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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Cluster Computing-The Journal of Networks Software Tools and Applications

ISSN

1386-7857

e-ISSN

1573-7543

Svazek periodika

neuveden

Číslo periodika v rámci svazku

23. srpna 2022

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

nestrankovano

Kód UT WoS článku

000843429400005

EID výsledku v databázi Scopus

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