Neural Networks in Imaging

Neural networks in imaging are computational models inspired by the brain’s structure, used to analyze and interpret visual data. They consist of interconnected layers of algorithms that learn patterns from images, such as shapes, textures, and colors. By training on large datasets, these networks improve their ability to recognize objects, detect anomalies, or enhance image quality. They are widely used in medical diagnostics, facial recognition, and image editing, enabling machines to understand and process visual information in a way that closely mimics human perception.