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3D SAACNet with GBM for the classification of benign and malignant lung nodules.

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第一作者： Zhitao,Guo

第一单位： School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300401, China. Electronic address: mrnow@hebut.edu.cn.

作者： Zhitao,Guo ^[1] ; Jikai,Yang ^[2] ; Linlin,Zhao ^[3] ; Jinli,Yuan ^[4] ; Hengyong,Yu ^[5]

作者单位： School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300401, China. Electronic address: mrnow@hebut.edu.cn. ^[1] School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300401, China. Electronic address: jkyang1999@163.com. ^[2] School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300401, China. Electronic address: zhaolinlin0813@163.com. ^[3] School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300401, China. Electronic address: jinli_yuan@hebut.edu.cn. ^[4] Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, 01854, USA. Electronic address: hengyong_yu@uml.edu. ^[5]

关键词 Asymmetric convolution Attention mechanism Image processing Lung nodule classification Multiscale

医学主题词人类(Humans);肺肿瘤(Lung Neoplasms);体层摄影术, X线计算机(Tomography, X-Ray Computed);曲线下面积(Area Under Curve);肺(Lung);孤立性肺结节(Solitary Pulmonary Nodule)

DOI 10.1016/j.compbiomed.2022.106532

PMID 36623436

发布时间 2023-11-21

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准确一般字义偏差语意偏离译文作用小提交