  PURPOSE: This study aimed to develop a deep learning-based tool to detect and
localize lung nodules with chest radiographs(CXRs). We expected it to enhance
the efficiency of interpreting CXRs and reduce the possibilities of delayed
diagnosis of lung cancer.
  MATERIALS AND METHODS: We collected CXRs from NCKUH database and VBD, an
open-source medical image dataset, as our training and validation data. A
number of CXRs from the Ministry of Health and Welfare(MOHW) database served as
our test data. We built a segmentation model to identify lung areas from CXRs,
and sliced them into 16 patches. Physicians labeled the CXRs by clicking the
patches. These labeled patches were then used to train and fine-tune a deep
neural network(DNN) model, classifying the patches as positive or negative.
Finally, we test the DNN model with the lung patches of CXRs from MOHW.
  RESULTS: Our segmentation model identified the lung regions well from the
whole CXR. The Intersection over Union(IoU) between the ground truth and the
segmentation result was 0.9228. In addition, our DNN model achieved a
sensitivity of 0.81, specificity of 0.82, and AUROC of 0.869 in 98 of 125
cases. For the other 27 difficult cases, the sensitivity was 0.54, specificity
0.494, and AUROC 0.682. Overall, we obtained a sensitivity of 0.78, specificity
of 0.79, and AUROC 0.837.
  CONCLUSIONS: Our two-step workflow is comparable to state-of-the-art
algorithms in the sensitivity and specificity of localizing lung nodules from
CXRs. Notably, our workflow provides an efficient way for specialists to label
the data, which is valuable for relevant researches because of the relative
rarity of labeled medical image data.
