Artificial Intelligence and Data Science Methods for Automatic Detection of White Blood Cells in Images.

Journal: Journal of imaging informatics in medicine
Published Date: May 16, 2025

Abstract

Data scieQuerynce (DS) methods and artificial intelligence (AI) are critical in today's healthcare services operations. This study focuses on evaluating the effectiveness of AI and DS in biomedical diagnostics, including automatic detection and counting of white blood cells (WBCs) and types, which provide valuable information for diagnosing and treating blood diseases such as leukemia. Automating these tasks using AI and DS saves time and avoids or minimizes errors compared to manual processes, which can be complex and error prone. The study utilizes bibliographic data from SCOPUS to evaluate research on applying AI algorithms and DS methods for mapping and classifying WBC images for treatment of blood diseases, such as leukemia using literature survey and science mapping methodology. The results show the potency of different DS methods and AI algorithms, such as machine learning, deep learning, and classification algorithms that enable the automatic detection of WBC images. AI and DS algorithms offer critical benefits in effectively and efficiently analyzing microscopic images of blood cells. The automatic identification, localization, and classification of WBCs speed up the patient diagnosis process, allowing hematologists to focus on interpreting results. Automatic processes identify specific abnormalities and patterns, enhancing accuracy and timely diagnoses. Future work will examine the application of generative AI in blood cells diagnostics.

Authors

  • Yawo M Kobara
    Odette School of Business, University of Windsor, Windsor, ON, Canada.
  • Ikpe Justice Akpan
    Department of Management and Information Systems, Kent State University at Tuscarawas, New Philadelphia, Ohio, USA.
  • Alima Damipe Nam
    Residency Program, National Institute of Hygiene, University of Lomé, Lome, Togo.
  • Firas H AlMukthar
    Department of Information Technology, Catholic University in Erbil, Erbil, Kurdistan Region, Iraq.
  • Mbuotidem Peter
    Healthcare Research Group, Ibom International Center for Research and Scholarship, Windsor, ON, Canada.

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