An Automatic Nucleus Segmentation and Classification of White Blood Cell with ResUNet

Nasser, Soraya; Belalem, Ghalem; Mahmoudi, Saïd

doi:10.18280/isi.300102

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An Automatic Nucleus Segmentation and Classification of White Blood Cell with ResUNet

Nasser, Soraya; Belalem, Ghalem; Mahmoudi, Saïd

2025 • In Ingénierie des Systèmes d'Information, 30 (1), p. 11 - 20

Peer reviewed

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https://hdl.handle.net/20.500.12907/55402

DOI
10.18280/isi.300102

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Keywords :

nucleus segmentation; ResNet; UNet; white blood cells (WBC); Information Systems

Abstract :

[en] Leukocytes, another name for white blood cells, or WBCs, are essential components of our immune system, playing a crucial role in protecting our bodies from infection and disease. When we look at immune disorders and bacterial infections, we see that lymphocytes play a central role in the adaptive immune response, while neutrophils are essential in the fight against bacterial infections, and basophils are involved in allergic and inflammatory reactions. When one of these three types of white blood cell (WBC) is affected, it can have a variety of consequences for the immune system and the body's overall health, leading to serious illnesses such as AIDS, leukemia and severe allergic reactions such as anaphylaxis. The diagnosis of some disorders can benefit greatly from the segmentation of the white blood cell nucleus. Analysis of cell morphology, in particular the shape and size of the nucleus in microscopic images, can provide indications of a cell's state of health. In this work, we suggest a fully automatic method for segmenting the nuclei of the three types of WBC (neutrophils, lymphocytes, basophils) using a convolutional neural network named WCSegNet based on the Unet architecture consisting of residual convolution blocks activated by the LeakyRlu activation function. Our technique succeeded in segmenting the cell nucleus and classifying microscopic images according to their type. The results obtained are encouraging, with precision scores in excess of 0.90.

Disciplines :

Computer science

Author, co-author :

Nasser, Soraya ; Higher School of Biological Sciences of Oran (ESSBO), Oran, Algeria ; Computer Science Department, LIO Laboratory, University of Oran1, Oran, Algeria

Belalem, Ghalem ; Computer Science Department, LIO Laboratory, University of Oran1, Oran, Algeria

Mahmoudi, Saïd ; Université de Mons - UMONS > Faculté Polytechnique > Service Informatique, Logiciel et Intelligence artificielle

Language :

English

Title :

An Automatic Nucleus Segmentation and Classification of White Blood Cell with ResUNet

Publication date :

11 March 2025

Journal title :

Ingénierie des Systèmes d'Information

ISSN :

1633-1311

Publisher :

International Information and Engineering Technology Association

Volume :

Issue :

Pages :

11 - 20

Peer reviewed :

Peer reviewed

Additional URL :

https://www.iieta.org/journals/isi/paper/10.18280/isi.300102

Research unit :

F114 - Informatique, Logiciel et Intelligence artificielle

Research institute :

R300 - Institut de Recherche en Technologies de l'Information et Sciences de l'Informatique

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since 15 January 2026

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