Soft Computing Approaches in Enhancing Healthcare Data Security Using Deep Neural Networks and Reversible Data Hiding

The rapid growth in the volume of electronic medical records (EMRs) being collected, stored, and transmitted across networks can be attributed to recent advances in medical informatics and health information technologies. Given the sensitive nature of this data, securing its transmission has become a critical concern for researchers. While conventional methods like encryption and password protection are commonly used to secure medical images with embedded data, traditional encryption and data hiding techniques often prove inadequate, leading to security vulnerabilities and inefficiencies during network transmission. To address these challenges, more robust strategies are needed to ensure both the integrity and confidentiality of patient information when shared with healthcare providers. Experimental studies on medical test images demonstrate that the proposed approach significantly outperforms existing methods, achieving a higher data embedding rate while preserving superior image quality. The findings are unique due to the combination of high data embedding capacity and enhanced security, addressing the limitations of traditional methods. Furthermore, the proposed technique minimizes image distortion, ensuring an optimal balance between security and image fidelity. This innovation has the potential to significantly improve the efficiency and security of medical data exchange in healthcare systems, providing better protection for sensitive patient information.