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 Dodamani, Pooja Shivanand; Palanisamy, Kanmani; Danti, Ajit Title A name given to the resource Novel Approach for Osteoporosis Classification Using 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 Biomedical and Pharmacology Journal;Volume;18;pp.203-216 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.13005/bpj/3082" target="_blank" rel="noreferrer noopener">https://doi.org/10.13005/bpj/3082</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105001193909?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105001193909?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 Dodamani P.S., Computer Science and Engineering, Christ University, Bangalore, India; Palanisamy K., Christ University, Computer Science and Engineering, CSE, Bangalore, India; Danti A., Computer Science and Engineering, Christ University, Bangalore, India Description An account of the resource This research delves into the technical advancements of image segmentation and classification models, specifically the refined Pix2Pix and Vision Transformer (ViT) architectures, for the crucial task of osteoporosis detection using X-ray images. The improved Pix2Pix model demonstrates noteworthy strides in image segmentation, achieving a specificity of 97.24% and excelling in the reduction of false positives. Simultaneously, the modified ViT models, especially the MViT-B/16 variant, exhibit superior accuracy at 96.01% in classifying osteoporosis cases, showcasing their proficiency in identifying critical medical conditions. These models are poised to revolutionize osteoporosis diagnosis, providing clinicians with accurate tools for early detection and intervention. The synergies between the Pix2Pix and ViT models open avenues for nuanced approaches in automated diagnostic systems, with the potential to significantly improve clinical results and contribute to the broader landscape of medical image analysis. As osteoporosis remains a prevalent and often undiagnosed condition, the technical insights from this study hold substantial importance in advancing the field, emphasizing the critical role of accurate diagnostic tools in improving patient care and health outcomes. 2025 Oriental Scientific Publishing Company. All rights reserved. Subject The topic of the resource BMD; Osteoporosis; Pix2Pix Segmentation; ViT Classification; X-ray Images Publisher An entity responsible for making the resource available Oriental Scientific Publishing Company Relation A related resource ISSN: 9746242; 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 All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online