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International Conference on Neural Information Processing

ICONIP 2019: Neural Information Processing pp 148–158Cite as

Convolutional Neural Network to Detect Thorax Diseases from Multi-view Chest X-Rays

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1142))

Abstract

Chest radiography is the most common examination for a radiologist. This demands correct and immediate diagnosis of a patient’s thorax to avoid life threatening diseases. Not only certified radiologists are hard to find, stress, fatigue and experience contribute to the quality of an examination. It is ideal that a chest X-ray can be interpreted by an automated deep learning algorithm. In this paper, we proposed a stage-wise model that is founded on a ResNet-50 based deep convolutional neural networks architecture to detect the presence and absence of twelve thorax diseases. This novel model has incorporated various recent techniques such as transfer learning, fine tuning, fit one cycle function and discriminative learning rates. The experiments were performed on 10% of the largest collection of chest X-rays to date, the MIMIC-CXR dataset. The model was trained for eight epochs using a subset of the available multi-view chest X-rays. The absolute labelling performance has achieved an encouraging average AUC of 0.779.

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Notes

  1. 1.

    https://github.com/ncbi-nlp/NegBio.

  2. 2.

    https://github.com/stanfordmlgroup/chexpert-labeler.

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Author information

Authors and Affiliations

  1. School of Computer Science, University of Sydney, Sydney, Australia

    Maram Mahmoud A. Monshi, Josiah Poon & Vera Chung

  2. Department of Information Technology, Taif University, Taif, Saudi Arabia

    Maram Mahmoud A. Monshi

Authors
  1. Maram Mahmoud A. Monshi
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  2. Josiah Poon
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  3. Vera Chung
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Corresponding author

Correspondence to Maram Mahmoud A. Monshi .

Editor information

Editors and Affiliations

  1. Australian National University, Canberra, ACT, Australia

    Tom Gedeon

  2. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  3. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

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Monshi, M.M.A., Poon, J., Chung, V. (2019). Convolutional Neural Network to Detect Thorax Diseases from Multi-view Chest X-Rays. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-030-36808-1_17

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  • DOI: https://doi.org/10.1007/978-3-030-36808-1_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36807-4

  • Online ISBN: 978-3-030-36808-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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