Cargando…

Deep CNN models for predicting COVID-19 in CT and x-ray images

Purpose: Coronavirus disease 2019 (COVID-19) is a new infection that has spread worldwide and with no automatic model to reliably detect its presence from images. We aim to investigate the potential of deep transfer learning to predict COVID-19 infection using chest computed tomography (CT) and x-ra...

Descripción completa

Detalles Bibliográficos
Autores principales: Chaddad, Ahmad, Hassan, Lama, Desrosiers, Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society of Photo-Optical Instrumentation Engineers 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8071782/
https://www.ncbi.nlm.nih.gov/pubmed/33912622
http://dx.doi.org/10.1117/1.JMI.8.S1.014502
_version_ 1783683783277412352
author Chaddad, Ahmad
Hassan, Lama
Desrosiers, Christian
author_facet Chaddad, Ahmad
Hassan, Lama
Desrosiers, Christian
author_sort Chaddad, Ahmad
collection PubMed
description Purpose: Coronavirus disease 2019 (COVID-19) is a new infection that has spread worldwide and with no automatic model to reliably detect its presence from images. We aim to investigate the potential of deep transfer learning to predict COVID-19 infection using chest computed tomography (CT) and x-ray images. Approach: Regions of interest (ROI) corresponding to ground-glass opacities (GGO), consolidations, and pleural effusions were labeled in 100 axial lung CT images from 60 COVID-19-infected subjects. These segmented regions were then employed as an additional input to six deep convolutional neural network (CNN) architectures (AlexNet, DenseNet, GoogleNet, NASNet-Mobile, ResNet18, and DarkNet), pretrained on natural images, to differentiate between COVID-19 and normal CT images. We also explored the model’s ability to classify x-ray images as COVID-19, non-COVID-19 pneumonia, or normal. Performance on test images was measured with global accuracy and area under the receiver operating characteristic curve (AUC). Results: When using raw CT images as input to the tested models, the highest accuracy of 82% and AUC of 88.16% is achieved. Incorporating the three ROIs as an additional model inputs further boosts performance to an accuracy of 82.30% and an AUC of 90.10% (DarkNet). For x-ray images, we obtained an outstanding AUC of 97% for classifying COVID-19 versus normal versus other. Combing chest CT and x-ray images, DarkNet architecture achieves the highest accuracy of 99.09% and AUC of 99.89% in classifying COVID-19 from non-COVID-19. Our results confirm the ability of deep CNNs with transfer learning to predict COVID-19 in both chest CT and x-ray images. Conclusions: The proposed method could help radiologists increase the accuracy of their diagnosis and increase efficiency in COVID-19 management.
format Online
Article
Text
id pubmed-8071782
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Society of Photo-Optical Instrumentation Engineers
record_format MEDLINE/PubMed
spelling pubmed-80717822021-04-27 Deep CNN models for predicting COVID-19 in CT and x-ray images Chaddad, Ahmad Hassan, Lama Desrosiers, Christian J Med Imaging (Bellingham) Computer-Aided Diagnosis Purpose: Coronavirus disease 2019 (COVID-19) is a new infection that has spread worldwide and with no automatic model to reliably detect its presence from images. We aim to investigate the potential of deep transfer learning to predict COVID-19 infection using chest computed tomography (CT) and x-ray images. Approach: Regions of interest (ROI) corresponding to ground-glass opacities (GGO), consolidations, and pleural effusions were labeled in 100 axial lung CT images from 60 COVID-19-infected subjects. These segmented regions were then employed as an additional input to six deep convolutional neural network (CNN) architectures (AlexNet, DenseNet, GoogleNet, NASNet-Mobile, ResNet18, and DarkNet), pretrained on natural images, to differentiate between COVID-19 and normal CT images. We also explored the model’s ability to classify x-ray images as COVID-19, non-COVID-19 pneumonia, or normal. Performance on test images was measured with global accuracy and area under the receiver operating characteristic curve (AUC). Results: When using raw CT images as input to the tested models, the highest accuracy of 82% and AUC of 88.16% is achieved. Incorporating the three ROIs as an additional model inputs further boosts performance to an accuracy of 82.30% and an AUC of 90.10% (DarkNet). For x-ray images, we obtained an outstanding AUC of 97% for classifying COVID-19 versus normal versus other. Combing chest CT and x-ray images, DarkNet architecture achieves the highest accuracy of 99.09% and AUC of 99.89% in classifying COVID-19 from non-COVID-19. Our results confirm the ability of deep CNNs with transfer learning to predict COVID-19 in both chest CT and x-ray images. Conclusions: The proposed method could help radiologists increase the accuracy of their diagnosis and increase efficiency in COVID-19 management. Society of Photo-Optical Instrumentation Engineers 2021-04-21 2021-01 /pmc/articles/PMC8071782/ /pubmed/33912622 http://dx.doi.org/10.1117/1.JMI.8.S1.014502 Text en © 2021 The Authors https://creativecommons.org/licenses/by/4.0/Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
spellingShingle Computer-Aided Diagnosis
Chaddad, Ahmad
Hassan, Lama
Desrosiers, Christian
Deep CNN models for predicting COVID-19 in CT and x-ray images
title Deep CNN models for predicting COVID-19 in CT and x-ray images
title_full Deep CNN models for predicting COVID-19 in CT and x-ray images
title_fullStr Deep CNN models for predicting COVID-19 in CT and x-ray images
title_full_unstemmed Deep CNN models for predicting COVID-19 in CT and x-ray images
title_short Deep CNN models for predicting COVID-19 in CT and x-ray images
title_sort deep cnn models for predicting covid-19 in ct and x-ray images
topic Computer-Aided Diagnosis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8071782/
https://www.ncbi.nlm.nih.gov/pubmed/33912622
http://dx.doi.org/10.1117/1.JMI.8.S1.014502
work_keys_str_mv AT chaddadahmad deepcnnmodelsforpredictingcovid19inctandxrayimages
AT hassanlama deepcnnmodelsforpredictingcovid19inctandxrayimages
AT desrosierschristian deepcnnmodelsforpredictingcovid19inctandxrayimages