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Gpmb-yolo: a lightweight model for efficient blood cell detection in medical imaging

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Abstract

In the field of biomedical science, blood cell detection in microscopic images is crucial for aiding physicians in diagnosing blood-related diseases and plays a pivotal role in advancing medicine toward more precise and efficient treatment directions. Addressing the time-consuming and error-prone issues of traditional manual detection methods, as well as the challenge existing blood cell detection technologies face in meeting both high accuracy and real-time requirements, this study proposes a lightweight blood cell detection model based on YOLOv8n, named GPMB-YOLO. This model utilizes advanced lightweight strategies and PGhostC2f design, effectively reducing model complexity and enhancing detection speed. The integration of the simple parameter-free attention mechanism (SimAM) significantly enhances the model’s feature extraction ability. Furthermore, we have designed a multidimensional attention-enhanced bidirectional feature pyramid network structure, MCA-BiFPN, optimizing the effect of multi-scale feature fusion. And use genetic algorithms for hyperparameter optimization, further improving detection accuracy. Experimental results validate the effectiveness of the GPMB-YOLO model, which realized a 3.2% increase in mean Average Precision (mAP) compared to the baseline YOLOv8n model and a marked reduction in model complexity. Furthermore, we have developed a blood cell detection system and deployed the model for application. This study serves as a valuable reference for the efficient detection of blood cells in medical images.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This work is supported by the National Natural Science Foundation of China (Nos. 62272418, 62102058) and the basic public welfare research program of Zhejiang Province (No. LGG18E050011).

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Authors and Affiliations

  1. School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China

    Chenyang Shi, Donglin Zhu & Changjun Zhou

  2. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, China

    Shi Cheng

  3. College of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China

    Chengye Zou

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  1. Chenyang Shi
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Correspondence to Changjun Zhou or Chengye Zou.

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Shi, C., Zhu, D., Zhou, C. et al. Gpmb-yolo: a lightweight model for efficient blood cell detection in medical imaging. Health Inf Sci Syst 12, 24 (2024). https://doi.org/10.1007/s13755-024-00285-8

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