Three-Branch AI Fusion Reaches 99.3% Accuracy in White Blood Cell Classification

Microscopic smear images of different white blood cell subtypes can look strikingly similar, and their appearance is further altered by staining variation, differences in microscope and acquisition setup, and the presence of surrounding background content such as red blood cells, platelets, and staining artifacts.

Rather than fixing a single preprocessing pipeline, the team evaluated four conditions: not cropped and minimally prepared, not cropped and processed, cropped and minimally prepared, and cropped and processed.

Subject of Research: Deep learning classification of five white blood cell types from microscopic smear images Article Title: Attention guided hybrid fusion of MobileNetV3 DenseNet121 and DINOv2 for five class white blood cell classification Article References: Shehan, M. S. H., Islam, M., Rahim, A., Uddin, M., Rahman, M. M., Rana, M. S., Islam, M. A., Bakhsh, M. M., Kabtia, M., & Raihan, D. I.

Attention guided hybrid fusion of MobileNetV3 DenseNet121 and DINOv2 for five class white blood cell classification.

Discover Analytics, 4(1), Article 37. https://doi.org/10.1007/s44257-026-00086-1 Image Credits: AI Generated DOI: 10.1007/s44257-026-00086-1