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Analysis of Multidimensional Microscopy Data Using Cell-ACDC

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Published on: November 7, 2025

Multispectral Blood Cell Image Analysis via Deep Learning With YOLOv5.

Gang Li¹, Guiming Fu¹, Honghui Zeng²

¹Medical School, Tianjin University, Tianjin, China.

Journal of Biophotonics

|September 28, 2025

Summary

This summary is machine-generated.

Multispectral imaging significantly improves automated blood cell recognition accuracy. This advanced technique enhances the detection of all cell types, including rare white blood cells, for better medical diagnosis.

Keywords:

YOLOv5 blood cell recognition microscopic images multispectral images

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Area of Science:

Biomedical Engineering
Medical Imaging
Computational Biology

Background:

Automated blood cell counting is crucial for medical diagnosis.
Traditional microscopic imaging offers limited cellular information.
Multispectral imaging captures enhanced optical characteristics for improved cell delineation.

Purpose of the Study:

To develop and evaluate a blood cell recognition method using multispectral imaging and YOLOv5.
To compare the performance of multispectral imaging against single-wavelength imaging for blood cell recognition.

Main Methods:

Blood cell images were captured at five different wavelengths and fused to create multispectral data.
Standard and modified YOLOv5 models were trained and tested using both single-wavelength and multispectral images.
Performance was evaluated based on identification precision for red blood cells, platelets, and white blood cells.

Main Results:

Multispectral imaging significantly enhanced blood cell recognition compared to single-wavelength methods.
Achieved identification precision of 99.9% for red blood cells and 96.1% for platelets.
Recognition precision for scarce white blood cells reached 98.9%, a 12.26% improvement over single-wavelength models.

Conclusions:

Multispectral imaging offers superior performance for automated blood cell recognition.
This technique demonstrates significant potential for high-precision detection, particularly for rare cell types.
Advanced imaging holds promise for improving diagnostic accuracy in hematology.