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dc.contributor.authorLobna M., AbouEl-Magd-
dc.contributor.authorAshraf, Darwish-
dc.contributor.authorVaclav Snasel, Snasel-
dc.date.accessioned2023-03-31T03:36:15Z-
dc.date.available2023-03-31T03:36:15Z-
dc.date.issued2023-
dc.identifier.urihttps://link.springer.com/article/10.1007/s10586-022-03703-2-
dc.identifier.urihttps://dlib.phenikaa-uni.edu.vn/handle/PNK/7371-
dc.descriptionCC BYvi
dc.description.abstractCoronavirus disease (COVID-19) is rapidly spreading worldwide. Recent studies show that radiological images contain accurate data for detecting the coronavirus. This paper proposes a pre-trained convolutional neural network (VGG16) with Capsule Neural Networks (CapsNet) to detect COVID-19 with unbalanced data sets. The CapsNet is proposed due to its ability to define features such as perspective, orientation, and size. Synthetic Minority Over-sampling Technique (SMOTE) was employed to ensure that new samples were generated close to the sample center, avoiding the production of outliers or changes in data distribution.vi
dc.language.isoenvi
dc.publisherSpringervi
dc.subjectCapsNetvi
dc.subjectVGG16vi
dc.titleA pre-trained convolutional neural network with optimized capsule networks for chest X-rays COVID-19 diagnosisvi
dc.typeBookvi
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