# MS-YOLO: A Multi-Scale Model for Accurate and Efficient Blood Cell Detection

arXiv: https://arxiv.org/abs/2506.03972 · HTML: https://mlsanity.com/paper/2506.03972/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 71 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [YOLOv11: An Overview of the Key Architectural Enhancements](https://mlsanity.com/paper/2410.17725/) (2024)
- [YOLOv12: Attention-Centric Real-Time Object Detectors](https://mlsanity.com/paper/2502.12524/) (2025)
- [No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects](https://mlsanity.com/paper/2208.03641/) (2022)
- [Refining activation downsampling with SoftPool](https://mlsanity.com/paper/2101.00440/) (2021)
- [Accurate Leukocyte Detection Based on Deformable-DETR and Multi-Level Feature Fusion for Aiding Diagnosis of Blood Diseases](https://mlsanity.com/paper/2401.00926/) (2024)
- [TE-YOLOF: Tiny and efficient YOLOF for blood cell detection](https://mlsanity.com/paper/2108.12313/) (2021)
- [DWRSeg: Rethinking Efficient Acquisition of Multi-scale Contextual Information for Real-time Semantic Segmentation](https://mlsanity.com/paper/2212.01173/) (2022)
- [CAF-YOLO: A Robust Framework for Multi-Scale Lesion Detection in Biomedical Imagery](https://mlsanity.com/paper/2408.01897/) (2024)
- [LSM-YOLO: A Compact and Effective ROI Detector for Medical Detection](https://mlsanity.com/paper/2408.14087/) (2024)
- [CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin Transformer](https://mlsanity.com/paper/2306.14590/) (2023)

## Abstract (arXiv)

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.