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Title: A pre-trained convolutional neural network with optimized capsule networks for chest X-rays COVID-19 diagnosis
Authors: Lobna M., AbouEl-Magd
Ashraf, Darwish
Vaclav Snasel, Snasel
Issue Date: 2023
Publisher: Springer
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.
Description: CC BY
URI: https://link.springer.com/article/10.1007/s10586-022-03703-2
https://dlib.phenikaa-uni.edu.vn/handle/PNK/7371
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