A Secure IoT-Enabled AI Framework for Cancer Cell Detection using Mathematical Morphology and Cyber Security Protocols

Abstract

The integration of Artificial Intelligence (AI), Internet of Things (IoT), and cybersecurity provides new opportunities for developing intelligent and secure healthcare systems. This study proposes a secure IoT-enabled AI framework for cancer cell detection by combining mathematical morphology, artificial neural networks, and cybersecurity protocols. A synthetic microscopic cell dataset consisting of normal and cancerous cell images is developed to provide a controlled environment for evaluating the proposed framework. Mathematical morphological operations, including erosion, dilation, opening, and closing, are employed to refine cellular regions and facilitate nuclear segmentation. Quantitative morphological descriptors, including nuclear area, perimeter, circularity, aspect ratio, solidity, and nucleus-to-cell ratio, are extracted from the segmented regions and used as input features for an artificial neural network classifier. The IoT layer provides a conceptual communication framework between the microscopic imaging device, gateway, and AI server, while authentication, encryption, and data-integrity verification are incorporated to protect the transmitted information. In the synthetic experimental setting, the proposed AI classification framework demonstrates the ability to distinguish normal and cancerous cell patterns using the extracted morphological characteristics. The cybersecurity layer additionally provides mechanisms for rejecting unauthenticated devices and detecting modified data packets. The study demonstrates the feasibility of integrating mathematical morphology, AI, IoT connectivity, and cybersecurity within a unified computational framework for cancer-cell analysis. Since the current investigation is based on synthetic data, the reported results represent a proof of concept rather than clinical diagnostic performance. Future work will focus on validation using diverse, annotated real-world histopathological datasets and assessment under realistic clinical and IoT network conditions.