Classification of brain lesions using a machine learning approach with cross-sectional ADC value dynamics

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dc.contributor.authorSolár, Petercs
dc.contributor.authorValeková, Hanacs
dc.contributor.authorMarcoň, Petrcs
dc.contributor.authorMikulka, Jancs
dc.contributor.authorBarák, Martincs
dc.contributor.authorHendrych, Michalcs
dc.contributor.authorStránský, Matyášcs
dc.contributor.authorNovotná, Kateřinacs
dc.contributor.authorKostial, Martincs
dc.contributor.authorHolíková, Kláracs
dc.contributor.authorBrychta, Jindřichcs
dc.contributor.authorJančálek, Radimcs
dc.coverage.issue1cs
dc.coverage.volume13cs
dc.date.accessioned2024-02-16T10:42:51Z
dc.date.available2024-02-16T10:42:51Z
dc.date.issued2023-07-15cs
dc.description.abstractDiffusion-weighted imaging (DWI) and its numerical expression via apparent diffusion coefficient (ADC) values are commonly utilized in non-invasive assessment of various brain pathologies. Although numerous studies have confirmed that ADC values could be pathognomic for various ring-enhancing lesions (RELs), their true potential is yet to be exploited in full. The article was designed to introduce an image analysis method allowing REL recognition independently of either absolute ADC values or specifically defined regions of interest within the evaluated image. For this purpose, the line of interest (LOI) was marked on each ADC map to cross all of the RELs’ compartments. Using a machine learning approach, we analyzed the LOI between two representatives of the RELs, namely, brain abscess and glioblastoma (GBM). The diagnostic ability of the selected parameters as predictors for the machine learning algorithms was assessed using two models, the k-NN model and the SVM model with a Gaussian kernel. With the k-NN machine learning method, 80% of the abscesses and 100% of the GBM were classified correctly at high accuracy. Similar results were obtained via the SVM method. The proposed assessment of the LOI offers a new approach for evaluating ADC maps obtained from different RELs and contributing to the standardization of the ADC map assessment.en
dc.formattextcs
dc.format.extent11cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationScientific Reports. 2023, vol. 13, issue 1, 11 p.en
dc.identifier.doi10.1038/s41598-023-38542-7cs
dc.identifier.issn2045-2322cs
dc.identifier.orcid0000-0001-7349-8426cs
dc.identifier.orcid0000-0003-3270-1795cs
dc.identifier.other184472cs
dc.identifier.researcheridD-2123-2012cs
dc.identifier.researcheridK-1324-2012cs
dc.identifier.scopus37063396000cs
dc.identifier.scopus11140405800cs
dc.identifier.scopus57192558700cs
dc.identifier.urihttps://hdl.handle.net/11012/244998
dc.language.isoencs
dc.publisherSpringer Naturecs
dc.relation.ispartofScientific Reportscs
dc.relation.urihttps://doi.org/10.1038/s41598-023-38542-7cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2045-2322/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectDWIen
dc.subjectADCen
dc.subjectbrain lesionsen
dc.subjectsegmentationen
dc.subjectclassificationen
dc.subjectartifical intelligenceen
dc.titleClassification of brain lesions using a machine learning approach with cross-sectional ADC value dynamicsen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-184472en
sync.item.dbtypeVAVen
sync.item.insts2024.02.16 11:42:51en
sync.item.modts2024.02.16 11:12:54en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav teoretické a experimentální elektrotechnikycs
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