Author(s)

Congcong Jiang ¹

Affiliation(s)

¹ Wuhan University of Bioengineering, Hubei, Wuhan, 430000, China

Corresponding Author

Congcong Jiang

ABSTRACT

Blood cell detection plays a key role in clinical medical diagnosis, while traditional manual microscopic observation methods have obvious deficiencies such as strong subjectivity and low efficiency. Addressing this problem, this research designs a high-precision image segmentation and feature extraction algorithm system by improving the U-Net network architecture through residual modules and dual attention mechanisms, optimizing the training process with multi-component loss functions, and constructing multi-scale feature fusion and attention-based feature selection methods. Experimental results show that the improved algorithm achieves 96.8% pixel-level accuracy and a Dice coefficient of 0.921 on the BCCD test set, while the feature extraction method reaches 97.2% accuracy in white blood cell five-classification tasks. The algorithm significantly improves the automation level and accuracy of blood cell detection, providing reliable technical support for clinical applications.

KEYWORDS

Blood cell detection; Image segmentation; Deep learning; U-Net improvement; Multi-scale feature fusion

CITE THIS PAPER

Congcong Jiang, High-Precision Image Segmentation and Feature Extraction Algorithm Design for Blood Cell Detection. Social Medicine and Health Management (2025) Vol. 6: 72-80. DOI: http://dx.doi.org/10.23977/socmhm.2025.060110.

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