A Hybrid Framework for RBC Labeling Using Elliptical Fitting, Autoencoding, and Data Augmentation

Red blood cell (RBC) morphology is critical for diagnosing hematological disorders, particularly in regions such as Southeast Asia where anemia and thalassemia are highly prevalent. However, manual microscopic assessment is labor-intensive, subjective, and dependent on expert availability, while existing automated methods often rely on small, curated datasets that fail to represent real-world smear variability. This study proposes a hybrid framework integrating preprocessing, unsupervised autoencoding, k-means clustering, ellipse fitting, expert-in-the-loop validation, and targeted data augmentation to establish a robust RBC labeling pipeline. High-resolution smear images from confirmed anemia and thalassemia cases were processed to extract over 14,000 single-cell patches, filtered systematically into quality-assured subsets. Latent features from a CNN autoencoder enabled clustering into 80 morphological groups, quantified using ellipse-based geometric metrics and validated by hematology experts. Data augmentation addressed class imbalance, expanding rare morphologies while preserving realistic cell structure. The resulting dataset captures clinically relevant morphological diversity specific to the Thai population and provides a scalable, interpretable framework for medical analysis and future AI model development in hematology.