Improving Quality of Medical Scans using GANs

Improving the quality of medical images is essential for precise diagnosis and treatment planning. When low quality images are used to train the neural network model, the good accuracy cannot be achieved. Nowadays, Generative Adversarial Networks (GANs) have become a potent image enhancement tool that can provide a fresh method for raising the caliber of medical images. In order to improve medical images, this paper presents a GAN-based framework that reduces noise, increases resolution, and corrects artifacts. The suggested technique makes use of a generator network to convert low-quality images into their high-quality equivalents, and a discriminator network to assess the veracity of the improved images. To ensure robustness across various modalities, the model is trained on a diverse dataset of medical images, including MRI, CT, and X-ray scans. Our experimental results show that GAN-based method significantly improves the image quality when compared to conventional methods, as evidenced by enhanced peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) according to quantitative evaluations. This study emphasizes the value of incorporating deep learning methods into medical image processing pipelines and the potential of GANs to advance medical imaging technology so that a robust neural network model can be designed.