A hybrid DenseNet121-UNet model for brain tumor segmentation from MR Images

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dc.authorid0000-0002-5106-6240en_US
dc.contributor.authorÇınar, Necip
dc.contributor.authorÖzcan, Alper
dc.contributor.authorKaya, Mehmet
dc.date.accessioned2023-04-03T11:40:59Z
dc.date.available2023-04-03T11:40:59Z
dc.date.issued2022en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractSeveral techniques are used to detect brain tumors in the medical research field; however, Magnetic Resonance Imaging (MRI) is still the most effective technique used by experts. Recently, researchers have proposed different MRI techniques to detect brain tumors with the possibility of uploading and visualizing the image. In the current decade, deep learning techniques have shown promising results in every research area, especially in bioinfor-matics and medical image analysis. This paper aims to segment brain tumors using deep learning methods of MR images. The UNet architecture, one of the deep learning networks, is used as a hybrid model with pre-trained DenseNet121 architecture for the segmentation process. During training and testing of the model, we focus on smaller sub-regions of tumors that comprise the complex structure. The proposed model is validated on BRATS 2019 publicly available brain tumor dataset that contains high-grade and low-grade glioma tumors. The experimental results indicate that our model performs better than other state-of-the-art methods presented in this particular area. Specifically, the best Dice Similarity Coefficient (DSC) are obtained by using the proposed approach to segment whole tumor (WT), core tumor (CT), and enhancing tumor (ET).en_US
dc.identifier.citationÇınar, N., Özcan, A. ve Kaya, M. (2022). A hybrid DenseNet121-UNet model for brain tumor segmentation from MR Images. Biomedical Signal Processing and Control, 76,103647.en_US
dc.identifier.doi10.1016/j.bspc.2022.103647
dc.identifier.endpage9en_US
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85126584694
dc.identifier.scopusqualityQ1
dc.identifier.startpage1en_US
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S1746809422001690?via%3Dihub
dc.identifier.urihttps://hdl.handle.net/11468/11594
dc.identifier.volume76en_US
dc.identifier.wosWOS:000783315500004
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorÇınar, Necip
dc.language.isoenen_US
dc.publisherElsevier SCI LTD.en_US
dc.relation.ispartofBiomedical Signal Processing and Control
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep learningen_US
dc.subjectImage processingen_US
dc.subjectBrain tumor segmentationen_US
dc.subjectArtificial neural network modelsen_US
dc.subjectImage segmentationen_US
dc.subjectUNeten_US
dc.subjectDenseNet121en_US
dc.titleA hybrid DenseNet121-UNet model for brain tumor segmentation from MR Imagesen_US
dc.titleA hybrid DenseNet121-UNet model for brain tumor segmentation from MR Images
dc.typeArticleen_US

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