The analysis of blood smear images for the classification of white blood cell (WBC) subtypes, known as the differential count, is fundamental to hematological diagnosis. However, conventional manual analysis is time-consuming, labor-intensive, and prone to inter-observer variability. To address these challenges, this study presents SEPO-WBC (Software-based Evaluation Process for Optimized White Blood Cell analysis). This open-source software automates WBC detection and classification in canine blood smear images to calculate differential counts. A two-stage pipeline is employed, utilizing the You Only Look Once (YOLO) model for detection and the Densely Connected Convolutional Networks (DenseNet) model for classifying detected WBC subtypes, with each model selected for optimal performance in its respective task. Experimental results demonstrate that SEPO-WBC achieves an mAP 50 value of 0.935 for detection and an accuracy of 0.878 for classification. A clinical comparison with five experts confirms detection equivalence with an intraclass correlation coefficient (ICC) of 0.969 and classification agreement of 0.855-0.883 within an inter-expert range of 0.845-0.970, while also realizing much faster processing. • SEPO-WBC is the novel open-source software for automated canine WBC analysis. • SEPO-WBC employs a two-stage pipeline for WBC detection and subtype classification. • The pipeline achieves an mAP50 of 0.935 for detection and 0.878 for classification. • Clinical validation confirms expert-level agreement and much faster processing.
Jeong et al. (Fri,) studied this question.