An enhanced brain tumor segmentation using an improved 3D U-Net topology: Solution to BraTS 2020 challenge
DOI:
https://doi.org/10.52292/j.laar.2024.3238Keywords:
Brain Tumor; U-Net; BraTS2020 dataset; Modified U-Net; CNN; SegmentationAbstract
Accurate tumor delineation is crucial for accurate diagnosis, strategic treatment planning, and the identification of risk factors associated with brain tumors. Manual segmentation of this tumors from MRI images is a time-consuming process and is susceptible to errors. To address this, deep learning (DL) algorithms have recently shown outstanding performance. This research introduces the utilization of the U-Net architecture for improved tumor segmentation. This study employs the BraTS2020 dataset to evaluate the effectiveness of the proposed approach. The U-Net model developed in this research achieves an impressive accuracy of 99.8 %, surpassing the previous existing topologies. The simulation results confirm that the proposed Improved U-Net (IUNet) design outperforms existing models in terms of accuracy.
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