Multiscale Rotation-Invariant Convolutional Neural Networks for Lung Texture Classification.
IEEE J Biomed Health Inform
; 22(1): 184-195, 2018 01.
Article
em En
| MEDLINE
| ID: mdl-28333649
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 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.
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Base de dados:
MEDLINE
Assunto principal:
Reconhecimento Automatizado de Padrão
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Interpretação de Imagem Assistida por Computador
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Redes Neurais de Computação
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Doenças Pulmonares Intersticiais
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Pulmão
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article