Pulmonary Textures Classification Using A Deep Neural Network with Appearance and Geometry Cues

Rui Xu; Zhen Cong; Xinchen Ye; Yasushi Hirano; Shoji Kido

ICASSP 2018accepted0 citations

Pulmonary Textures Classification Using A Deep Neural Network with Appearance and Geometry Cues

Rui Xu, Zhen Cong, Xinchen Ye, Yasushi Hirano, Shoji Kido

Abstract

Classification of pulmonary textures on CT images is essential for the development of a computer-aided diagnosis system of diffuse lung diseases. In this paper, we propose a novel method to classify pulmonary textures by using a deep neural network, which can make full use of appearance and geometry cues of textures via a dual-branch architecture. The proposed method has been evaluated by a dataset that includes seven kinds of typical pulmonary textures. Experimental results show that our method outperforms the state-of-the-art methods including feature engineering based method and convolutional neural network based method.

BibTeX

@inproceedings{icassp2018_pulmonarytexture,
  title = {Pulmonary Textures Classification Using A Deep Neural Network with Appearance and Geometry Cues},
  author = {Rui Xu and Zhen Cong and Xinchen Ye and Yasushi Hirano and Shoji Kido},
  booktitle = {ICASSP 2018},
  year = {2018}
}