A Comprehensive Review of Securing Healthcare Data with Quaternion-Based Evolutionary Gravitational Neocognitron Neural Networks and Encoder-Elliptic Curve Deep Neural Networks Integrated with Blockchain

Abstract

The rapid digitization of healthcare systems has significantly amplified concerns surrounding the privacy, integrity, and security of sensitive medical data, as traditional protection methods increasingly fail to address the complexity of modern distributed environments. This review presents an integrated framework combining Quaternion-Based Evolutionary Gravitational Neocognitron Neural Networks (QEG-NNNs), Encoder-Elliptic Curve Deep Neural Networks (EECDNNs), and blockchain technology to comprehensively enhance healthcare data security. QEG-NNNs efficiently process multidimensional medical data while preserving critical spatial relationships, with evolutionary gravitational optimization further improving learning efficiency and convergence. EECDNNs integrate deep learning with elliptic curve cryptography to deliver secure, lightweight encryption well suited for resource-constrained medical devices. Blockchain technology complements these components by enabling decentralized, tamper-proof data management and secure inter-institutional data sharing. The framework is evaluated across established datasets including MIMIC-III and NIH Chest X-ray, with deployment analysis spanning cloud, edge, and IoMT environments. Key challenges such as scalability, interoperability, and regulatory compliance are examined alongside emerging solutions including federated learning and homomorphic encryption. Overall, this study demonstrates that hybrid AI-cryptographic-blockchain frameworks hold significant promise in substantially strengthening data confidentiality, integrity, and availability, establishing a robust foundation for securing next-generation healthcare systems against increasingly sophisticated cyber threats.