A deep learning method for counting white blood cells in bone marrow images

BMC Bioinformatics

Maxwell Hwang¹  Kefeng Ding¹  Da Wang¹  Kao-Shing Hwang²  Hsiao Chien Chang²  Wei-Cheng Jiang³ 

[1] Department of Colorectal Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang, China;Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan, China;Department of Electrical Engineering, Tunghai University, Taichung, Taiwan, China;

关键词: Medical image;    Leukemia;    Deep learning;    Object detection;    Classification;   

DOI  :  10.1186/s12859-021-04003-z

来源: Springer

【 摘 要 】

BackgroundDifferentiating and counting various types of white blood cells (WBC) in bone marrow smears allows the detection of infection, anemia, and leukemia or analysis of a process of treatment. However, manually locating, identifying, and counting the different classes of WBC is time-consuming and fatiguing. Classification and counting accuracy depends on the capability and experience of operators.ResultsThis paper uses a deep learning method to count cells in color bone marrow microscopic images automatically. The proposed method uses a Faster RCNN and a Feature Pyramid Network to construct a system that deals with various illumination levels and accounts for color components' stability. The dataset of The Second Affiliated Hospital of Zhejiang University is used to train and test.ConclusionsThe experiments test the effectiveness of the proposed white blood cell classification system using a total of 609 white blood cell images with a resolution of 2560 × 1920. The highest overall correct recognition rate could reach 98.8% accuracy. The experimental results show that the proposed system is comparable to some state-of-art systems. A user interface allows pathologists to operate the system easily.

【 授权许可】

CC BY