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 Patil, Ashwini P.; Hiremath, Manjunatha Title A name given to the resource Utilizing Deep Learning Features to Categorize WBCs in Blood Smear 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 Lecture Notes in Electrical Engineering;Volume;1399 LNEE;pp.327-335 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-4430-8_23" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-4430-8_23</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105012431540?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105012431540?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 Patil A.P., Department of Computer Science, CHRIST (Deemed to Be University), Bengaluru, India, Department of Computer Applications, CMR Institute of Technology, Bengaluru, India; Hiremath M., Department of Computer Science, CHRIST (Deemed to Be University), Bengaluru, India Description An account of the resource Automated categorization of white blood cells (WBCs) is essential not just to identify infections, autoimmune ailments, and blood-related disorders, but also in the pivotal decision-making process concerning patient treatment and the efficient management of diseases. In this paper, an advanced approach for WBC type classification using smear images is proposed. The VGG16 model is utilized to capture intricate features of the images, which are then provided to an XGBoost classifier. This integration enables precise classification into 5 distinct WBC types. Our model shows a significant accuracy score of 92.3%, demonstrating its capability in accurately identifying WBC types from smear images. Proposed technique provides a promising pathway for automating WBC classification, thereby enhancing efficiency in disease diagnosis and decision-making within clinical settings. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Blood smear image; Deep learning; VGG16; WBC; XGBoost Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 18761100; ISBN: 978-981964429-2; 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