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Automated Chest X-ray Image View Classification using Force Histogram

  • K. C. SantoshEmail author
  • Laurent Wendling
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 709)

Abstract

To advance and/or ease computer aided diagnosis (CAD) system, chest X-ray (CXR) image view information is required. In other words, separating CXR image view: frontal and lateral can be considered as a crucial step to effective subsequent processes, since the techniques that work for frontal CXRs may not equally work for lateral ones. With this motivation, in this paper, we present a novel machine learning technique to classify frontal and lateral CXR images, where we introduce a force histogram to extract features and apply three different state-of-the-art classifiers: support vector machine (SVM), random forest (RF) and multi-layer perceptron (MLP). We validated our fully automatic technique on a set of 8100 images hosted by National Library of Medicine (NLM), National Institutes of Health (NIH), and achieved an accuracy close to 100%.

Keywords

Automation Chest X-ray Force histograms Image view Classification 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceThe University of South DakotaVermillionUSA
  2. 2.LIPADE – Université Paris Descartes (Paris V)Paris Cedex 06France

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