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IRCM‐Caps: An X‐ray image detection method for COVID‐19

OBJECTIVE: COVID‐19 is ravaging the world, but traditional reverse transcription‐polymerase reaction (RT‐PCR) tests are time‐consuming and have a high false‐negative rate and lack of medical equipment. Therefore, lung imaging screening methods are proposed to diagnose COVID‐19 due to its fast test s...

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Detalles Bibliográficos
Autores principales: Qiu, Shuo, Ma, Jinlin, Ma, Ziping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10214581/
https://www.ncbi.nlm.nih.gov/pubmed/36922395
http://dx.doi.org/10.1111/crj.13599
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author Qiu, Shuo
Ma, Jinlin
Ma, Ziping
author_facet Qiu, Shuo
Ma, Jinlin
Ma, Ziping
author_sort Qiu, Shuo
collection PubMed
description OBJECTIVE: COVID‐19 is ravaging the world, but traditional reverse transcription‐polymerase reaction (RT‐PCR) tests are time‐consuming and have a high false‐negative rate and lack of medical equipment. Therefore, lung imaging screening methods are proposed to diagnose COVID‐19 due to its fast test speed. Currently, the commonly used convolutional neural network (CNN) model requires a large number of datasets, and the accuracy of the basic capsule network for multiple classification is limital. For this reason, this paper proposes a novel model based on CNN and CapsNet. METHODS: The proposed model integrates CNN and CapsNet. And attention mechanism module and multi‐branch lightweight module are applied to enhance performance. Use the contrast adaptive histogram equalization (CLAHE) algorithm to preprocess the image to enhance image contrast. The preprocessed images are input into the network for training, and ReLU was used as the activation function to adjust the parameters to achieve the optimal. RESULT: The test dataset includes 1200 X‐ray images (400 COVID‐19, 400 viral pneumonia, and 400 normal), and we replace CNN of VGG16, InceptionV3, Xception, Inception‐Resnet‐v2, ResNet50, DenseNet121, and MoblieNetV2 and integrate with CapsNet. Compared with CapsNet, this network improves 6.96%, 7.83%, 9.37%, 10.47%, and 10.38% in accuracy, area under the curve (AUC), recall, and F1 scores, respectively. In the binary classification experiment, compared with CapsNet, the accuracy, AUC, accuracy, recall rate, and F1 score were increased by 5.33%, 5.34%, 2.88%, 8.00%, and 5.56%, respectively. CONCLUSION: The proposed embedded the advantages of traditional convolutional neural network and capsule network and has a good classification effect on small COVID‐19 X‐ray image dataset.
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spelling pubmed-102145812023-05-27 IRCM‐Caps: An X‐ray image detection method for COVID‐19 Qiu, Shuo Ma, Jinlin Ma, Ziping Clin Respir J Original Articles OBJECTIVE: COVID‐19 is ravaging the world, but traditional reverse transcription‐polymerase reaction (RT‐PCR) tests are time‐consuming and have a high false‐negative rate and lack of medical equipment. Therefore, lung imaging screening methods are proposed to diagnose COVID‐19 due to its fast test speed. Currently, the commonly used convolutional neural network (CNN) model requires a large number of datasets, and the accuracy of the basic capsule network for multiple classification is limital. For this reason, this paper proposes a novel model based on CNN and CapsNet. METHODS: The proposed model integrates CNN and CapsNet. And attention mechanism module and multi‐branch lightweight module are applied to enhance performance. Use the contrast adaptive histogram equalization (CLAHE) algorithm to preprocess the image to enhance image contrast. The preprocessed images are input into the network for training, and ReLU was used as the activation function to adjust the parameters to achieve the optimal. RESULT: The test dataset includes 1200 X‐ray images (400 COVID‐19, 400 viral pneumonia, and 400 normal), and we replace CNN of VGG16, InceptionV3, Xception, Inception‐Resnet‐v2, ResNet50, DenseNet121, and MoblieNetV2 and integrate with CapsNet. Compared with CapsNet, this network improves 6.96%, 7.83%, 9.37%, 10.47%, and 10.38% in accuracy, area under the curve (AUC), recall, and F1 scores, respectively. In the binary classification experiment, compared with CapsNet, the accuracy, AUC, accuracy, recall rate, and F1 score were increased by 5.33%, 5.34%, 2.88%, 8.00%, and 5.56%, respectively. CONCLUSION: The proposed embedded the advantages of traditional convolutional neural network and capsule network and has a good classification effect on small COVID‐19 X‐ray image dataset. John Wiley and Sons Inc. 2023-03-15 /pmc/articles/PMC10214581/ /pubmed/36922395 http://dx.doi.org/10.1111/crj.13599 Text en © 2023 The Authors. The Clinical Respiratory Journal published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
Qiu, Shuo
Ma, Jinlin
Ma, Ziping
IRCM‐Caps: An X‐ray image detection method for COVID‐19
title IRCM‐Caps: An X‐ray image detection method for COVID‐19
title_full IRCM‐Caps: An X‐ray image detection method for COVID‐19
title_fullStr IRCM‐Caps: An X‐ray image detection method for COVID‐19
title_full_unstemmed IRCM‐Caps: An X‐ray image detection method for COVID‐19
title_short IRCM‐Caps: An X‐ray image detection method for COVID‐19
title_sort ircm‐caps: an x‐ray image detection method for covid‐19
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10214581/
https://www.ncbi.nlm.nih.gov/pubmed/36922395
http://dx.doi.org/10.1111/crj.13599
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