Fusion-Driven Medical Image Encryption Framework with Entropy-Calibrated Control and Integrity Assurance

The safety of transmission and storage of medical images is an urgent issue in the modern health care system, and high security guarantees are required to be obtained without impairing computational performance or the quality of diagnosis.To ensure these needs, the paper proposes a Fusion-Driven Medical Image Encryption Framework (FDMIEF), which is a novel seven step encryption framework that has been specially developed to meet medical imaging requirements.The proposed framework incorporates entropy-calibrated control seeding, plaintext-coupled key-stream generation, multi-component fusion encoding, adaptive substitution, diffusion expansion, spatial reordering, and embedded integrity checking to gain high security and complete reversibility.A large-scale experiment on a set of retinopathy image data of diabetics shows that FDMIEF performs more effectively than the state-of-theart algorithms with a maximum PSNR of 48.92 dB, SSIM of 0.9986, and MSE of 0.021, which means that it can perform high-fidelity decryption.Also, the suggested framework eliminates the processing time by about 29 percent of computationally intensive schemes like VFHE-DWOA at higher levels of the iteration.Security analysis also attests to great resistance to the statistical, differential, chosen-plaintext and chosen-ciphertext attacks.Such findings make FDMIEF a powerful, effective, and scalable platform of secure management of medical image in the real-life healthcare settings.