Brain tumor classification using MRI images and convolutional neural networks

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dc.authorid0000-0001-7853-1731
dc.contributor.authorHafeez, Muhammad Adeel
dc.contributor.authorKayasandık, Cihan Bilge
dc.contributor.authorDoğan, Merve Yüşra
dc.date.accessioned2022-10-11T11:22:33Z
dc.date.available2022-10-11T11:22:33Z
dc.date.issued2022
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Biyomedikal Mühendisliği Bölümü
dc.description.abstractThe brain tumor has become one of the most prominent types of cancers affecting a huge population across the globe every year. It has the lowest life expectancy rate and the risk of death is highly associated with the type, shape, and location of the tumor. The Magnetic Resonance Imaging (MRI) is a strong tool to detect different brain lesions and is extensively used by radiologists and physicians. For the early and accurate diagnosis of the brain tumor using MRI, it is important to consider automated computer-assisted diagnosis which is more flexible and efficient. In this paper, we have proposed a Convolutional Neural Network (CNN) based approach for the classification of three types of brain tumors (meningiomas, gliomas, and pituitary tumors). A publicly available dataset that contains 3064 T1-weighted brain CE-MRI images collected from 233 patients has been used in the study. We propose a 15 layers CNN model for the classification of three types of brain tumors from the mentioned dataset. We obtained an accuracy, precision, recall, and f1-score of 98.6%, 99%, 98.3%, and 98.6% from our proposed model which is higher than previously reported results.
dc.identifier.citationHafeez, M. A., Kayasandık, C. B. ve Doğan, M. Y. (2022). Brain tumor classification using MRI images and convolutional neural networks. 30th Signal Processing and Communications Applications Conference, SIU 2022. Safranbolu, 15-18 May 2022. https://doi.org/10.1109/SIU55565.2022.9864962
dc.identifier.doi10.1109/SIU55565.2022.9864962
dc.identifier.isbn9781665450928
dc.identifier.scopus2-s2.0-85138734005
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/SIU55565.2022.9864962
dc.identifier.urihttps://hdl.handle.net/20.500.12511/9823
dc.indekslendigikaynakScopus
dc.institutionauthorHafeez, Muhammad Adeel
dc.institutionauthorKayasandık, Cihan Bilge
dc.institutionauthorDoğan, Merve Yüşra
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof30th Signal Processing and Communications Applications Conference, SIU 2022en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectBrain Tumor
dc.subjectCNN
dc.subjectMRI
dc.titleBrain tumor classification using MRI images and convolutional neural networks
dc.typeConference Object

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