Accurate and efficient blood cell detection in microscopic images remains challenging due to dense object distribution, frequent occlusion, and the small size of platelets, which often lead to missed detections and redundant computations in existing detectors. To address these issues, we propose YOLO-ECL, a lightweight blood cell detection framework built upon YOLOv11n. YOLO-ECL introduces three targeted improvements: (i) C3k2EMBC, which replaces the original C3k2 backbone module by integrating MBConv with Squeeze-and-Excitation (SE) attention to enhance fine-grained feature extraction for minute cells; (ii) ContextGuidedBlockDown in the neck to jointly model local cues and surrounding context, improving robustness in dense and overlapping regions; and (iii) a Lightweight Shared Convolutional Detection head (LSCD) with a scale layer to reduce parameter redundancy while preserving multi-scale representation. Extensive experiments on the BCCD dataset (1344 images; RBC/WBC/platelet) show that YOLO-ECL achieves 95. 8% mAP@0. 5 with only 2. 50 M parameters and 6. 1 GFLOPs, outperforming representative lightweight and mainstream YOLO variants under comparable settings. Moreover, cross-domain evaluation on the LISC dataset demonstrates improved generalization, reaching 86. 2% mAP@0. 5. Deployment results on embedded platforms (Jetson Nano, RK3588, Snapdragon 8 Gen 2) confirm real-time inference (≥ 25 FPS) with low energy consumption (< 0. 25 J/image) and limited memory footprint (< 600 MB), supporting practical integration into portable intelligent medical testing equipment.
Liang et al. (Sun,) studied this question.