CNNs for advanced data processing and pattern recognition while incorporating blockchain to ensure decentralized, tamper-proof data management. Federated learning enables collaborative model training across multiple healthcare institutions without compromising patient data privacy. The proposed system addresses key challenges such as data breaches, scalability, and interoperability in cloud-based healthcare environments. Through a comprehensive survey and implementation, this research will demonstrate the effectiveness of combining CNNs, blockchain, and federated learning in delivering a secure, efficient, and patient-centric healthcare ecosystem.