2019/10/02 by Changhun Jung, Mohammed Abuhamad, Jung, Changhun +9
Computer Science · Engineering · Immunology and Microbiology · Medicine · #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #Digital Imaging for Blood Diseases #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Image Processing Techniques and Applications #Image and Video Processing (eess.IV) #Immunotherapy and Immune Responses #Quantitative Methods (q-bio.QM) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1910.01091
openalex publication_date 2019/10/02 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28
Computer-aided methods for analyzing white blood cells (WBC) have become\nwidely popular due to the complexity of the manual process. Recent works have\nshown highly accurate segmentation and detection of white blood cells from\nmicroscopic blood images. However, the classification of the observed cells is\nstill a challenge and highly demanded as the distribution of the five types\nreflects on the condition of the immune system. This work proposes W-Net, a\nCNN-based method for WBC classification. We evaluate W-Net on a real-world\nlarge-scale dataset, obtained from The Catholic University of Korea, that\nincludes 6,562 real images of the five WBC types. W-Net achieves an average\naccuracy of 97%.\n