MRI and CT Scan Images Quality Enhancement Using Generative Adversarial Network

In the present era, medical imaging is of vital importance within the healthcare system. It offers critical information for precise and accurate diagnosis and therapy planning. However, subpar image quality still severely limits the ability to fully harness the potential of the deep learning models in the medical field. A dual strategy, combining both conventional Generative Adversarial Networks (GANs) and Pix2Pix GANs is proposed in this research paper to improve the quality of the medical photographs. This GAN framework produces visually enhanced and high-resolution medical images through adversarial training. Moreover, the incorporation of Pix2Pix GAN enhances this process even more, emphasizing the preservation of pertinent details in the photos and structural coherence. The combined effect of these two architectures of GAN guarantees a thorough and subtle enhancement of the visual integrity of medical images specifically of brain tumour. The process yields to improve photos that are a useful dataset for deep learning model for the training purpose. It is anticipated that the deep learning model that has been tuned, would perform better in the medical image processing tasks as compared to the normal ones, which will lead to the more precise and accurate diagnosis and treatment. By bridging the gap between the image quality enhancement methods and the demands for the strong deep learning models in the medical field, this research holds promise for improvements in patient care and healthcare domain.