IoT enabled smart healthcare system for COVID-19 classification using optimized robust spatiotemporal graph convolutional networks

Healthcare organizations and academics are paying close attention to the development of smart medical sensors, gadgets, cloud computing, and other health-related technology. To actively diagnose and control the spread of COVID-19, an effectual automated system is required. Therefore, this paper proposes an IoT enabled Smart Healthcare System for COVID-19 Classification Using Optimized Robust Spatiotemporal Graph Convolutional Networks (IoT-RSGCN-SGWOA-CD19). Here, the input images are collected through Chest X-Ray dataset. The input images are preprocessed by utilizing Adaptive two-stage unscented Kalman filter (ATSUKF). Next, the preprocessed images are fed into Two-Dimensional Spectral Graph Wavelets (2DSGW) for extracting features. The extracted features are supplied to the feature selection to select the appropriate features using Clouded Leopard Optimization (CLO). Then, Robust Spatiotemporal Graph Convolutional Network (RSGCN) is proposed to classify the disease as pneumonia, normal and COVID-19. The weight parameter of RSGCN is optimally tuned by Sunflower based Grey Wolf Optimization Algorithm(SFGWOA), improving its accuracy in disease screening and infectious disease categorization. The effectiveness of the proposed IoT-RSGCN-SGWOA-CD19 method is implemented in MATLAB and evaluated through performance metrics, likes accuracy, precision, recall, ROC, AUC, loss. The IoT-RSGCN SGWOA-CD19 method attains 23.64 %, 20.98 % and 24.33 % higher accuracy, 13.24 %, 30.43 % and 28.71 % higher precision and 27.79 %, 23.84 % and 26.62 % higher recall when analyzed with the existing models. The experimental results confirm that the IoT-RSGCN-SGWOA-CD19 method offers a significant advancement in automated COVID-19 screening, with superior classification accuracy and reliability. The proposed system can be a valuable tool in pandemic control by providing rapid and accurate diagnoses.