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Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks

  • Jia Ding
  • Aoxue Li
  • Zhiqiang Hu
  • Liwei WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)

Abstract

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 0.893, ranking the 1st place over all submitted results), which outperforms the best result on the leaderboard of the LUNA16 Challenge (average FROC-score of 0.864).

Notes

Acknowledgements

This work was partially supported by National Basic Research Program of China (973 Program) (grant no. 2015CB352502), NSFC (61573026) and the MOE-Microsoft Key Laboratory of Statistics and Machine Learning, Peking University. We would like to thank the anonymous reviewers for their valuable comments on our paper.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.The Key Laboratory of Machine Perception (MOE), School of Electronics Engineering and Computer SciencePeking UniversityBeijingChina

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