Multiscale Rotation-Invariant Convolutional Neural Networks for Lung Texture Classification

作者：Wang, Qiangchang; Zheng, Yuanjie; Yang, Gongping; Jin, Weidong; Chen, Xinjian^*; Yin, Yilong^*

来源：IEEE Journal of Biomedical and Health Informatics, 2018, 22(1): 184-195.

DOI：10.1109/JBHI.2017.2685586

• Convolutional neural network (CNN)
• gabor filter
• interstitial lung disease (ILD) classification
• local binary pattern (LBP)
• lung classification

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.

• 出版日期2018-1
• 单位苏州大学; 山东师范大学; 山东财经大学; 山东大学