An Improved Yolov10n for Detection of Bronchoalveolar Lavage Cells
Bronchoalveolar lavage fluid (BALF) is a liquid sample that reflects the biological status of lung tissues, containing a wealth of components such as cells and proteins. These components provide a non-invasive method to obtain pathological information about the lungs, serving as a powerful complemen...
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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/10848101/ |
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| author | Peihe Jiang Shaoqi Li Yanfen Lu Xiaogang Song |
| author_facet | Peihe Jiang Shaoqi Li Yanfen Lu Xiaogang Song |
| author_sort | Peihe Jiang |
| collection | DOAJ |
| description | Bronchoalveolar lavage fluid (BALF) is a liquid sample that reflects the biological status of lung tissues, containing a wealth of components such as cells and proteins. These components provide a non-invasive method to obtain pathological information about the lungs, serving as a powerful complement to traditional lung biopsies. However, the similarity in morphology and function of cells in BALF, combined with the diversity of sample processing and analysis methods, can lead to confusion in recognizing and distinguishing these cellular features. This study presents an improved Yolov10 method for the detection and classification of BALF cells, specifically targeting macrophages, lymphocytes, neutrophils, and eosinophils. The backbone network incorporates the PLWA module in place of the PSA module to enhance the acquisition of useful information, and the C2f-DC module replaces the C2f module to improve image feature extraction capabilities. Furthermore, the head network employs the Cross-Attention Fusion module (CAP) to enhance the retrieval of image information. Experimental results demonstrate that the model achieves a mean Average Precision (mAP) of 86.5% and a recall rate of 79.1%, confirming the model’s effectiveness. |
| format | Article |
| id | doaj-art-abfc6ad0092f41a5b39f4754e847939e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-abfc6ad0092f41a5b39f4754e847939e2025-02-07T00:01:22ZengIEEEIEEE Access2169-35362025-01-0113227642277310.1109/ACCESS.2025.353249310848101An Improved Yolov10n for Detection of Bronchoalveolar Lavage CellsPeihe Jiang0https://orcid.org/0000-0003-0971-7561Shaoqi Li1https://orcid.org/0009-0008-9501-1277Yanfen Lu2https://orcid.org/0009-0005-3240-2906Xiaogang Song3https://orcid.org/0009-0009-6008-7834School of Physics and Electronic Information, Yantai University, Yantai, ChinaSchool of Physics and Electronic Information, Yantai University, Yantai, ChinaDepartment of Pulmonary and Critical Care Medicine, Yantai Yuhuangding Hospital, Qingdao University, Yantai, ChinaDepartment of Pulmonary and Critical Care Medicine, Yantai Yuhuangding Hospital, Qingdao University, Yantai, ChinaBronchoalveolar lavage fluid (BALF) is a liquid sample that reflects the biological status of lung tissues, containing a wealth of components such as cells and proteins. These components provide a non-invasive method to obtain pathological information about the lungs, serving as a powerful complement to traditional lung biopsies. However, the similarity in morphology and function of cells in BALF, combined with the diversity of sample processing and analysis methods, can lead to confusion in recognizing and distinguishing these cellular features. This study presents an improved Yolov10 method for the detection and classification of BALF cells, specifically targeting macrophages, lymphocytes, neutrophils, and eosinophils. The backbone network incorporates the PLWA module in place of the PSA module to enhance the acquisition of useful information, and the C2f-DC module replaces the C2f module to improve image feature extraction capabilities. Furthermore, the head network employs the Cross-Attention Fusion module (CAP) to enhance the retrieval of image information. Experimental results demonstrate that the model achieves a mean Average Precision (mAP) of 86.5% and a recall rate of 79.1%, confirming the model’s effectiveness.https://ieeexplore.ieee.org/document/10848101/Bronchoalveolar lavage fluidclassificationdetectionmachine learning |
| spellingShingle | Peihe Jiang Shaoqi Li Yanfen Lu Xiaogang Song An Improved Yolov10n for Detection of Bronchoalveolar Lavage Cells IEEE Access Bronchoalveolar lavage fluid classification detection machine learning |
| title | An Improved Yolov10n for Detection of Bronchoalveolar Lavage Cells |
| title_full | An Improved Yolov10n for Detection of Bronchoalveolar Lavage Cells |
| title_fullStr | An Improved Yolov10n for Detection of Bronchoalveolar Lavage Cells |
| title_full_unstemmed | An Improved Yolov10n for Detection of Bronchoalveolar Lavage Cells |
| title_short | An Improved Yolov10n for Detection of Bronchoalveolar Lavage Cells |
| title_sort | improved yolov10n for detection of bronchoalveolar lavage cells |
| topic | Bronchoalveolar lavage fluid classification detection machine learning |
| url | https://ieeexplore.ieee.org/document/10848101/ |
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