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Research on Blood Cell Image Detection Method Based on Fourier Ptychographic Microscopy.

Mingjing Li¹, Le Yang¹, Shu Fang¹

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

Sensors (Basel, Switzerland)

|February 13, 2025

Summary

A new algorithm, SCD-YOLOv7, enhances red blood cell detection using Fourier Ptychographic Microscopy (FPM) imaging. This method improves accuracy and efficiency for clinical diagnostics and understanding oxygen-carrying capacity.

Keywords:

Fourier ptychographic microscopic imaging YOLOv7 (You Only Look Once version 7)blood cell detection feature fusion

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Area of Science:

Pathology
Medical Imaging
Computer Vision

Background:

Autonomous Fourier Ptychographic Microscopy (FPM) enables high-resolution, large field-of-view imaging crucial for detailed pathological observations.
Red blood cells are vital biomarkers for assessing oxygen transport and guiding clinical decisions.

Purpose of the Study:

To develop an advanced algorithm for accurate and robust detection of red blood cells using FPM.
To enhance the generalization and robustness of blood cell detection models through data preprocessing and architectural innovations.

Main Methods:

Construction of a blood cell dataset utilizing an FPM experimental platform.
Implementation of four image enhancement strategies to improve model generalization.
Proposal of the SCD-YOLOv7 algorithm featuring C-MP and DELAN modules for optimized feature extraction, a Sim-Head detection head for improved small target detection, and a Focal-EIoU loss function for enhanced accuracy and convergence.

Main Results:

The SCD-YOLOv7 algorithm achieved a blood cell detection accuracy of 92.4% on the FPM dataset, an improvement of 7.2%.
The algorithm demonstrated a reduction in computational load by 14.6 G.
Ablation and comparative experiments validated the effectiveness of the proposed modules and loss function.

Conclusions:

The SCD-YOLOv7 algorithm significantly advances automated blood cell detection in pathology.
The integration of FPM imaging with sophisticated deep learning models offers a powerful tool for clinical diagnostics.
The developed method shows potential for improving the efficiency and accuracy of hematological analyses.