SEU2-Net: multi-scale U2-Net with SE attention mechanism for liver occupying lesion CT image segmentation

• Published in: PeerJ. Computer science Vol. 10; p. e1751
• Main Authors: Liu, Lizhuang, Wu, Kun, Wang, Ke, Han, Zhenqi, Qiu, Jianxing, Qiao Zhan, Wu, Tian, Xu, Jinghang, Zeng, Zheng
• Format: Journal Article
• Language: English
• Published: San Diego PeerJ, Inc 25.01.2024; PeerJ Inc
• Subjects: Algorithms; Artificial Intelligence; Automation; Bioinformatics; Clustering; Computed tomography; Computer Vision; Datasets; Deep learning; Excitation; Image segmentation; Kappa coefficient; Lesions; Liver; Liver occupying lesion segmentation; Machine learning; SE attention; Semantics; Tumors; U2-Net

Liver occupying lesions can profoundly impact an individual’s health and well-being. To assist physicians in the diagnosis and treatment of abnormal areas in the liver, we propose a novel network named SEU2-Net by introducing the channel attention mechanism into U2-Net for accurate and automatic liv...