Synthesis of Microscopic Cell Images Obtained from Bone Marrow Aspirate Smears through Generative Adversarial Networks

SIMPLE SUMMARY: This paper proposes a hybrid generative adversarial networks model—WGAN-GP-AC—to generate synthetic microscopic cell images. We generate the synthetic data for the cell types containing fewer data to obtain a balanced dataset. A balanced dataset would help enhance the classification accuracy of each cell type and help with an easy and quick diagnosis that is critical for leukemia patients. In this work, we combine images from three datasets to form a single concrete dataset with variations of multiple microscopic cell images. We provide experimental results that prove the correlation between the original and our synthetically generated data. We also deliver classification results to showcase that the generated synthetic data can be used for real-life experiments and the advancement of the medical domain. ABSTRACT: Every year approximately 1.24 million people are diagnosed with blood cancer. While the rate increases each year, the availability of data for each kind of blood cancer remains scarce. It is essential to produce enough data for each blood cell type obtained from bone marrow aspirate smears to diagnose rare types of cancer. Generating data would help easy and quick diagnosis, which are the most critical factors in cancer. Generative adversarial networks (GAN) are the latest emerging framework for generating synthetic images and time-series data. This paper takes microscopic cell images, preprocesses them, and uses a hybrid GAN architecture to generate synthetic images of the cell types containing fewer data. We prepared a single dataset with expert intervention by combining images from three different sources. The final dataset consists of 12 cell types and has 33,177 microscopic cell images. We use the discriminator architecture of auxiliary classifier GAN (AC-GAN) and combine it with the Wasserstein GAN with gradient penalty model (WGAN-GP). We name our model as WGAN-GP-AC. The discriminator in our proposed model works to identify real and generated images and classify every image with a cell type. We provide experimental results demonstrating that our proposed model performs better than existing individual and hybrid GAN models in generating microscopic cell images. We use the generated synthetic data with classification models, and the results prove that the classification rate increases significantly. Classification models achieved 0.95 precision and 0.96 recall value for synthetic data, which is higher than the original, augmented, or combined datasets.