Classification of brain tumors using MRI images based on convolutional neural network and supervised machine learning algorithms

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dc.contributor.authorAli, Rawaa
dc.contributor.authorAl-Jumaili, Saif
dc.contributor.authorDuru, Adil Deniz
dc.contributor.authorUçan, Osman Nuri
dc.contributor.authorBoyacı, Aytuğ
dc.contributor.authorDuru, Dilek Göksel
dc.date.accessioned2022-12-27T16:47:20Z
dc.date.available2022-12-27T16:47:20Z
dc.date.issued2022en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Bilişim Teknolojileri Ana Bilim Dalıen_US
dc.description.abstractBrain tumor is abnormal cells that originate from cranial tissue and is considered one of the most destructive diseases, and lead to the cause of death, where the early diagnosis is crucial for accelerating the therapy of brain tumors. Examining the patient's MRI scans is one traditional way of distinguishing brain cancers. The conventional approaches take a long time and are prone to human error, especially when dealing with huge amounts of data and diverse brain tumor classes. Artificial Intelligence (AI) is extremely useful for the strict detection and classification of several diseases in the brain. Convolutional Neural Network (CNN) is one of the modes techniques which act as a tumor classifier due to it shows high effectiveness for diagnosing brain tumors. That's why, in this research, we presented a hybrid method that merged a group of pre-Trained deep learning CNN patterns with a group of supervised classifiers in machine learning called, k-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA). We used an MRI image that consist of images of four brain tumor classes, namely glioma, meningioma, pituitary, and no tumor. We deduced the features extracted from the images by hiring three types of CNN called (GoogleNet, Shuffle-Net, and NasNet-Mobile). Depending upon the experimental consequences, ShuffleN et with SVM achieved the highest results according to the four categories of metrics evaluation that are Accuracy of 98.40%, Precision of 97%, Recall of 96.75%, and Fl-Score of 96.75%. Finally, we compared our results with different state-of-The-Art papers recently published and our proposed method show outperforms compared them.en_US
dc.identifier.citationAli, R., Al-jumaili, S., Duru, A. D., Uçan, O. N., Boyacı, A., Duru, D. G. (2022). Classification of brain tumors using MRI images based on convolutional neural network and supervised machine learning algorithms. In 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 822-827). IEEE.en_US
dc.identifier.endpage827en_US
dc.identifier.isbn9781665470131
dc.identifier.scopus2-s2.0-85142785700
dc.identifier.scopusqualityN/A
dc.identifier.startpage822en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/3166
dc.indekslendigikaynakScopus
dc.institutionauthorAli, Rawaa
dc.institutionauthorAl-Jumaili, Saif
dc.institutionauthorUçan, Osman Nuri
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings
dc.relation.isversionof10.1109/ISMSIT56059.2022.9932690en_US
dc.relation.publicationcategoryKonferans Öğesi - Ulusal - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBrain Tumorsen_US
dc.subjectClassification Brain Tumorsen_US
dc.subjectCNNen_US
dc.subjectDeep Learningen_US
dc.subjectMachine Learningen_US
dc.titleClassification of brain tumors using MRI images based on convolutional neural network and supervised machine learning algorithms
dc.typeConference Object

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