Abstract
Medical images are more crucial than other images in the majority of applications, particularly in real-time applications like telemedicine, a high level of safety and security is necessary for medical image transmission via open access. Privacy as well as security of patient medical picture data have become a crucial concern in recent image privacy protection research due to the quick development of artificial intelligence (AI) technology. This study suggests a unique method for encrypting medical images that integrates hybrid deep learning with cloud IoT network cyber security analysis. Here, the medical picture has been encrypted utilising deep, extreme convolutional networks and a stream crypto cypher in quantum techniques. After that, a secure cloud IoT architecture was used to store this encrypted image. The peak signal-to-noise ratio (PSNR), root mean square error (RMSE), structural similarity index measure (SSIM), mean average precision (MAP), and encryption speed are used in the experimental study. Analyses and simulations that were undertaken experimentally were done to gauge how well the suggested method worked. the proposed technique attained PSNR 92%, RMSE 85%, SSIM 68%, MAP 52% and encryption speed 88%.
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The experimental data used to support the findings of this study are available from the corresponding author upon request.
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P.A.: Conceived and design the analysis, Writing- Original draft preparation, Collecting the Data; A.S.P.: Contributed data and analysis stools, Performed and analysis; M.A.A. and A.B.: Wrote the Paper, Editing and Figure Design.
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Priyadarshini, A., Abirami, S.P., Ahmed, M.A. et al. Quantum-enhanced cybersecurity analysis and medical image encryption in cloud IoT networks. Opt Quant Electron 56, 674 (2024). https://doi.org/10.1007/s11082-023-06018-7
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DOI: https://doi.org/10.1007/s11082-023-06018-7