Blockchain-Integrated Deep Learning Framework for Secure Medical Image Transfer Over Public Networks

Abstract

The three main issues involved with safely sending medical images over a public network are data confidentiality and integrity, and the problem of unauthorized access. This research provides a secure, verifiable, and efficient solution for the inter-institutional transfer of medical images within a Blockchain-Integrated Deep Learning Framework. Deep Learning Models are used to devise intelligent strategies for the encryption, compression, and anomaly detection of medical images so that diagnostic-quality images can be sent within extremely limited bandwidths. Other aspects of the proposed framework used Blockchain to provide a secure, logged, and decentralized transaction and verified authentication and access control, as well as cryptographic defenses for the integrity of images. For the maintenance of the authenticity, traceability, and auditability of data and accountability of custodians, each transaction is recorded in a permissioned blockchain. To conform to the healthcare regulatory standards of HIPAA and GDPR, smart contracts are used to implement access control and consent verification policies in a healthcare context. Adversarial experimentation shows that transfers of medical images using this framework are more efficient and provide enhanced confidentiality and integrity than competing solutions. This paradigm is intended to create secure end-to-end transfers of medical images over public networks, while preserving the diagnostic quality of the images. This framework can potentially support future advances in telemedicine and cross-institutional medical collaborations. The proposed BIDL-SMIT framework also achieved the desired goals of a framework for trust and integrity of medical data by combining security, efficiency, and diagnostic quality, making it an appropriate framework to use for an actual application in secure medical image sharing healthcare networks.