High-Resolution Guided Image Synthesis Using Latent Diffusion Model Technology

We introduce the latent diffusion model used in medical ultrasound image synthesis. We point out that precision is the issue, and installing ultrasound images was completed with an accuracy of 97.47% since ultrasound demands greater accuracy. It has some particular disadvantages because it operates in real-time and requires operator settings. Considering these challenges, our model has a lot of promise to provide accurate and lifelike ultrasound images. Even though it is hard to calculate the precise answer for this optimization, applying the backpropagation method merely once can produce an approximation. In order to train a diffusion model with the value and outcomes (FID: 2.870, CLIP: 0.209, SSIM: 0.9923, and LPIPS: 0.92) that we promised, we generated synthetic images of roughly 300 ultrasound images. were acquired. Expanding the use of artificial intelligence in medical imaging is the aim of this endeavor. Since this is a novel problem, the study will serve as a foundation and source of inspiration for researchers looking into possible applications of diffusion models in medical image production. The URL https://www.kaggle.com/datasets/suraahmed56/computer-vision-medical-images provides access to synthetic images