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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
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Research and Optimization of White Blood Cell Classification Methods Based on Deep Learning and Fourier Ptychographic

Mingjing Li1, Junshuai Wang1, Shu Fang1

  • 1College of Electronic Information Engineering, Changchun University, Changchun 130022, China.

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|May 14, 2025
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Summary

This study introduces CCE-YOLOv7, an enhanced algorithm for classifying white blood cells (WBCs) using deep learning. The novel method significantly improves accuracy and efficiency in automated hematopathology diagnostics.

Keywords:
CARAFE upsamplingEMA mechanismFourier ptychographic microscopySoft-NMSYOLOv7white blood cell classification

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Area of Science:

  • Medical imaging analysis
  • Computational pathology
  • Artificial intelligence in diagnostics

Background:

  • Traditional white blood cell (WBC) classification methods face limitations in receptive fields and contextual information utilization, impacting diagnostic accuracy.
  • Hematopathology and clinical diagnostics rely heavily on accurate WBC classification for disease identification and patient management.

Purpose of the Study:

  • To develop an enhanced WBC classification algorithm, CCE-YOLOv7, that overcomes the limitations of traditional methods by improving detection accuracy and model efficiency.
  • To leverage deep learning, specifically the YOLOv7 framework, with novel architectural components for superior feature representation and classification performance.

Main Methods:

  • Proposed CCE-YOLOv7 integrates a novel Conv2Former backbone for combined local and global feature extraction.
  • Incorporated Content-Aware ReAssembly of Features (CARAFE) upsampling and an Efficient Multi-scale Attention (EMA) module for enhanced feature maps and fusion.
  • Utilized Soft Non-Maximum Suppression (Soft-NMS) to improve handling of overlapping cells, reducing classification errors.

Main Results:

  • CCE-YOLOv7 achieved 89.3% detection accuracy on a WBC image dataset, a 7.8% improvement over baseline YOLOv7.
  • The model demonstrated enhanced efficiency, reducing parameters by 2 million and computational complexity by 5.7 GFLOPs compared to YOLOv7.
  • CCE-YOLOv7 outperformed YOLOv8 in accuracy by 4.1% and offered better computational efficiency than YOLOv11.

Conclusions:

  • CCE-YOLOv7 presents a robust, accurate, and computationally efficient solution for automated WBC classification.
  • The algorithm's improvements in accuracy and efficiency suggest significant potential for real-time clinical applications in hematopathology.
  • The study highlights the effectiveness of combining convolutional and transformer architectures with advanced attention and feature fusion mechanisms for medical image analysis.