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

BACKGROUND: Differentiating 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. RESULTS: This 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. CONCLUSIONS: The 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.