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All Journal Journal of Mathematics Education and Application (JMEA) Academica : Journal of Multidisciplinary Studies Jurnal Kreativitas PKM Jurnal Kesehatan Budi Luhur: Jurnal Ilmu-Ilmu Kesehatan Masyarakat, Keperawatan, dan Kebidanan Jurnal Ilmiah Mahasiswa Pendidikan Jurnal Inovasi Pembelajaran dan Pendidikan Islam Jurnal Horizon Pendidikan Jurnal Kesehatan Bakti Tunas Husada JPS (Jurnal Perbankan Syariah) Pinisi: Physics Journal
Hasanah, Annisa Nur
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Chemistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Health Professions Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Application of Artificial Intelligence in White Blood Cell Classification Based on Microscopic Images: A Scoping Review Hasanah, Annisa Nur; Sas, Oktafirani Al; Kosen, Yosua Darmadi
Jurnal Kesehatan Budi Luhur: Jurnal Ilmu-Ilmu Kesehatan Masyarakat, Keperawatan, dan Kebidanan Vol. 18 No. 2 (2025): July 2025
Publisher : STIKes Budi Luhur Cimahi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62817/jkbl.v18i2.432

Abstract

White blood cell (WBC) classification plays a crucial role in hematological diagnosis and is typically performed manually using microscopic images. However, manual analysis is limited by subjectivity and time inefficiency. With recent technological advances, artificial intelligence (AI) offers promising solutions for automated WBC classification that enhance accuracy and efficiency. This study presents a scoping review of 20 scientific publications discussing AI applications in microscopic image-based WBC classification. Literature searches were conducted in PubMed, ScienceDirect, Institute of Electrical and Electronics Enginers (IEEE) Xplore, and Google Scholar using relevant keywords such as “AI”, “white blood cell”, and “microscopic image”. Findings indicate that the most commonly used method is Convolutional Neural Network (CNN), either standalone or hybrid (e.g., YOLOv5, ResNet, Vision Transformer), achieving accuracies up to 99.7%. The datasets were mostly public Blood Cell Count and Detection (BCCD), Leucocyte Images for Segmentation and Classification (LISC), Raabin-WBC or local laboratory sources. The reviewed studies aimed at automatic WBC detection, classification, and morphological identification. Despite encouraging outcomes, challenges such as external validation and limited access to real clinical data remain. Overall, AI has proven effective in enhancing speed, accuracy, and objectivity in WBC classification. Further research is needed to support AI integration into real-world clinical laboratory practice.
Co-Authors Ainni, Siti Nur Ellis Mardiana Panggabean Fauzan, Abd Rasyid Anhar Handini, Resa Irvan Irvan Izdahara, Dini Kasmanto, Hendro Kosen, Yosua Darmadi Ningsih, Sri Rahayu Adi Novita, Anggi Nur, Wahyuna Nurzannah Oktafirani Al Sas pratama, Indra Mulia Pratama, Nurrahma Ramba, Rezha Julyanti Rianti Nurpalah, Rianti Umiyati Umiyati, Umiyati Wati Wati