AffiliationUniv Arizona, Dept Elect & Comp Engn
Univ Arizona, Dept Med Imaging
Univ Arizona, Data Sci Inst
Univ Arizona, Dept Radiol
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CitationMajdi, M. S., Salman, K. N., Morris, M. F., Merchant, N. C., & Rodriguez, J. J. (2020, March). Deep learning classification of chest x-ray images. In 2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) (pp. 116-119). IEEE.
RightsCopyright © 2020, IEEE
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractWe 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.
VersionFinal accepted manuscript