Watermarking Protocol Inspired Kidney Stone Segmentation in IoMT.

The rapid explosion of medical data, exarcebated by the demands of smart healthcare, poses significant challenges for authentication and integrity verification. Moreover, the surge in cybercrime targeting healthcare data jeopardizes patient privacy, compromising both trust and diagnostic reliability. To address these concerns, we propose a robust healthcare system that integrates a kidney stone segmentation framework with a watermarking protocol tailored for Internet of Medical Things (IoMT) applications. Drawing upon patient information and biometrics, chaotic keys are generated for obfuscation and randomization, along with the watermark for integrity verification and authentication. The watermark is imperceptibly embedded into the obfuscated medical image using Singular Value Decomposition (SVD) and adaptive quantization, followed by randomization. Upon reception, successful watermark extraction and verification ensure secure access to unaltered medical data, enabling precise segmentation. To facilitate this, a ResNeXt-50 inspired encoder and attention-guided decoder are introduced within the U-Net architecture to enhance comprehensive feature learning. The effectiveness and practicality of the proposed system have been evaluated through comprehensive experiments on kidney CT scans. Comparative analysis with state-of-the-art techniques highlights its superior performance.