A Reproducible Deep Learning Pipeline for Augmentation-Enhanced Classification of Anemia-Related Blood-Cell Images

Abstract

This study presents a reproducible end-to-end deep learning pipeline for proof-of-concept classification of anemia-related and control-like blood cell images using a publicly accessible online dataset. The workflow integrates online data download, automatic extraction, dataset restructuring, class balancing, augmentation-enhanced preprocessing, transfer-learning-based model training, visualization, and structured metric generation within a single executable framework. A balanced binary dataset was constructed to reduce the effect of severe class imbalance, and augmentation strategies, including flipping, rotation, color jitter, and affine transformation, were applied only during training. A ResNet18-based classifier showed strong convergence under a fixed random seed and single train-validation-test split. On a small held-out test subset of 32 images, all images were correctly classified. However, this result should be interpreted cautiously because the test subset was small, no cross-validation or external validation was performed, and performance on limited balanced data may overestimate generalizability. Class-distribution plots, representative sample panels, learning curves, confusion matrices, prediction files, and metric summaries support transparent inspection and reproducibility. The main contribution of this study is a reproducible engineering workflow for blood-cell image classification, rather than a claim of algorithmic novelty or clinical readiness.