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

Rui Xu, Zhen Cong, Xinchen Ye*

Dalian University of Technology  

* Corresponding author

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.

Index Terms— residual network, pulmonary texture, Hessian matrix, CAD, CT

METHOD

PUBLICATIONS 

[1] Rui Xu, Zhen Cong, Xinchen Ye*, Pulmonary Textures Classification via a Multi-Scale Attention Network, IEEE journal of Biomedical and Health Informatics, 24(7), 2041-2052, 2020.

[2] Rui Xu, Zhen Cong, Xinchen Ye*, Pulmonary Textures Classification Using A Deep Neural Network with Appearence and Geometry Cues, IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2018, Calgary, Alberta, Canada.（CCF-B）

[3] Rui Xu, Jiao Pan, Xinchen Ye, S. Kido and S. Tanaka, "A pilot study to utilize a deep convolutional network to segment lungs with complex opacities," IEEE Chinese Automation Congress (CAC), Jinan, 2017, pp. 3291-3295.