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%.
@article{arxiv.1910.01091,
title = {W-Net: A CNN-based Architecture for White Blood Cells Image Classification},
author = {Changhun Jung and Mohammed Abuhamad and Jumabek Alikhanov and Aziz Mohaisen and Kyungja Han and DaeHun Nyang},
journal= {arXiv preprint arXiv:1910.01091},
year = {2019}
}