A hybrid crypto-compression model for secure brain mri image transmission

Medical image encryption is a major issue in healthcare applications where memory, energy, and computational resources are constrained. The modern technological architecture of digital healthcare systems is, in fact, insufficient to handle both the current and future requirements for data. Security has been raised to the highest priority. By meeting these conditions, the hybrid crypto-compression technique introduced in this study can be used for securing the transfer of healthcare images. The approach consists of two components. In order to construct a cutting-edge generative lossy compression system, we first combine generative adversarial networks (GANs) with oearned compression. As a result, the second phase might address this problem by using highly effective picture cryptography techniques. A randomly generated public key is subjected to the DNA technique. In this application, pseudo-random bits are produced by using a logistic chaotic map algorithm. During the substitution process, an additional layer of security is provided to boost the technique's fault resilience. Our proposed system and security investigations show that the method provides trustworthy and long-lasting encryption and several multidimensional aspects that have been discovered in various public health and healthcare issues. As a result, the recommended hybrid crypto-compression technique may significantly reduce a photo's size and remain safe enough to be used for medical image encryption.

Automated summary

Medical image encryption is a crucial concern in healthcare applications, and the current technological architecture is inadequate for secure data transfer. This study introduces a hybrid crypto-compression technique that combines generative adversarial networks and learned compression to address the issue. The proposed system provides trustworthy and long-lasting encryption, and has various multidimensional applications in public health and healthcare.