Title
Multiscale Rotation-Invariant Convolutional Neural Networks for Lung Texture Classification.
Abstract
We propose a new multiscale 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 analysis-invariance to image scales and rotations. In addition, we offer an approach to deal with the problems caused by imbalanced number of samp...
Year
DOI
Venue
2018
10.1109/JBHI.2017.2685586
IEEE Journal of Biomedical and Health Informatics
Keywords
Field
DocType
Lungs,Feature extraction,Informatics,Support vector machines,Neural networks,Biomedical imaging,Computed tomography
Computer vision,Binary pattern,Pattern recognition,Medical imaging,Convolutional neural network,Computer science,Support vector machine,Feature extraction,Artificial intelligence,Invariant (mathematics),Computed tomography,Artificial neural network
Journal
Volume
Issue
ISSN
22
1
2168-2194
Citations 
PageRank 
References 
6
0.50
45
Authors
6
Name
Order
Citations
PageRank
Qiangchang Wang160.50
Yuanjie Zheng267155.01
Gongping Yang341442.17
Weidong Jin460.50
Xinjian Chen5397.50
Yilong Yin6966135.80