A dataset of COVID-19 x-ray chest images

The distinction between normal chest x-ray (CXR) images and abnormal ones containing features of disease (e.g., opac-ities, consolidation, etc.) is important for accurate medical di-agnosis. CXR images contain valuable information concerning the physiological and pathological state of the lungs and air-ways. In addition, they provide information about the heart, chest bones, and some arteries (e.g., Aorta and pulmonary ar-teries). Deep learning artificial intelligence has taken great strides in the development of sophisticated medical mod-els in a wide range of applications. More specifically, it has been shown to provide highly accurate diagnosis and de-tection tools. The dataset presented in this article contains the chest x-ray images from the examination of confirmed COVID-19 subjects, who were admitted for a multiday stay at a local hospital in northern Jordan. To provide a diverse dataset, only one CXR image per subject was included in the data. The dataset can be used for the development of automated methods that detect COVID-19 from CXR images (COVID-19 vs. normal) and distinguish pneumonia caused by COVID-19 from other pulmonary diseases. (c) 202x The Au-thor(s). Published by Elsevier Inc. This is an open access arti-cle under the CC BY-NC-ND license ( http://creativecommons. org/licenses/by- nc-nd/4.0/ )(c) 2023 Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)