资源介绍
# Introduction Due to relatively cheap price and easy accessibility chest X-ray (CXR) imaging is used widely for health monitoring and diagnosis of many lung diseases (pneumonia, tuberculosis, cancer, etc.). Manual analysis and detection by CXR of marks of these diseases is carried out by expert radiologists, which is a long and complicated process. Nevertheless, the modern [evolution of general-purpose graphic processing cards (GPU) hardware][1] (1) and [software for medical image analysis][2] (2), especially [deep learning techniques][3] (3), allows scientists to detect automatically many lung diseases from CXR images [at a level exceeding certified radiologists][4] (4). Despite these successes the strong belief exists among experts that deep learning techniques become efficient for the very big datasets (>10 000 images), because for the smaller datasets (<1000 images) they produce bad predictions (if any at all) with the very low accuracies. # Dataset Description This dataset contains manually segmented lung masks for [Shenzhen Hospital X-ray Set][5] (5) that was used in our recent paper for the description of the lung segmentation technique in combination with lossless and lossy data augmentation which allow us to get the statistically reliable predictions of lung diseases (availability of tuberculosis) for such a small dataset (<1000 images) even. The details on this dataset and related research could be found in the related publication ["Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation"][6] (6). # Acknowledgements Shenzhen Hospital X-ray Set: X-ray images in this data set have been collected by Shenzhen No.3 Hospital in Shenzhen, Guangdong providence, China. The x-rays were acquired as part of the routine care at Shenzhen Hospital. The set contains images in JPEG format. There are 326 normal x-rays and 336 abnormal x-rays showing various manifestations of tuberculosis. These segmentation masks for Shenzhen Hospital X-ray Set were prepared manually by students and teachers of [Computer Engineering Department, Faculty of Informatics and Computer Engineering, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute
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