As one of the most ubiquitous diagnostic imaging tests in medical practice, chest radiography requires timely reporting of potential findings and diagnosis of diseases in the images. Automated, fast, and reliable detection of diseases based on chest radiography is a critical step in radiology workflow. In response to this issue, artificial intelligence methods such as deep learning are promising options for automatic diagnosis because they have achieved state-of-the-art performance in the analysis of visual information and a wide range of medical images. This paper presents a survey of deep learning for lung disease detection in chest X ray images. The taxonomy presents five attributes that are common in the surveyed articles: types of deep learning algorithms, features used for detection of abnormalities, data augmentation, transfer learning, and types of lung diseases. The presented taxonomy could prove to be extremely useful for other researchers to ideate their research contributions in this area. The potential future direction suggested could further improve the efficiency and increase the number of deep learning aided lung disease detection applications.