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https://hdl.handle.net/20.500.11851/7781
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zarandi, Mohammad Hossein Fazel | - |
dc.contributor.author | Zarinbal, M. | - |
dc.contributor.author | Zarinbal, A. | - |
dc.contributor.author | Türkşen, İsmail Burhan | - |
dc.contributor.author | İzadi, M. | - |
dc.date.accessioned | 2021-09-11T15:59:44Z | - |
dc.date.available | 2021-09-11T15:59:44Z | - |
dc.date.issued | 2010 | en_US |
dc.identifier.citation | 2010 IEEE World Congress on Computational Intelligence -- JUL 18-23, 2010 -- Barcelona, SPAIN | en_US |
dc.identifier.isbn | 978-1-4244-6920-8 | - |
dc.identifier.issn | 1098-7584 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/7781 | - |
dc.description.abstract | Fuzzy 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.sponsorship | IEEE, IEEE Computat Intelligence Soc, Int Neural Network Soc, Evolut Program Soc, IET | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | 2010 IEEE International Conference On Fuzzy Systems (Fuzz-IEEE 2010) | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Interval-Valued Type-2 Fuzzy Logic | en_US |
dc.subject | Fuzzy Function | en_US |
dc.subject | Image Processing | en_US |
dc.subject | Brain Tumors Diagnosis | en_US |
dc.subject | T-1-weighted MRI | en_US |
dc.title | Using Type-2 Fuzzy Function for Diagnosing Brain Tumors Based on Image Processing Approach | en_US |
dc.type | Conference Object | en_US |
dc.relation.ispartofseries | IEEE International Conference on Fuzzy Systems | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Industrial Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü | tr_TR |
dc.identifier.wos | WOS:000287453600015 | en_US |
dc.identifier.scopus | 2-s2.0-78549295911 | en_US |
dc.institutionauthor | Türkşen, İsmail Burhan | - |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.relation.conference | 2010 IEEE World Congress on Computational Intelligence | en_US |
dc.identifier.scopusquality | - | - |
item.openairetype | Conference Object | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
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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