We propose a novel deep neural network architecture for normalcy detection in chest x-ray images. This architecture treats the problem as fine-grained binary classification in which the normal cases are well-defined as a class while leaving all other cases in the broad class of abnormal. It employs several components that allow generalization and prevent overfitting across demographics. The model is trained and validated on a large public dataset of frontal chest X-ray images. It is then tested independently on images from a clinical institution of differing patient demographics using a three radiologist consensus for ground truth labeling. The model provides an area under ROC curve of 0.96 when tested on 1271 images. We can automatically remove nearly a third of disease-free chest X-ray screening images from the workflow, without introducing any false negatives (100% sensitivity to disease) thus raising the potential of expediting radiology workflows in hospitals in future.