{"title":"利用优化的鲁棒时空图卷积网络进行 COVID-19 分类的物联网智能医疗系统","authors":"","doi":"10.1016/j.bspc.2024.107104","DOIUrl":null,"url":null,"abstract":"<div><div>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 pre-processed 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.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"IoT enabled smart healthcare system for COVID-19 classification using optimized robust spatiotemporal graph convolutional networks\",\"authors\":\"\",\"doi\":\"10.1016/j.bspc.2024.107104\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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 pre-processed 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.</div></div>\",\"PeriodicalId\":55362,\"journal\":{\"name\":\"Biomedical Signal Processing and Control\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomedical Signal Processing and Control\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1746809424011625\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Signal Processing and Control","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1746809424011625","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
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 pre-processed 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.
期刊介绍:
Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management.
Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.