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.