Developing deep transfer and machine learning models of chest X-ray for diagnosing COVID-19 cases using probabilistic single-valued neutrosophic hesitant fuzzy

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dc.authoridDeveci, Muhammet/0000-0002-3712-976X
dc.authoridQahtan, Sarah/0000-0002-5636-5901
dc.authoridA.Alsattar, Hassan/0000-0003-1182-936X
dc.authoridMartinez, Luis/0000-0003-4245-8813
dc.authorwosidDeveci, Muhammet/V-8347-2017
dc.authorwosidQahtan, Sarah/E-9160-2019
dc.authorwosidA.Alsattar, Hassan/S-1079-2017
dc.authorwosidMartinez, Luis/A-1746-2009
dc.contributor.authorAlsattar, Hassan A.
dc.contributor.authorQahtan, Sarah
dc.contributor.authorZaidan, Aws Alaa
dc.contributor.authorDeveci, Muhammet
dc.contributor.authorMartinez, Luis
dc.contributor.authorPamucar, Dragan
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2024-05-19T14:39:59Z
dc.date.available2024-05-19T14:39:59Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThis study presents a novel dynamic localisation-based decision (DLBD) with fuzzy weighting with zero inconsistency (FWZIC) under a probabilistic single-valued neutrosophic hesitant fuzzy set (PSVNHFS) environment to benchmark Hybrid Multi Deep Transfer and Machine Learning (HMDTML) models. The novel DLBD method is proposed to generate a dynamic localisation decision matrix based on the upper and lower boundaries and the length of the scale. The superiority of DLBD derives from its ability to manage dynamic changes with boundary value consequences. In addition, the utilization of PSVNHFS in conjunction with DLBD and FWZIC has proven to effectively address the challenges posed by vagueness, uncertainty and hesitancy in the benchmarking procedure. The proposed methodology consists of three primary three steps: i) the adaptation of 48 HMDTML models, including 4 deep transfer learning models and 12 machine learning models trained on a dataset of 936 chest Xray images obtained from both COVID-19 patients and individuals without the disease. Then, these models were evaluated based on seven evaluation criteria, and a decision matrix was proposed. ii) The development of a PSVNH-FWZIC to assign weights to the evaluation criteria. iii) The formulation of a PSVNH-DLBD for the purpose of benchmarking HMDTML models. Results of the PSVNH-FWZIC revealed that AUC and time were the most important evaluation criteria, while precision was the least important. Furthermore, the results from PSVNH-DLBD, reveal that Model M24 (Painters-Decision Tree) earned the highest rank when & lambda; = 2,3,4, 5and6, followed by Model M25 (SqueezeNet-AdaBoost) and Model M34 (DeepLoc-kNN), while Model M39 (DeepLocSVM) had the lowest rank (rank = 48) across all & lambda; values. The proposed method underwent sensitivity and comparison analyses to confirm its reliability and robustness.en_US
dc.identifier.doi10.1016/j.eswa.2023.121300
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85172890907en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.eswa.2023.121300
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4886
dc.identifier.volume236en_US
dc.identifier.wosWOS:001070936100001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectChest X-Ray Imagesen_US
dc.subjectCovid-19en_US
dc.subjectDeep Transfer Machine Learning Modelsen_US
dc.subjectFwzicen_US
dc.subjectMcdmen_US
dc.titleDeveloping deep transfer and machine learning models of chest X-ray for diagnosing COVID-19 cases using probabilistic single-valued neutrosophic hesitant fuzzyen_US
dc.typeArticleen_US

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