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Siju John,

S.N. Kumar,
- 1 Research Scholar, Lincoln University College, Jalan Lembah Sireh, 15050 Kota Bharu, Kelantan, Malaysia, 1Assistant Professor, Department of Computer Science and engineering, Amal Jyothi College of Engineering, Kanjirappally, Kerala, India
- Associate Professor, Department of Electrical and Electronics Engineering, Amal Jyothi College of Engineering, Kanjirappally, Kerala, India
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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: encryption, medical imaging, deep learning
[This article belongs to Journal of Image Processing & Pattern Recognition Progress (joipprp)]
Siju John, S.N. Kumar. Deep Learning Algorithms for Medical Image Encryption to Ensure Secure Data Transfer. Journal of Image Processing & Pattern Recognition Progress. 2024; 11(03):-.
Siju John, S.N. Kumar. Deep Learning Algorithms for Medical Image Encryption to Ensure Secure Data Transfer. Journal of Image Processing & Pattern Recognition Progress. 2024; 11(03):-. Available from: https://journals.stmjournals.com/joipprp/article=2024/view=0
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Journal of Image Processing & Pattern Recognition Progress
| Volume | 11 |
| Issue | 03 |
| Received | 31/08/2024 |
| Accepted | 23/09/2024 |
| Published | 11/10/2024 |
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