Analysis of Deep Learning Based Secure and Robust System for the Renovation of Medical Images on IoMT

Medical healthcare transformations resulted from Internet of Medical Things (IoMT) integration because this technology allows physicians to carry out medical imaging examinations promptly and diagnose patient conditions in real time. Medical imaging systems built on the Internet of Medical Things framework experience crucial issues that involve weakening of images and privacy risks as well as cyber security threats. This research delivers an extensive investigation about an advanced DL framework which ensures secure medical image restoration inside IoMT platforms. GANs coupled with a Residual Attention Mechanism (RAM) allow the proposed framework to recover medical images that become degraded while retaining essential anatomical elements. A Hybrid Homomorphic Encryption (HHE) scheme brings heightened security through complete confidentiality of data whether it is transmitted or stored. The Adaptive Noise Reduction (ANR) model serves as an integral part to reduce transmission errors and their resulting artifacts and distortions. The model proves its superiority through evaluations on medical imaging benchmarks which yield better Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) values compared to contemporary state-of-the-art approaches while users report improved subjective judicial scores (MOS) at the same time. The framework has established its capability for improving image clarity and data security while maintaining patient privacy roles as a promising solution for secure medical image restoration in IoMT platforms.