Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/10345
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dc.contributor.authorÖztürk, Ahmet Cankat-
dc.contributor.authorHaznedar, Hilal-
dc.contributor.authorHaznedar, Bulent-
dc.contributor.authorIlgan, Seyfettin-
dc.contributor.authorEroğul, Osman-
dc.contributor.authorKalınlı, Adem-
dc.date.accessioned2023-04-16T10:01:15Z-
dc.date.available2023-04-16T10:01:15Z-
dc.date.issued2023-
dc.identifier.issn2075-4418-
dc.identifier.urihttps://doi.org/10.3390/diagnostics13040740-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/10345-
dc.description.abstractThe thyroid nodule risk stratification guidelines used in the literature are based on certain well-known sonographic features of nodules and are still subjective since the application of these characteristics strictly depends on the reading physician. These guidelines classify nodules according to the sub-features of limited sonographic signs. This study aims to overcome these limitations by examining the relationships of a wide range of ultrasound (US) signs in the differential diagnosis of nodules by using artificial intelligence methods. An innovative method based on training Adaptive-Network Based Fuzzy Inference Systems (ANFIS) by using Genetic Algorithm (GA) is used to differentiate malignant from benign thyroid nodules. The comparison of the results from the proposed method to the results from the commonly used derivative-based algorithms and Deep Neural Network (DNN) methods yielded that the proposed method is more successful in differentiating malignant from benign thyroid nodules. Furthermore, a novel computer aided diagnosis (CAD) based risk stratification system for the thyroid nodule's US classification that is not present in the literature is proposed.en_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.ispartofDiagnosticsen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectthyroiden_US
dc.subjectthyroid noduleen_US
dc.subjectclassificationen_US
dc.subjectANFISen_US
dc.subjectdeep neural networken_US
dc.subjectguidelineen_US
dc.subjectAssociation Guidelinesen_US
dc.subjectWhite Paperen_US
dc.subjectManagementen_US
dc.subjectDiagnosisen_US
dc.titleDifferentiation of Benign and Malignant Thyroid Nodules With Anfis by Using Genetic Algorithm and Proposing a Novel Cad-Based Risk Stratification System of Thyroid Nodulesen_US
dc.typeArticleen_US
dc.departmentTOBB ETÜen_US
dc.identifier.volume13en_US
dc.identifier.issue4en_US
dc.identifier.wosWOS:000939170500001en_US
dc.identifier.scopus2-s2.0-85148941737en_US
dc.institutionauthor-
dc.identifier.pmid36832228en_US
dc.identifier.doi10.3390/diagnostics13040740-
dc.authorscopusid58118595300-
dc.authorscopusid58118523500-
dc.authorscopusid57201991740-
dc.authorscopusid6701613821-
dc.authorscopusid56247443100-
dc.authorscopusid56619948200-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.2. Department of Biomedical Engineering-
Appears in Collections:PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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