标题:Multiscale Rotation-Invariant Convolutional Neural Networks for Lung Texture Classification
作者:Wang, Qiangchang; Zheng, Yuanjie; Yang, Gongping; Jin, Weidong; Chen, Xinjian; Yin, Yilong
作者机构:[Wang, Qiangchang; Yang, Gongping; Yin, Yilong] Shandong Univ, Sch Comp Sci & Technol, Jinan 250101, Shandong, Peoples R China.; [Zheng, Yuanjie] Sh 更多
通讯作者:Chen, Xinjian
通讯作者地址:[Yin, YL]Shandong Univ, Sch Comp Sci & Technol, Jinan 250101, Shandong, Peoples R China;[Chen, XJ]Soochow Univ, Sch Elect & Informat Engn, Suzhou 2150 更多
来源:IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
出版年:2018
卷:22
期:1
页码:184-195
DOI:10.1109/JBHI.2017.2685586
关键词:Convolutional neural network (CNN); gabor filter; interstitial lung; disease (ILD) classification; local binary pattern (LBP); lung; classification
摘要:We propose a newmultiscale rotation-invariant convolutional neural network (MRCNN) model for classifying various lung tissue types on high-resolution computed tomography. MRCNN employs Gabor-local binary pattern that introduces a good property in image analysisinvariance to image scales and rotations. In addition, we offer an approach to deal with the problems caused by imbalanced number of samples between different classes in most of the existing works, accomplished by changing the overlapping size between the adjacent patches. Experimental results on a public interstitial lung disease database show a superior performance of the proposed method to state of the art.
收录类别:EI;SCOPUS;SCIE
WOS核心被引频次:1
Scopus被引频次:2
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040364846&doi=10.1109%2fJBHI.2017.2685586&partnerID=40&md5=5bbbcc5ae0d74043d45b267312cf7206
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