Choquet Integral and Coalition Game-based Ensemble of Deep Learning Models for COVID-19 Screening from Chest X-ray Images
DC Field | Value | Language |
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dc.contributor.author | Bhowal, P. | - |
dc.contributor.author | Sen, S. | - |
dc.contributor.author | Yoon, Jin Hee | - |
dc.contributor.author | Geem, Zong Woo | - |
dc.contributor.author | Sarkar, R. | - |
dc.date.accessioned | 2021-12-22T00:40:39Z | - |
dc.date.available | 2021-12-22T00:40:39Z | - |
dc.date.created | 2021-09-18 | - |
dc.date.issued | 2021-12 | - |
dc.identifier.issn | 2168-2194 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/82975 | - |
dc.description.abstract | Under the present circumstances, when we are still under the threat of different strains of coronavirus, and since the most widely used method for COVID-19 detection, RT-PCR is a tedious and time-consuming manual procedure with poor precision, the application of Artificial Intelligence (AI) and Computer-Aided Diagnosis (CAD) is inevitable. In this work, we have analyzed Chest X-ray (CXR) images for the detection of the coronavirus. The primary agenda of this proposed research study is to leverage the classification performance of the deep learning models using ensemble learning. Many papers have proposed different ensemble learning techniques in this field, some methods using aggregation functions like Weighted Arithmetic Mean (WAM) among others. However, none of these methods take into consideration the decisions that subsets of the classifiers take. In this paper, we have applied Choquet integral for ensemble and propose a novel method for the evaluation of fuzzy measures using Coalition Game Theory, Information Theory, and Lambda fuzzy approximation. Three different sets of Fuzzy Measures are calculated using three different weighting schemes along with information theory and coalition game theory. Using these three sets of fuzzy measures three Choquet Integrals are calculated and their decisions are finally combined.We have created a database by combining several image repositories developed recently. Impressive results on the newly developed dataset and the challenging COVIDx dataset support the efficacy and robustness of the proposed method. To the best of our knowledge, our experimental results outperform many recently proposed methods. Source code available at https://github.com/subhankar01/Covid-Chestxray-lambda-fuzzy Author | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.relation.isPartOf | IEEE Journal of Biomedical and Health Informatics | - |
dc.title | Choquet Integral and Coalition Game-based Ensemble of Deep Learning Models for COVID-19 Screening from Chest X-ray Images | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000728140900014 | - |
dc.identifier.doi | 10.1109/JBHI.2021.3111415 | - |
dc.identifier.bibliographicCitation | IEEE Journal of Biomedical and Health Informatics, v.25, no.12, pp.4328 - 4339 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85114729742 | - |
dc.citation.endPage | 4339 | - |
dc.citation.startPage | 4328 | - |
dc.citation.title | IEEE Journal of Biomedical and Health Informatics | - |
dc.citation.volume | 25 | - |
dc.citation.number | 12 | - |
dc.contributor.affiliatedAuthor | Geem, Zong Woo | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Biomedical imaging | - |
dc.subject.keywordAuthor | Biomedical measurement | - |
dc.subject.keywordAuthor | Chest X-Ray Images | - |
dc.subject.keywordAuthor | Choquet Integral | - |
dc.subject.keywordAuthor | Coalition Game | - |
dc.subject.keywordAuthor | COVID-19 | - |
dc.subject.keywordAuthor | COVID-19 | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Deep Learning | - |
dc.subject.keywordAuthor | Feature extraction | - |
dc.subject.keywordAuthor | Information Theory | - |
dc.subject.keywordAuthor | Lambda Fuzzy | - |
dc.subject.keywordAuthor | Solid modeling | - |
dc.subject.keywordAuthor | X-ray imaging | - |
dc.subject.keywordPlus | Computer aided diagnosis | - |
dc.subject.keywordPlus | Fuzzy systems | - |
dc.subject.keywordPlus | Game theory | - |
dc.subject.keywordPlus | Information theory | - |
dc.subject.keywordPlus | Integral equations | - |
dc.subject.keywordPlus | Learning systems | - |
dc.subject.keywordPlus | Aggregation functions | - |
dc.subject.keywordPlus | Chest X-ray image | - |
dc.subject.keywordPlus | Classification performance | - |
dc.subject.keywordPlus | Computer Aided Diagnosis(CAD) | - |
dc.subject.keywordPlus | Ensemble learning | - |
dc.subject.keywordPlus | Fuzzy approximation | - |
dc.subject.keywordPlus | Image repository | - |
dc.subject.keywordPlus | Weighting scheme | - |
dc.subject.keywordPlus | Deep learning | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalResearchArea | Medical Informatics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Medical Informatics | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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