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: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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)
Title of host publication2020 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages116-119
Number of pages4
ISBN (Electronic)9781728157450
DOIs
StatePublished - Mar 2020
Event2020 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2020 - Santa Fe, United States
Duration: Mar 29 2020Mar 31 2020

Publication series

NameProceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation
Volume2020-March

Conference

Conference2020 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2020
CountryUnited States
CitySanta Fe
Period3/29/203/31/20

Keywords

  • cardiomegaly
  • chest X-ray
  • classification
  • deep learning
  • pulmonary nodule

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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  • Cite this

    Majdi, M. S., Salman, K. N., Morris, M. F., Merchant, N. C., & Rodriguez, J. J. (2020). Deep Learning Classification of Chest X-Ray Images. In 2020 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2020 - Proceedings (pp. 116-119). [9094612] (Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation; Vol. 2020-March). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSIAI49293.2020.9094612