Magnetic resonance imaging (MRI) for brain tumor and seizures classification using recurrent neural network

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dc.contributor.authorAljaleeli, Marwah
dc.contributor.authorNahar, Ali
dc.contributor.authorMahmood, Mohammed
dc.contributor.authorBayat, Oğuz
dc.date.accessioned2021-05-15T12:49:38Z
dc.date.available2021-05-15T12:49:38Z
dc.date.issued2020
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Bilgisayar Mühendisliği Bölümüen_US
dc.description4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 -- 22 October 2020 through 24 October 2020 -- -- 165025
dc.description.abstractThe aim of this paper is to use develop and evaluate a Magnetic Resonance Imaging (MRI) for brain tumor and seizures classification using Recurrent Neural Network (RNN). Medical Science in Image Processing is an emerging field which has proposed a lot of advanced techniques in detection and analysis of a particular disease. Treatment of brain tumors in recent years is getting more and more challenging due to complex structure, shape and texture of the tumor. Therefore, by advancing in image processing, various methodologies have been proposed to identify the tumors in the brain. The advancement in this field created an urge in me to research more on the techniques and methodologies developed for tumor extraction. Hence, we propose a system to extract Tumor from the brain using MRI images. This technique involves different image processing methodologies such as noise removal, filtering, segmentation and morphological operations. Extraction of Brain tumor can be accomplished successfully by performing these operations on MATLAB software. Cross-correlation is computed between the target variable vector and the tumor region to determine how pixels' values of the tumor region are closely related using image processing and RNN technique achieving an accuracy of 99.71% © 2020 IEEE.en_US
dc.identifier.doi10.1109/ISMSIT50672.2020.9254648
dc.identifier.isbn9781728190907
dc.identifier.scopus2-s2.0-85097667443
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/ISMSIT50672.2020.9254648
dc.identifier.urihttps://hdl.handle.net/20.500.12939/1078
dc.indekslendigikaynakScopus
dc.institutionauthorBayat, Oğuz
dc.institutionauthorAljaleeli, Marwah
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 - Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBrainen_US
dc.subjectClassificationen_US
dc.subjectMRIen_US
dc.subjectRNNen_US
dc.subjectSegmentationen_US
dc.subjectSeizureen_US
dc.titleMagnetic resonance imaging (MRI) for brain tumor and seizures classification using recurrent neural network
dc.typeConference Object

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