  Chest radiography is an effective screening tool for diagnosing pulmonary
diseases. In computer-aided diagnosis, extracting the relevant region of
interest, i.e., isolating the lung region of each radiography image, can be an
essential step towards improved performance in diagnosing pulmonary disorders.
Methods: In this work, we propose a deep learning approach to enhance abnormal
chest x-ray (CXR) identification performance through segmentations. Our
approach is designed in a cascaded manner and incorporates two modules: a deep
neural network with criss-cross attention modules (XLSor) for localizing lung
region in CXR images and a CXR classification model with a backbone of a
self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR
data sets. The proposed pipeline is evaluated on Shenzhen Hospital (SH) data
set for the segmentation module, and COVIDx data set for both segmentation and
classification modules. Novel statistical analysis is conducted in addition to
regular evaluation metrics for the segmentation module. Furthermore, the
results of the optimized approach are analyzed with gradient-weighted class
activation mapping (Grad-CAM) to investigate the rationale behind the
classification decisions and to interpret its choices. Results and Conclusion:
Different data sets, methods, and scenarios for each module of the proposed
pipeline are examined for designing an optimized approach, which has achieved
an accuracy of 0.946 in distinguishing abnormal CXR images (i.e., Pneumonia and
COVID-19) from normal ones. Numerical and visual validations suggest that
applying automated segmentation as a pre-processing step for classification
improves the generalization capability and the performance of the
classification models.
