B. Jabber, K. Rajesh, D. Haritha, C. Z. Basha, Syed Nazia Parveen
{"title":"An Intelligent System for Classification of Brain Tumours With GLCM and Back Propagation Neural Network","authors":"B. Jabber, K. Rajesh, D. Haritha, C. Z. Basha, Syed Nazia Parveen","doi":"10.1109/ICECA49313.2020.9297541","DOIUrl":null,"url":null,"abstract":"Currently, technology has shown a lot of advancement in the field of medicine. Modalities available for capturing the brain images are Magnetic Resonance Imaging (MRIs), Positron Emission Tomography (PET) scan, and Computed Tomography (CT) scan. Among these MR is the most significantly used tool for judgment related to the anatomy of the brain. It is very essential for the classification of tumors in early-stage which supports avoiding the deaths due to brain tumors. Computerized classification of the tumor using MRI is proposed where features are extracted using the Gray Level Co-occurrence Matrices (GLCM) and classification using the BPNN. An accuracy of 94% is achieved with the proposed methodology.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"233 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICECA49313.2020.9297541","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
Currently, technology has shown a lot of advancement in the field of medicine. Modalities available for capturing the brain images are Magnetic Resonance Imaging (MRIs), Positron Emission Tomography (PET) scan, and Computed Tomography (CT) scan. Among these MR is the most significantly used tool for judgment related to the anatomy of the brain. It is very essential for the classification of tumors in early-stage which supports avoiding the deaths due to brain tumors. Computerized classification of the tumor using MRI is proposed where features are extracted using the Gray Level Co-occurrence Matrices (GLCM) and classification using the BPNN. An accuracy of 94% is achieved with the proposed methodology.
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