Classification of Pulmonary Emphysema in CT Images Based on Multi-Scale Deep Convolutional Neural Networks

In this work, we aim at classifying emphysema in computed tomography (CT) images of lungs. Most previous works are limited to extracting low-level features or mid-level features without enough high-level information. Moreover, these approaches do not take the characteristics (scales) of different emphysema into account, which are crucial for feature extraction. In contrast to previous works, we propose a novel deep learning method based on multiscale deep convolutional neural networks. There are three contributions for this paper. First, we propose to use a base residual network with 20 layers to extract more high-level information. To the best of our knowledge, this is the first deep learning method for classification of emphysema. Second, we incorporate multi-scale information into our deep neural networks so as to take full consideration of the characteristics of different emphysema. Finally, we established a high-quality emphysema dataset which contains 91 high-resolution computed tomography (HRCT) volumes, annotated manually by two experienced radiologists and checked by one experienced chest radiologist. A 92.68% classification accuracy is achieved on this dataset. The results show that (1) the multi-scale method is highly effective in comparison to the single scale setting; (2) the proposed approach is superior to the state-of-the-art techniques.