Denoising Degraded PCOS Ultrasound Images Using an Enhanced Denoising Diffusion Probabilistic Model

Abstract

Currently, for polycystic ovary syndrome (PCOS), diagnostic methods are mainly divided into hormonal indicators and ultrasound imaging. However, ultrasound images are often affected by noise and artifacts during the imaging process. This significantly degrades image quality and increases the difficulty of diagnosis. This paper proposes a PCOS ultrasound image denoising method based on an improved DDPM. During the forward diffusion process of the original model, Gaussian noise is progressively added using a cosine-based scheduling strategy. In the reverse diffusion process, a conditional noise predictor is introduced and combined with the original ultrasound image information to iteratively denoise and recover a clear image. Additionally, we fine-tuned and optimized the model to better suit the requirements of PCOS ultrasound image denoising. Experimental results show that our model outperforms state-of-the-art methods in both noise suppression and structural fidelity. It delivers a fully automated PCOS-ultrasound denoising pipeline whose diffusion-based restoration preserves clinically salient anatomy, improving the reliability of downstream assessments.