  Early detection of lung cancer is an effective way to improve the survival
rate of patients. It is a critical step to have accurate detection of lung
nodules in computed tomography (CT) images for the diagnosis of lung cancer.
However, due to the heterogeneity of the lung nodules and the complexity of the
surrounding environment, robust nodule detection has been a challenging task.
In this study, we propose a two-stage convolutional neural network (TSCNN)
architecture for lung nodule detection. The CNN architecture in the first stage
is based on the improved UNet segmentation network to establish an initial
detection of lung nodules. Simultaneously, in order to obtain a high recall
rate without introducing excessive false positive nodules, we propose a novel
sampling strategy, and use the offline hard mining idea for training and
prediction according to the proposed cascaded prediction method. The CNN
architecture in the second stage is based on the proposed dual pooling
structure, which is built into three 3D CNN classification networks for false
positive reduction. Since the network training requires a significant amount of
training data, we adopt a data augmentation method based on random mask.
Furthermore, we have improved the generalization ability of the false positive
reduction model by means of ensemble learning. The proposed method has been
experimentally verified on the LUNA dataset. Experimental results show that the
proposed TSCNN architecture can obtain competitive detection performance.
