Deep Neural Network for Foreign Object Detection in Chest X-Rays

In automated Chest X-Ray (CXR) screening process, foreign objects, such as coins/buttons, medical tubes and devices, and jewelries can adversely impact the performance. In an automated process, conventional machine learning algorithms did not separately consider them into account, and as a consequen...

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Bibliographic Details
Main Authors: Santosh, K.C., Dhar, Mrinal K, Rajbhandari, Ramina, Neupane, Amul
Format: Conference Proceeding
Language:English
Subjects:
Biomedical imaging
Chest X-rays
Deep Neural Networks
Faster R-CNN
Feature extraction
Foreign Objects
Lung
Object detection
Proposals
Tuberculosis
X-rays
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Summary:In automated Chest X-Ray (CXR) screening process, foreign objects, such as coins/buttons, medical tubes and devices, and jewelries can adversely impact the performance. In an automated process, conventional machine learning algorithms did not separately consider them into account, and as a consequence, they results in false positive cases. In this paper, we address the use of Deep Neural Network (DNN) to detect circle-like foreign objects of difference sizes in CXRs. We present faster Region-based Convolutional Neural Network (R-CNN) for foreign object detection on a set of 400 publicly available CXR images hosted by LHNCBC, U.S. National Library of Medicine (NLM), National Institutes of Health (NIH). The proposed DNN achieved 97% precision, 90% recall, and 93% F1-score. The results are comparable with the existing techniques.
ISSN:2372-9198
DOI:10.1109/CBMS49503.2020.00107