A Deep Learning-Driven Multimodal Biometric Medical Image Protection System with Secure Encryption
Received: 8 November 2025 | Revised: 12 January 2026 and 14 February 2026 | Accepted: 15 February 2026 | Online: 4 April 2026
Corresponding author: S. N. Kavitha
Abstract
The Industrial Internet of Things (IIoT) has transformed healthcare by enabling remote diagnosis and efficient exchange of medical data, but it also raises serious privacy and security concerns. Encryption alone cannot fully protect medical images after decryption, while unimodal biometrics security lacks reliability. To overcome these issues, this work proposes BioMedShield, a deep learning-driven multimodal biometric medical image protection system that integrates encryption, data hiding, and biometric fusion for comprehensive security. The framework uses Dual-SegNet (DS-Net) for accurate medical image segmentation and MSE-Net for robust biometric feature extraction. DS-Net achieves high performance with 98.8% accuracy and 98.7% recall. Secure Biometric Blowfish Encryption (SBBE) further ensures strong confidentiality and resistance to unauthorized access in IIoT healthcare systems.
Keywords:
deep learning, multimodal biometrics, medical image protection, data hiding, encryption, Industrial Internet of Things (IIoT), secure healthcare systemDownloads
References
R. Mekala, "Deep Learning in the Cloud for Anomaly Detection Improves Medical Monitoring," Journal of International Exercise Sciences, vol. 2, no. 1, pp. 44–59, 2023.
F. Castro, D. Impedovo, and G. Pirlo, "A Medical Image Encryption Scheme for Secure Fingerprint-Based Authenticated Transmission," Applied Sciences, vol. 13, no. 10, May 2023. DOI: https://doi.org/10.3390/app13106099
M. Singh, N. Baranwal, K. N. Singh, A. K. Singh, and H. Zhou, "Deep learning-based biometric image feature extraction for securing medical images through data hiding and joint encryption–compression," Journal of Information Security and Applications, vol. 79, Dec. 2023, Art. no. 103628. DOI: https://doi.org/10.1016/j.jisa.2023.103628
Z. Liu and R. Xue, "Medical Image Encryption using Biometric Image Texture Fusion," Journal of Medical Systems, vol. 47, no. 1, Nov. 2023, Art. no. 112. DOI: https://doi.org/10.1007/s10916-023-02003-5
W. El-Shafai, I. Almomani, A. Ara, and A. Alkhayer, "An optical-based encryption and authentication algorithm for color and grayscale medical images," Multimedia Tools and Applications, vol. 82, no. 15, pp. 23735–23770, June 2023. DOI: https://doi.org/10.1007/s11042-022-14093-3
N. Santos, B. Ghita, and G. L. Masala, "Medical Systems Data Security and Biometric Authentication in Public Cloud Servers," IEEE Transactions on Emerging Topics in Computing, vol. 12, no. 2, pp. 572–582, Apr. 2024. DOI: https://doi.org/10.1109/TETC.2023.3271957
J. Selvaraj, W. C. Lai, B. P. Kavin, K. C, and G. H. Seng, "Cryptographic Encryption and Optimization for Internet of Things Based Medical Image Security," Electronics, vol. 12, no. 7, Mar. 2023. DOI: https://doi.org/10.3390/electronics12071636
D. Zhang, L. Ren, M. Shafiq, and Z. Gu, "A Privacy Protection Framework for Medical Image Security without Key Dependency Based on Visual Cryptography and Trusted Computing," Computational Intelligence and Neuroscience, vol. 2023, no. 1, 2023, Art. no. 6758406. DOI: https://doi.org/10.1155/2023/6758406
A. S. Jamil, R. A. Azeez, and N. F. Hassan, "An Image Feature Extraction to Generate a Key for Encryption in Cyber Security Medical Environments," International Journal of Online and Biomedical Engineering (iJOE), vol. 19, no. 01, pp. 93–106, Jan. 2023. DOI: https://doi.org/10.3991/ijoe.v19i01.36901
A. A. Alhussan, H. A. Abdallah, S. Alsodairi, and A. A. Ateya, "Hybrid Watermarking and Encryption Techniques for Securing Medical Images," Computer Systems Science and Engineering, vol. 46, no. 1, pp. 403–416, 2023. DOI: https://doi.org/10.32604/csse.2023.035048
A. Odeh and A. A. Taleb, "A Multi-Faceted Encryption Strategy for Securing Patient Information in Medical Imaging," Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, vol. 14, no. 4, pp. 164–176, Dec. 2023. DOI: https://doi.org/10.58346/JOWUA.2023.I4.012
Y. Qin and B. Zhang, "Privacy-Preserving Biometrics Image Encryption and Digital Signature Technique Using Arnold and ElGamal," Applied Sciences, vol. 13, no. 14, July 2023. DOI: https://doi.org/10.3390/app13148117
K. N. Singh, N. Baranwal, A. K. Singh, A. K. Agrawal, and H. Zhou, "Using Multimodal Biometrics, Data Hiding, and Encryption for Secure Healthcare Imaging System," IEEE Transactions on Consumer Electronics, vol. 71, no. 2, pp. 6711–6718, Feb. 2025. DOI: https://doi.org/10.1109/TCE.2024.3438356
K. N. Singh, N. Baranwal, O. P. Singh, and A. K. Singh, "DeepENC: Deep Learning-Based ROI Selection for Encryption of Medical Images Through Key Generation With Multimodal Information Fusion," IEEE Transactions on Consumer Electronics, vol. 70, no. 3, pp. 6149–6156, Dec. 2024. DOI: https://doi.org/10.1109/TCE.2024.3406963
S. B. Jadhav, N. K. Deshmukh, and S. B. Pawar, "Robust Authentication System with Privacy Preservation for Hybrid Deep Learning-Based Person Identification System Using Multi-Modal Palmprint, Ear, and Face Biometric Features," International Journal of Image and Graphics, vol. 25, no. 05, Sept. 2025, Art. no. 2550049. DOI: https://doi.org/10.1142/S0219467825500494
H. Xie, Y. Zhang, J. Bian, and H. Zhang, "A secure and privacy-preserving technique based on coupled chaotic system and plaintext encryption for multimodal medical images," Multimedia Tools and Applications, vol. 84, no. 20, pp. 22701–22726, June 2025. DOI: https://doi.org/10.1007/s11042-024-19956-5
G. Latif, J. Alghazo, N. Mohammad, S. E. Abdelhamid, G. B. Brahim, and K. Amjad, "A Novel Fragmented Approach for Securing Medical Health Records in Multimodal Medical Images," Applied Sciences, vol. 14, no. 14, July 2024. DOI: https://doi.org/10.3390/app14146293
S. Vatchala et al., "Multi-Modal Biometric Authentication: Leveraging Shared Layer Architectures for Enhanced Security," IEEE Access, vol. 13, pp. 28029–28041, 2025. DOI: https://doi.org/10.1109/ACCESS.2025.3534223
F. Sayeed, K. R. Ahmed, and S. M. Swamy, "Development of a multimodal biometric recognition system with feature optimization and deep learning," Multimedia Tools and Applications, vol. 84, no. 31, pp. 38399–38422, Sept. 2025. DOI: https://doi.org/10.1007/s11042-025-20709-1
M. S. Nair and S. Dharan, "Design a Multimodal Biometric Based Protection System by Generation of a Revocable Cryptographic Key Using Separately Extracted Feature Fusion-based Convolutional Neural Network With Bat Optimization." Research Square, Aug. 29, 2024.
"Multimodal_Iris_Fingerprint_Biometric_data." Kaggle, [Online]. Available: https://www.kaggle.com/datasets/ninadmehendale/multimodal-iris-fingerprint-biometric-data.
"COVID-19 CT scan lesion segmentation dataset." Kaggle, [Online]. Available: https://www.kaggle.com/datasets/maedemaftouni/covid19-ct-scan-lesion-segmentation-dataset.
S. Bian, X. Xu, W. Jiang, Y. Shi, and T. Sato, "BUNET: Blind Medical Image Segmentation Based on Secure UNET," in Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 2020, pp. 612–622. DOI: https://doi.org/10.1007/978-3-030-59713-9_59
S. Subathra and V. Thanikaiselvan, "Enhanced security for medical images using a new 5D hyper chaotic map and deep learning based segmentation," Scientific Reports, vol. 15, no. 1, July 2025, Art. no. 22628. DOI: https://doi.org/10.1038/s41598-025-04906-4
H. Farah, A. Bennourm, H. Soltani, M. Nahas, R. R. Marie, and M. Al-Sarem, "Attention U-Net for Precision Skeletal Segmentation in Chest X-Ray Imaging: Advancing Person Identification Techniques in Forensic Science," Computers, Materials and Continua, vol. 85, no. 2, pp. 3335–3348, Sept. 2025. DOI: https://doi.org/10.32604/cmc.2025.067226
Q. Chen, H. Li, S. B. Ariffin, and N. A. B. Mustapa, "A Comprehensive Study on the Homomorphic Encryption for Secure Image Data Processing," Engineering, Technology & Applied Science Research, vol. 15, no. 2, pp. 21783–21790, Apr. 2025. DOI: https://doi.org/10.48084/etasr.10007
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