The detection and classification of blood cells from microscopic images plays a vital role in medical diagnosis. However, it is challenging because of the small size, varying shape and dense clustering of cells. Conventional methods rely on manual inspection, which is labor-intensive and depends on expert knowledge. In contrast, Deep Learning (DL) based approaches significantly speed up the process and provide more reliable and consistent results. However, there should be a trade-off between computational costs, model size and accuracy. In this work, we propose the Blood Cell You Only Look Once (BC-YOLO) model built on the YOLOv11 architecture. The backbone of the model is enhanced by replacing traditional convolution structures with MBConv-ECA blocks, resulting in lighter and more efficient feature extraction. This modification reduces computational complexity while increasing accuracy, resulting in a more robust object detection model. The proposed model accurately detects red blood cells (RBC), white blood cells (WBC) and platelets, automating the analysis of microscopic images. Experimental results show that the BC-YOLO Medium model achieves 95.89% mAP@0.5, 92.2% precision and 96.3% recall with 18.5 million parameters and 57.4 GFLOP. The model outperforms not only YOLOv11 but also other YOLO variants and existing studies in the literature. Furthermore, a user-friendly interface has been developed so that end-users can easily upload and analyze microscopic blood cell images as well as inspect the detected cells. In addition, Grad-CAM-based explainability visualizations are also presented to better understand the model’s decision-making processes and increase transparency for medical professionals.