Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7781
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dc.contributor.authorZarandi, Mohammad Hossein Fazel-
dc.contributor.authorZarinbal, M.-
dc.contributor.authorZarinbal, A.-
dc.contributor.authorTürkşen, İsmail Burhan-
dc.contributor.authorİzadi, M.-
dc.date.accessioned2021-09-11T15:59:44Z-
dc.date.available2021-09-11T15:59:44Z-
dc.date.issued2010en_US
dc.identifier.citation2010 IEEE World Congress on Computational Intelligence -- JUL 18-23, 2010 -- Barcelona, SPAINen_US
dc.identifier.isbn978-1-4244-6920-8-
dc.identifier.issn1098-7584-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/7781-
dc.description.abstractFuzzy functions are used to identify the structure of system models and reasoning with them. Fuzzy functions can be determined by any function identification method such as Least Square Estimates (LSE), Maximum Likelihood Estimates (MLE) or Support Vector Machine Estimates (SVM). However, estimating fuzzy functions using LSE method is structurally a new and unique approach for determining fuzzy functions. By using this approach, there is no need to know or to develop an in-depth understanding of essential concepts for developing and using the membership functions and selecting the t-norms, co-norms and implication operators. Furthermore, there is no need to apply fuzzification and defuzzification methods. The goal of this paper is to improve the Type-2 fuzzy image processing expert system based on Type-2 fuzzy function to diagnose the Astrocytoma tumors (most important category of brain tumors) in T-1-weighted MR Images with contrast. This expert system has four steps, Pre-processing, Segmentation, Feature extraction and Approximate reasoning. The focus of this paper is to improve the last step, Approximate reasoning step, by using fuzzy function strategy instead of fuzzy rule-base approach. The results show that Type-2 fuzzy function approach requires less computation steps with less computational complexity and could provide better results.en_US
dc.description.sponsorshipIEEE, IEEE Computat Intelligence Soc, Int Neural Network Soc, Evolut Program Soc, IETen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2010 IEEE International Conference On Fuzzy Systems (Fuzz-IEEE 2010)en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectInterval-Valued Type-2 Fuzzy Logicen_US
dc.subjectFuzzy Functionen_US
dc.subjectImage Processingen_US
dc.subjectBrain Tumors Diagnosisen_US
dc.subjectT-1-weighted MRIen_US
dc.titleUsing Type-2 Fuzzy Function for Diagnosing Brain Tumors based on Image Processing Approachen_US
dc.typeConference Objecten_US
dc.relation.ispartofseriesIEEE International Conference on Fuzzy Systemsen_US
dc.departmentFaculties, Faculty of Engineering, Department of Industrial Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümütr_TR
dc.identifier.wosWOS:000287453600015en_US
dc.identifier.scopus2-s2.0-78549295911en_US
dc.institutionauthorTürkşen, İsmail Burhan-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.relation.conference2010 IEEE World Congress on Computational Intelligenceen_US
dc.identifier.scopusquality--
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeConference Object-
item.cerifentitytypePublications-
item.languageiso639-1en-
Appears in Collections:Endüstri Mühendisliği Bölümü / Department of Industrial Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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