RGA-Unet: An improved U-net segmentation model based on residual grouped convolution and convolutional block attention module for brain tumor MRI image segmentation

Siyi Xun, Yan Zhang, Sixu Duan, Huachao Chen, Mingwei Wang, Jiangang Chen, Tao Tan

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

In recent years, brain tumor diseases have seriously threatened brain health. With the rapid development of medical imaging diagnostic technology, magnetic resonance imaging (MRI) has become the preferred imaging method for the diagnosis and treatment of brain tumor diseases. However, the target structure of brain tumors is complex and individual variation is great, which makes the detection and treatment of brain tumors very challenging. To solve the problem of brain tumor MRI image segmentation, we propose a U-net segmentation network based on residual grouped convolution and convolutional block attention module (CBAM), named RGA-Unet. Through the experiment on The Cancer Imaging Archive (TCIA) brain tumor image dataset, the Dice score of segmentation results reaches 95.904%, which is 10.761% higher than that of a traditional U-net network. The experimental results show that this method can effectively realize the automatic and accurate segmentation of brain tumor images, which has certain research significance.

原文English
主出版物標題CSSE 2022 - 2022 5th International Conference on Computer Science and Software Engineering
主出版物子標題Conference Proceedings
發行者Association for Computing Machinery
頁面319-324
頁數6
ISBN(電子)9781450397780
DOIs
出版狀態Published - 21 10月 2022
事件5th International Conference on Computer Science and Software Engineering, CSSE 2022 - Guilin, China
持續時間: 21 10月 202223 10月 2022

出版系列

名字ACM International Conference Proceeding Series

Conference

Conference5th International Conference on Computer Science and Software Engineering, CSSE 2022
國家/地區China
城市Guilin
期間21/10/2223/10/22

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