Enhancing Medical Imaging Resolution: Exploring SRGAN for High-Quality Medical Image Reconstruction

In healthcare diagnostics, medical imaging is essential for accurate diagnosis and treatment planning. A major challenge is the limited resolution of some medical images, which can obscure crucial details and affect diagnostic accuracy, especially in telemedicine. This study examines the use of Super-Resolution Generative Adversarial Networks (SRGANs) to improve the resolution of medical images. SRGANs, known for enhancing conventional images, are adapted for medical imaging using a deep convolutional neural network (CNN) generator and discriminator, with a perceptual loss function guiding the reconstruction process. The research involves training the network on a varied dataset of low-resolution medical images, including MRI, CT scans, and ultrasound, aiming to capture intrinsic features of medical imagery. The performance of the model is assessed using a mix of quantitative and qualitative metrics.