Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Singh, Kuljeet; Gaur, Anubha; Kumar, Sachin; Shastri, Sourabh; Mansotra, Vibhakar Title A name given to the resource Deep CP-CXR: A Deep Learning Model for Classification of Covid-19 and Pneumonia Disease Using Chest X-Ray Images Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Annals of Data Science; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s40745-025-00601-3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s40745-025-00601-3</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105002645084?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105002645084?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Singh K., Department of Computer Science, School of Sciences, Christ University, Delhi-NCR, India; Gaur A., Information Technology, KIET Group of Institutions, Ghaziabad, India; Kumar S., Department of Computer Science &amp; IT, University of Jammu, Jammu &amp; Kashmir, Jammu, India; Shastri S., Department of Computer Science &amp; IT, University of Jammu, Jammu &amp; Kashmir, Jammu, India; Mansotra V., Department of Computer Science &amp; IT, University of Jammu, Jammu &amp; Kashmir, Jammu, India Description An account of the resource The global spread of the Coronavirus has caused a disastrous effect, affecting millions of people and making it crucial to take action. Numerous experts have worked extensively to create viable vaccines in the fight against this infectious disease. The current study offers hope by suggesting a deep learning model, Deep CP-CXR, for determining patients with Covid-19 and pneumonia. Our study encompasses two significant investigations. First, we used images from chest X-rays for binary classification to distinguish Covid-19-diagnosed patients from normal patients. Second, using chest X-ray images, we expanded the study to include several groups, such as pneumonia, Covid-19, and normal instances. The results of our studies were extremely promising. The binary classification achieved a remarkable average accuracy of 100%, allowing for accurate classification between Covid-19 patients and normal cases. In addition, the multiple-category classification was able to distinguish between Covid-19, pneumonia, and normal individuals with a remarkable average accuracy of 98.57%. These astounding findings lead us to conclude that the Deep CP-CXR method weve suggested for classifying Covid-19 and pneumonia patients enables medical professionals to perform it accurately. Healthcare providers worldwide will benefit significantly from this development since it has the potential to enhance both the detection and treatment of these ailments. The proposed deep learning approach improves the speed and precision in classifying the disease with which doctors can diagnose and treat their patients effectively. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. Subject The topic of the resource Chest X-ray; Classification; Covid-19; Deep learning; Pneumonia Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21985804; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online