Brain tumor segmentation of MRI images: a comprehensive review on the application of artificial intelligence tools

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dc.authoridErfan Babaee Tirkolaee / 0000-0003-1664-9210en_US
dc.authorscopusidErfan Babaee Tirkolaee / 57196032874en_US
dc.authorwosidErfan Babaee Tirkolaee / U-3676-2017en_US
dc.contributor.authorRanjbarzadeh, Ramin
dc.contributor.authorCaputo, Annalina
dc.contributor.authorTirkolaee, Erfan Babaee
dc.contributor.authorJafarzadeh Ghoushchi, Saeid
dc.contributor.authorBendechache, Malika
dc.date.accessioned2022-12-21T08:13:00Z
dc.date.available2022-12-21T08:13:00Z
dc.date.issued2023en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractBackground: Brain cancer is a destructive and life-threatening disease that imposes immense negative effects on patients’ lives. Therefore, the detection of brain tumors at an early stage improves the impact of treatments and increases the patients survival rates. However, detecting brain tumors in their initial stages is a demanding task and an unmet need. Methods: The present study presents a comprehensive review of the recent Artificial Intelligence (AI) methods of diagnosing brain tumors using MRI images. These AI techniques can be divided into Supervised, Unsupervised, and Deep Learning (DL) methods. Results: Diagnosing and segmenting brain tumors usually begin with Magnetic Resonance Imaging (MRI) on the brain since MRI is a noninvasive imaging technique. Another existing challenge is that the growth of technology is faster than the rate of increase in the number of medical staff who can employ these technologies. It has resulted in an increased risk of diagnostic misinterpretation. Therefore, developing robust automated brain tumor detection techniques has been studied widely over the past years. Conclusion: The current review provides an analysis of the performance of modern methods in this area. Moreover, various image segmentation methods in addition to the recent efforts of researchers are summarized. Finally, the paper discusses open questions and suggests directions for future research. © 2022 Elsevier Ltden_US
dc.identifier.citationRanjbarzadeh, R., Caputo, A., Tirkolaee, E. B., Ghoushchi, S. J., & Bendechache, M. (2022). Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools. Computers in Biology and Medicine, 106405.en_US
dc.identifier.doi10.1016/j.compbiomed.2022.106405en_US
dc.identifier.issn0010-4825en_US
dc.identifier.scopus2-s2.0-85143655131en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.compbiomed.2022.106405
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3748
dc.identifier.wosWOS:000913219100010en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorTirkolaee, Erfan Babaee
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofComputers in Biology and Medicineen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectBrain Tumoren_US
dc.subjectMRI Modalitiesen_US
dc.subjectTumor Classificationen_US
dc.subjectTumor Segmentationen_US
dc.titleBrain tumor segmentation of MRI images: a comprehensive review on the application of artificial intelligence toolsen_US
dc.typeArticleen_US

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