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

IF 4.9 2区 医学 Q1 ENGINEERING, BIOMEDICAL
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引用次数: 0

摘要

医疗机构和学术界都在密切关注智能医疗传感器、小工具、云计算和其他健康相关技术的发展。为了积极诊断和控制 COVID-19 的传播,需要一个有效的自动化系统。因此,本文提出了一种物联网智能医疗系统,利用优化的鲁棒时空图卷积网络(IoT-RSGCN-SGWOA-CD19)对 COVID-19 进行分类。输入图像通过胸部 X 光数据集收集。输入图像通过自适应两级无香卡尔曼滤波器(ATSUKF)进行预处理。然后,将预处理后的图像输入二维频谱图小波(2DSGW)以提取特征。提取的特征将提供给特征选择,以使用云豹优化(CLO)选择合适的特征。然后,提出鲁棒时空图卷积网络(RSGCN),将疾病分为肺炎、正常和 COVID-19。通过基于向日葵的灰狼优化算法(SFGWOA)对 RSGCN 的权重参数进行优化,提高了其在疾病筛查和传染病分类中的准确性。在 MATLAB 中实现了所提出的 IoT-RSGCN-SGWOA-CD19 方法,并通过准确率、精确度、召回率、ROC、AUC、损失等性能指标对其有效性进行了评估。物联网-RSGCN-SGWOA-CD19 方法与现有模型相比,准确度分别提高了 23.64 %、20.98 % 和 24.33 %,精确度分别提高了 13.24 %、30.43 % 和 28.71 %,召回率分别提高了 27.79 %、23.84 % 和 26.62 %。实验结果证实,IoT-RSGCN-SGWOA-CD19 方法在 COVID-19 自动筛查方面取得了重大进展,具有卓越的分类准确性和可靠性。所提出的系统可以提供快速准确的诊断,是大流行病控制的重要工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
Biomedical Signal Processing and Control
Biomedical Signal Processing and Control 工程技术-工程:生物医学
CiteScore
9.80
自引率
13.70%
发文量
822
审稿时长
4 months
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.
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