Secure Image Management in Healthcare and Industry through Deep Learning and Cryptographic Approaches

Securing electronic health records (EHRs) within the Internet of Medical Things (IoMT) ecosystem remains a major challenge due to the complex and evolving nature of healthcare environments. As digital systems expand, maintaining the confidentiality, integrity, and accessibility of medical image data becomes increasingly difficult. Cryptographic techniques offer a foundational approach for protecting sensitive medical images during transmission and storage, while deep learning provides new opportunities to transform traditional encryption processes. This study investigates the integration of deep learning and cryptography to strengthen medical image security. It examines methods such as weight analysis to enhance encryption robustness and the use of chaotic systems to generate highly secure, undetectable encryption patterns. The study also reviews current deep learning–based anomaly detection approaches used in operational settings, focusing on network architectures, supervision models, and evaluation standards. Findings indicate that combining deep learning with cryptographic methods provides strong protection, improved resolution, and enhanced detection capabilities for medical image security. The paper further identifies challenges and opportunities in healthcare and industrial image protection, highlighting the need for continued research to address emerging threats and optimize system performance. By bridging the gap between deep learning and cryptography, this work contributes to improved privacy, integrity, and availability of critical image data across healthcare and industrial sectors.