Enhancing Medical Images with Generative Adversarial Networks (GANs): A Review of Techniques and Applications

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Generative Adversarial Networks (GANs) have demonstrated substantial utility in the domain of synthetic image generation for medical imaging. There are several obstacles to the growing need for high-quality photographs for illness detection, including expense, time, and radiation exposure. Generative adversarial networks have found applications in various image-to-image translation tasks such as virtual reconstruction of 7T MRIs from 3T images, enhancing low-quality resolution images into higher quality ones, and similar converting CT scans to MRIs. By creating multimodal datasets from a single modality, these methods greatly enhance data quality and eliminate the need for repeated scans. This paper examines several GAN models and how they are used in medical image processing, emphasizing how they may improve picture quality for more accurate diagnosis.