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1.2 SSIAI Version Final submission ...
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Final Accepted Manuscript
Affiliation
Univ Arizona, Dept Elect & Comp EngnUniv Arizona, Dept Med Imaging
Univ Arizona, Data Sci Inst
Univ Arizona, Dept Radiol
Issue Date
2020-05-18
Metadata
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IEEECitation
Majdi, 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.Rights
Copyright © 2020, IEEE.Collection Information
This 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 repository@u.library.arizona.edu.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.ISSN
1550-5782Version
Final accepted manuscriptae974a485f413a2113503eed53cd6c53
10.1109/ssiai49293.2020.9094612