Low-Dose CT Image Denoising based on EfficientNetV2 and Wasserstein GAN

Medical professionals use low-dose computed tomography (LDCT) to reduce radiation exposure in patients, but this can create noisy images and artifacts that complicate interpretation. Recent research focuses on using deep learning techniques to improve image quality in LDCT scans. In this study, we suggest a new method that combines EfficientNetV2 with a generative adversarial network (GAN) using Wasserstein distance and perceptual similarity. This approach helps reduce noise while maintaining LDCT image structures, potentially enhancing diagnostic accuracy and patient safety. By integrating EfficientNetV2 with a GAN and utilizing perceptual similarity and Wasserstein distance, we achieved excellent results with a PSNR of 32.6058 and SSIM of 0.9135 on the AAPM-Mayo Clinic LDCT Grand Challenge Dataset. The significant improvement over existing methods highlights the potential of our proposed method in enhancing LDCT image quality.