Brain tumor classification using modified local binary patterns (LBP) feature extraction methods

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dc.authoridKaplan, Kaplan/0000-0001-8036-1145
dc.authoridKUNCAN, Melih/0000-0002-9749-0418
dc.contributor.authorKaplan, Kaplan
dc.contributor.authorKaya, Yilmaz
dc.contributor.authorKuncan, Melih
dc.contributor.authorErtunc, H. Metin
dc.date.accessioned2024-12-24T19:27:29Z
dc.date.available2024-12-24T19:27:29Z
dc.date.issued2020
dc.departmentSiirt Üniversitesi
dc.description.abstractAutomatic classification of brain tumor types is very important for accelerating the treatment process, planning and increasing the patient's survival rate. Today, MR images are used to determine the type of brain tumor. Manual diagnosis of brain tumor type depends on the experience and sensitivity of radiologists. Therefore, researchers have developed many brain tumor classification models to minimize the human factor. In this study, two different feature extraction (nLBP and alpha LBP) approaches were used to classify the most common brain tumor types; Glioma, Meningioma, and Pituitary brain tumors. nLBP is formed based on the relationship for each pixel around the neighbors. The nLBP method has a d parameter that specifies the distance between consecutive neighbors for comparison. Different patterns are obtained for different d parameter values. The alpha LBP operator calculates the value of each pixel based on an angle value. The angle values used for calculation are 0, 45, 90 and 135. To test the proposed methods, it was applied to images obtained from the brain tumor database collected from Nanfang Hospital, Guangzhou, China, and Tianjin Medical University General Hospital between the years of 2005 and 2010. The classification process was performed by using K-Nearest Neighbor (Knn) and Artificial Neural Networks (ANN), Random Forest (RF), A1DE, Linear Discriminant Analysis (LDA) classification methods, with the feature matrices obtained with nLBP, alpha LBP and classical LBP from the images in the data set. The highest success rate in brain tumor classification was 95.56% with the nLBPd = 1 feature extraction method and Knn model.
dc.identifier.doi10.1016/j.mehy.2020.109696
dc.identifier.issn0306-9877
dc.identifier.issn1532-2777
dc.identifier.pmid32234609
dc.identifier.scopus2-s2.0-85082449856
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.mehy.2020.109696
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6659
dc.identifier.volume139
dc.identifier.wosWOS:000531083000005
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherChurchill Livingstone
dc.relation.ispartofMedical Hypotheses
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectBrain tumor classification
dc.subjectLBP
dc.subjectNLBP and aLBP
dc.subjectMachine learning techniques
dc.titleBrain tumor classification using modified local binary patterns (LBP) feature extraction methods
dc.typeArticle

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