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Updated: Jan 9, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
Lightweight semantic compression visual cryptography for secure medical image transmission in IoT systems
A John Blesswin1, G Selva Mary2, Suresh Sankaranarayanan3
1Directorate of Learning and Development, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India. johnb@srmist.edu.in.
Abstract:
Ensuring the secure transmission of medical images is a crucial challenge, particularly in resource-constrained IoT-based healthcare environments, where maintaining data confidentiality, integrity, and authenticity is of paramount importance. Given the sensitivity of medical data, it is imperative to develop efficient and robust security mechanisms that address the computational and storage limitations of IoT devices while ensuring reliable and secure communication. In this paper, a novel framework for lightweight visual cryptography is proposed based on semantic compression for secure transmission of medical images. The proposed framework encodes grayscale medical images into multiple visual shares to achieve confidentiality, integrity, and robust reconstruction. Modular arithmetic-based lightweight encryption techniques minimize computational overheads for IoT devices. Performance evaluation is done using medical images, with high quality reconstruction of images where PSNR is more than 34 dB, and SSIM is close to 0.99. The proposed framework is secure against tampering and eavesdropping; hence, there is robust security during transmission. This framework achieves an effective balance between computational efficiency, reconstruction quality, and security, demonstrating strong potential for secure transmission of medical images within IoT-based environments.
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