  The recent progress of computing, machine learning, and especially deep
learning, for image recognition brings a meaningful effect for automatic
detection of various diseases from chest X-ray images (CXRs). Here efficiency
of lung segmentation and bone shadow exclusion techniques is demonstrated for
analysis of 2D CXRs by deep learning approach to help radiologists identify
suspicious lesions and nodules in lung cancer patients. Training and validation
was performed on the original JSRT dataset (dataset #01), BSE-JSRT dataset,
i.e. the same JSRT dataset, but without clavicle and rib shadows (dataset #02),
original JSRT dataset after segmentation (dataset #03), and BSE-JSRT dataset
after segmentation (dataset #04). The results demonstrate the high efficiency
and usefulness of the considered pre-processing techniques in the simplified
configuration even. The pre-processed dataset without bones (dataset #02)
demonstrates the much better accuracy and loss results in comparison to the
other pre-processed datasets after lung segmentation (datasets #02 and #03).
