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Autore principale:	Hatice Catal Reis
Natura:	Artículo científico
Lingua:	en
Pubblicazione:	Universidade Estadual de Maringá 2021
Soggetti:	Ingeniería SVM AdaBoost Coronavirus InceptionV3 NASNetMobile
Accesso online:	https://www.redalyc.org/articulo.oa?id=303271763049 https://www.redalyc.org/journal/3032/303271763049/ https://www.redalyc.org/journal/3032/303271763049/html/ https://www.redalyc.org/journal/3032/303271763049/303271763049.epub https://www.redalyc.org/journal/3032/303271763049/movil https://doi.org/10.4025/actascitechnol.v43i1.55189
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author	Hatice Catal Reis
author_facet	Hatice Catal Reis
contents	Automatic Classification of COVID-19 using CT-Scan Images Hatice Catal Reis Ingeniería SVM AdaBoost Coronavirus InceptionV3 NASNetMobile Medicine and engineering sciences have been working in close contact for common purposes. Machine learning algorithms are used in the medical field for early diagnosis prediction. The major aim of this study is to evaluate machine learning algorithms and deep learning algorithms using computed tomography scan (CT-scan) images for automated detection of the coronavirus disease 2019 (COVID-19) patients. We obtained seven hundred and fifty-seven (757) CT-scan images from a public platform. We applied four automated traditional classification methods to predict COVID-19 using deep learning and machine learning. These algorithms are SVM, AdaBoost, NASNetMobile, and InceptionV3. Comparative analyses are presented among the four models by considering metric performance factors to find the best model. The results show that the InceptionV3 model achieves better performance in terms of accuracy, precision, recall, Cohen’s kappa, F.- score, root mean squared error (RMSE), and receiver operating characteristic- area under the curve (ROC-AUC), in comparison with the other Covid-19 classifiers. Accordingly, the InceptionV3 approach is recommended for the automatic diagnosis of Covid-19 and assessments. This research can present a second point of view to medical experts and it can save time for researchers as the performance of standard machine learning methods in detecting COVID-19 is evaluated. 2021 artículo científico 1806-2563 https://www.redalyc.org/articulo.oa?id=303271763049 https://www.redalyc.org/journal/3032/303271763049/ https://www.redalyc.org/journal/3032/303271763049/html/ https://www.redalyc.org/journal/3032/303271763049/303271763049.epub https://www.redalyc.org/journal/3032/303271763049/movil https://doi.org/10.4025/actascitechnol.v43i1.55189 en http://www.redalyc.org/revista.oa?id=3032 Acta Scientiarum. Technology application/pdf Universidade Estadual de Maringá Acta Scientiarum. Technology (Brasil) Vol.43
format	Artículo científico
id	redalyc_303271763049
language	en
publishDate	2021
publisher	Universidade Estadual de Maringá
spellingShingle	Automatic Classification of COVID-19 using CT-Scan Images Hatice Catal Reis Ingeniería SVM AdaBoost Coronavirus InceptionV3 NASNetMobile Automatic Classification of COVID-19 using CT-Scan Images Hatice Catal Reis Ingeniería SVM AdaBoost Coronavirus InceptionV3 NASNetMobile Medicine and engineering sciences have been working in close contact for common purposes. Machine learning algorithms are used in the medical field for early diagnosis prediction. The major aim of this study is to evaluate machine learning algorithms and deep learning algorithms using computed tomography scan (CT-scan) images for automated detection of the coronavirus disease 2019 (COVID-19) patients. We obtained seven hundred and fifty-seven (757) CT-scan images from a public platform. We applied four automated traditional classification methods to predict COVID-19 using deep learning and machine learning. These algorithms are SVM, AdaBoost, NASNetMobile, and InceptionV3. Comparative analyses are presented among the four models by considering metric performance factors to find the best model. The results show that the InceptionV3 model achieves better performance in terms of accuracy, precision, recall, Cohen’s kappa, F.- score, root mean squared error (RMSE), and receiver operating characteristic- area under the curve (ROC-AUC), in comparison with the other Covid-19 classifiers. Accordingly, the InceptionV3 approach is recommended for the automatic diagnosis of Covid-19 and assessments. This research can present a second point of view to medical experts and it can save time for researchers as the performance of standard machine learning methods in detecting COVID-19 is evaluated. 2021 artículo científico 1806-2563 https://www.redalyc.org/articulo.oa?id=303271763049 https://www.redalyc.org/journal/3032/303271763049/ https://www.redalyc.org/journal/3032/303271763049/html/ https://www.redalyc.org/journal/3032/303271763049/303271763049.epub https://www.redalyc.org/journal/3032/303271763049/movil https://doi.org/10.4025/actascitechnol.v43i1.55189 en http://www.redalyc.org/revista.oa?id=3032 Acta Scientiarum. Technology application/pdf Universidade Estadual de Maringá Acta Scientiarum. Technology (Brasil) Vol.43
title	Automatic Classification of COVID-19 using CT-Scan Images
topic	Ingeniería SVM AdaBoost Coronavirus InceptionV3 NASNetMobile
url	https://www.redalyc.org/articulo.oa?id=303271763049 https://www.redalyc.org/journal/3032/303271763049/ https://www.redalyc.org/journal/3032/303271763049/html/ https://www.redalyc.org/journal/3032/303271763049/303271763049.epub https://www.redalyc.org/journal/3032/303271763049/movil https://doi.org/10.4025/actascitechnol.v43i1.55189

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// Anecdotal testing Today and Yesterday seems to indicate destroy() // is called by the garbage collector and everything is good. // Something to keep in mind though. return 0; } /** * Write function that is called when session data is to be saved. * * @param string $sessId The current session ID * @param string $data The session data to write * * @return bool */ public function write($sessId, $data): bool { if ($this->writesDisabled) { return true; } return $this->saveSession($sessId, $data); } /** * A function that is called internally when session data is to be saved. * * @param string $sessId The current session ID * @param string $data The session data to write * * @return bool */ abstract protected function saveSession($sessId, $data): bool; }

* * @return void */ public function writeClose() { // The assumption is that we're using PHP's ext/session. // session_write_close() will actually overwrite $_SESSION with an // empty array on completion -- which leads to a mismatch between what // is in the storage object and $_SESSION. To get around this, we // temporarily reset $_SESSION to an array, and then re-link it to // the storage object. // // Additionally, while you _can_ write to $_SESSION following a // session_write_close() operation, no changes made to it will be // flushed to the session handler. As such, we now mark the storage // object isImmutable. $storage = $this->getStorage(); if (! $storage->isImmutable()) { $_SESSION = $storage->toArray(true); session_write_close(); $storage->fromArray($_SESSION); $storage->markImmutable(); } } /** * Attempt to set the session name * * If the session has already been started, or if the name provided fails * validation, an exception will be raised. * * @param string $name * @return SessionManager * @throws Exception\InvalidArgumentException */ public function setName($name) { if ($this->sessionExists()) { throw new Exception\InvalidArgumentException( 'Cannot set session name after a session has already started'

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