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Related Experiment Video

Updated: May 6, 2026

Controlled Microfluidic Environment for Dynamic Investigation of Red Blood Cell Aggregation
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Automated Blood Cell Detection and Classification in Microscopic Images Using YOLOv11 and Optimized Weights.

Halenur Sazak1, Muhammed Kotan1

  • 1Department of Information Systems Engineering, Faculty of Computer and Information Sciences, Sakarya University, Sakarya 54050, Turkey.

Diagnostics (Basel, Switzerland)
|January 11, 2025
PubMed
Summary

This study introduces YOLOv11 for automated blood cell classification, achieving 93.8% mAP accuracy. This advancement enhances hematological analysis and aids in diagnosing conditions through precise red blood cell, white blood cell, and platelet identification.

Keywords:
YOLOv11automated detectionblood cell detectioncomputer visionmedical imaging

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Area of Science:

  • Medical Imaging
  • Hematology
  • Computer Vision

Background:

  • Accurate blood cell detection is vital for diagnosing hematological disorders.
  • Automating this process can significantly improve diagnostic efficiency.

Purpose of the Study:

  • To develop and evaluate advanced YOLOv10 and YOLOv11 models for automated blood cell detection and classification.
  • To focus on identifying red blood cells (RBCs), white blood cells (WBCs), and platelets for complete blood count (CBC) analysis.

Main Methods:

  • Utilized the Blood Cell Count Detection (BCCD) dataset, enhanced with data augmentation.
  • Conducted experiments with complete weight initialization and advanced optimization for YOLOv11.
  • Performed meticulous hyperparameter tuning for the YOLOv11 architecture.

Main Results:

  • The YOLOv11-l model achieved a mean Average Precision (mAP) of 93.8%.
  • Demonstrated robust accuracy in classifying multiple blood cell types.

Conclusions:

  • The YOLOv11 architecture is highly effective for automated blood cell classification.
  • This technology shows significant potential for improving hematological analyses and clinical diagnosis.