Review of Recent Advances in Secure and Lossless Compression Techniques for Medical Images Using Multi-Model Approaches
Mohammed M., Manga I., Nathan N.
Abstract
This paper reviewed existing literature on the topic "Secure Lossless Compression of Medical Images Based on a Multi-Model Framework," focusing on how recent approaches aim to balance image compression efficiency with strong data security. Many studies have explored hybrid methods that combine advanced compression techniques like Neural Transformation Coding with encryption algorithms such as RSA and AES. Faster processing times were observed for JPG files due to smaller file sizes, suggesting a trade-off between compression quality and speed. Despite these promising outcomes, the review identified gaps in current research, including a lack of real-world testing, limited dataset diversity, and minimal exploration of integration with medical systems. To address these issues, future work should focus on applying these frameworks in clinical settings, evaluating performance across various imaging modalities, and adopting lightweight, scalable encryption techniques. Incorporating technologies like blockchain and AI-driven adaptive compression could further enhance security, efficiency, and practical deployment in healthcare environments.
Keywords
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