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High-performance medical image secret sharing using super-resolution for CAD systems

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Abstract

Visual Secret Sharing (VSS) is a field of Visual Cryptography (VC) in which the secret image (SI) is distributed to a certain number of participants in the form of different encrypted shares. The decryption then uses authorized shares in a pre-defined manner to obtain that secret information. Medical image secret sharing (MISS) is an emerging VSS field to address the performance challenges in sharing medical images, such as efficiency and effectiveness. Here, we propose a novel MISS for the histopathological medical images to achieve high performance in these two parameters. The novelty here is the Graphics Processing Unit (GPU) to exploit the data-parallelism in MISS during encryption and super-resolution (SR), supplementing effectiveness with efficiency. A Convolution Neural Network (CNN) for SR produces a high-contrast reconstructed image. We evaluate the presented model using standard objective assessment parameters and the Computer-Aided Diagnosis (CAD) systems. The result analysis confirmed the high-performance of the proposed MISS with a 98% SSIM of the deciphered image. Compared with the state-of-art deep learning models designed for the histopathological medical images, MISS outperformed with 99.71% accuracy. Also, we achieved a categorization precision that fits the CAD systems. We attained an overall speedup of \( 800\times \) over the sequential model. This speedup is significant compared to the speedups of the benchmark GPGPU-based medical image reconstruction models.

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Authors and Affiliations

  1. Information Security Research Laboratory, Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, 575025, India

    M. Raviraja Holla & Alwyn R. Pais

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  1. M. Raviraja Holla
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  2. Alwyn R. Pais
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Holla, M.R., Pais, A.R. High-performance medical image secret sharing using super-resolution for CAD systems. Appl Intell 52, 16852–16868 (2022). https://doi.org/10.1007/s10489-021-03095-7

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