Brain Pathology Classification of MR Images Using Machine Learning Techniques

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dc.authoridSyafrudin, Muhammad/0000-0002-5640-4413
dc.authoridHameed, Alaa Ali/0000-0002-8514-9255
dc.authoridAlfian, Ganjar/0000-0002-3273-1452
dc.authoridFitriyani, Norma Latif/0000-0002-1133-3965
dc.authoridRAMAHA, NEHAD T.A/0000-0003-2600-4125
dc.authorwosidSyafrudin, Muhammad/P-9657-2017
dc.authorwosidHameed, Alaa Ali/ABI-8417-2020
dc.authorwosidAlfian, Ganjar/P-5217-2018
dc.authorwosidFitriyani, Norma Latif/S-4105-2018
dc.authorwosidRAMAHA, NEHAD T.A/JWA-5356-2024
dc.contributor.authorRamaha, Nehad T. A.
dc.contributor.authorMahmood, Ruaa M.
dc.contributor.authorHameed, Alaa Ali
dc.contributor.authorFitriyani, Norma Latif
dc.contributor.authorAlfian, Ganjar
dc.contributor.authorSyafrudin, Muhammad
dc.date.accessioned2024-05-19T14:43:07Z
dc.date.available2024-05-19T14:43:07Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractA brain tumor is essentially a collection of aberrant tissues, so it is crucial to classify tumors of the brain using MRI before beginning therapy. Tumor segmentation and classification from brain MRI scans using machine learning techniques are widely recognized as challenging and important tasks. The potential applications of machine learning in diagnostics, preoperative planning, and postoperative evaluations are substantial. Accurate determination of the tumor's location on a brain MRI is of paramount importance. The advancement of precise machine learning classifiers and other technologies will enable doctors to detect malignancies without requiring invasive procedures on patients. Pre-processing, skull stripping, and tumor segmentation are the steps involved in detecting a brain tumor and measurement (size and form). After a certain period, CNN models get overfitted because of the large number of training images used to train them. That is why this study uses deep CNN to transfer learning. CNN-based Relu architecture and SVM with fused retrieved features via HOG and LPB are used to classify brain MRI tumors (glioma or meningioma). The method's efficacy is measured in terms of precision, recall, F-measure, and accuracy. This study showed that the accuracy of the SVM with combined LBP with HOG is 97%, and the deep CNN is 98%.en_US
dc.identifier.doi10.3390/computers12080167
dc.identifier.issn2073-431X
dc.identifier.issue8en_US
dc.identifier.scopus2-s2.0-85169064097en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org10.3390/computers12080167
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5323
dc.identifier.volume12en_US
dc.identifier.wosWOS:001057681800001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofComputersen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectMachine Learningen_US
dc.subjectTumor Segmentationen_US
dc.subjectClassificationen_US
dc.subjectFeature Extractionen_US
dc.subjectMri Imageen_US
dc.titleBrain Pathology Classification of MR Images Using Machine Learning Techniquesen_US
dc.typeArticleen_US

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