SegSwin-Net: A Segmentation-Guided Vision Transformer for Improved Leukemia Detection and Classification

This study introduces a novel hybrid framework that enhances automated leukemia detection by using cell segmentation to eliminate background noise prior to Swin Transformer classification. By isolating these key diagnostic features, the proposed model achieves an exceptional 99% accuracy, significantly outperforming baseline CNN architectures.