Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer

  • Yu. Gordienko
  • Peng Gang
  • Jiang Hui
  • Wei Zeng
  • Yu. Kochura
  • O. Alienin
  • O. Rokovyi
  • S. Stirenko
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 754)


The recent progress of computing, machine learning, and especially deep learning, for image recognition brings a meaningful effect for automatic detection of various diseases from chest X-ray images (CXRs). Here efficiency of lung segmentation and bone shadow exclusion techniques is demonstrated for analysis of 2D CXRs by deep learning approach to help radiologists identify suspicious lesions and nodules in lung cancer patients. Training and validation was performed on the original JSRT dataset (dataset #01), BSE-JSRT dataset, i.e. the same JSRT dataset, but without clavicle and rib shadows (dataset #02), original JSRT dataset after segmentation (dataset #03), and BSE-JSRT dataset after segmentation (dataset #04). The results demonstrate the high efficiency and usefulness of the considered pre-processing techniques in the simplified configuration even. The pre-processed dataset without bones (dataset #02) demonstrates the much better accuracy and loss results in comparison to the other pre-processed datasets after lung segmentation (datasets #02 and #03).


Deep learning Convolutional neural network Tensorflow GPU JSRT Chest X-ray Segmentation Bone shadow exclusion Lung cancer 



The work was partially supported by Huizhou Science and Technology Bureau and Huizhou University (Huizhou, P.R. China) in the framework of Platform Construction for China-Ukraine Hi-Tech Park Project # 2014C050012001.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Yu. Gordienko
    • 1
  • Peng Gang
    • 2
  • Jiang Hui
    • 2
  • Wei Zeng
    • 2
  • Yu. Kochura
    • 1
  • O. Alienin
    • 1
  • O. Rokovyi
    • 1
  • S. Stirenko
    • 1
  1. 1.National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”KyivUkraine
  2. 2.Huizhou UniversityHuizhou CityChina

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