Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/42976
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dc.creatorChandra, Tej Bahadur-
dc.creatorVerma, Kesari-
dc.creatorSingh, Bikesh Kumar-
dc.creatorJain, Deepak-
dc.creatorNetam, Satyabhuwan Singh-
dc.date.accessioned2020-09-10T17:09:05Z-
dc.date.available2020-09-10T17:09:05Z-
dc.date.issued2021-03-
dc.identifier.citationCHANDRA, T. B. et al. Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble. Expert Systems with Applications, [S.l.], v. 165, Mar. 2021.pt_BR
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0957417420307041pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/42976-
dc.description.abstractNovel coronavirus disease (nCOVID-19) is the most challenging problem for the world. The disease is caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2), leading to high morbidity and mortality worldwide. The study reveals that infected patients exhibit distinct radiographic visual characteristics along with fever, dry cough, fatigue, dyspnea, etc. Chest X-Ray (CXR) is one of the important, non-invasive clinical adjuncts that play an essential role in the detection of such visual responses associated with SARS-COV-2 infection. However, the limited availability of expert radiologists to interpret the CXR images and subtle appearance of disease radiographic responses remains the biggest bottlenecks in manual diagnosis. In this study, we present an automatic COVID screening (ACoS) system that uses radiomic texture descriptors extracted from CXR images to identify the normal, suspected, and nCOVID-19 infected patients. The proposed system uses two-phase classification approach (normal vs. abnormal and nCOVID-19 vs. pneumonia) using majority vote based classifier ensemble of five benchmark supervised classification algorithms. The training-testing and validation of the ACoS system are performed using 2088 (696 normal, 696 pneumonia and 696 nCOVID-19) and 258 (86 images of each category) CXR images, respectively. The obtained validation results for phase-I (accuracy (ACC) = 98.062%, area under curve (AUC) = 0.956) and phase-II (ACC = 91.329% and AUC = 0.831) show the promising performance of the proposed system. Further, the Friedman post-hoc multiple comparisons and z-test statistics reveals that the results of ACoS system are statistically significant. Finally, the obtained performance is compared with the existing state-of-the-art methods.pt_BR
dc.languageen_USpt_BR
dc.publisherElsevierpt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceExpert Systems with Applicationspt_BR
dc.subjectCoronaviruspt_BR
dc.subjectChest X-Raypt_BR
dc.titleCoronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemblept_BR
dc.typeArtigopt_BR
Aparece nas coleções:FCS - Artigos sobre Coronavirus Disease 2019 (COVID-19)

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