Real-Time White Blood Cell Classification with YOLO

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dc.contributor.authorEamkong, Anoma
dc.contributor.authorPintavirooj, Chuchart
dc.contributor.authorTreebupachatsakul, Treesukon
dc.date.accessioned2026-08-06T10:49:16Z
dc.date.available2026-08-06T10:49:16Z
dc.date.issued2025-01-01
dc.description.abstractWhite blood cell (WBC) classification plays a crucial role in diagnosing various hematological conditions, including infections, immune disorders, and leukemia. This study presents an automated approach for WBC detection and classification using the YOLOv5 deep learning model. The system integrates a 1.3MP microscope camera with a stepper motor-driven platform for real-time imaging and classification. The dataset consists of five WBC types: basophils, eosinophils, lymphocytes, monocytes, and neutrophils, with image enhancement and data augmentation applied to improve model performance. The trained YOLOv5 model achieved a classification accuracy of 92.61% and a validation accuracy of 95.86%, demonstrating high precision and recall in WBC identification. The results indicate that this system can effectively automate WBC analysis, reducing manual effort and improving diagnostic accuracy. This approach has potential applications in clinical hematology, offering a rapid and reliable method for WBC classification.
dc.identifier.citationBmeicon 2025 17th Biomedical Engineering International Conference, 2025
dc.identifier.doi10.1109/BMEICON66226.2025.11113710
dc.identifier.other2-s2.0-105015571484
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/16471
dc.sourceBmeicon 2025 17th Biomedical Engineering International Conference
dc.subjectDeep Learning
dc.subjectMicroscope camera
dc.subjectWhite Blood Cells (WBCs)
dc.subjectYOLOv5
dc.titleReal-Time White Blood Cell Classification with YOLO
dc.typeConference Paper

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