Medical image enhancement for improved diagnostic accuracy using generative adversarial network

Abstract

Background: Enhancing the quality of images from retinal, MRI and echocardiography imaging shows promise with SR-GANs for medical imaging use. Using these networks, it is possible to produce high-quality images even from low-quality medical scans. Methods: To do this, SR-GANs make use of growth from low to high resolutions in two 2× stages, multiple sizes of filters and powerful loss functions. The medical super-resolution network and denoising SR-GAN focus on problems such as image noise and artifacts to improve a photo’s stability, ability to extract features and how it looks. Results: Assessment by numbers has found that using SR-GAN-based approaches leads to marked improvements such as increases in the PSNR by up to 4.85 dB and improvements in the SSIM by between 0.04 and 0.05. Such improvements are better than traditional super-resolution methods which help doctors achieve clear images of the mitral valve in cardiac ultrasonography. Conclusion: It is anticipated that applying SR-GANs in clinical tasks will increase the accuracy of diagnoses, ease the workload for patients and widen the application of super-resolution methods in various medical procedures. The results prove that SR-GANs improve the picture quality of echocardiograms used for diagnosing mitral valve problems. Having proven the model in research settings, future studies should try to apply it to real-world clinical cases, test for its use across a range of imaging devices and perfect the system to ensure it is efficient for use in medical settings.