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Deep CNN-Based Method for Segmenting Lung Fields in Digital Chest Radiographs

  • Simranpreet KaurEmail author
  • Rahul Hooda
  • Ajay Mittal
  • Akashdeep
  • Sanjeev Sofat
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 712)

Abstract

Lung Field Segmentation (LFS) is an indispensable step for detecting austere lung diseases in various computer-aided diagnosis. This paper presents a deep learning-based Convolutional Neural Network (CNN) for segmenting lung fields in chest radiographs. The proposed CNN network consists of three sets of convolutional-layer and rectified linear unit (ReLU) layer, followed by a fully connected layer. At each convolutional layer, 64 filters retrieve the representative features. Japanese Society of Radiological Technology (JSRT) dataset is used for training and validation. Test results have 98.05% average accuracy, 93.4% average overlap, 96.25% average sensitivity, and 98.80% average specificity. The obtained results are promising and better than many of the existing state-of-the-art LFS techniques.

Keywords

Lung Field Segmentation (LFS) Convolutional Neural Network (CNN) Deep learning Chest radiography 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Simranpreet Kaur
    • 1
    Email author
  • Rahul Hooda
    • 2
  • Ajay Mittal
    • 1
  • Akashdeep
    • 1
  • Sanjeev Sofat
    • 2
  1. 1.UIET, Panjab UniversityChandigarhIndia
  2. 2.PEC University of TechnologyChandigarhIndia

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