Research Article
Improvements in Brain Tumor Segmentation Methods Based on Convolutional Neural Networks
@ARTICLE{10.4108/eetel.6080, author={Yuzhuo Li and Lihong Zhang and Yingbo Liang and Chongxin Xu and Tong Liu}, title={Improvements in Brain Tumor Segmentation Methods Based on Convolutional Neural Networks}, journal={EAI Endorsed Transactions on e-Learning}, volume={11}, number={1}, publisher={EAI}, journal_a={EL}, year={2025}, month={4}, keywords={Brain tumor images, Image segmentation, Deep learning, Convolutional neural network, Network architecture}, doi={10.4108/eetel.6080} }- Yuzhuo Li
Lihong Zhang
Yingbo Liang
Chongxin Xu
Tong Liu
Year: 2025
Improvements in Brain Tumor Segmentation Methods Based on Convolutional Neural Networks
EL
EAI
DOI: 10.4108/eetel.6080
Abstract
Convolutional Neural Networks (CNNs) have emerged as a prominent research area in deep learning in recent years. U-Net, an essential model within CNNs, has gradually become a research focus in the field of medical image segmentation due to its remarkable segmentation performance. This paper presents a comprehensive overview of brain tumor segmentation methods based on CNNs. Firstly, it introduces common medical image datasets in the field of brain tumor segmentation. Secondly, it offers detailed reviews on the common improvements to 2D U-Net, 3D U-Net, and improvements based on other CNNs for brain tumor segmentation. Finally, it discusses the future development directions of CNNs for brain tumor segmentation.
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