Cognitive Computing-Based COVID-19 Detection in Multi-Access Edge Computing Environment

The COVID-19 pandemic has highlighted the need for fast, accurate, and scalable diagnostic systems. Conventional diagnostic techniques often rely on centralized cloud infrastructure, leading to high latency and potential privacy concerns. This paper presents a cognitive computing framework for COVID-19 detection within a Multi-Access Edge Computing (MEC) environment. The proposed system integrates artificial intelligence (AI), federated learning, and edge intelligence to enable real-time, privacy-preserving diagnostics. CT scan images are obtained from the publicly available SARS-CoV-2 dataset and undergo image resizing during pre-processing. Federated learning facilitates distributed model training across edge nodes without transferring sensitive patient data, enhancing both data privacy and efficiency. Simulation results demonstrate a classification accuracy of 98.2% with an inference latency of 120 ms—significantly lower than the 320 ms observed in traditional cloud-based systems. Patient data is securely maintained in the cloud layer, ensuring integrity and confidentiality. The results validate the potential of cognitive edge computing for intelligent medical diagnostics and lay the groundwork for future applications in decentralized healthcare systems.