MRI Brain Tumor Image Semantic Segmentation Based on Full Convolution Generative Adversarial Network
SUN Chuanyou
SHAO Haijian
DENG Xing
Abstract：This research introduces a novel type of brain tumor segmentation network called NFGAN that handles the difficul-ties of model design and sample category imbalance.The generator model based on the full convolutional network can actually real-ize the segmentation network's end-to-end output and enrich the semantic information of brain tumors through lateral connections.NFGAN can also strengthen the essential characteristics of the learning data through confrontation training in order to improve the segmentation accuracy of brain tumors.This paper also proposes a novel loss function that is a linear combination of weighted cross entropy loss and generalized dice loss in order to reduce the influence of category imbalance.On the BraTs2018 dataset,the pro-posed model shows segmentation accuracies of 87.9%,81.2%and 77.8%,with superior segmentation outcomes than the classic U-Net model.
Keywords：brain tumor segmentationfully convolutional networkMRI imagingloss functionclass imbalance
Publication Date：2025-12-20
Online Publishing Date：2026-03-09(First online date of this platform, not the publication date of the document)
Pages：6( 3495-3500 )