This paper introduces a novel approach to securing medical image transmission through the integration of deep learning techniques into cryptographic processes. Leveraging the capabilities of Backpropagation (BP), Convolutional Neural Networks (CNN), Residual Networks (ResNet), and Generative Adversarial Network (GAN), our method aims to enhance the privacy and security of medical images in real-time applications like telemedicine. The proposed system focuses on optimizing performance metrics including Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error (RMSE), Structural Similarity Index Measure (SSIM), Mean Average Precision (MAP), and encryption speed. Through experimental evaluation, our approach demonstrates promising results in terms of encryption efficiency and preservation of image quality. By addressing the critical need for secure transmission methods in healthcare, this research contributes to advancing the field of medical image cryptography and lays the groundwork for further exploration in deep learning-based security solutions for healthcare data.
Keywords
Medical Image Security, Deep Learning, Homomorphic Encryption, Cryptography, Feature Extraction.
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Author(s) thanks to Dr. Murugan D for this research completion and support.
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Suvitha B
Suvitha B
Department of Computer Science and Engineering, Manonmaniam Sundaranar
University, Tirunelveli, Tamil Nadu, India.
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Cite this article
Suvitha B and Murugan D, “Integrating Deep Learning and Homomorphic Encryption
for Secure Image Transmission”, Journal of Machine and Computing, pp. 1206-1219, October 2024.
doi:10.53759/7669/jmc202404111.
