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Segmentation of tumor regions using 3D-UNet in magnetic resonance imaging

Divya Mohan, Ulagamuthalvi Venugopal, Nisha Joseph, Kulanthaivel Govindarajan

Abstract


Brain tumor has been a severe problem for a few decades ago. With the advancement in medical technologies, a brain tumor can be treated if observed earlier. This paper aims to segment and classify the tumor regions from Magnetic Resonance Imaging (MRI). The work consists of two steps. In step1, the 3D MRI images are pre-processed by the Salient Object Detection method to improve efficiency. In step2, the improved 3D-Res2UNet segments the tumor regions. The segmented tumors are partitioned into two classes using a Support Vector Machine (SVM) classifier. The method is tested using BRATS 2017 and 2018 datasets and obtained 87.1% and 99.2% dice score for BRATS 2017 and 2018, respectively. The performance of the proposed method is better compared to most recent methods.


Keywords


tumor; residual network; UNet; classifier

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References


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DOI: https://doi.org/10.32629/jai.v7i5.1058

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Copyright (c) 2024 Divya Mohan, Ulagamuthalvi Venugopal, Nisha Joseph, Kulanthaivel Govindarajan

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