Deep Convolutional Neural Network Mechanism Assessment of COVID-19 Severity.
As an epidemic, COVID-19's core test instrument still has serious flaws. To improve the present condition, all capabilities and tools available in this field are being used to combat the pandemic. Because of the contagious characteristics of the unique coronavirus (COVID-19) infection, an overwhelmi...
| Publicado en: | BioMed Research International pp. 1 - 15 |
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| Autores principales: | , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
8/23/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158677285&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158677285 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/23/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 158677285 158677285 158677285 10.1155/2022/1289221 158677285 ppf: 1 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Convolutional Neural Network Mechanism Assessment of COVID-19 Severity. aug: au: Nirmaladevi, J. Vidhyalakshmi, M. Edwin, E. Bijolin Venkateswaran, N. Avasthi, Vinay Alarfaj, Abdullah A. Hirad, Abdurahman Hajinur Rajendran, R. K. Hailu, TegegneAyalew affil: Department of Information Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu 638401, India sug: subj: COVID-19 Severity of Illness Neural Networks (Computer) Radiography, Thoracic Decision Support Systems, Clinical Artificial Intelligence Sensitivity and Specificity ab: As an epidemic, COVID-19's core test instrument still has serious flaws. To improve the present condition, all capabilities and tools available in this field are being used to combat the pandemic. Because of the contagious characteristics of the unique coronavirus (COVID-19) infection, an overwhelming comparison with patients queues up for pulmonary X-rays, overloading physicians and radiology and significantly impacting the quality of care, diagnosis, and outbreak prevention. Given the scarcity of clinical services such as intensive care and motorized ventilation systems in the aspect of this vastly transmissible ailment, it is critical to categorize patients as per their risk categories. This research describes a novel use of the deep convolutional neural network (CNN) technique to COVID-19 illness assessment seriousness. Utilizing chest X-ray images as contribution, an unsupervised DCNN model is constructed and suggested to split COVID-19 individuals into four seriousness classrooms: low, medium, serious, and crucial with an accuracy level of 96 percent. The efficiency of the DCNN model developed with the proposed methodology is demonstrated by empirical findings on a suitably huge sum of chest X-ray scans. To the evidence relating, it is the first COVID-19 disease incidence evaluation research with four different phases, to use a reasonably high number of X-ray images dataset and a DCNN with nearly all hyperparameters dynamically adjusted by the variable selection optimization task. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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