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Abstract

Modern deep learning technology is without a doubt catalyzing a transformative revolution across various critical domains, including image analysis, natural language processing, and expert systems. It stands as an indispensable technique with a profound potential for shaping future applications. Recently, Magnetic Resonance Imaging (MRI) has garnered substantial attention due to its non-invasive attributes and its remarkable ability to provide intricate soft tissue contrasts within the body. Leveraging the significant advancements in deep learning, researchers have proposed ingenious approaches to augment the processing and analysis of MRI images. This article endeavors to present a comprehensive overview of how deep learning is being effectively employed for MRI image processing and analysis. The narrative commences with a succinct introduction to the fundamental concept of deep learning, followed by an elucidation of the diverse imaging modalities employed within the realm of MRI. Subsequently, the article delves into a comprehensive exploration of prevalent deep learning architectures. Building upon this foundation, the article navigates through a diverse array of applications made possible by harnessing deep learning within the domain of MRI. This encompassing exploration includes an emphasis on fundamental deep learning techniques, the transfer of knowledge between varying domains, classification paradigms, as well as the intricate domain of image segmentation. However, the article's exploration does not conclude here; it extends to deliberating on the strengths and limitations inherent in widely adopted tools. Additionally, it introduces specific deep learning tools that have been meticulously tailored to cater to the unique demands of MRI applications. In the final stretch, the article provides an impartial assessment of the role and impact of deep learning within the context of MRI. It also offers insightful projections into the landscape of future advancements and emerging trends. With a discerning eye on the trajectory ahead, the article articulates the immense potential for deep learning to significantly advance MRI image analysis, solidifying its pivotal role as a leading-edge technology shaping the landscape of medical imaging. In summation, the fusion of deep learning techniques with MRI analysis is poised to bring forth transformative advancements. The amalgamation of these disciplines stands to propel the boundaries of MRI image analysis, redefining the horizons of medical imaging while fortifying deep learning's status as a cornerstone technology driving this evolution

Keywords

magnetic resonance imaging deep learning segmentation classification transfer learning

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How to Cite
Lamyaa Fahem Katran, Ebtesam N. AlShemmary, & Waleed Ameen Al Jawher. (2023). Deep Learning’s Impact on MRI Image Analysis: A Comprehensive Survey. Texas Journal of Engineering and Technology, 25, 63–80. Retrieved from https://zienjournals.com/index.php/tjet/article/view/4572

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