A Secure and Intelligent Framework for Multimodal Healthcare Data Processing using Deep Neural Networks and Blockchain-Based Trust Management

Abstract

The explosion of multimodal healthcare data such as medical images, physiological signals, and electronic health records has posed major security storage problems, credible data sharing, and correct disease diagnosis. Traditional healthcare is usually vulnerable to privacy concerns, unauthorized access, and poor analytic abilities. To handle the above challenges, this paper suggests STMD-BTNet, an intelligent and secure architecture that combines blockchain-based trust management and deep neural networks to process multimodal healthcare data reliably. The proposed system secures patient data with a dynamic hash-based session key generation system and secures the transmission with a verified blockchain bridge that uses a trust-conscious Proof-of-Stake consensus system. Access control with smart contracts can be used to provide access to sensitive records by authorized medical professionals. A multimodal deep learning model that incorporates convolutional neural networks to process medical images, long short-term memory networks to process physiological signals, and fusion layer to combine features of clinical attributes allows patients to receive the correct diagnosis. Experimental results on publicly accessible healthcare datasets show better performance with accuracy of 98.62, precision of 98.45, recall of 98.30 and AUC of 99.12 in the combined application of the multimodal, and low latency and improved data security. Comparative analysis has proved that STMD-BTNet is more reliable to diagnose, scale, and trust well than current deep learning and blockchain-based methods and is therefore applicable in next-generation intelligent healthcare infrastructures.