Key points are not available for this paper at this time.
Since the COVID-19 outbreak, there has been a growing need for contactless healthcare to meet medical diagnosis demands. Electronic health systems using the Internet of Things (IoT) are rapidly advancing, transmitting significant amounts of private medical data online. In telemedicine, where patients are diagnosed remotely, sensitive information, such as patient records, may be embedded into medical images for security purposes. Due to the large file sizes of medical images produced by equipment, compression is essential for fast transmission. To safeguard medical images in telemedicine and address bandwidth limitations, we utilize data hiding techniques and absolute moment block truncation coding (AMBTC) compression to introduce an IoT-driven electronic health protection mechanism. Our mechanism employs diverse methods to compress and embed data across various image blocks. Additionally, it offers a flexible adaptation to meet different application requirements concerning embedding capacity, visual quality, and file size by adjusting thresholds and variants. Compared to alternative methods, our approach delivers superior payload capacity and efficiency while preserving visual fidelity.
Lin et al. (Mon,) studied this question.