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Page 27
allied
academies
Microbiology: Current Research
Volume 2
International Conference on
Emerging Diseases, Outbreaks & Case Studies
&
16
th
Annual Meeting on
March 28-29, 2018 | Orlando, USA
Influenza
O
ur primary motivator is the need for screening HIV+
populations in resource-constrained regions for
exposure to Tuberculosis (TB), using poster anterior chest
radiographs (CXRs). The proposed method is motivated by
the observation that radiological examinations routinely
conduct bilateral comparisons of the lung field. Also,
abnormal CXRs tend to exhibit changes in the lung shape, size
and content (textures), and in overall, reflection symmetry
between them. We analyze lung region symmetry using
multi-scale shape features, and edge plus texture features.
Shape features exploit local and global representation of the
lung regions, while edge and texture features take internal
content, including spatial arrangements of the structures. For
classification, we have performed voting-based combination
of three different classifiers: Bayesian network (BN),
multilayer perception (MLP) neural networks and random
forest (RF). We have used three CXR benchmark collections
made available by the US National Library of Medicine, and
National Institute of Tuberculosis and Respiratory Diseases,
India, and have achieved maximum abnormality detection
accuracy (ACC) of 91.00% and area under the ROC curve
(AUC) of 0.96. The proposed method outperforms the
previously reported methods by more than 5% in ACC and
3% in AUC.
e:
santosh.kc@usd.eduAutomated chest X-ray screening: Can lung region symmetry help detect pulmonary abnormalities?
K C Santosh
1
and
Sameer Antani
2
1
The University of South Dakota, USA
2
National Institutes of Health, USA