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Automated Analysis of Chest Radiographs for Cystic Fibrosis Scoring

  • Zhaowei HuangEmail author
  • Chen Ding
  • Lei Zhang
  • Min-Zhao Lee
  • Yang Song
  • Hiran Selvadurai
  • Dagan Feng
  • Yanning Zhang
  • Weidong Cai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10989)

Abstract

We present a framework to analyze chest radiographs for cystic fibrosis using machine learning methods. We compare the representational power of deep learning features with traditional texture features. Specifically, we respectively employ VGG-16 based deep learning features, Tamura and Gabor filter based textural features to represent the cystic fibrosis images. We demonstrate that VGG-16 features perform best, with a maximum agreement of 82%. In addition, due to limited dimensionality, Tamura features for unsegmented images achieve no more than 50% agreement; however, after segmentation, the accuracy of Tamura can reach 78%. In combination with using the deep learning features, we also compare back propagation neural network and sparse coding classifiers to the typical SVM classifier with polynomial kernel function. The result shows that neural network and sparse coding classifiers outperform SVM in most cases. Only with insufficient training samples does SVM demonstrate higher accuracy.

Keywords

Cystic fibrosis Computer-assisted score Deep learning feature VGG-16 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Zhaowei Huang
    • 1
    Email author
  • Chen Ding
    • 2
  • Lei Zhang
    • 2
  • Min-Zhao Lee
    • 1
  • Yang Song
    • 1
  • Hiran Selvadurai
    • 3
  • Dagan Feng
    • 1
  • Yanning Zhang
    • 2
  • Weidong Cai
    • 1
  1. 1.Biomedical and Multimedia Information Technology (BMIT) Research Group, School of ITUniversity of SydneySydneyAustralia
  2. 2.Shaanxi Key Lab of Speech and Image Information Processing (SAIIP), School of Computer ScienceNorthwestern Polytechnical UniversityXi’anChina
  3. 3.Children’s Hospital at Westmead, Sydney Children’s Hospitals NetworkSydneyAustralia

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