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http://dx.doi.org/10.9718/JBER.2017.38.1.25

Evaluation on the Usefulness of X-ray Computer-Aided Detection (CAD) System for Pulmonary Tuberculosis (PTB) using SegNet  

Lee, J.H. (Dept. of Biomedical Science & Engineering, Gwangju Institute of Science & Technology)
Ahn, H.S. (Dept. of Biomedical Engineering, Konyang University)
Choi, D.H. (Dept. of Biomedical Engineering, Konyang University)
Tae, Ki Sik (Dept. of Biomedical Engineering, Konyang University)
Publication Information
Journal of Biomedical Engineering Research / v.38, no.1, 2017 , pp. 25-31 More about this Journal
Abstract
Testing TB in chest X-ray images is a typical method to diagnose presence and magnitude of PTB lesion. However, the method has limitation due to inter-reader variability. Therefore, it is essential to overcome this drawback with automatic interpretation. In this study, we propose a novel method for detection of PTB using SegNet, which is a deep learning architecture for semantic pixel wise image labelling. SegNet is composed of a stack of encoders followed by a corresponding decoder stack which feeds into a soft-max classification layer. We modified parameters of SegNet to change the number of classes from 12 to 2 (TB or none-TB) and applied the architecture to automatically interpret chest radiographs. 552 chest X-ray images, provided by The Korean Institute of Tuberculosis, used for training and test and we constructed a receiver operating characteristic (ROC) curve. As a consequence, the area under the curve (AUC) was 90.4% (95% CI:[85.1, 95.7]) with a classification accuracy of 84.3%. A sensitivity was 85.7% and specificity was 82.8% on 431 training images (TB 172, none-TB 259) and 121 test images (TB 63, none-TB 58). This results show that detecting PTB using SegNet is comparable to other PTB detection methods.
Keywords
Pulmonary tuberculosis (PTB); SegNet; Deep learning; Pixel-wise image labelling; PTB detection;
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Times Cited By KSCI : 1  (Citation Analysis)
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