Tuberculosis (TB) is a severe comorbidity of HIV and chest x-ray (CXR) analysis is a necessary step in screening
for the infective disease. Automatic analysis of digital CXR images for detecting pulmonary abnormalities is
critical for population screening, especially in medical resource constrained developing regions. In this article,
we describe steps that improve previously reported performance of NLM’s CXR screening algorithms and help
advance the state of the art in the field. We propose a local-global classifier fusion method where two complementary
classification systems are combined. The local classifier focuses on subtle and partial presentation of the
disease leveraging information in radiology reports that roughly indicates locations of the abnormalities. In addition,
the global classifier models the dominant spatial structure in the gestalt image using GIST descriptor for
the semantic differentiation. Finally, the two complementary classifiers are combined using linear fusion, where
the weight of each decision is calculated by the confidence probabilities from the two classifiers. We evaluated
our method on three datasets in terms of the area under the Receiver Operating Characteristic (ROC) curve,
sensitivity, specificity and accuracy. The evaluation demonstrates the superiority of our proposed local-global
fusion method over any single classifier.
Meng Ding, Sameer Antani, Stefan Jaeger, Zhiyun Xue, Sema Candemir, Marc Kohli, and George Thoma, "Local-global classifier fusion for screening chest radiographs," Proc. SPIE 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications, 101380A (Presented at SPIE Medical Imaging: February 15, 2017; Published: 13 March 2017); https://doi.org/10.1117/12.2252459.
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