COVID-19 Diagnosis Using Capsule Network and Fuzzy C-Means and Mayfly Optimization Algorithm.
The COVID-19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer-aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagno...
| Publicado en: | BioMed Research International pp. 1 - 12 |
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| Autores principales: | , , , , , |
| Formato: | computer program diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
10/19/2021
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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=153094479&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153094479 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 10/19/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 153094479 153094479 153094479 10.1155/2021/2295920 153094479 ppf: 1 ppct: 11 formats: fmt: @attributes: type: P tig: atl: COVID-19 Diagnosis Using Capsule Network and Fuzzy C-Means and Mayfly Optimization Algorithm. aug: au: Farki, Ali Salekshahrezaee, Zahra Tofigh, Arash Mohammadi Ghanavati, Reza Arandian, Behdad Chapnevis, Amirahmad affil: Department of Information Technology Engineering, Industrial and Systems Engineering Faculty, Tarbiat Modares University, Tehran, Iran sug: subj: COVID-19 Diagnosis Algorithms Software Diagnosis, Laboratory Methods Technology Neural Networks (Computer) Methods Human Early Diagnosis COVID-19 Pathology Radiography, Thoracic Registries, Disease Simulations Predictive Value of Tests Reliability and Validity Descriptive Statistics Sensitivity and Specificity COVID-19 Prevention and Control ab: The COVID-19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer-aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagnosis of COVID-19. Here, a method based on fuzzy C -ordered means (FCOM) along with an improved version of the enhanced capsule network (ECN) has been proposed for this purpose. The proposed ECN method is improved based on mayfly optimization (MFO) algorithm. The suggested technique is then implemented on the chest X-ray COVID-19 images from publicly available datasets. Simulation results are assessed by considering a comparison with some state-of-the-art methods, including FOMPA, MID, and 4S-DT. The results show that the proposed method with 97.08% accuracy and 97.29% precision provides the highest accuracy and reliability compared with the other studied methods. Moreover, the results show that the proposed method with a 97.1% sensitivity rate has the highest ratio. And finally, the proposed method with a 97.47% F 1 -score rate gives the uppermost value compared to the others. pubtype: Academic Journal doctype: computer program diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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