  Early detection of pulmonary cancer is the most promising way to enhance a
patient's chance for survival. Accurate pulmonary nodule detection in computed
tomography (CT) images is a crucial step in diagnosing pulmonary cancer. In
this paper, inspired by the successful use of deep convolutional neural
networks (DCNNs) in natural image recognition, we propose a novel pulmonary
nodule detection approach based on DCNNs. We first introduce a deconvolutional
structure to Faster Region-based Convolutional Neural Network (Faster R-CNN)
for candidate detection on axial slices. Then, a three-dimensional DCNN is
presented for the subsequent false positive reduction. Experimental results of
the LUng Nodule Analysis 2016 (LUNA16) Challenge demonstrate the superior
detection performance of the proposed approach on nodule detection(average
FROC-score of 0.891, ranking the 1st place over all submitted results).
