CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images.

This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	M Rubaiyat Hossain Mondal, Subrato Bharati, Prajoy Podder
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/369f1099b3ed41eab223728556259c8e
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:369f1099b3ed41eab223728556259c8e
record_format	dspace
spelling	oai:doaj.org-article:369f1099b3ed41eab223728556259c8e2021-12-02T20:13:23ZCO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images.1932-620310.1371/journal.pone.0259179https://doaj.org/article/369f1099b3ed41eab223728556259c8e2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0259179https://doaj.org/toc/1932-6203This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. The proposed CO-IRv2 is applied to a new dataset of 2481 computed tomography (CT) images formed by collecting two independent datasets. Data resizing and normalization are performed, and the evaluation is run up to 25 epochs. Various performance metrics, including precision, recall, accuracy, F1-score, area under the receiver operating characteristics (AUC) curve are used as performance metrics. The effectiveness of three optimizers known as Adam, Nadam and RMSProp are evaluated in classifying suspected COVID-19 patients and normal people. Results show that for CO-IRv2 and for CT images, the obtained accuracies of Adam, Nadam and RMSProp optimizers are 94.97%, 96.18% and 96.18%, respectively. Furthermore, it is shown here that for the case of CT images, CO-IRv2 with Nadam optimizer has better performance than existing DL algorithms in the diagnosis of COVID-19 patients. Finally, CO-IRv2 is applied to an X-ray dataset of 1662 images resulting in a classification accuracy of 99.40%.M Rubaiyat Hossain MondalSubrato BharatiPrajoy PodderPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0259179 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q M Rubaiyat Hossain Mondal Subrato Bharati Prajoy Podder CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images.
description	This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. The proposed CO-IRv2 is applied to a new dataset of 2481 computed tomography (CT) images formed by collecting two independent datasets. Data resizing and normalization are performed, and the evaluation is run up to 25 epochs. Various performance metrics, including precision, recall, accuracy, F1-score, area under the receiver operating characteristics (AUC) curve are used as performance metrics. The effectiveness of three optimizers known as Adam, Nadam and RMSProp are evaluated in classifying suspected COVID-19 patients and normal people. Results show that for CO-IRv2 and for CT images, the obtained accuracies of Adam, Nadam and RMSProp optimizers are 94.97%, 96.18% and 96.18%, respectively. Furthermore, it is shown here that for the case of CT images, CO-IRv2 with Nadam optimizer has better performance than existing DL algorithms in the diagnosis of COVID-19 patients. Finally, CO-IRv2 is applied to an X-ray dataset of 1662 images resulting in a classification accuracy of 99.40%.
format	article
author	M Rubaiyat Hossain Mondal Subrato Bharati Prajoy Podder
author_facet	M Rubaiyat Hossain Mondal Subrato Bharati Prajoy Podder
author_sort	M Rubaiyat Hossain Mondal
title	CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images.
title_short	CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images.
title_full	CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images.
title_fullStr	CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images.
title_full_unstemmed	CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images.
title_sort	co-irv2: optimized inceptionresnetv2 for covid-19 detection from chest ct images.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/369f1099b3ed41eab223728556259c8e
work_keys_str_mv	AT mrubaiyathossainmondal coirv2optimizedinceptionresnetv2forcovid19detectionfromchestctimages AT subratobharati coirv2optimizedinceptionresnetv2forcovid19detectionfromchestctimages AT prajoypodder coirv2optimizedinceptionresnetv2forcovid19detectionfromchestctimages
_version_	1718374757919883264

CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images.

Ejemplares similares