Refining Clarity Medical Image via U-Net with Tailored Architectural Modifications and Transfer Learning

Development, implementation, and quality assessment of an augmented neural network with transfer learning methodologies, with a specific focus on denoising medical images are presented. The principal aim of research is to elevate the quality and dependability of medical images in conditions of limited initial data. The described method is based on the use of the improved U-Net architecture and is intended for well-established operation in the field of biomedical image segmentation, configured for the nuanced task of eliminating image noise.