White blood cell classification using multi-hop attention graph neural networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10256829" target="_blank" >RIV/61989100:27240/25:10256829 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0957417425003471?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0957417425003471?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2025.126725" target="_blank" >10.1016/j.eswa.2025.126725</a>

Jazyk výsledku

angličtina

Název v původním jazyce

White blood cell classification using multi-hop attention graph neural networks

Popis výsledku v původním jazyce

The process of creating blood cells (hematopoiesis) occurs in the bone marrow, where Hematopoietic Stem Cells (HSCs) are located. The division and differentiation of hematopoietic stem cells are tightly regulated to ensure a balance between blood cell lineages. Disturbances in the process can lead to blood diseases such as anemia, high White Blood Cell (WBC) count, or thrombocytopenia. Detecting malignant leukemia cells based on images is crucial in diagnosing and treating leukemia, helping doctors make accurate diagnoses, and providing appropriate treatment. The author proposes a new method for recognizing and classifying WBC images using the Multi-hop Attention Graph Neural Networks method. The YOLO-v10 method is used for object detection and image preprocessing through the Centre Net network architecture. The Salp Swarm Optimization (SSO) method is deployed to select the features of the WBC images optimally and put the image features into each node in the architecture of the Graph Neural Network (GNN) model to perform classification. The dataset used has an image quality of approximately 42 pixels per 1 μ mresolution with a total of 16,027 annotated White Blood Cell images classified into 9 types of WBC with characteristic images of clinically significant pathologies. The classification accuracy of the system of the YLSSOGNN model is 99.18%, and the classification accuracy of the system of the YLGNN model is 99.03%. The WBC image recognition and classification model using the post-learning method has a GNN architecture with object recognition function using the YOLO-v10 method and feature extraction and optimization using the SSO method and performs WBC image classification using Multi-hop Attention Graph Neural Networks model, which helps to bring high performance and can apply the model to other types of image objects.

Název v anglickém jazyce

White blood cell classification using multi-hop attention graph neural networks

Popis výsledku anglicky

The process of creating blood cells (hematopoiesis) occurs in the bone marrow, where Hematopoietic Stem Cells (HSCs) are located. The division and differentiation of hematopoietic stem cells are tightly regulated to ensure a balance between blood cell lineages. Disturbances in the process can lead to blood diseases such as anemia, high White Blood Cell (WBC) count, or thrombocytopenia. Detecting malignant leukemia cells based on images is crucial in diagnosing and treating leukemia, helping doctors make accurate diagnoses, and providing appropriate treatment. The author proposes a new method for recognizing and classifying WBC images using the Multi-hop Attention Graph Neural Networks method. The YOLO-v10 method is used for object detection and image preprocessing through the Centre Net network architecture. The Salp Swarm Optimization (SSO) method is deployed to select the features of the WBC images optimally and put the image features into each node in the architecture of the Graph Neural Network (GNN) model to perform classification. The dataset used has an image quality of approximately 42 pixels per 1 μ mresolution with a total of 16,027 annotated White Blood Cell images classified into 9 types of WBC with characteristic images of clinically significant pathologies. The classification accuracy of the system of the YLSSOGNN model is 99.18%, and the classification accuracy of the system of the YLGNN model is 99.03%. The WBC image recognition and classification model using the post-learning method has a GNN architecture with object recognition function using the YOLO-v10 method and feature extraction and optimization using the SSO method and performs WBC image classification using Multi-hop Attention Graph Neural Networks model, which helps to bring high performance and can apply the model to other types of image objects.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Expert Systems with Applications

ISSN

0957-4174

e-ISSN

1873-6793

Svazek periodika

272

Číslo periodika v rámci svazku

5 May 2025

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

20

Strana od-do

1-20

Kód UT WoS článku

001426744000001

EID výsledku v databázi Scopus

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