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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Lobna M., AbouEl-Magd | - |
| dc.contributor.author | Ashraf, Darwish | - |
| dc.contributor.author | Vaclav Snasel, Snasel | - |
| dc.date.accessioned | 2023-03-31T03:36:15Z | - |
| dc.date.available | 2023-03-31T03:36:15Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | https://link.springer.com/article/10.1007/s10586-022-03703-2 | - |
| dc.identifier.uri | https://dlib.phenikaa-uni.edu.vn/handle/PNK/7371 | - |
| dc.description | CC BY | vi |
| dc.description.abstract | Coronavirus 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.iso | en | vi |
| dc.publisher | Springer | vi |
| dc.subject | CapsNet | vi |
| dc.subject | VGG16 | vi |
| dc.title | A pre-trained convolutional neural network with optimized capsule networks for chest X-rays COVID-19 diagnosis | vi |
| dc.type | Book | vi |
| Appears in Collections | OER - Công nghệ thông tin | |
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