  Chest X-ray examination plays an important role in lung disease detection.
The more accuracy of this task, the more experienced radiologists are required.
After ChestX-ray14 dataset containing over 100,000 frontal-view X-ray images of
14 diseases was released, several models were proposed with high accuracy. In
this paper, we develop a work flow for lung disease diagnosis in chest X-ray
images, which can improve the average AUROC of the state-of-the-art model from
0.8414 to 0.8445. We apply image preprocessing steps before feeding to the 14
diseases detection model. Our project includes three models: the first one is
DenseNet-121 to predict whether a processed image has a better result, a
convolutional auto-encoder model for bone shadow exclusion is the second one,
and the last is the original CheXNet.
