Automated Blood Cell Segmentation and Classification Using YOLOv11n: An End-to- End Deep Learning Approach

Abstract: This research presents an efficient deep learning solution for detecting and counting blood cells in microscope images using the YOLOv11n object detection model. Leveraging a robust annotated dataset, data augmentation, and advanced inference, the system achieves high detection accuracy (mAP 90.5%) for RBCs, WBCs, and platelets. Automated results improve laboratory workflows and reliability, demonstrating strong real-world impact for digital hematology.