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The Multimodal Brain Tumor Image Segmentation specification (BraTS 2020) dataset’s setup and results have been applied in this research. For the brain tumor segmentation, CNN models ResNet and VGG16 have been used in conjunction with Attention U-Net. When the accuracy of the models was assessed, it was discovered that the Attention U-Net performed better than the others in terms of segmentation. As an ongoing benchmarking resource, the BraTS 2020 MRI scan data set and manual annotations are publicly accessible via an online evaluation system. On the BraTS 2020 dataset, the Attention U-Net obtained an accuracy of 98.31%, followed by VGG16 at 96.20% and ResNet at 97.89%.

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Attention U-Net and CNN-Based Hybrid Models for Precise Brain Tumor Segmentation on BraTS

  • 会议论文

  • 发表日期：

    2026年

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  • Saurabh Dixit,
  • Nigmendra Pratap Yadav,
  • Devesh Pandey,
  • Ajai Pratap Singh

摘要

The Multimodal Brain Tumor Image Segmentation specification (BraTS 2020) dataset’s setup and results have been applied in this research. For the brain tumor segmentation, CNN models ResNet and VGG16 have been used in conjunction with Attention U-Net. When the accuracy of the models was assessed, it was discovered that the Attention U-Net performed better than the others in terms of segmentation. As an ongoing benchmarking resource, the BraTS 2020 MRI scan data set and manual annotations are publicly accessible via an online evaluation system. On the BraTS 2020 dataset, the Attention U-Net obtained an accuracy of 98.31%, followed by VGG16 at 96.20% and ResNet at 97.89%.