Changes in blood cell morphology are a primary indicator of serious diseases such as leukemia. In this study, an automated blood cell diagnostic approach is proposed by integrating the latest object detection algorithm, YOLOv11, with the statistical Neyman–Pearson hypothesis. The study utilizes an extended BCCD dataset consisting of 2,000 images (80% training, 10% testing, and 10% validation). The results demonstrate that the YOLOv11 model significantly outperforms the conventional Faster R-CNN algorithm in terms of higher accuracy (95.8% mAP) and real-time processing speed, thereby enhancing the efficiency of clinical diagnostics.

YOLOv11, Deep Learning, Leukemia

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