Deep residual learning-based denoiser for medical X-ray images

Effective image denoising is vital for accurate biomedical image analysis, serving as a foundational step for subsequent diagnostic and analytical tasks. Medical diagnostic instruments often introduce noise during image acquisition, necessitating robust denoising algorithms to ensure diagnostic accuracy. Although deep learning has shown potential in this area, existing solutions often use resized natural images for training deep learning models. However, maintaining picture details is crucial for accurate analysis because medical images are usually high-resolution and contain significant pixel-level features. To address this challenge, a novel approach of preparing training data by dividing images into smaller square tiles is proposed in this study. This study introduces a deep residual learning-based model, utilizing multiple residual learning blocks and rectified linear unit activations, to perform end-to-end mapping from noisy inputs to denoised outputs. The proposed method was evaluated on 50 degraded images, showing superior performance with an Structural Similarity Index (SSIM) of 0.9844 for mammogram images, surpassing the DCNN approach, which averaged an SSIM of 0.9811. Our model achieves enhanced peak signal-to-noise ratio (PSNR) and SSIM in X-ray image denoising, underscoring the effectiveness of training image partitioning over traditional resizing methods. This work highlights the importance of preserving high-resolution details in medical imaging and introduces a novel data preparation technique that significantly improves denoising outcomes.