Paper Title
Review of Multi-Model Fusion Techniques Driven by Artificial Intelligence for MRI Imaging Analysis of Motor Neuron Disease
Article Identifiers
Registration ID: IJNRD_224688
Published ID: IJNRD2407065
DOI: http://doi.one/10.1729/Journal.40439
Authors
Gaurav N Borse
Keywords
MRI ,AI ,Machine Learning , Multimodel Techniques , CNN, Imaging
Abstract
Abstract Particularly in improving the interpretation of magnetic resonance imaging (MRI) data for the diagnosis and monitoring of complicated neurological diseases, artificial intelligence (AI) and machine learning (ML) have become rather effective tools in the field of medical imaging. The use of AI-driven multi-model fusion approaches for MRI imaging analysis of motor neuron illness is investigated in this work. The paper covers the importance of artificial intelligence in transforming medical imaging, summarizes motor neuron illness and its diagnosis, and includes many AI/ML models and methods for MRI image processing. It then explores the practical uses of multi-model fusion methods and their importance in enhancing the accuracy of MRI image motor neuron disease identification. Furthermore, discussed in the study are the difficulties and constraints in combining several artificial intelligence models using fusion techniques and possible future developments and ideas that can increase the efficiency of these methods even further. (Kim & Jewells.,2020; Liu et al.,2020).
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How To Cite (APA)
Gaurav N Borse (July-2024). Review of Multi-Model Fusion Techniques Driven by Artificial Intelligence for MRI Imaging Analysis of Motor Neuron Disease . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(7), a615-a621. http://doi.one/10.1729/Journal.40439
Issue
Volume 9 Issue 7, July-2024
Pages : a615-a621
Other Publication Details
Paper Reg. ID: IJNRD_224688
Published Paper Id: IJNRD2407065
Downloads: 000121990
Research Area: Medical Science
Country: pune, Maharastra, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2407065.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2407065
Crossref DOI: http://doi.one/10.1729/Journal.40439
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