# W-Net: A CNN-based Architecture for White Blood Cells Image Classification

arXiv: https://arxiv.org/abs/1910.01091 · HTML: https://mlsanity.com/paper/1910.01091/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [Leveraging Sparse Annotations for Leukemia Diagnosis on the Large Leukemia Dataset](https://mlsanity.com/paper/2504.02602/) (2025)
- [Shape-Aware Fine-Grained Classification of Erythroid Cells](https://mlsanity.com/paper/2212.13695/) (2022)

## Abstract (arXiv)

Computer-aided methods for analyzing white blood cells (WBC) have become widely popular due to the complexity of the manual process. Recent works have shown highly accurate segmentation and detection of white blood cells from microscopic blood images. However, the classification of the observed cells is still a challenge and highly demanded as the distribution of the five types reflects on the condition of the immune system. This work proposes W-Net, a CNN-based method for WBC classification. We evaluate W-Net on a real-world large-scale dataset, obtained from The Catholic University of Korea, that includes 6,562 real images of the five WBC types. W-Net achieves an average accuracy of 97%.