A Unified CNN-Based Instance Segmentation Architecture for Blood Cell Classification and Early Cancer Abnormality Recognition

This study presents a YOLOv11-YOLOv12based deep learning framework for automated detection, segmentation, and classification of five hematologic cell types—Basophil, Erythroblast, Monocyte, Myeloblast, and Segmented Neutrophil-from microscopic blood images. A dataset of annotated cell images was preprocessed and augmented to enhance model generalization, and transfer learning was applied to optimize performance on unseen samples. Quantitative evaluations demonstrated exceptional accuracy, with mAP50 scores exceeding 0.99 for both bounding box and mask predictions, an overall mAP@0.5 of 0.992, and F1-scores peaking at 0.98. Precision-recall, precision-confidence, and recall-confidence curves further confirmed…