  Chest radiographs are primarily employed for the screening of pulmonary and
cardio-/thoracic conditions. Being undertaken at primary healthcare centers,
they require the presence of an on-premise reporting Radiologist, which is a
challenge in low and middle income countries. This has inspired the development
of machine learning based automation of the screening process. While recent
efforts demonstrate a performance benchmark using an ensemble of deep
convolutional neural networks (CNN), our systematic search over multiple
standard CNN architectures identified single candidate CNN models whose
classification performances were found to be at par with ensembles. Over 63
experiments spanning 400 hours, executed on a 11:3 FP32 TensorTFLOPS compute
system, we found the Xception and ResNet-18 architectures to be consistent
performers in identifying co-existing disease conditions with an average AUC of
0.87 across nine pathologies. We conclude on the reliability of the models by
assessing their saliency maps generated using the randomized input sampling for
explanation (RISE) method and qualitatively validating them against manual
annotations locally sourced from an experienced Radiologist. We also draw a
critical note on the limitations of the publicly available CheXpert dataset
primarily on account of disparity in class distribution in training vs. testing
sets, and unavailability of sufficient samples for few classes, which hampers
quantitative reporting due to sample insufficiency.
