Secure IoT-Integrated Cloud-Based Medical Image Processing Using Optimized Stereoscopic Scalable Quantum CNN for Efficient Diagnosis

Healthcare data have increased significantly as a result of the quick development of medical imaging technology, necessitating accurate diagnosis, safe transmission, and effective storage. Using a hybrid model known as stereoscopic scalable quantum convolutional neural network-Gooseneck Barnacle optimization (SSQ-CNN-GBO), this study suggests an innovative, safe, and scalable cloud-based medical image analysis framework that is integrated with the Internet of Things (IoT). The system uses quasi-cross bilateral filtering (QBF) for feature preservation and noise reduction, as well as dual elliptic curve-based lightweight authentication and data encryption (DEC-LADE) to guarantee data security. The GBO algorithm is utilized to optimize anSSQ-CNN for classification, while the multiview fuzzy clustering based on anchor graph (MVFCAG) approach is employed for accurate segmentation. Tests on the brain tumor MRI and chestX-ray14 datasets show that the suggested model outperforms current techniques in terms of diagnosis and encryption efficiency, achieving 99.97% accuracy and 98.57% precision. IoT-enabled healthcare systems can process medical images securely, accurately, and in real time thanks to this integrated solution.