AI-powered body fluid cell classification: Development and validation using Roboflow and YOLOv11n framework
Accurate classification of body fluid cells is crucial for diagnosing various medical conditions. Traditional manual methods are often time-consuming and subject to inter-observer variability. In this study, we developed and compared two AI-based classification models, YOLOv11n and Roboflow 3.0, for...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Telematics and Informatics Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S277250302500057X |
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| Summary: | Accurate classification of body fluid cells is crucial for diagnosing various medical conditions. Traditional manual methods are often time-consuming and subject to inter-observer variability. In this study, we developed and compared two AI-based classification models, YOLOv11n and Roboflow 3.0, for body fluid cell classification. A dataset comprising 12,966 cells across 14 distinct categories was used for training (70 %), validation (20 %), and testing (10 %). Expert consensus from three medical technologists ensured robust labeling, achieving a Fleiss’ Kappa of 0.877. Both models were validated against expert consensus on unseen images, with YOLOv11n achieving a Kappa of 0.932 and Roboflow 3.0 a Kappa of 0.930, indicating almost perfect agreement. The Roboflow 3.0 model demonstrated a mean average precision (mAP50) of 78.9 %, with a precision of 74.3 %, recall of 75.0 %, and F1 score of 74.6 %, while YOLOv11n achieved an mAP50 of 73.9 %, precision of 69.8 %, recall of 75.0 %, and F1 score of 72.3 %. Both models showed high accuracy for common cell types but faced challenges with rarer categories and in distinguishing between morphologically similar cells like macrophages and monocytes. Notably, these models offer a consistent and efficient tool for cell classification, providing an advantage for educational purposes and facilitating the reskilling of medical technologists in the application of AI in diagnostics. |
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| ISSN: | 2772-5030 |