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Residual Block Based Nested U-Type Architecture for Multi-Modal Brain Tumor Image Segmentation
2022
Frontiers in Neuroscience
Multi-modal magnetic resonance imaging (MRI) segmentation of brain tumors is a hot topic in brain tumor processing research in recent years, which can make full use of the feature information of different modalities in MRI images, so that tumors can be segmented more effectively. In this article, convolutional neural networks (CNN) is used as a tool to improve the efficiency and effectiveness of segmentation. Based on this, Dense-ResUNet, a multi-modal MRI image segmentation model for brain
doi:10.3389/fnins.2022.832824
pmid:35356052
pmcid:PMC8959850
fatcat:wcgz6kxgmfa6rkwozu5oimdr3u