Research on deep learning-based blood cell detection method

Yao Liu; Peng Chen; Songyan Liu

doi:10.1117/12.3044408

27 August 2024 Research on deep learning-based blood cell detection method

Yao Liu, Peng Chen, Songyan Liu

Author Affiliations +

Proceedings Volume 13252, Fourth International Conference on Biomedicine and Bioinformatics Engineering (ICBBE 2024); 132521A (2024) https://doi.org/10.1117/12.3044408
Event: 2024 Fourth International Conference on Biomedicine and Bioinformatics Engineering (ICBBE 2024), 2024, Kaifeng, China

Abstract

An assessment of a patient's health status significantly relies on the analysis of blood components, particularly through the accurate enumeration of red blood cells, white blood cells, and platelets. Traditional algorithms for target detection typically encounter challenges such as inadequate accuracy, as well as frequent false positives and false negatives in the detection of blood cells. In this study, a multi-scale fusion blood cell detection model AGF-YOLOv8 is proposed, which firstly introduces the CBAM attention mechanism between the ConvModule and the Batch Normalization layer to improve the attention to key features; Secondly, it combines the structure of the SPPF and the FPN to realize multi-scale feature fusion, and adopts the Ghost module to reduce model parameters and computational complexity; Finally, to address sample imbalance, the Focal Loss is used in the Darknet Bottleneck to optimize the recognition of difficult-to-divide samples. Experimental findings indicate that, relative to the original YOLOv8, the detection accuracy of the AGF-YOLOv8 model has increased by four percentage points, which can provide auxiliary diagnosis for healthcare professionals in the future.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yao Liu, Peng Chen, and Songyan Liu "Research on deep learning-based blood cell detection method", Proc. SPIE 13252, Fourth International Conference on Biomedicine and Bioinformatics Engineering (ICBBE 2024), 132521A (27 August 2024); https://doi.org/10.1117/12.3044408

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