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 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.