Hierarchical Deep Learning for Reliable Bone Marrow Cell Classification in Digital Healthcare

Accurate bone marrow cell classification can support the morphological assessment of hematological disorders, but manual examination is labor-intensive and observer-dependent. This study presents a Taguchi-guided, six-stage hierarchical deep-learning framework that decomposes seven-class bone marrow cell classification into clinically interpretable binary decisions involving identifiability, lineage, maturity, and subtype. ResNet-101, DenseNet-201, and EfficientNet-B0 were evaluated as stage-specific transfer-learning backbones, with solver, learning rate, and mini-batch size screened using an L₉ orthogonal array. The framework was evaluated on a curated balanced dataset of 7,000 annotated images. Balanced node-level test accuracies ranged from 95 to 98%, with F1-scores from 0.95 to 0.98. End-to-end evaluation on the locked 700-image test set produced an overall accuracy of 96.3%, Micro-F1 of 0.963, and Macro-F1 of 0.963. Flat seven-class baselines achieved 30.0–37.0% accuracy under the recorded experimental settings. These results suggest that hierarchical decomposition was beneficial for this dataset, although image-level partitioning, the absence of external validation, and possible propagation of early routing errors limit generalizability. The system was implemented as a standalone MATLAB proof-of-concept interface. Prospective evaluation with hematopathologists and external patient- or slide-level validation are required before clinical use.