An Advanced GAN-Based Framework for Medical Image Enhancement

Low contrast, noise, and low visual detail are major medical image problems that pose a negative effect on diagnostic accuracy. This paper suggests a more developed model using deep generative networks (GANs) to enhance the quality of medical iris images. The framework has a sequence of preprocessing steps that include contrast enhancement (CLAHE), noise removal (Bilateral Filter), and edge enhancement (Unsharp Masking), and then the stage of enhanced generation with an attention-assisted generator (Adam) with fine-tuned parameters. SSIM, PSNR and LPIPS measures were used to evaluate the performance of the model. The findings revealed that there were significant visual and perceptual structure of images as results showed that, average SSIM was improved by 0.9383 to 0.9783, LPIPS was reduced by 0.0137 to 0.0078 and PSNR had increased by 28.62 to 32.23 than the default parameters. These results confirm the usefulness of fine-tuning at enhancing perceptual and structural image measures. This model improves the diagnosis abilities in the medical field and minimizes the use of costly refined imaging methods, hence it can be applied in large scale clinical setting.