Hybrid Deep Neural Cryptographic Framework for Intelligent Healthcare Monitoring and Secure Medical Data Transmission

Abstract

The rapid integration of Internet of Medical Things (IoMT) technologies has revolutionized the healthcare sector, enabling real-time patient monitoring, intelligent diagnosis, and seamless data exchange between devices and cloud servers. However, this interconnected ecosystem exposes sensitive medical information to serious privacy and security threats. To address these challenges, this research proposes a Hybrid Deep Neural Cryptographic Framework (HDNCF) for intelligent healthcare monitoring and secure medical data transmission. The proposed HDNCF integrates advanced deep learning and cryptographic methods to ensure both analytical intelligence and end-to-end data confidentiality. In the proposed model, patient data collected from IoMT healthcare monitoring and security undergoes preprocessing for noise removal and normalization. A hybrid deep learning architecture, Deep Convolutional with Stacked Long Short-Term Memory (DCon-Stacked LSTM) networks, combines Deep Convolutional Neural Networks (CNN) for feature extraction, and Stacked LSTMis applied to anomaly detection and disease prediction. To guarantee privacy and security during transmission, the framework employs Rivest–Shamir–Adleman(RSA) that enhances encryption strength while reducing computational complexity. This integration enables lightweight and efficient secure communication suitable for resource-constrained IoMT devices. The experimental evaluation conducted on benchmark healthcare datasets demonstrates that HDNCF overcame these restrictions by obtaining 99.12% accuracy, 97.8% precision, 98.5% recall, and 98.7% F1-score, as well as greatly improved encryption and decryption speeds of 18.0 and 17.6 ms, respectively, to cyberattacks compared to existing models. Overall, the proposed framework establishes a robust foundation for intelligent, secure, and privacy-preserving healthcare ecosystems, fostering trust and efficiency in modern digital healthcare environments.