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Enhanced Blood Cell Detection in YOLOv11n Using Gradient Accumulation and Loss Reweighting.

Min Feng1,2, Juncai Xu3,4

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This study optimized the YOLOv11n model for automated blood cell detection, significantly improving diagnostic accuracy for hematological diseases. The enhanced model efficiently handles challenging cell images, aiding timely medical intervention.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Hematology

Background:

  • Automated blood cell detection is crucial for diagnosing hematological diseases.
  • Current methods face challenges with dense, overlapping cells and image artifacts.
  • Advancements in AI can enhance diagnostic speed and accuracy.

Purpose of the Study:

  • To optimize the YOLOv11n model for improved blood cell detection in clinical images.
  • To enhance the accuracy and efficiency of automated hematological diagnostics.
  • To address limitations in current automated cell detection systems.

Main Methods:

  • Optimized the YOLOv11n model using gradient accumulation and loss reweighting.
  • Evaluated model performance on clinical blood cell images.
  • Conducted ablation experiments to validate technique contributions.

Main Results:

  • The optimized YOLOv11n model achieved high performance: mAP50 of 0.9356 and mAP50-95 of 0.6620.
  • Demonstrated superior precision and recall compared to existing methods.
  • Successfully addressed issues of dense cell distribution, overlap, and artifacts.

Conclusions:

  • The optimized YOLOv11n model offers significant improvements for automated blood cell detection.
  • Gradient accumulation and loss reweighting show a synergistic effect, boosting accuracy without computational overhead.
  • The model shows strong potential for real-time clinical integration in hematology workflows.