Brain Tumor MRI Image Segmentation Based on Improved Deep Learning Network Model U2-Net
Year: 2025
Type: article
Abstract: At present, the image segmentation network architecture based on deep learning will lose some important information and key details when extracting shallow image features, which may cause errors in clinical diagnosis. The image segmentation method based on a convolutional neural network still needs to be improved. For the first time, the MobileViT block is combined with the nested U-shaped structure of U2-Net. Through the collaborative optimization of lightweight global context modeling and the channel-spatial attention mechanism, the problems of blurred boundaries and insufficient fusion of multi-scale features in brain tumor MRI images are solved. Aiming at the problem that the overall segmentation accuracy of brain glioma images needs to be improved, this paper proposes an improved brain glioma MRI image segmentation method based on U2-Net. In this model, the original U-type residual block encoder at the bottom of U2-Net is replaced with a MobileViT block module to capture the feature information of global remote context association, and an attention module is added between the U-type residual block encoder and U-type residual block decoder. The attention module consists of the spatial attention method and the channel attention method. The residual connection is used to grasp the global contour of the image to improve the accuracy of the segmentation method. Finally, the effectiveness of the improved image segmentation network model is indicated by the parameters of mIOU and Dice, the segmentation visualization results, thermal maps and other relevant experimental data obtained by the experiment, and the segmentation accuracy of the improved image segmentation method is significantly improved over that of the traditional method.
Authors: Pan Luhai, Hu Ruo, Xu Hong, Zhao Huimin
Institution: Guangdong Polytechnic Normal University
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W4413182086