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 Conference Paper Faculty Publications- Conference Papers 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 Nanjundan, Preethi; Mishra, Suraj; George, Jossy P.; Dash, Bibhuti Bhusan Title A name given to the resource Explainable AI in Healthcare: A Hybrid CNN-ViT Approach for Pneumonia Detection Using SHAP Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Communications in Computer and Information Science;Volume;2730 CCIS;pp.286-308 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-12232-2_20" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-12232-2_20</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105027520190?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105027520190?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 Nanjundan P., Department of Data Science, CHRIST University, Bangalore, India; Mishra S., Department of Data Science, CHRIST University, Bangalore, India; George J.P., Department of Data Science, CHRIST University, Bangalore, India; Dash B.B., School of Computer Applications, KIIT Deemed to be University, Bhubaneswar, India Description An account of the resource The adoption of Artificial Intelligence (AI) in healthcare has improved diagnostic accuracy, particularly in medical imaging. However, the opaque nature of deep learning models raises concerns about interpretability in high-stakes applications such as pneumonia diagnosis. This study proposes an Explainable AI (XAI) framework that integrates a Hybrid CNN-ViT architecture with SHAP (SHapley Additive Explanations) for pneumonia detection from chest X-rays. Our approach achieves competitive diagnostic performance (94% accuracy) while enhancing transparency by highlighting clinically relevant features such as lobar consolidations and ground-glass opacities. By grounding explanations in established radiological findings, the framework supports clinical trust and regulatory compliance. This work contributes to bridging the gap between AI performance and medical accountability, positioning explainable deep learning as a trustworthy tool for real-world healthcare deployment. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource CNN-ViT; Explainable AI; Medical Imaging; Pneumonia Detection; SHAP Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 18650929; ISBN: 978-303212231-5; Language A language of the resource English Type The nature or genre of the resource Conference paper 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