{"title":"Performance Analysis of Walsh-Hadamard Transform-Based Gabor Filter Feature Extraction Method and GLCM Feature Extraction Method for Brain Tumor Detection","authors":"Rita B. Patil","doi":"10.22214/ijraset.2024.63543","DOIUrl":null,"url":null,"abstract":"Abstract: Brain tumor detection through MRI imaging is a crucial step in medical diagnostics. This paper presents a comparative performance analysis of two feature extraction methods: the Walsh-Hadamard Transform (WHT) based Gabor Filter method and the Gray-Level Co-occurrence Matrix (GLCM) method. We evaluate these techniques based on accuracy, computational efficiency, and robustness using a benchmark MRI dataset. Our results indicate the strengths and limitations of each method, providing insights for their application in automated brain tumor detection systems.","PeriodicalId":13718,"journal":{"name":"International Journal for Research in Applied Science and Engineering Technology","volume":"54 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal for Research in Applied Science and Engineering Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22214/ijraset.2024.63543","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract
Abstract: Brain tumor detection through MRI imaging is a crucial step in medical diagnostics. This paper presents a comparative performance analysis of two feature extraction methods: the Walsh-Hadamard Transform (WHT) based Gabor Filter method and the Gray-Level Co-occurrence Matrix (GLCM) method. We evaluate these techniques based on accuracy, computational efficiency, and robustness using a benchmark MRI dataset. Our results indicate the strengths and limitations of each method, providing insights for their application in automated brain tumor detection systems.
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