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Flow Cytometry

Flow Cytometry

The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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A two stage blood cell detection and classification algorithm based on improved YOLOv7 and EfficientNetv2.

XinZheng Wang¹, GuangJian Pan², ZhiGang Hu²

¹College of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, 471023, China. wxinzheng@haust.edu.cn.

Scientific Reports

|March 12, 2025

Summary

This study introduces an automated two-stage method for detecting and identifying blood cells, improving upon manual diagnoses of leukemia. The system achieves high accuracy in classifying white blood cells, red blood cells, and platelets, aiding preliminary diagnosis.

Keywords:

ASPP BCE EfficientNetv2 Multihead attention SIoU loss function YOLOv7

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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

Area of Science:

Medical Imaging
Computational Pathology
Artificial Intelligence in Medicine

Background:

Manual blood cell morphology analysis for leukemia diagnosis is time-consuming and subjective.
Current diagnostic methods face challenges with high workload and limited efficiency.

Purpose of the Study:

To develop an automated two-stage method for accurate blood cell detection and identification.
To enhance the efficiency and objectivity of preliminary leukemia diagnosis.

Main Methods:

An improved YOLOv7 model with multihead attention and SIoU loss for blood cell detection (WBCs, RBCs, platelets).
An improved EfficientNetv2 model with ASPP and BCE loss for white blood cell classification.
Utilized four public datasets: BCCD, LDWBC, LISC, and Raabin.

Main Results:

The detection model achieved 94.7% average accuracy on the BCCD dataset.
Achieved a mean average precision (mAP) of 97.17% at IoU=0.5.
Attained 95.12% average precision (AP) and 97% average recall (AR) for WBC classification on other datasets.

Conclusions:

The proposed two-stage method accurately detects and identifies blood cells.
This facilitates automated analysis, classification, and quantification of blood cell images.
The system can assist physicians in preliminary leukemia diagnosis.