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An improved algorithm for salient object detection of microscope based on U2-Net

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Abstract

With the rapid advancement of modern medical technology, microscopy imaging systems have become one of the key technologies in medical image analysis. However, manual use of microscopes presents issues such as operator dependency, inefficiency, and time consumption. To enhance the efficiency and accuracy of medical image capture and reduce the burden of subsequent quantitative analysis, this paper proposes an improved microscope salient object detection algorithm based on U2-Net, incorporating deep learning technology. The improved algorithm first enhances the network’s key information extraction capability by incorporating the Convolutional Block Attention Module (CBAM) into U2-Net. It then optimizes network complexity by constructing a Simple Pyramid Pooling Module (SPPM) and uses Ghost convolution to achieve model lightweighting. Additionally, data augmentation is applied to the slides to improve the algorithm’s robustness and generalization. The experimental results show that the size of the improved algorithm model is 72.5 MB, which represents a 56.85% reduction compared to the original U2-Net model size of 168.0 MB. Additionally, the model’s prediction accuracy has increased from 92.24 to 97.13%, providing an efficient means for subsequent image processing and analysis tasks in microscopy imaging systems.

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Acknowledgements

We would like to express our sincere gratitude to all the authors and colleagues involved in this study for their outstanding contributions to data collection and analysis. We also thank the Hubei Provincial Education Department for their guidance and encouragement throughout the research process.

Funding

We would like to express our sincere gratitude to the Hubei Provincial Education Department for their generous support and funding (Project No. D20201705). This project played an important role in advancing our research and achieving our goals.

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Authors and Affiliations

  1. School of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan, 430200, China

    Yunchai Li, Run Fang, Nangang Zhang, Chengsheng Liao, Xiaochang Chen, Xiaoyu Wang, Yunfei Luo, Leheng Li, Min Mao & Yunlong Zhang

  2. State Key Laboratory of New Textile Materials and Advanced Processing Technologies, Wuhan Textile University, Wuhan, 430200, China

    Yunchai Li, Run Fang, Nangang Zhang, Chengsheng Liao, Xiaochang Chen, Xiaoyu Wang, Yunfei Luo, Leheng Li, Min Mao & Yunlong Zhang

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  1. Yunchai Li
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Correspondence to Run Fang.

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Li, Y., Fang, R., Zhang, N. et al. An improved algorithm for salient object detection of microscope based on U2-Net. Med Biol Eng Comput 63, 383–397 (2025). https://doi.org/10.1007/s11517-024-03205-w

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