W-Net: A CNN-based Architecture for White Blood Cells Image Classification
This work addresses a specific problem in medical imaging for healthcare diagnostics, but it is incremental as it applies a CNN-based method to a known bottleneck in WBC analysis.
The paper tackled the challenge of classifying white blood cells from microscopic images, achieving an average accuracy of 97% on a dataset of 6,562 real images.
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%.