Secure Medical Data Transmission Using a Deep Learning–Based Quantum-Resistant Hybrid Stegno-Crypto Model

Document Type : Research Paper

Authors

1 Research Scholar (Part Time), Research Centre -Department of Electronics and Instrumentation Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka, India; Assistant Professor, Department of Electronics and Instrumentation Engineering, Dr. Ambedkar Institute of Technology, Bengaluru, Karnataka, India.

2 Associate Professor, Department of Electronics and Instrumentation Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka – 560078 Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India.

10.22059/jitm.2026.106255

Abstract

This study focuses on the secure transmission of medical data, an important element of contemporary healthcare systems in which sensitive patient data is shared over digital networks to facilitate diagnosis, monitoring, and decision-making. Due to the growing volume of medical records and electronic medical imaging data, it has become critical to ensure the integrity, confidentiality, and accuracy of the data throughout transmission. The two significant problems the study will cover include high-frequency noise in medical records that reduces diagnostic accuracy and the high sensitivity of traditional encryption techniques to quantum-computing-based attacks that jeopardize data privacy. To address these problems, a smart, noise-hardy, and quantum-resistant dual-layer design is proposed to provide an efficient and secure transfer of medical information without compromising quality or computational efficiency. The methodology includes two major steps. In the first step, a Noise-Resilient Pre-Encryption Data Pre-processing Algorithm is created using a Random Forest-Wavelet Filter hybrid model, in which the Random Forest employs adaptive parameter selection and the wavelet filter employs multilevel decomposition to remove noise. Genetic algorithms (GA) are used to optimize these parameters more dynamically in response to shifting noise levels. PSNR and SSIM are used to validate the pre-processing performance, and it is proven that the data fidelity is enhanced. The second phase proposes a Hybrid Quantum-resistant Steganography-Cryptography Algorithm that combines CNN-based Steganography with AES encryption. The CNN safely encodes medical information in non-sensitive carriers using encrypted messages to transmit it invisibly without tampering. In contrast, the AES uses dynamic key generation to resist classical and quantum attacks. The results of the experiment show that accuracy (96.89%), precision (89%), recall (94.50%), F1-score (91.60%), and system efficiency were increased significantly, with computational time (8.47 ms), latency (8.50 ms), and throughput (117.60 OPS) also increasing.

Keywords

References

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Journal of Information Technology Management
Volume 18, Issue 1
2026
Pages 80-101

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