Lightweight White Blood Cells Detection Using Fusion of YOLOv5 and Attention Model

The human body is protected by an immune system which mainly consists of white blood cells (WBCs). There are five types of white blood cells, and each type will fight certain viruses and bacteria that are encountered in the human body. This defence system helps to maintain human health. Consequently...

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Published in:Journal of Advanced Research in Applied Sciences and Engineering Technology
Main Author: Rohaziat N.; Tomari M.R.M.; Zakaria W.N.W.; Das D.
Format: Article
Language:English
Published: Semarak Ilmu Publishing 2025
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199751285&doi=10.37934%2faraset.48.1.117136&partnerID=40&md5=3ce891b01210dd331754a2065877cade
id 2-s2.0-85199751285
spelling 2-s2.0-85199751285
Rohaziat N.; Tomari M.R.M.; Zakaria W.N.W.; Das D.
Lightweight White Blood Cells Detection Using Fusion of YOLOv5 and Attention Model
2025
Journal of Advanced Research in Applied Sciences and Engineering Technology
48
1
10.37934/araset.48.1.117136
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199751285&doi=10.37934%2faraset.48.1.117136&partnerID=40&md5=3ce891b01210dd331754a2065877cade
The human body is protected by an immune system which mainly consists of white blood cells (WBCs). There are five types of white blood cells, and each type will fight certain viruses and bacteria that are encountered in the human body. This defence system helps to maintain human health. Consequently, healthy WBCs keep humans healthy. Abnormality in WBCs can cause harmful viruses or bacterial infections. Leukaemia is a common WBCs disease which affects the production of good cells. Early detection is important for advanced treatment for cancer patient. One of the detection methods is by visual detection of the blood microscopic image since the five types of the WBCs are visually distinctive. In current practice, the pathologist will perform the diagnosis manually which may take time if there are many samples to examine. This procedure can be improved by automating it using a computer aided detection system. This paper studied the deep learning detection model of YOLOv5s and the effect of fusing the Squeeze-Excitation (SE) and Convolutional Block Attention Model (CBAM) into the YOLOv5s. It was performed on the four types of the WBCs, eosinophil, lymphocyte, monocyte, and the neutrophil taken from a public dataset. Based on the findings, the proposed method of YOLOv5s-SE, YOLOv5s-CBAM, and YOLOv5s-SE-CBAM produced overall accuracy of 99.5%, 99.5% and 99.4% mAP value and the performance are at par with the deeper model YOLOv5m with 65.8% of a smaller number of hyperparameters. © 2025, Semarak Ilmu Publishing. All rights reserved.
Semarak Ilmu Publishing
24621943
English
Article

author Rohaziat N.; Tomari M.R.M.; Zakaria W.N.W.; Das D.
spellingShingle Rohaziat N.; Tomari M.R.M.; Zakaria W.N.W.; Das D.
Lightweight White Blood Cells Detection Using Fusion of YOLOv5 and Attention Model
author_facet Rohaziat N.; Tomari M.R.M.; Zakaria W.N.W.; Das D.
author_sort Rohaziat N.; Tomari M.R.M.; Zakaria W.N.W.; Das D.
title Lightweight White Blood Cells Detection Using Fusion of YOLOv5 and Attention Model
title_short Lightweight White Blood Cells Detection Using Fusion of YOLOv5 and Attention Model
title_full Lightweight White Blood Cells Detection Using Fusion of YOLOv5 and Attention Model
title_fullStr Lightweight White Blood Cells Detection Using Fusion of YOLOv5 and Attention Model
title_full_unstemmed Lightweight White Blood Cells Detection Using Fusion of YOLOv5 and Attention Model
title_sort Lightweight White Blood Cells Detection Using Fusion of YOLOv5 and Attention Model
publishDate 2025
container_title Journal of Advanced Research in Applied Sciences and Engineering Technology
container_volume 48
container_issue 1
doi_str_mv 10.37934/araset.48.1.117136
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199751285&doi=10.37934%2faraset.48.1.117136&partnerID=40&md5=3ce891b01210dd331754a2065877cade
description The human body is protected by an immune system which mainly consists of white blood cells (WBCs). There are five types of white blood cells, and each type will fight certain viruses and bacteria that are encountered in the human body. This defence system helps to maintain human health. Consequently, healthy WBCs keep humans healthy. Abnormality in WBCs can cause harmful viruses or bacterial infections. Leukaemia is a common WBCs disease which affects the production of good cells. Early detection is important for advanced treatment for cancer patient. One of the detection methods is by visual detection of the blood microscopic image since the five types of the WBCs are visually distinctive. In current practice, the pathologist will perform the diagnosis manually which may take time if there are many samples to examine. This procedure can be improved by automating it using a computer aided detection system. This paper studied the deep learning detection model of YOLOv5s and the effect of fusing the Squeeze-Excitation (SE) and Convolutional Block Attention Model (CBAM) into the YOLOv5s. It was performed on the four types of the WBCs, eosinophil, lymphocyte, monocyte, and the neutrophil taken from a public dataset. Based on the findings, the proposed method of YOLOv5s-SE, YOLOv5s-CBAM, and YOLOv5s-SE-CBAM produced overall accuracy of 99.5%, 99.5% and 99.4% mAP value and the performance are at par with the deeper model YOLOv5m with 65.8% of a smaller number of hyperparameters. © 2025, Semarak Ilmu Publishing. All rights reserved.
publisher Semarak Ilmu Publishing
issn 24621943
language English
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