EfficientNet-B0-Based Screening of WBC-Diff Scattergram Images for Hematological Abnormalities

White blood cell differential (WBC-Diff) scattergrams are used to screen for hematological abnormalities in potential hematopoietic malignancies and abnormal conditions. However, these potential indications can be easily missed. We applied deep learning models to develop a new and rapid workflow by learning the characteristics of WBC-Diff scattergram images for early hematological abnormality screening. A total of 4297 WBC-Diff scattergram images, including healthy controls, were included and divided into 19 categories. Eleven models encompassing both CNNs (ResNet18, ResNet50, VGG16, InceptionV3, DenseNet121, MobileNetV2, EfficientNet-B0, and EfficientNet-B1) and Vision Transformers (Swin-Tiny, DeiT-Base, and ConvNeXt-Tiny) were trained and compared. Grad-CAM interpretability analysis, hierarchical clustering, and multivariate analysis of variance (MANOVA) were used to characterize the learned feature space. Among all models, Swin-Tiny achieved the highest accuracy (70.44%), while EfficientNet-B0 was selected as the primary model due to its favorable balance of classification performance (67.90% accuracy) and computational efficiency (4.0 M parameters, 0.42 G floating-point operations (GFLOPs), 2.8 × faster CPU inference than Swin-Tiny). The 5-run stability evaluation confirmed reproducible performance, and evaluation on an independent hold-out dataset confirmed patient-independent generalizability. As an upstream triage endpoint (normal vs. abnormal), the model achieved a sensitivity of 0.975 (test) and 0.984 (independent hold-out set) with a false-negative rate of 2.5% and 1.6%, respectively. At the screening level, only 2.2% of abnormal images were mis-routed to "normal," reflecting a sensitivity-first, specificity-as-safeguard design. Grad-CAM analysis revealed disease-specific activation patterns that mapped to established cell population zones on the WBC-Diff scattergram (e.g., myeloblast zone for M1 and neutrophil-basophil cluster for CML), providing support for the biological plausibility of model decisions, with significant differences among disease categories in the visual feature space (MANOVA, p < 0.01). This study demonstrated that a new automated hematology workflow incorporating EfficientNet-B0 could provide early and rapid screening of potential hematological abnormalities, particularly suitable for deployment in resource-constrained clinical settings.