Albert Souza

Classification of Normal versus Leukemic Cells with Swin Transformer and Balanced Data Augmentation

Acute lymphoblastic leukemia (ALL) diagnosis still relies heavily on the manual microscopic examination of stained blood or bone marrow smears, a process that is inherently subjective and time-consuming. To address these limitations, we present an automated classification framework built upon the Swin Transformer Tiny architecture, designed to distinguish leukemic from normal lymphocytes. The hierarchical attention mechanism of the model enables the capture of long-range spatial relationships. To counter dataset imbalance, the training pipeline incorporates geometric data augmentation and a weighted random sampling strategy. Experiments conducted on the C-NMC 2019 dataset employed transfer learning and fine-tuning to achieve optimal performance. The proposed approach achieved an F1-score of 99.44%, outperforming previously reported CNN-based and handcrafted feature methods. Statistical robustness was confirmed via 30-run Monte Carlo validation (p < 0.001). These results highlight the potential of attention-based transformer architectures to deliver accurate and computationally efficient diagnostic tools suitable for clinical deployment in leukemia detection.