关键词: 肺癌, 肺结节, 肺结节检测系统, 噪声, 残差网络

Abstract:

The early detection and diagnosis of lung cancer is the key to improve the survival rate of lung cancer patients. Due to the small nodules in the early stage of lung cancer, the existing pulmonary nodules detection system is easy to miss the diagnosis when detecting these nodules. Accurate detection of early lung nodules is crucial to improve the cure rate of lung cancer. In order to reduce the missed rate of early nodules in the detection system, it is necessary to optimize the extraction procedure of candidate nodules. The shortcut of residual network is introduced into U-Net, which effectively solves the disadvantage of poor results caused by the lack of depth in the traditional U-Net. On the basis of this improvement, a U-type noise residual network NRU（Noisy Residual U-Net） is proposed to enhance the sensitivity of neural network to small nodules by using the characteristics of hopping layer connection and adding noise to the convolutional layer. The Lung Nodule Analysis 2016 and Alibaba Tianchi lung cancer detection competition data sets are used to train the neural network. Comparison experiments between U-Net and NRU show that the sensitivity of the algorithm to small nodules with a diameter of 3-5 mm （97.1%） is greater than that of U-Net（90.5%）.

Key words: lung cancer, pulmonary nodules, pulmonary nodules detection system, noise, residual network