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Fusion of multi-scale bag of deep visual words features of chest X-ray images to detect COVID-19 infection
Chest X-ray (CXR) images have been one of the important diagnosis tools used in the COVID-19 disease diagnosis. Deep learning (DL)-based methods have been used heavily to analyze these images. Compared to other DL-based methods, the bag of deep visual words-based method (BoDVW) proposed recently is...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8668931/ https://www.ncbi.nlm.nih.gov/pubmed/34903792 http://dx.doi.org/10.1038/s41598-021-03287-8 |
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author | Sitaula, Chiranjibi Shahi, Tej Bahadur Aryal, Sunil Marzbanrad, Faezeh |
author_facet | Sitaula, Chiranjibi Shahi, Tej Bahadur Aryal, Sunil Marzbanrad, Faezeh |
author_sort | Sitaula, Chiranjibi |
collection | PubMed |
description | Chest X-ray (CXR) images have been one of the important diagnosis tools used in the COVID-19 disease diagnosis. Deep learning (DL)-based methods have been used heavily to analyze these images. Compared to other DL-based methods, the bag of deep visual words-based method (BoDVW) proposed recently is shown to be a prominent representation of CXR images for their better discriminability. However, single-scale BoDVW features are insufficient to capture the detailed semantic information of the infected regions in the lungs as the resolution of such images varies in real application. In this paper, we propose a new multi-scale bag of deep visual words (MBoDVW) features, which exploits three different scales of the 4th pooling layer’s output feature map achieved from VGG-16 model. For MBoDVW-based features, we perform the Convolution with Max pooling operation over the 4th pooling layer using three different kernels: [Formula: see text] , [Formula: see text] , and [Formula: see text] . We evaluate our proposed features with the Support Vector Machine (SVM) classification algorithm on four CXR public datasets (CD1, CD2, CD3, and CD4) with over 5000 CXR images. Experimental results show that our method produces stable and prominent classification accuracy (84.37%, 88.88%, 90.29%, and 83.65% on CD1, CD2, CD3, and CD4, respectively). |
format | Online Article Text |
id | pubmed-8668931 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-86689312021-12-15 Fusion of multi-scale bag of deep visual words features of chest X-ray images to detect COVID-19 infection Sitaula, Chiranjibi Shahi, Tej Bahadur Aryal, Sunil Marzbanrad, Faezeh Sci Rep Article Chest X-ray (CXR) images have been one of the important diagnosis tools used in the COVID-19 disease diagnosis. Deep learning (DL)-based methods have been used heavily to analyze these images. Compared to other DL-based methods, the bag of deep visual words-based method (BoDVW) proposed recently is shown to be a prominent representation of CXR images for their better discriminability. However, single-scale BoDVW features are insufficient to capture the detailed semantic information of the infected regions in the lungs as the resolution of such images varies in real application. In this paper, we propose a new multi-scale bag of deep visual words (MBoDVW) features, which exploits three different scales of the 4th pooling layer’s output feature map achieved from VGG-16 model. For MBoDVW-based features, we perform the Convolution with Max pooling operation over the 4th pooling layer using three different kernels: [Formula: see text] , [Formula: see text] , and [Formula: see text] . We evaluate our proposed features with the Support Vector Machine (SVM) classification algorithm on four CXR public datasets (CD1, CD2, CD3, and CD4) with over 5000 CXR images. Experimental results show that our method produces stable and prominent classification accuracy (84.37%, 88.88%, 90.29%, and 83.65% on CD1, CD2, CD3, and CD4, respectively). Nature Publishing Group UK 2021-12-13 /pmc/articles/PMC8668931/ /pubmed/34903792 http://dx.doi.org/10.1038/s41598-021-03287-8 Text en © The Author(s) 2022, corrected publication 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Sitaula, Chiranjibi Shahi, Tej Bahadur Aryal, Sunil Marzbanrad, Faezeh Fusion of multi-scale bag of deep visual words features of chest X-ray images to detect COVID-19 infection |
title | Fusion of multi-scale bag of deep visual words features of chest X-ray images to detect COVID-19 infection |
title_full | Fusion of multi-scale bag of deep visual words features of chest X-ray images to detect COVID-19 infection |
title_fullStr | Fusion of multi-scale bag of deep visual words features of chest X-ray images to detect COVID-19 infection |
title_full_unstemmed | Fusion of multi-scale bag of deep visual words features of chest X-ray images to detect COVID-19 infection |
title_short | Fusion of multi-scale bag of deep visual words features of chest X-ray images to detect COVID-19 infection |
title_sort | fusion of multi-scale bag of deep visual words features of chest x-ray images to detect covid-19 infection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8668931/ https://www.ncbi.nlm.nih.gov/pubmed/34903792 http://dx.doi.org/10.1038/s41598-021-03287-8 |
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