A pre-trained convolutional neural network with optimized capsule networks for chest X-rays COVID-19 diagnosis

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10251924" target="_blank" >RIV/61989100:27240/22:10251924 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s10586-022-03703-2" target="_blank" >https://link.springer.com/article/10.1007/s10586-022-03703-2</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10586-022-03703-2" target="_blank" >10.1007/s10586-022-03703-2</a>

Result language
angličtina
Original language name
A pre-trained convolutional neural network with optimized capsule networks for chest X-rays COVID-19 diagnosis
Original language description
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. As the results may change by changing capsule network parameters (Capsule dimensionality and routing number), the Gaussian optimization method has been used to optimize these parameters. Four experiments have been done, (1) CapsNet with the unbalanced data sets, (2) CapsNet with balanced data sets based on class weight, (3) CapsNet with balanced data sets based on SMOTE, and (4) CapsNet hyperparameters optimization with balanced data sets based on SMOTE. The performance has improved and achieved an accuracy rate of 96.58% and an F1- score of 97.08%, a competitive optimized model compared to other related models.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical
Cluster Computing-The Journal of Networks Software Tools and Applications
ISSN
1386-7857
e-ISSN
1573-7543
Volume of the periodical
neuveden
Issue of the periodical within the volume
23. srpna 2022
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
nestrankovano
UT code for WoS article
000843429400005
EID of the result in the Scopus database
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