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A Pulmonary Nodule Detection Method Based on Residual Learning and Dense Connection

  • Feng Zhang
  • Yutong Xie
  • Yong XiaEmail author
  • Yanning Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11795)

Abstract

Pulmonary nodule detection using chest CT scan is an essential but challenging step towards the early diagnosis of lung cancer. Although a number of deep learning-based methods have been published in the literature, these methods still suffer from less accuracy. In this paper, we propose a novel pulmonary module detection method, which uses a 3D residual U-Net (3D RU-Net) for nodule candidate detection and a 3D densely connected CNN (3D DC-Net) for false positive reduction. 3D RU-Net contains residual blocks in both contracting and expansive paths, and 3D DC-Net leverages three dense blocks to facilitate gradients flow. We evaluated our method on the benchmark LUng Nodule Analysis 2016 (LUNA16) dataset and achieved a CPM score of 0.941, which is higher than those achieved by five competing methods. Our results suggest that the proposed method can effectively detect pulmonary nodules on chest CT.

Keywords

Pulmonary nodule detection Residual learning Dense connection Chest CT 

Notes

Acknowledgement

This work was supported in part by the Science and Technology Innovation Committee of Shenzhen Municipality, China, under Grants JCYJ20180306171334997, in part by the National Natural Science Foundation of China under Grants 61771397, in part by Synergy Innovation Foundation of the University and Enterprise for Graduate Students in Northwestern Polytechnical University (NPU) under Grants XQ201911, in part by the Seed Foundation of Innovation and Creation for Graduate Students in NPU under Grants ZZ2019029, and in part by the Project for Graduate Innovation team of NPU. We appreciate the efforts devoted by LUNA16 challenge organizers to collect and share the data.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Feng Zhang
    • 1
    • 2
  • Yutong Xie
    • 2
  • Yong Xia
    • 1
    • 2
    Email author
  • Yanning Zhang
    • 2
  1. 1.Research & Development Institute of Northwestern Polytechnical University in ShenzhenShenzhenChina
  2. 2.National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and EngineeringNorthwestern Polytechnical UniversityXianChina

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