Deep learning classification of chest x-ray images

Mohammad S. Majdi, Khalil N. Salman, Michael F. Morris, Nirav C. Merchant, Jeffrey J. Rodriguez

Research output: Contribution to journalArticlepeer-review


We propose a deep learning based method for classification of commonly occurring pathologies in chest X-ray images. The vast number of publicly available chest X-ray images provides the data necessary for successfully employing deep learning methodologies to reduce the misdiagnosis of thoracic diseases. We applied our method to the classification of two example pathologies, pulmonary nodules and cardiomegaly, and we compared the performance of our method to three existing methods. The results show an improvement in AUC for detection of nodules and cardiomegaly compared to the existing methods.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - May 19 2020


  • Cardiomegaly
  • Chest X-ray
  • Classification
  • Deep learning
  • Pulmonary nodule

ASJC Scopus subject areas

  • General

Fingerprint Dive into the research topics of 'Deep learning classification of chest x-ray images'. Together they form a unique fingerprint.

Cite this