COVID-19 PNEUMONIA CHEST X-RAY PATTERN SYNTHESIS BY STABLE DIFFUSION

In this study, we fine-tuned a stable diffusion model to synthesize high resolution chest X-ray images (512×512) with bilateral lung edema caused by COVID-19 pneumonia using the class-specific prior preservation strategy. 300 positive images were selected from the MIDRC dataset as subject instances with an additional 400 negative images for class prior preservation. We synthesized images respectively using the new technique and the conventional technique for comparison. The synthetic images by the stable diffusion fine-tuned by the prior preservation technique have the Frechet inception distance (FID) of 9.2158 and kernel inception distance (KID) 0.0818 computed with the real positive images, which is superior to the synthetic images using the conventional methods such as WGAN and DDIM. The classification accuracy is 0.9975 with precision of 1.0 and recall of 0.9950 when the synthetic positive images with the real negative images were classified by a trained vision transformer (ViT). We conclude that the stable diffusion model can synthesize high-quality and high-resolution chest x-ray images using the prior preservation strategy with a small number of real images as subject instances and text prompt as guidance for the designated patterns.