Utilizing Deep Learning Features to Categorize WBCs in Blood Smear Images

Title

Utilizing Deep Learning Features to Categorize WBCs in Blood Smear Images

Creator

Patil, Ashwini P.; Hiremath, Manjunatha

Description

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.

Source

Lecture Notes in Electrical Engineering;Volume;1399 LNEE;pp.327-335

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Blood smear image; Deep learning; VGG16; WBC; XGBoost

Coverage

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

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 18761100; ISBN: 978-981964429-2;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-96-4430-8_23

https://www.scopus.com/pages/publications/105012431540?origin=resultslist