Identifying Cardiomegaly in ChestX-ray8 Using Transfer Learning

Sicheng Zhou, Xinyuan Zhang, Rui Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations


Recently, the National Institutes of Health (NIH) published a chest X-ray image database named “ChestX-ray8”, which contains 108,948 X-ray images that are labeled with eight types of diseases. Identifying the pathologies from the clinical images is a challenging task even for human experts, and to develop computer-aided diagnosis systems to help humans identify the pathologies from images is an urgent need. In this study, we applied the deep learning methods to identify the cardiomegaly from the X-ray images. We tested our algorithms on a dataset containing 600 images, and obtained the best performance with an area under the curve (AUC) of 0.87 using the transfer learning method. This result indicates the feasibility of developing computer-aided diagnosis systems for different pathologies from X-rays using deep learning techniques.

Original languageEnglish (US)
Title of host publicationMEDINFO 2019
Subtitle of host publicationHealth and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics
EditorsBrigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi
PublisherIOS Press
Number of pages5
ISBN (Electronic)9781643680026
StatePublished - Aug 21 2019
Event17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France
Duration: Aug 25 2019Aug 30 2019

Publication series

NameStudies in health technology and informatics
ISSN (Print)0926-9630


Conference17th World Congress on Medical and Health Informatics, MEDINFO 2019

Bibliographical note

Publisher Copyright:
© 2019 International Medical Informatics Association (IMIA) and IOS Press.


  • Cardiomegaly
  • Machine Learning
  • X-rays
  • Algorithms
  • Diagnosis, Computer-Assisted
  • Area Under Curve
  • Humans
  • Deep Learning

PubMed: MeSH publication types

  • Journal Article


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