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

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

SOURCE CODE 

Opening soon. The source code is only for the non-commercial use.

PUBLICATIONS 

[1]   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）

[2]  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.