Diffusion-based Wasserstein generative adversarial network for blood cell image augmentation

White blood cells (WBC) are vital elements of the immune system, and their number and differential count are crucial for diagnosing blood-related disorders. While existing research has primarily focused on classifying easily distinguishable major WBC types, our study delves into a model encompassing up to 19 WBC classes, some of which exhibit irregular shapes and are challenging to differentiate manually. Convolutional Neural Networks (CNNs) have shown remarkable progress in accurately classifying these intricate WBC classes. However, the accuracy of these models depends mainly on the availability of enough appropriate datasets, which can be challenging to obtain for rare WBC classes. To address this, we introduce a generative model, the diffusion-based Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). This model innovatively combines the Denoising Diffusion Probabilistic Model (DDPM) forward diffusion process with the WGAN-GP, leveraging DDPM's noisy vectors as inputs for WGAN-GP's generator. This fusion accelerates the generative process and significantly enhances the output's fidelity, particularly for complex WBC images. Our model demonstrated its effectiveness on a dataset comprising 4,503 images across 19 WBC classes from Soonchunhyang University Bucheon Hospital, Korea, showing significant improvement in generating high-quality images for rare WBC classes and addressing data imbalance. We further combined pre-trained CNNs with Support Vector Machines (SVM) for classification, where our augmentation strategy led to the ResNet50-SVM model achieving an average accuracy of 95% in classifying the 19 WBC classes. This study not only addresses the data imbalance but also sets a new benchmark in WBC image analysis, demonstrating our model's efficacy in generating high-quality data for rare classes.