{'fraction_non_alphanumeric': 0.02494154325798909, 'fraction_numerical': 0.00779423226812159, 'mean_word_length': 5.263414634146342, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An approach for chest tube detection in chest radiographs

It is known that overlapping tissues cause highly complex projections in chest radiographs. In addition, artificial objects, such as catheters, chest tubes and pacemakers can appear on these radiographs. It is important that the anomaly detection algorithms are not confused by these objects. To achieve this goal, the authors propose an approach to train a convolutional neural network (CNN) to detect chest tubes present on radiographs. To detect the chest tube skeleton as the final output in a better manner, non-uniform rational B-spline curves are used to automatically fit with the CNN output. This is the first study conducted to automatically detect artificial objects in the lung region of chest radiographs. Other automatic detection schemes work on the mediastinum. The authors evaluated the performance of the model using a pixel-based receiver operating characteristic (ROC) analysis. Each true positive, true negative, false positive and false negative pixel is counted and used for calculating average accuracy, sensitivity and specificity percentages. The results were 99.99% accuracy, 59% sensitivity and 99.99% specificity. Therefore they obtained promising results on the detection of artificial objects.