Clinical Application of Using Diffusion-Based Wasserstein Generative Adversarial Network for Morphologic Analysis of Blood Cells

Background: Morphologic analysis of peripheral blood smears is essential for diagnosing hematologic diseases and patient management. Although manual microscopy is the traditional gold standard, it is time-consuming and subjective. Digital morphology analyzers have improved automation and accuracy; however, challenges remain, particularly in classifying certain cell types. Recently, the diffusion-based Wasserstein generative adversarial network with gradient penalty (DWGAN-GP) showed potential by enhancing image quality and addressing data imbalance. We aim to investigate the accuracy of blood cell classification using the DWGAN-GP model. Methods: In this study, the DWGAN-GP model in conjunction with the EfficientNetB3 classification model was evaluated using 78,494 peripheral blood cell images. Samples were collected from patients with normal and abnormal hematologic conditions. Data were balanced by augmenting underrepresented classes with synthetic images, resulting in equal representation across 13 cell classes. Performance was compared with PBIA (ANI Co., Suwon, Korea), a commercial digital morphology analyzer. Results: DWGAN-GP augmentation significantly improved classification accuracy of the EfficientNetB3 model to 97.74% with an F1-score of 91.13%. This result surpassed both the unbalanced dataset (accuracy 95.68%, F1-score 82.12%) and PBIA system (accuracy 95%). Notably, improvements were significant in minority classes such as blasts and myelocytes, which are critical in diagnosing leukemia. Conclusion: Incorporating synthetic data using DWGAN-GP significantly enhanced model performance and addressed class imbalance. This method shows promise for more accurate and consistent blood cell classification, offering potential improvements in clinical diagnostics for hematologic disorders.