ABSTRACT
New digital technologies present unprecedented opportunities for healthcare systems all over the world to enhance the quality of service they provide to patients. Medical science has made great strides. However, the lack of emotional recognition, together with the scarcity of tailored and pervasive health apps and emotional smart devices, necessitates the integration of intelligent sensors into health systems using developing technologies. Although smart and connected health care has come a long way, more study, dissemination, and technology are required to unbundle new possibilities. Smart decision-making is just the beginning of a paradigm shift in healthcare. A secure Internet of Medical Things (IoMT) based transfer learning strategy is considered. In the smart healthcare business, disease prediction is performed using a deep learning model called a Hybrid Capsule Network 5.0. Using a safe IoMT-based transfer learning strategy, the research can accurately and quickly predict cancer in humans. Before uploading data to the cloud using a strong encryption technique. The best cancer forecast in the smart healthcare sector is also validated as using the suggested secure IoMT-based transfer learning algorithms. In the field of smart healthcare, the accuracy of cancer disease predictions using the proposed secure IoMT-based transfer learning tactic outperformed that of earlier state-of-the-art methodologies.
