LWF-YOLO: A Lightweight framework based YOLO for blood cell detection

Accurate detection of blood cells, red blood cells, white blood cells, and platelets is essential for diagnosing hematological disorders such as anemia and leukemia. However, traditional approaches like manual microscopy and automated analyzers are limited by subjectivity and inefficiency in resolving complex cellular patterns. To overcome these challenges, we propose LWF-YOLO, a lightweight and high mean average precision (mAP) object detection framework built upon YOLOv11, tailored for blood cell detection. LWF-YOLO introduces the Multi-Scale Edge-Aware Feature Enhancement (MSEFE) module, which leverages Sobel operators and multi-scale fusion to sharpen cell boundary delineation. Complementing MSEFE, the Dynamic Channel-Mixing Gated Former (DCGFormer) replaces conventional convolutional modules with a novel architecture that employs identity mapping, dynamic channel splitting, and adaptive gating. This design preserves fine-grained spatial details while optimizing feature modulation through depthwise convolutions and localized dependency aggregation, significantly reducing computational redundancy for lightweight deployment. Additionally, the Dynamic Deformable Convolution Network (DyDCN) integrates adaptive sampling with multi-dimensional attention to improve robust localization across diverse cell scales. These components demonstrate LWF-YOLO's strong performance and multi-scale adaptability with minimal computational overhead. Extensive experiments on multiple datasets demonstrate LWF-YOLO's exceptional performance, achieving competitive or superior performance compared to state-of-the-art models like YOLOv11 and RT-DETR-R18 while maintaining a lightweight profile. By prioritizing efficiency, LWF-YOLO enables real-time diagnostics on resource-constrained devices, advancing auto-mated hematological analysis.