MS-YOLO: A Multi-Scale Model for Accurate and Efficient Blood Cell Detection
This work addresses the need for automated, cost-effective blood cell detection to improve clinical diagnostics, though it is incremental as it builds on the YOLOv11 framework with specific architectural enhancements.
The study tackled the problem of accurate and efficient blood cell detection in clinical diagnostics by proposing MS-YOLO, a multi-scale model based on YOLOv11, which achieved an mAP@50 of 97.4% on the CBC benchmark, outperforming existing models and demonstrating robust generalization on additional datasets.
Complete blood cell detection holds significant value in clinical diagnostics. Conventional manual microscopy methods suffer from time inefficiency and diagnostic inaccuracies. Existing automated detection approaches remain constrained by high deployment costs and suboptimal accuracy. While deep learning has introduced powerful paradigms to this field, persistent challenges in detecting overlapping cells and multi-scale objects hinder practical deployment. This study proposes the multi-scale YOLO (MS-YOLO), a blood cell detection model based on the YOLOv11 framework, incorporating three key architectural innovations to enhance detection performance. Specifically, the multi-scale dilated residual module (MS-DRM) replaces the original C3K2 modules to improve multi-scale discriminability; the dynamic cross-path feature enhancement module (DCFEM) enables the fusion of hierarchical features from the backbone with aggregated features from the neck to enhance feature representations; and the light adaptive-weight downsampling module (LADS) improves feature downsampling through adaptive spatial weighting while reducing computational complexity. Experimental results on the CBC benchmark demonstrate that MS-YOLO achieves precise detection of overlapping cells and multi-scale objects, particularly small targets such as platelets, achieving an mAP@50 of 97.4% that outperforms existing models. Further validation on the supplementary WBCDD dataset confirms its robust generalization capability. Additionally, with a lightweight architecture and real-time inference efficiency, MS-YOLO meets clinical deployment requirements, providing reliable technical support for standardized blood pathology assessment.