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Related Concept Videos

Flow Cytometry01:23

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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Classification of Leukocytes01:30

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
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Blood Flow01:29

Blood Flow

Blood is pumped by the heart into the aorta, the largest artery in the body, and then into increasingly smaller arteries, arterioles, and capillaries. The velocity of blood flow decreases with increased cross-sectional blood vessel area. As blood returns to the heart through venules and veins, its velocity increases. The movement of blood is encouraged by smooth muscle in the vessel walls, the movement of skeletal muscle surrounding the vessels, and one-way valves that prevent backflow.
Blood Transfusion01:15

Blood Transfusion

Blood transfusion is a critical medical procedure that saves lives and treats various medical conditions. It involves transferring blood from a donor to a recipient. This process requires a thorough understanding of the ABO blood group system and its associated antigens and antibodies.
Blood Transfusion Overview
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Lymphoid Cells and Tissues01:18

Lymphoid Cells and Tissues

Lymphoid cells and tissues are integral to the immune system, which is crucial in maintaining our body's defense against harmful pathogens. They form the building blocks of lymphoid organs, which include the spleen, thymus, and lymph nodes.
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Blood Typing01:10

Blood Typing

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Related Experiment Videos

LWF-YOLO: a lightweight framework based YOLO for blood cell detection.

Rui Mao1, Dazhi Huang2, Yuanyuan Wu1

  • 1School of Computer Science and Cyber Security, Chengdu University of Technology, No.1, East Third Road, Erxianqiao, Chengdu 610059, Sichuan, People's Republic of China.

Biomedical Physics & Engineering Express
|June 15, 2026
PubMed
Summary

A new lightweight object detection framework, LWF-YOLO, enhances blood cell detection accuracy. This method improves diagnosis of hematological disorders by offering efficient and precise analysis on resource-constrained devices.

Keywords:
LWF-YOLOlightweightmedical image detectionobject detection

Related Experiment Videos

Area of Science:

  • Medical imaging
  • Computer vision
  • Hematology

Background:

  • Accurate blood cell detection is crucial for diagnosing hematological disorders like anemia and leukemia.
  • Traditional methods face limitations in subjectivity and efficiency for complex cellular patterns.

Purpose of the Study:

  • To introduce LWF-YOLO, a lightweight object detection framework for enhanced blood cell detection.
  • To improve the accuracy and efficiency of automated hematological analysis.

Main Methods:

  • Developed LWF-YOLO based on YOLOv11, incorporating a Multi-Scale Edge-Aware Feature Enhancement (MSEFE) module for cell boundary delineation.
  • Introduced a Dynamic Channel-Mixing Gated Former (DCGFormer) and Dynamic Deformable Convolution Network (DyDCN) for optimized feature modulation and robust localization.
  • Utilized Sobel operators, multi-scale fusion, identity mapping, dynamic channel splitting, adaptive gating, and multi-dimensional attention.

Main Results:

  • LWF-YOLO achieved high mean average precision (mAP) in blood cell detection.
  • Demonstrated competitive or superior performance compared to state-of-the-art models like YOLOv11 and RT-DETR-R18.
  • Maintained a lightweight profile with minimal computational overhead, enabling efficient deployment.

Conclusions:

  • LWF-YOLO offers a significant advancement in automated hematological analysis.
  • The framework enables real-time diagnostics on resource-constrained devices, improving accessibility and speed.
  • LWF-YOLO addresses the limitations of traditional methods by providing accurate and efficient blood cell detection.