Radiology “forensics”: determination of age and sex from chest radiographs using deep learning
Radiology “forensics”: determination of age and sex from chest radiographs using deep learning
About this item
Full title
Author / Creator
Publisher
Cham: Springer International Publishing
Journal title
Language
English
Formats
Publication information
Publisher
Cham: Springer International Publishing
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Scope and Contents
Contents
Purpose
To develop and test the performance of deep convolutional neural networks (DCNNs) for automated classification of age and sex on chest radiographs (CXR).
Methods
We obtained 112,120 frontal CXRs from the NIH ChestX-ray14 database performed in 48,780 females (44%) and 63,340 males (56%) ranging from 1 to 95 years old. The dataset wa...
Alternative Titles
Full title
Radiology “forensics”: determination of age and sex from chest radiographs using deep learning
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_proquest_miscellaneous_2537646569
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_2537646569
Other Identifiers
ISSN
1070-3004
E-ISSN
1438-1435
DOI
10.1007/s10140-021-01953-y
