Abstract: Pulmonary Nodule Detection is the most promising way in early detection of pulmonary cancer. False positive reduction is one of the most crucial steps for improving the accuracy in automatic pulmonary nodule detection. For quickly and accurately discriminate true nodules from a large number of candidates, a 3D convolutional neural networks (CNN) is proposed for false positive reduction. In the proposed network, identity mapping and residual unit are adopted to accelerate network training. At the same time, single connected path is explored to get new features. Compared with 2D CNN based pulmonary nodule detection methods, the proposed method based on the proposed 3D CNN can take full advantage of space structure of CT. Compared with other 3D CNN based pulmonary nodule detection methods, the proposed method has fewer parameters that can make the training very fast. Experimental results on the public LUNA16 dataset demonstrate superior performance of the proposed method.

Key words:  3D CNN, pulmonary nodules, false positive reduction