{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T07:10:39Z","timestamp":1779174639323,"version":"3.51.4"},"reference-count":18,"publisher":"World Scientific Pub Co Pte Ltd","issue":"10","funder":[{"DOI":"10.13039\/501100003213","name":"Beijing Municipal Education Commission","doi-asserted-by":"publisher","award":["22019821001"],"award-info":[{"award-number":["22019821001"]}],"id":[{"id":"10.13039\/501100003213","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhiyuan Science Foundation","award":["2023015"],"award-info":[{"award-number":["2023015"]}]},{"name":"Climbing Program Foundation, Beijing Institute of Petrochemical Technology","award":["BIPTAAI-2021-004"],"award-info":[{"award-number":["BIPTAAI-2021-004"]}]},{"name":"Undergraduate Research Training (URT) Project of Beijing institute of petrochemical technology","award":["2026J00179"],"award-info":[{"award-number":["2026J00179"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:p>The imaging flow cytometer enables real-time capture of microscopic cell images under dynamic conditions of continuous high-speed flow, thereby acquiring instantaneous morphological and structural information of cells in motion. This significantly enhances the information dimensionality and feature integrity of cellular feature data. Detecting and identifying different types of flow cytometry images facilitates early disease screening and auxiliary diagnosis. To enhance the detection efficiency and accuracy of cells in flow imaging, this paper proposes an improved model named YOLO-AF for real-time detection of high-speed flowing cell images. Based on YOLOv12, our model integrates two key improvements: First, the lightweight attention module AFE (Attention Feature Enhancement) is introduced. Combining channel and spatial attention mechanisms, AFE adopts adaptive compression and spatial information aggregation strategies to enhance the model\u2019s ability to extract features of small cells. Second, the Feature Pyramid Network (FPN) is reconstructed to further improve multi-scale feature fusion, thereby enhancing detection performance for cell targets of varying sizes. Additionally, this study constructs a high-resolution microscopic image dataset of animal cells, named AnimalRBC, using a lab-made imaging flow cytometer. The dataset includes three types of red blood cells: guinea pig, chicken, and sheep. Experimental results show that YOLO-AF achieves 95.5% mAP@0.5 on this dataset, a 3.2% improvement over the baseline model YOLOv12. This method significantly enhances recognition accuracy while maintaining high detection speed, providing robust support for cell-related early disease screening.<\/jats:p>","DOI":"10.1142\/s0218001426570065","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T08:49:24Z","timestamp":1773910164000},"source":"Crossref","is-referenced-by-count":0,"title":["YOLO-AF: An Improved Lightweight Architecture for Real-Time Cell Detection in Imaging Flow Cytometry"],"prefix":"10.1142","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2298-5023","authenticated-orcid":false,"given":"Zhe","family":"Liu","sequence":"first","affiliation":[{"name":"Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3207-5832","authenticated-orcid":false,"given":"Qiang","family":"Yong","sequence":"additional","affiliation":[{"name":"Shunyi Women\u2019s and Children\u2019s Hospital, Beijing Children\u2019s Hospital, Beijing 101300, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8457-1095","authenticated-orcid":false,"given":"Yuyang","family":"Han","sequence":"additional","affiliation":[{"name":"Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3455-9106","authenticated-orcid":false,"given":"Liming","family":"Chen","sequence":"additional","affiliation":[{"name":"Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3869-3247","authenticated-orcid":false,"given":"Yixiang","family":"Lu","sequence":"additional","affiliation":[{"name":"Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1221-4784","authenticated-orcid":false,"given":"Ruotong","family":"Mu","sequence":"additional","affiliation":[{"name":"Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9270-3521","authenticated-orcid":false,"given":"Ming","family":"Yang","sequence":"additional","affiliation":[{"name":"Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5488-3904","authenticated-orcid":false,"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1813-8799","authenticated-orcid":false,"given":"Shaomin","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Pathology, School of Basic Medical Sciences, Third Hospital, Peking University Health Science Center, Beijing 100191, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5767-4637","authenticated-orcid":false,"given":"Qiang","family":"Yang","sequence":"additional","affiliation":[{"name":"Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3699-5858","authenticated-orcid":false,"given":"Qiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2026,4,30]]},"reference":[{"key":"S0218001426570065BIB001","doi-asserted-by":"publisher","DOI":"10.3109\/07388551.2015.1128876"},{"issue":"14","key":"S0218001426570065BIB002","first-page":"1409003","volume":"2","author":"Chen X.","year":"2025","journal-title":"Acta Opt. Sin."},{"key":"S0218001426570065BIB003","author":"Chen Y.","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"S0218001426570065BIB004","doi-asserted-by":"publisher","DOI":"10.52202\/068431-1189"},{"key":"S0218001426570065BIB005","unstructured":"G. Jocher\n                      et al.\n                      , ultralytics\/yolov5: v3.0,\n                      Zenodo\n                      (2020)."},{"key":"S0218001426570065BIB006","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP51287.2024.10647618"},{"key":"S0218001426570065BIB007","unstructured":"R. Khanam and M. Hussain, Yolov11: An overview of the key architectural enhancements, preprint arXiv:2410.17725 (2024)."},{"key":"S0218001426570065BIB008","doi-asserted-by":"publisher","DOI":"10.3390\/app12168140"},{"key":"S0218001426570065BIB009","unstructured":"C. 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