Leukocytes Classification Methods: Effectiveness and Robustness in a Real Application Scenario

Classification and differentiation of leukocyte sub-types are important in peripheral blood smear analysis. Fully-automated systems for leukocyte analysis are grouped into segmentation- and detection-based methods. The accuracy of classification depends on the accuracy of segmentation and detection steps. Real-world applications often produce inaccurate ROIs due to image quality factors, e.g., colour and lighting conditions, absence of standards, or even density and presence of overlapping cells. To this end, we investigated the scenario in-depth with ROIs simulating segmentation and detection methods and evaluating different image descriptors on two tasks: differentiation of leukocyte sub-types and leukaemia detection. The obtained results show that even simpler approaches can lead to accurate and robust results in both tasks when exploiting appropriate images for model training. Traditional handcrafted features are more effective when extracted from tight bounding boxes or masks, while deep features are more effective when extracted from large bounding boxes or masks.