Cargando…

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

Detalles Bibliográficos
Autores principales: Mondal, M. Rubaiyat Hossain, Bharati, Subrato, Podder, Prajoy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8553063/
https://www.ncbi.nlm.nih.gov/pubmed/34710175
http://dx.doi.org/10.1371/journal.pone.0259179
_version_ 1784591504899047424
author Mondal, M. Rubaiyat Hossain
Bharati, Subrato
Podder, Prajoy
author_facet Mondal, M. Rubaiyat Hossain
Bharati, Subrato
Podder, Prajoy
author_sort Mondal, M. Rubaiyat Hossain
collection PubMed
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 Online
Article
Text
id pubmed-8553063
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-85530632021-10-29 CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images Mondal, M. Rubaiyat Hossain Bharati, Subrato Podder, Prajoy PLoS One Research Article 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%. Public Library of Science 2021-10-28 /pmc/articles/PMC8553063/ /pubmed/34710175 http://dx.doi.org/10.1371/journal.pone.0259179 Text en © 2021 Mondal et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Mondal, M. Rubaiyat Hossain
Bharati, Subrato
Podder, Prajoy
CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images
title 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_short 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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8553063/
https://www.ncbi.nlm.nih.gov/pubmed/34710175
http://dx.doi.org/10.1371/journal.pone.0259179
work_keys_str_mv AT mondalmrubaiyathossain coirv2optimizedinceptionresnetv2forcovid19detectionfromchestctimages
AT bharatisubrato coirv2optimizedinceptionresnetv2forcovid19detectionfromchestctimages
AT podderprajoy coirv2optimizedinceptionresnetv2forcovid19detectionfromchestctimages