Deep Learning for Medical Image Cryptography: A Comprehensive Review
Source abstract
Abstract
Electronic health records (EHRs) security is a critical challenge in the implementation and administration of Internet of Medical Things (IoMT) systems within the healthcare sector’s heterogeneous environment. As digital transformation continues to advance, ensuring privacy, integrity, and availability of EHRs become increasingly complex. Various imaging modalities, including PET, MRI, ultrasonography, CT, and X-ray imaging, play vital roles in medical diagnosis, allowing healthcare professionals to visualize and assess the internal structures, functions, and abnormalities within the human body. These diagnostic images are typically stored, shared, and processed for various purposes, including segmentation, feature selection, and image denoising. Cryptography techniques offer a promising solution for protecting sensitive medical image data during storage and transmission. Deep learning has the potential to revolutionize cryptography techniques for securing medical images. This paper explores the application of deep learning techniques in medical image cryptography, aiming to enhance the privacy and security of healthcare data. It investigates the use of deep learning models for image encryption, image resolution enhancement, detection and classification, encrypted compression, key generation, and end-to-end encryption. Finally, we provide insights into the current research challenges and promising directions for future research in the field of deep learning applications in medical image cryptography.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Media Forensic Detection · AI in cancer detection · Brain Tumor Detection and Classification
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electronics and Communication Engineering, The LNM Institute of Information Technology, Jaipur 302031, India
Department of Information and Communication Technology, University of Agder, 4879 Grimstad, Norway
References (100+)
Show source-provided references
1. Lee, D., and Yoon, S.N. (2021). Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges. Int. J. Environ. Res. Public Health, 18.10.3390/ijerph18010271 ↗
2. Impacts of Healthcare 4.0 Digital Technologies on the Resilience of Hospitals10.1016/j.techfore.2021.120666 ↗
3. Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0
4. Internet of Things: A Survey of Enabling Technologies in Healthcare and Its Applications10.1016/j.comnet.2019.03.006 ↗
5. IoMT-SAF: Internet of Medical Things Security Assessment Framework10.1016/j.iot.2019.100123 ↗
6. Hatzivasilis, G., Soultatos, O., Ioannidis, S., Verikoukis, C., Demetriou, G., and Tsatsoulis, C. (2019, January 29–31). Review of Security and Privacy for the Internet of Medical Things (IoMT). Proceedings of the 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), Santorini Island, Greece.10.1109/DCOSS.2019.00091 ↗
7. Review of Security Challenges in Healthcare Internet of Things10.1007/s11276-020-02340-0 ↗
8. Koutras, D., Stergiopoulos, G., Dasaklis, T., Kotzanikolaou, P., Glynos, D., and Douligeris, C. (2020). Security in IoMT Communications: A Survey. Sensors, 20.10.3390/s20174828 ↗
9. Priyadharshini, A., Umamaheswari, R., Jayapandian, N., and Priyananci, S. (2021, January 19–20). Securing Medical Images Using Encryption and LSB Steganography. Proceedings of the 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), Bhilai, India.
10. Security of Medical Images for Telemedicine: A Systematic Review10.1007/s11042-022-11956-7 ↗
11. Liu, X., Song, L., Liu, S., and Zhang, Y. (2021). A Review of Deep-Learning-Based Medical Image Segmentation Methods. Sustainability, 13.10.3390/su13031224 ↗
12. A Review of the Application of Deep Learning in Medical Image Classification and Segmentation10.21037/atm.2020.02.44 ↗
13. A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis10.1016/j.media.2021.102062 ↗
14. Deep Learning in Medical Image Analysis: A Third Eye for Doctors10.1016/j.jormas.2019.06.002 ↗
15. Decoding Deep Learning Applications for Diagnosis and Treatment Planning10.1590/2177-6709.27.5.e22spe5 ↗
16. Endoscopic Diagnosis and Treatment Planning for Colorectal Polyps Using a Deep-Learning Model10.1038/s41598-019-56697-0 ↗
17. Medical Imaging Data Analysis Using 3D Deep Learning Models towards Improving the Individual Treatment Plans10.1016/j.nima.2022.167951 ↗
18. MR-Based Treatment Planning in Radiation Therapy Using a Deep Learning Approach10.1002/acm2.12554 ↗
19. Scope and Performance of Artificial Intelligence Technology in Orthodontic Diagnosis, Treatment Planning, and Clinical Decision-Making—A Systematic Review10.1016/j.jds.2020.05.022 ↗
20. The Role of Neural Artificial Intelligence for Diagnosis and Treatment Planning in Endodontics: A Qualitative Review10.1016/j.sdentj.2022.04.004 ↗
21. Privacy-Preserving Artificial Intelligence in Healthcare: Techniques and Applications10.1016/j.compbiomed.2023.106848 ↗
22. DeepKeyGen: A Deep Learning-Based Stream Cipher Generator for Medical Image Encryption and Decryption10.1109/TNNLS.2021.3062754 ↗
23. Secure, Privacy-Preserving and Federated Machine Learning in Medical Imaging10.1038/s42256-020-0186-1 ↗
24. Securing Medical Image Privacy in Cloud Using Deep Learning Network10.1186/s13677-023-00422-w ↗
25. (2023, June 28). Virtual Clinical Trials Market Size, Share and Analysis|Forecast-2030. Available online: https://www.acumenresearchandconsulting.com/virtual-clinical-trials-market.
26. (2023, June 05). One in Four US Consumers Have Had Their Healthcare Data Breached, Accenture Survey Reveals. Available online: https://newsroom.accenture.com/news/one-in-four-us-consumers-have-had-their-healthcare-data-breached-accenture-survey-reveals.htm.
27. (2023, June 05). Cost of a Data Breach. Available online: https://www.ibm.com/reports/data-breach.
28. (2023, June 10). Global Cyberattacks Continue to Rise with Africa and APAC Suffering Most. Available online: https://blog.checkpoint.com/research/global-cyberattacks-continue-to-rise/.
29. The Quality of Medical Care, Behavioral Risk Factors, and Longevity Growth10.1007/s10754-010-9086-y ↗
30. Medical Image Analysis Using Convolutional Neural Networks: A Review10.1007/s10916-018-1088-1 ↗
31. Automated Quantification of Baseline Imaging PET Metrics on FDG PET/CT Images of Pediatric Hodgkin Lymphoma Patients10.1186/s40658-020-00346-3 ↗
32. Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis10.3389/fonc.2021.638182 ↗
33. Clinical Application of Artificial Intelligence in Positron Emission Tomography: Imaging of Prostate Cancer10.1016/j.cpet.2021.09.002 ↗
34. A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)10.1109/ACCESS.2021.3058537 ↗
35. Deep Learning and Medical Image Processing for Coronavirus (COVID-19) Pandemic: A Survey10.1016/j.scs.2020.102589 ↗
36. Recent Advances and Clinical Applications of Deep Learning in Medical Image Analysis10.1016/j.media.2022.102444 ↗
37. Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., and Shpanskaya, K. (2017). CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. arXiv.
38. Korolev, S., Safiullin, A., Belyaev, M., and Dodonova, Y. (2017, January 18–21). Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification. Proceedings of the 2017 IEEE 14th International Symposium on Biomedical Imaging, Melbourne, VIC, Australia.10.1109/ISBI.2017.7950647 ↗
39. He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep Residual Learning for Image Recognition. arXiv, 770–778.10.1109/CVPR.2016.90 ↗
40. Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv.
41. A Review of Deep Learning-Based Multiple-Lesion Recognition from Medical Images: Classification, Detection and Segmentation10.1016/j.compbiomed.2023.106726 ↗
42. Detection of Tumors on Brain MRI Images Using the Hybrid Convolutional Neural Network Architecture10.1016/j.mehy.2020.109684 ↗
43. Chakraborty, C., Banerjee, A., Kolekar, M.H., Garg, L., and Chakraborty, B. (2021). Internet of Things for Healthcare Technologies, Springer. Studies in Big Data.10.1007/978-981-15-4112-4 ↗
44. A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique10.1109/ACCESS.2021.3079204 ↗
45. Deep Learning in Mammography Images Segmentation and Classification: Automated CNN Approach10.1016/j.aej.2021.03.048 ↗
46. Ahmad, B., Sun, J., You, Q., Palade, V., and Mao, Z. (2022). Brain Tumor Classification Using a Combination of Variational Autoencoders and Generative Adversarial Networks. Biomedicines, 10.10.3390/biomedicines10020223 ↗
47. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs10.1109/TPAMI.2017.2699184 ↗
48. Semi-Supervised Generative Adversarial Networks for the Segmentation of the Left Ventricle in Pediatric MRI10.1016/j.compbiomed.2020.103884 ↗
49. A Survey on Deep Learning in Medical Image Analysis10.1016/j.media.2017.07.005 ↗
50. Medical Image Segmentation Using Deep Learning10.1007/978-3-030-32606-7_2 ↗
51. Medical Image Detection Using Deep Learning10.1007/978-3-030-32606-7_1 ↗
52. Detecting Anatomical Landmarks from Limited Medical Imaging Data Using Two-Stage Task-Oriented Deep Neural Networks10.1109/TIP.2017.2721106 ↗
53. Validation, Comparison, and Combination of Algorithms for Automatic Detection of Pulmonary Nodules in Computed Tomography Images: The LUNA16 Challenge10.1016/j.media.2017.06.015 ↗
54. Pandey, S.K., and Bhandari, A.K. (2023). A Systematic Review of Modern Approaches in Healthcare Systems for Lung Cancer Detection and Classification. Arch Comput. Methods Eng.10.1007/s11831-023-09940-x ↗
55. Deep Learning Ensemble 2D CNN Approach towards the Detection of Lung Cancer10.1038/s41598-023-29656-z ↗
56. Park, Y.-J., Cho, H.-S., and Kim, M.-N. (2023). AI Model for Detection of Abdominal Hemorrhage Lesions in Abdominal CT Images. Bioengineering, 10.10.3390/bioengineering10040502 ↗
57. Xue, X., Chinnaperumal, S., Abdulsahib, G.M., Manyam, R.R., Marappan, R., Raju, S.K., and Khalaf, O.I. (2023). Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets. Bioengineering, 10.10.3390/bioengineering10030363 ↗
58. Convolutional Neural Networks for Breast Cancer Detection in Mammography: A Survey10.1016/j.compbiomed.2021.104248 ↗
59. Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges10.1007/s10278-019-00227-x ↗
60. Deep Neural Networks for Medical Image Segmentation10.1155/2022/9580991 ↗
61. Deep Learning Approaches to Biomedical Image Segmentation10.1016/j.imu.2020.100297 ↗
62. Medical Image Segmentation Using Deep Learning: A Survey10.1049/ipr2.12419 ↗
63. Gou, F., and Wu, J. (2022, January 6–8). An Attention-Based AI-Assisted Segmentation System for Osteosarcoma MRI Images. Proceedings of the 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Las Vegas, NV, USA.10.1109/BIBM55620.2022.9995391 ↗
64. A Survey of Deep Learning for Retinal Blood Vessel Segmentation Methods: Taxonomy, Trends, Challenges and Future Directions10.1109/ACCESS.2022.3163247 ↗
65. Deep Learning for Automatic Target Volume Segmentation in Radiation Therapy: A Review10.21037/qims-21-168 ↗
66. A Survey of Deep Learning for MRI Brain Tumor Segmentation Methods: Trends, Challenges, and Future Directions10.1007/s12553-023-00737-3 ↗
67. Deep Learning Based Brain Tumor Segmentation: A Survey10.1007/s40747-022-00815-5 ↗
68. Bonaldi, L., Pretto, A., Pirri, C., Uccheddu, F., Fontanella, C.G., and Stecco, C. (2023). Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies. Bioengineering, 10.10.3390/bioengineering10020137 ↗
69. Fredrikson, M., Jha, S., and Ristenpart, T. (2015, January 12). Model Inversion Attacks That Exploit Confidence Information and Basic Countermeasures. Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, New York, NY, USA. Association for Computing Machinery.10.1145/2810103.2813677 ↗
70. (2023, June 06). A Comprehensive Survey on Poisoning Attacks and Countermeasures in Machine Learning|ACM Computing Surveys. Available online: https://dl.acm.org/doi/10.1145/3551636.
71. A Comprehensive Review on Deep Learning Algorithms: Security and Privacy Issues10.1016/j.cose.2023.103297 ↗
72. Dey, N., Ashour, A.S., and Borra, S. (2018). Classification in BioApps: Automation of Decision Making, Springer International Publishing. Lecture Notes in Computational Vision and Biomechanics.10.1007/978-3-319-65981-7 ↗
73. Adversarial Attacks on Medical Machine Learning10.1126/science.aaw4399 ↗
74. Goodfellow, I.J., Shlens, J., and Szegedy, C. (2015). Explaining and Harnessing Adversarial Examples. arXiv.
75. Papernot, N., McDaniel, P., and Goodfellow, I. (2016). Transferability in Machine Learning: From Phenomena to Black-Box Attacks Using Adversarial Samples. arXiv.
76. Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A. (2019). Towards Deep Learning Models Resistant to Adversarial Attacks. arXiv.
77. Carlini, N., and Wagner, D. (2017). Towards Evaluating the Robustness of Neural Networks. arXiv.10.1109/SP.2017.49 ↗
78. Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., and Swami, A. (2015). The Limitations of Deep Learning in Adversarial Settings. arXiv.10.1109/EuroSP.2016.36 ↗
79. Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., and Frossard, P. (2017, January 26). Universal Adversarial Perturbations. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.17 ↗
80. (2023, June 30). DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks|IEEE Conference Publication|IEEE Xplore. Available online: https://ieeexplore.ieee.org/document/7780651.
81. Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors10.1016/j.media.2021.102141 ↗
82. Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., and Fichtinger, G. (2018, January 16–20). Generalizability vs. Robustness: Investigating Medical Imaging Networks Using Adversarial Examples. Proceedings of the Medical Image Computing and Computer Assisted Intervention—MICCAI 2018, Granada, Spain.10.1007/978-3-030-00931-1 ↗
83. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27–30). Rethinking the Inception Architecture for Computer Vision. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.308 ↗
84. Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2016). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. arXiv.10.1609/aaai.v31i1.11231 ↗
85. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv.
86. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation10.1109/TPAMI.2016.2644615 ↗
87. Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. (2015, January 5–9). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015, Munich, Germany.10.1007/978-3-319-24553-9 ↗
88. Jegou, S., Drozdzal, M., Vazquez, D., Romero, A., and Bengio, Y. (2017). The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation. arXiv, 11–19.10.1109/CVPRW.2017.156 ↗
89. Finlayson, S.G., Chung, H.W., Kohane, I.S., and Beam, A.L. (2019). Adversarial Attacks Against Medical Deep Learning Systems. arXiv.10.1126/science.aaw4399 ↗
90. Bortsova, G., Dubost, F., Hogeweg, L., Katramados, I., and de Bruijne, M. (2021). Adversarial Heart Attack: Neural Networks Fooled to Segment Heart Symbols in Chest X-ray Images. arXiv.
91. Pal, B., Gupta, D., Rashed-Al-Mahfuz, M., Alyami, S.A., and Moni, M.A. (2021). Vulnerability in Deep Transfer Learning Models to Adversarial Fast Gradient Sign Attack for COVID-19 Prediction from Chest Radiography Images. Appl. Sci., 11.10.3390/app11094233 ↗
92. Hirano, H., Koga, K., and Takemoto, K. (2020). Vulnerability of Deep Neural Networks for Detecting COVID-19 Cases from Chest X-ray Images to Universal Adversarial Attacks. PLoS ONE, 15.10.1371/journal.pone.0243963 ↗
93. COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-ray Images10.1038/s41598-020-76550-z ↗
94. Hirano, H., Minagi, A., and Takemoto, K. (2021). Universal Adversarial Attacks on Deep Neural Networks for Medical Image Classification. BMC Med. Imaging, 21.10.1186/s12880-020-00530-y ↗
95. Adversarial Perturbation on MRI Modalities in Brain Tumor Segmentation10.1109/ACCESS.2020.3030235 ↗
96. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)10.1109/TMI.2014.2377694 ↗
97. Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., and Summers, R.M. (2017, January 26). ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.369 ↗
98. (2023, June 07). ISIC|International Skin Imaging Collaboration. Available online: https://www.isic-archive.com.
99. (2023, June 07). Diabetic Retinopathy Detection|Kaggle. Available online: https://www.kaggle.com/c/diabetic-retinopathy-detection.
100. Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems10.1016/j.patcog.2020.107332 ↗