Abstract
Imaging is a vital component of the diagnostic and early detection processes for many medical disorders. However, the noise in the images can sometimes interfere with the accuracy of the diagnosis. Speckle noise, Poisson noise, salt and pepper noise, and Gaussian noise are a few instances of these disturbances, which are produced by imaging techniques and reduce diagnostic accuracy as well as image quality. Noise reduction methods, such as spatial filtering and transformational domain filtering, have a lot of problems when dealing with various kinds of noise. With the growth of deep learning, especially generative adversarial networks, the capabilities of image noise reduction are even superior to those of traditional techniques. This study compares the efficacy of GAN techniques with traditional de-noising techniques and illustrates the effects of various noise sources on medical imaging. Besides that, it describes how the GAN accomplishes the noise reduction task in medical imaging by discussing its advantages, uses, and efficiency in comparison to other techniques. The study's outcomes revealed a new approach to using GAN to filter out the noise in medical images and the possibility of utilizing this technique in real-world cases to generate accurate diagnosis and analysis. But, in addition to that, it serves as a passageway to more in-depth research that focuses on medical image enhancement and patient healthcare.
