Application of Artificial Intelligence in White Blood Cell Classification Based on Microscopic Images: A Scoping Review
DOI:
https://doi.org/10.62817/jkbl.v18i2.432Keywords:
artificial intelligence, white blood cells, classification, microscopic image, CNNAbstract
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.
References
Abou Ali, M. D.-C. (2023). White Blood Cell Classification: Convolutional Neural Network (CNN) and Vision Transformer (ViT) under Medical Microscope. Algorithms, 16(11). https://doi.org/10.3390/a16110525, 525.
Arksey, H. &. (2005). Scoping studies: towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19-32.
Bain, B. J. (2015). Blood Cells: A Practical Guide. Wiley-Blackwell.
Baydilli, e. a. (2021). Classification of white blood cells using SVM.
Caraka, B. S. (2018). Klasifikasi Sel Darah Putih Menggunakan Metode Support Vector Machine (SVM) Berbasis Pengolahan Citra Digital. Indonesian Journal of Electronics and Instrumentation Systems 8(2), https://doi.org/10.22146/ijeis.15420, 109–117.
Chen, Y. C. (2024). DAFFNet: A Dual Attention Feature Fusion Network for Classification of White Blood Cells. arXiv preprint arXiv:2405. https://arxiv.org/abs/2405.16220, 16220.
Jung, C. A. (2019). W-Net: A CNN-based Architecture for White Blood Cells Image Classification. arXiv preprint arXiv:1910.01091. https://arxiv.org/abs/1910.01091.
Levac, D. C. (2010). Scoping studies: advancing the methodology. Implementation Science, 5(1), 69.
Liang, G. H. (2018). Combining convolutional neural network with recursive neural network for blood cell image classification. IEEE Access, 36188–36197.
Lu, a. a. (2023). A real-time WBC detection system using YOLOv5 and hybrid CNN.
Mohamed, M. A. (2021). Leukocyte image classification using deep learning techniques. Scientific Reports, https://doi.org/10.1038/s41598-021-03469-3, 23972.
Rezatofighi, S. H.-Z. (2011). Automatic recognition of five types of white blood cells in peripheral blood. Computerized Medical Imaging and Graphics 35(4), 333–343.
Settouti, N. S. (2020). An instance and variable selection approach in pixel-based classification for automatic white blood cells segmentation. Pattern Analysis and Applications, 23(4). https://doi.org/10.1007/s10044-020-00873-w, 1709–1726.
Yao, X. S. (2021). Classification of white blood cells using weighted optimized deformable convolutional neural networks. Artificial Cells, Nanomedicine, and Biotechnology 49(1). https://doi.org/10.1080/21691401.2021.1879823, 147–155.
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Copyright (c) 2025 Annisa Nur Hasanah, Oktafirani Al Sas, Yosua Darmadi Kosen
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