  Lung cancer is the leading cause of cancer death worldwide and a good
prognosis depends on early diagnosis. Unfortunately, screening programs for the
early diagnosis of lung cancer are uncommon. This is in-part due to the at-risk
groups being located in rural areas far from medical facilities. Reaching these
populations would require a scaled approach that combines mobility, low cost,
speed, accuracy, and privacy. We can resolve these issues by combining the
chest X-ray imaging mode with a federated deep-learning approach, provided that
the federated model is trained on homogenous data to ensure that no single data
source can adversely bias the model at any point in time. In this study we show
that an image pre-processing pipeline that homogenizes and debiases chest X-ray
images can improve both internal classification and external generalization,
paving the way for a low-cost and accessible deep learning-based clinical
system for lung cancer screening. An evolutionary pruning mechanism is used to
train a nodule detection deep learning model on the most informative images
from a publicly available lung nodule X-ray dataset. Histogram equalization is
used to remove systematic differences in image brightness and contrast. Model
training is performed using all combinations of lung field segmentation, close
cropping, and rib suppression operators. We show that this pre-processing
pipeline results in deep learning models that successfully generalize an
independent lung nodule dataset using ablation studies to assess the
contribution of each operator in this pipeline. In stripping chest X-ray images
of known confounding variables by lung field segmentation, along with
suppression of signal noise from the bone structure we can train a highly
accurate deep learning lung nodule detection algorithm with outstanding
generalization accuracy of 89% to nodule samples in unseen data.
