White Blood Cell Detection Based on FBDM-YOLOv8s
White blood cells, also known as immune cells, help the body resist infectious diseases and foreign pathogens as part of the immune system. This article proposes a white blood cell detection method based on an improved YOLOv8s to enhance the accurate detection of different categories of normal white...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10934993/ |
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| author | Borui Sun Xiangsuo Fan Jie Meng Jinfeng Wang Huajin Chen Lei Liu |
| author_facet | Borui Sun Xiangsuo Fan Jie Meng Jinfeng Wang Huajin Chen Lei Liu |
| author_sort | Borui Sun |
| collection | DOAJ |
| description | White blood cells, also known as immune cells, help the body resist infectious diseases and foreign pathogens as part of the immune system. This article proposes a white blood cell detection method based on an improved YOLOv8s to enhance the accurate detection of different categories of normal white blood cells. In the YOLOv8s network, this study replaces the main framework of YOLOv8 with the Fasternet module to reduce computational redundancy and memory access. Subsequently, a BiLevel Routing Attention mechanism is proposed to achieve precise localization of white blood cells. Additionally, to attain diversified feature extraction, this article introduces the DBBNCSPELAN4 module, which can perform diversified feature extraction in a multi-branch manner while lightweighting the model, reducing the number of parameters, and speeding up computation. Finally, the MultiSEAM module is integrated with Detect to address detection errors caused by occlusion and connected white blood cells. Experiments were conducted on a dataset of five types of normal cells provided by DML-LZWH (Liuzhou Workers’ Hospital Medical Laboratory). The FBDM-YOLOv8s significantly outperforms other advanced object detection algorithms in terms of performance, with a 2.1% increase in mAP compared to the baseline YOLOv8s.We will release the source code on <uri>https://github.com/SSRR-LLL/FBDM-YOLOv8.git</uri>. |
| format | Article |
| id | doaj-art-a88432bf93a74474beca21d6eb07d16a |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a88432bf93a74474beca21d6eb07d16a2025-08-20T03:07:20ZengIEEEIEEE Access2169-35362025-01-0113569185692910.1109/ACCESS.2025.355313910934993White Blood Cell Detection Based on FBDM-YOLOv8sBorui Sun0https://orcid.org/0009-0004-6788-7846Xiangsuo Fan1https://orcid.org/0000-0002-1685-4989Jie Meng2Jinfeng Wang3Huajin Chen4https://orcid.org/0000-0002-8783-3691Lei Liu5School of Automation, Guangxi University of Science and Technology, Liuzhou, ChinaSchool of Automation, Guangxi University of Science and Technology, Liuzhou, ChinaMedical Science Laboratory, Liuzhou Worker’s Hospital, Liuzhou, ChinaSchool of Automation, Guangxi University of Science and Technology, Liuzhou, ChinaSchool of Electronic Engineering, Guangxi University of Science and Technology, Liuzhou, ChinaSichuan Provincial Corps Hospital, Chinese People’s Armed Police Force, Leshan, Sichuan, ChinaWhite blood cells, also known as immune cells, help the body resist infectious diseases and foreign pathogens as part of the immune system. This article proposes a white blood cell detection method based on an improved YOLOv8s to enhance the accurate detection of different categories of normal white blood cells. In the YOLOv8s network, this study replaces the main framework of YOLOv8 with the Fasternet module to reduce computational redundancy and memory access. Subsequently, a BiLevel Routing Attention mechanism is proposed to achieve precise localization of white blood cells. Additionally, to attain diversified feature extraction, this article introduces the DBBNCSPELAN4 module, which can perform diversified feature extraction in a multi-branch manner while lightweighting the model, reducing the number of parameters, and speeding up computation. Finally, the MultiSEAM module is integrated with Detect to address detection errors caused by occlusion and connected white blood cells. Experiments were conducted on a dataset of five types of normal cells provided by DML-LZWH (Liuzhou Workers’ Hospital Medical Laboratory). The FBDM-YOLOv8s significantly outperforms other advanced object detection algorithms in terms of performance, with a 2.1% increase in mAP compared to the baseline YOLOv8s.We will release the source code on <uri>https://github.com/SSRR-LLL/FBDM-YOLOv8.git</uri>.https://ieeexplore.ieee.org/document/10934993/BiLevelRoutingAttentionDBBNCSPELAN4FBDM-YOLOv8sFasterNetleukocyte target detectionMultiSEAM |
| spellingShingle | Borui Sun Xiangsuo Fan Jie Meng Jinfeng Wang Huajin Chen Lei Liu White Blood Cell Detection Based on FBDM-YOLOv8s IEEE Access BiLevelRoutingAttention DBBNCSPELAN4 FBDM-YOLOv8s FasterNet leukocyte target detection MultiSEAM |
| title | White Blood Cell Detection Based on FBDM-YOLOv8s |
| title_full | White Blood Cell Detection Based on FBDM-YOLOv8s |
| title_fullStr | White Blood Cell Detection Based on FBDM-YOLOv8s |
| title_full_unstemmed | White Blood Cell Detection Based on FBDM-YOLOv8s |
| title_short | White Blood Cell Detection Based on FBDM-YOLOv8s |
| title_sort | white blood cell detection based on fbdm yolov8s |
| topic | BiLevelRoutingAttention DBBNCSPELAN4 FBDM-YOLOv8s FasterNet leukocyte target detection MultiSEAM |
| url | https://ieeexplore.ieee.org/document/10934993/ |
| work_keys_str_mv | AT boruisun whitebloodcelldetectionbasedonfbdmyolov8s AT xiangsuofan whitebloodcelldetectionbasedonfbdmyolov8s AT jiemeng whitebloodcelldetectionbasedonfbdmyolov8s AT jinfengwang whitebloodcelldetectionbasedonfbdmyolov8s AT huajinchen whitebloodcelldetectionbasedonfbdmyolov8s AT leiliu whitebloodcelldetectionbasedonfbdmyolov8s |