Open AccessDeep learning-based pulmonary tuberculosis automated detection on chest radiography: large-scale independent testing
Author(s) -
Wenxia Zhou,
Guanxun Cheng,
Ziqi Zhang,
Litong Zhu,
Stefan Jaeger,
Fleming Lure,
Lin Guo
Publication year - 2022
Publication title -
quantitative imaging in medicine and surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.766
H-Index - 21
eISSN - 2223-4306
pISSN - 2223-4292
DOI - 10.21037/qims-21-676
Subject(s) - artificial intelligence , convolutional neural network , deep learning , artificial neural network , radiography , computer science , data set , tuberculosis , scale (ratio) , pattern recognition (psychology) , medicine , machine learning , radiology , pathology , cartography , geography
It is critical to have a deep learning-based system validated on an external dataset before it is used to assist clinical prognoses. The aim of this study was to assess the performance of an artificial intelligence (AI) system to detect tuberculosis (TB) in a large-scale external dataset.
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