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Bimonthly Since 1986 |
ISSN 1004-9037
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Publication Details |
Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
Distributed by:
China: All Local Post Offices
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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.
Keyword
medical imaging, clinical diagnosis, image processing, artificial intelligence, image enhancement.
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