Deep Learning For Medical Image Super-Resolution For Precision Diagnosis

Abstract

A study on deep learning for medical image super-resolution (MISR) and its prospective applications in medical imaging are presented in this research report. We investigate and evaluate the performance of two distinct methods to MISR employing CNNs and GANs on a dataset consisting of low-resolution medical pictures. Both of these techniques use artificial neural networks. Both CNN-based and GAN-based approaches were able to significantly improve the visual quality and diagnostic accuracy of medical images, with the GAN-based approach outperforming the CNN-based approach in terms of perceptual quality. Our experimental results show that both approaches can significantly improve the visual quality of medical images. We also examine the possible uses of deep learning for MISR in clinical diagnostics and medical technology, as well as assess the influence that various parameters have on the accuracy and visual quality of the models. This study makes a significant contribution to the expanding corpus of research on the use of deep learning to MISR and offers important new insights into the design, implementation, and improvement of deep learning models for use in medical imaging applications.