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Deep Convolutional Nets for Pulmonary Nodule Detection and Classification

  • Nannan Sun
  • Dongbao Yang
  • Shancheng Fang
  • Hongtao Xie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11062)

Abstract

In this study, a novel pulmonary nodule detection and classification system with 2D convolutional neural networks is proposed. The objective is to effectively address the challenges in lung cancer diagnosis and early treatment. The system consists of two stages: nodule detection and false positive reduction. For nodule detection, we introduce a detection framework based on Faster R-CNN, which integrates a deconvolution layer to enlarge the feature map and two region proposal networks to concatenate the useful information from the lower layer. In order to ensure high sensitivity, the conditions at this stage are simple and loose. Therefore, a boosting architecture based on 2D CNNs is designed for false positive reduction. In order to improve classification accuracy, every training model pays attention to those data that are not easy to classify. In experiments, our method is conducted on LUNA16 challenge. The sensitivity of nodule candidate detection achieves 86.42%. For false positive reduction, sensitivities of 73.4% and 74.4% at 1/8 and 1/4 false positives per scan are obtained, respectively. It proves that our method can maintain a satisfactory sensitivity even with extremely low false positive rates.

Keywords

Computed tomography Computer-aided detection Convolutional networks Lung cancer Pulmonary nodule 

Notes

Acknowledgment

This work was supported by National Key R&D Program 2016 under Grant No. 2016YFB0801305.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Nannan Sun
    • 1
    • 2
  • Dongbao Yang
    • 1
    • 2
  • Shancheng Fang
    • 1
    • 2
  • Hongtao Xie
    • 3
  1. 1.Institute of Information EngineeringChinese Academy of SciencesBeijingChina
  2. 2.School of Cyber SecurityUniversity of Chinese Academy of SciencesBeijingChina
  3. 3.School of Information Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina

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