  Access to high-quality datasets in the medical industry limits machine
learning model performance. To address this issue, we propose a Denoising
Diffusion Probabilistic Model (DDPM) combined with a UNet architecture for
X-ray image synthesis. Focused on pneumonia medical condition, our methodology
employs over 3000 pneumonia X-ray images obtained from Kaggle for training.
Results demonstrate the effectiveness of our approach, as the model
successfully generated realistic images with low Mean Squared Error (MSE). The
synthesized images showed distinct differences from non-pneumonia images,
highlighting the model's ability to capture key features of positive cases.
Beyond pneumonia, the applications of this synthesizer extend to various
medical conditions, provided an ample dataset is available. The capability to
produce high-quality images can potentially enhance machine learning models'
performance, aiding in more accurate and efficient medical diagnoses. This
innovative DDPM-based X-ray photo synthesizer presents a promising avenue for
addressing the scarcity of positive medical image datasets, paving the way for
improved medical image analysis and diagnosis in the healthcare industry.
