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CNN–RNN Network Integration for the Diagnosis of COVID-19 Using Chest X-ray and CT Images
The 2019 coronavirus disease (COVID-19) has rapidly spread across the globe. It is crucial to identify positive cases as rapidly as humanely possible to provide appropriate treatment for patients and prevent the pandemic from spreading further. Both chest X-ray and computed tomography (CT) images ar...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9919640/ https://www.ncbi.nlm.nih.gov/pubmed/36772394 http://dx.doi.org/10.3390/s23031356 |
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author | Kanjanasurat, Isoon Tenghongsakul, Kasi Purahong, Boonchana Lasakul, Attasit |
author_facet | Kanjanasurat, Isoon Tenghongsakul, Kasi Purahong, Boonchana Lasakul, Attasit |
author_sort | Kanjanasurat, Isoon |
collection | PubMed |
description | The 2019 coronavirus disease (COVID-19) has rapidly spread across the globe. It is crucial to identify positive cases as rapidly as humanely possible to provide appropriate treatment for patients and prevent the pandemic from spreading further. Both chest X-ray and computed tomography (CT) images are capable of accurately diagnosing COVID-19. To distinguish lung illnesses (i.e., COVID-19 and pneumonia) from normal cases using chest X-ray and CT images, we combined convolutional neural network (CNN) and recurrent neural network (RNN) models by replacing the fully connected layers of CNN with a version of RNN. In this framework, the attributes of CNNs were utilized to extract features and those of RNNs to calculate dependencies and classification base on extracted features. CNN models VGG19, ResNet152V2, and DenseNet121 were combined with long short-term memory (LSTM) and gated recurrent unit (GRU) RNN models, which are convenient to develop because these networks are all available as features on many platforms. The proposed method is evaluated using a large dataset totaling 16,210 X-ray and CT images (5252 COVID-19 images, 6154 pneumonia images, and 4804 normal images) were taken from several databases, which had various image sizes, brightness levels, and viewing angles. Their image quality was enhanced via normalization, gamma correction, and contrast-limited adaptive histogram equalization. The ResNet152V2 with GRU model achieved the best architecture with an accuracy of 93.37%, an F1 score of 93.54%, a precision of 93.73%, and a recall of 93.47%. From the experimental results, the proposed method is highly effective in distinguishing lung diseases. Furthermore, both CT and X-ray images can be used as input for classification, allowing for the rapid and easy detection of COVID-19. |
format | Online Article Text |
id | pubmed-9919640 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99196402023-02-12 CNN–RNN Network Integration for the Diagnosis of COVID-19 Using Chest X-ray and CT Images Kanjanasurat, Isoon Tenghongsakul, Kasi Purahong, Boonchana Lasakul, Attasit Sensors (Basel) Article The 2019 coronavirus disease (COVID-19) has rapidly spread across the globe. It is crucial to identify positive cases as rapidly as humanely possible to provide appropriate treatment for patients and prevent the pandemic from spreading further. Both chest X-ray and computed tomography (CT) images are capable of accurately diagnosing COVID-19. To distinguish lung illnesses (i.e., COVID-19 and pneumonia) from normal cases using chest X-ray and CT images, we combined convolutional neural network (CNN) and recurrent neural network (RNN) models by replacing the fully connected layers of CNN with a version of RNN. In this framework, the attributes of CNNs were utilized to extract features and those of RNNs to calculate dependencies and classification base on extracted features. CNN models VGG19, ResNet152V2, and DenseNet121 were combined with long short-term memory (LSTM) and gated recurrent unit (GRU) RNN models, which are convenient to develop because these networks are all available as features on many platforms. The proposed method is evaluated using a large dataset totaling 16,210 X-ray and CT images (5252 COVID-19 images, 6154 pneumonia images, and 4804 normal images) were taken from several databases, which had various image sizes, brightness levels, and viewing angles. Their image quality was enhanced via normalization, gamma correction, and contrast-limited adaptive histogram equalization. The ResNet152V2 with GRU model achieved the best architecture with an accuracy of 93.37%, an F1 score of 93.54%, a precision of 93.73%, and a recall of 93.47%. From the experimental results, the proposed method is highly effective in distinguishing lung diseases. Furthermore, both CT and X-ray images can be used as input for classification, allowing for the rapid and easy detection of COVID-19. MDPI 2023-01-25 /pmc/articles/PMC9919640/ /pubmed/36772394 http://dx.doi.org/10.3390/s23031356 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kanjanasurat, Isoon Tenghongsakul, Kasi Purahong, Boonchana Lasakul, Attasit CNN–RNN Network Integration for the Diagnosis of COVID-19 Using Chest X-ray and CT Images |
title | CNN–RNN Network Integration for the Diagnosis of COVID-19 Using Chest X-ray and CT Images |
title_full | CNN–RNN Network Integration for the Diagnosis of COVID-19 Using Chest X-ray and CT Images |
title_fullStr | CNN–RNN Network Integration for the Diagnosis of COVID-19 Using Chest X-ray and CT Images |
title_full_unstemmed | CNN–RNN Network Integration for the Diagnosis of COVID-19 Using Chest X-ray and CT Images |
title_short | CNN–RNN Network Integration for the Diagnosis of COVID-19 Using Chest X-ray and CT Images |
title_sort | cnn–rnn network integration for the diagnosis of covid-19 using chest x-ray and ct images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9919640/ https://www.ncbi.nlm.nih.gov/pubmed/36772394 http://dx.doi.org/10.3390/s23031356 |
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