STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Image Processing & Pattern Recognition Progress Original Research

Deep Learning Algorithms for Medical Image Encryption to Ensure Secure Data Transfer

  1. Siju John Research Scholar, Lincoln University College, Jalan Lembah Sireh, 15050 Kota Bharu
  2. S.N. Kumar Department of Electrical and Electronics Engineering, Amal Jyothi College of Engineering, Kanjirappally

Abstract

Deep learning has significantly impacted various fields, including medical imaging, by offering new ways to encrypt medical images for secure data transfer. This research work examines how deep learning algorithms are used to enhance medical image security during transmission. Given the high sensitivity and privacy requirements of medical data, it’s crucial to maintain its confidentiality. Traditional encryption techniques, while reliable, often struggle with issues like scalability, computational efficiency, and the ability to handle the specific features of medical images. This review focuses on recent developments in deep learning, such as convolutional neural networks (CNNs), Autoencoders, and generative adversarial networks (GANs), and their potential to create strong encryption methods. It compares these advanced algorithms with standard encryption methods to highlight the advantages of deep learning in security, efficiency, and flexibility. Additionally, the paper looks at the prospects and challenges in this developing area, seeking to foster new advancements for the secure transmission of medical data in today’s digital world.

Keywords

References (21)

  1. Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, et al. The history began from AlexNet: A comprehensive survey on deep learning approaches. [Preprint] arXiv:1803.01164. 2018.
  2. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-118. doi:10.1038/nature21056
  3. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016;316(22):2402. doi:10.1001/jama.2016.17216
  4. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, et al. TensorFlow: A system for large-scale machine learning. 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16); Nov 2-4, 2016; Savannah, GA, USA. p. 265–83.
  5. Tschandl P, Rosendahl C, Kittler H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data. 2018;5(1). doi:10.1038/sdata.2018.161
  6. Minaee S, Boykov YY, Porikli F, Plaza AJ, Kehtarnavaz N, Terzopoulos D. Image Segmentation Using Deep Learning: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2021:1-1. doi:10.1109/tpami.2021.3059968
  7. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44. DOI: 10.1038/
  8. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88. doi:10.1016/j.media.2017.005.
  9. Ding Y, Wu G, Chen D, Zhang N, Gong L, Cao M, et al. DeepEDN: A Deep-Learning-Based Image Encryption and Decryption Network for Internet of Medical Things. IEEE Internet of Things Journal. 2021;8(3):1504-1518. doi:10.1109/jiot.2020.3012452
  10. Panwar K, Singh A, Kukreja S, Singh KK, Shakhovska N, Boichuk A. Encipher GAN: An End-to-End Color Image Encryption System Using a Deep Generative Model. Systems. 2023;11(1):36. doi:10.3390/systems11010036
  11. Kingma DP. Auto-encoding variational Bayes. [Preprint] arXiv:1312.6114. 2013.
  12. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial nets. Adv Neural Inf Process Syst. 2014;27:2672–80.
  13. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. Lecture Notes in Computer Science. 2015:234-241. doi:10.1007/978-3-319-24574-4_28
  14. Chai X, Zhang J, Gan Z, Zhang Y. Medical image encryption algorithm based on Latin square and memristive chaotic system. Multimedia Tools and Applications. 2019;78(24):35419-35453. doi:10.1007/s11042-019-08168-x
  15. Pan C, Schoppe O, Parra-Damas A, Cai R, Todorov MI, Gondi G, et al. Deep Learning Reveals Cancer Metastasis and Therapeutic Antibody Targeting in the Entire Body. Cell. 2019;179(7):1661-1676.e19. doi:10.1016/j.cell.2019.11.013
  16. Vu L, Thuy HV, Nguyen QU, Ngoc TN, Nguyen DN, Hoang DT, et al. Time Series Analysis for Encrypted Traffic Classification: A Deep Learning Approach. 2018 18th International Symposium on Communications and Information Technologies (ISCIT). 2018:121-126. doi:10.1109/iscit.2018.8587975
  17. Xu M, Papageorgiou DP, Abidi SZ, Dao M, Zhao H, Karniadakis GE. A deep convolutional neural network for classification of red blood cells in sickle cell anemia. PLOS Computational Biology. 2017;13(10):e1005746. doi:10.1371/journal.pcbi.1005746
  18. Ciompi F, Chung K, van Riel SJ, Setio AAA, Gerke PK, Jacobs C, et al. Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Scientific Reports. 2017;7(1). doi:10.1038/srep46479
  19. Zhang Y, Xiang Y, Zhang LY, Rong Y, Guo S. Secure wireless communications based on compressive sensing: A survey. IEEE Commun Surv Tutor. 2018;21(2):1093–111. doi:10.1109/2018.2878943.
  20. Bhattacharya P, Tanwar S, Bodkhe U, Tyagi S, Kumar N. BinDaaS: Blockchain-Based Deep-Learning as-a-Service in Healthcare 4.0 Applications. IEEE Transactions on Network Science and Engineering. 2021;8(2):1242-1255. doi:10.1109/tnse.2019.2961932
  21. Hinton GE, Salakhutdinov RR. Reducing the Dimensionality of Data with Neural Networks. Science. 2006;313(5786):504-507. doi:10.1126/science.1127647
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