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How to Improve the Deep Residual Network to Segment Multi-Modal Brain Tumor Images

  • Published:2019 Issue: Volume:7 Page:152821-152831
  • ISSN:2169-3536
  • Container-title:IEEE Access
  • language:
  • Short-container-title:IEEE Access

Author:

Ding YiORCID,Li Chang,Yang Qiqi,Qin ZhenORCID,Qin Zhiguang

Funder

Natural Science Foundation of Guangdong Province

Neijiang Intelligent Showmanship Service Platform Project

Sichuan Science-Technology Support Plan Program

National Science Foundation of China - Guangdong Joint Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Cited by 30 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Unfolding Explainable AI for Brain Tumor Segmentation;Neurocomputing;2024-09

2. Enhanced Deep Learning Framework for Automatic MRI Brain Tumor Segmentation with Data Augmentation;2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP);2024-07-11

3. Full-automatic Segmentation Algorithm of Brain Tumor Based on RFE-UNet and Hybrid Focal Loss Function;Radioengineering;2024-06

4. Brain Tumour Detection and Classification using Deep Convolutional Neural Network (DCNN);International Journal of Scientific Research in Computer Science, Engineering and Information Technology;2024-03-21

5. Challenges and Difficulties of Multi-Spectral MRI Based Brain Tumor Detection and Segmentation;2023 1st International Conference on Health Science and Technology (ICHST);2023-12-28
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