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A two-tier feature selection method using Coalition game and Nystrom sampling for screening COVID-19 from chest X-Ray images
The world is still under the threat of different strains of the coronavirus and the pandemic situation is far from over. The method, that is widely used for the detection of COVID-19 is Reverse Transcription Polymerase chain reaction (RT-PCR), which is a time-consuming method and is prone to manual...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8455233/ https://www.ncbi.nlm.nih.gov/pubmed/34567278 http://dx.doi.org/10.1007/s12652-021-03491-4 |
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author | Bhowal, Pratik Sen, Subhankar Sarkar, Ram |
author_facet | Bhowal, Pratik Sen, Subhankar Sarkar, Ram |
author_sort | Bhowal, Pratik |
collection | PubMed |
description | The world is still under the threat of different strains of the coronavirus and the pandemic situation is far from over. The method, that is widely used for the detection of COVID-19 is Reverse Transcription Polymerase chain reaction (RT-PCR), which is a time-consuming method and is prone to manual errors, and has poor precision. Although many nations across the globe have begun the mass immunization procedure, the COVID-19 vaccine will take a long time to reach everyone. The application of artificial intelligence (AI) and computer-aided diagnosis (CAD) has been used in the domain of medical imaging for a long period. It is quite evident that the use of CAD in the detection of COVID-19 is inevitable. The main objective of this paper is to use convolutional neural network (CNN) and a novel feature selection technique to analyze Chest X-Ray (CXR) images for the detection of COVID-19. We propose a novel two-tier feature selection method, which increases the accuracy of the overall classification model used for screening COVID-19 CXRs. Filter feature selection models are often more effective than wrapper methods as wrapper methods tend to be computationally more expensive and are not useful for large datasets dealing with a large number of features. However, most filter methods do not take into consideration how a group of features would work together, rather they just look at the features individually and decide on a score. We have used approximate Shapley value, a concept of Coalition game theory, to deal with this problem. Further, in the case of a large dataset, it is important to work with shorter embeddings of the features. We have used CUR decomposition and Nystrom sampling to further reduce the feature space. To check the efficacy of this two-tier feature selection method, we have applied it to the features extracted by three standard deep learning models, namely VGG16, Xception and InceptionV3, where the features have been extracted from the CXR images of COVID-19 datasets and we have found that the selection procedure works quite well for the features extracted by Xception and InceptionV3. The source code of this work is available at https://github.com/subhankar01/covidfs-aihc. |
format | Online Article Text |
id | pubmed-8455233 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-84552332021-09-22 A two-tier feature selection method using Coalition game and Nystrom sampling for screening COVID-19 from chest X-Ray images Bhowal, Pratik Sen, Subhankar Sarkar, Ram J Ambient Intell Humaniz Comput Original Research The world is still under the threat of different strains of the coronavirus and the pandemic situation is far from over. The method, that is widely used for the detection of COVID-19 is Reverse Transcription Polymerase chain reaction (RT-PCR), which is a time-consuming method and is prone to manual errors, and has poor precision. Although many nations across the globe have begun the mass immunization procedure, the COVID-19 vaccine will take a long time to reach everyone. The application of artificial intelligence (AI) and computer-aided diagnosis (CAD) has been used in the domain of medical imaging for a long period. It is quite evident that the use of CAD in the detection of COVID-19 is inevitable. The main objective of this paper is to use convolutional neural network (CNN) and a novel feature selection technique to analyze Chest X-Ray (CXR) images for the detection of COVID-19. We propose a novel two-tier feature selection method, which increases the accuracy of the overall classification model used for screening COVID-19 CXRs. Filter feature selection models are often more effective than wrapper methods as wrapper methods tend to be computationally more expensive and are not useful for large datasets dealing with a large number of features. However, most filter methods do not take into consideration how a group of features would work together, rather they just look at the features individually and decide on a score. We have used approximate Shapley value, a concept of Coalition game theory, to deal with this problem. Further, in the case of a large dataset, it is important to work with shorter embeddings of the features. We have used CUR decomposition and Nystrom sampling to further reduce the feature space. To check the efficacy of this two-tier feature selection method, we have applied it to the features extracted by three standard deep learning models, namely VGG16, Xception and InceptionV3, where the features have been extracted from the CXR images of COVID-19 datasets and we have found that the selection procedure works quite well for the features extracted by Xception and InceptionV3. The source code of this work is available at https://github.com/subhankar01/covidfs-aihc. Springer Berlin Heidelberg 2021-09-22 2023 /pmc/articles/PMC8455233/ /pubmed/34567278 http://dx.doi.org/10.1007/s12652-021-03491-4 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Research Bhowal, Pratik Sen, Subhankar Sarkar, Ram A two-tier feature selection method using Coalition game and Nystrom sampling for screening COVID-19 from chest X-Ray images |
title | A two-tier feature selection method using Coalition game and Nystrom sampling for screening COVID-19 from chest X-Ray images |
title_full | A two-tier feature selection method using Coalition game and Nystrom sampling for screening COVID-19 from chest X-Ray images |
title_fullStr | A two-tier feature selection method using Coalition game and Nystrom sampling for screening COVID-19 from chest X-Ray images |
title_full_unstemmed | A two-tier feature selection method using Coalition game and Nystrom sampling for screening COVID-19 from chest X-Ray images |
title_short | A two-tier feature selection method using Coalition game and Nystrom sampling for screening COVID-19 from chest X-Ray images |
title_sort | two-tier feature selection method using coalition game and nystrom sampling for screening covid-19 from chest x-ray images |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8455233/ https://www.ncbi.nlm.nih.gov/pubmed/34567278 http://dx.doi.org/10.1007/s12652-021-03491-4 |
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