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Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

Deep Learning (DL) algorithms enabled computational models consist of multiple processing layers that represent data with multiple levels of abstraction. In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image ana...

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Autores principales: Nadeem, Muhammad Waqas, Ghamdi, Mohammed A. Al, Hussain, Muzammil, Khan, Muhammad Adnan, Khan, Khalid Masood, Almotiri, Sultan H., Butt, Suhail Ashfaq
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7071415/
https://www.ncbi.nlm.nih.gov/pubmed/32098333
http://dx.doi.org/10.3390/brainsci10020118
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author Nadeem, Muhammad Waqas
Ghamdi, Mohammed A. Al
Hussain, Muzammil
Khan, Muhammad Adnan
Khan, Khalid Masood
Almotiri, Sultan H.
Butt, Suhail Ashfaq
author_facet Nadeem, Muhammad Waqas
Ghamdi, Mohammed A. Al
Hussain, Muzammil
Khan, Muhammad Adnan
Khan, Khalid Masood
Almotiri, Sultan H.
Butt, Suhail Ashfaq
author_sort Nadeem, Muhammad Waqas
collection PubMed
description Deep Learning (DL) algorithms enabled computational models consist of multiple processing layers that represent data with multiple levels of abstraction. In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics. Consequently, deep learning has dramatically changed and improved the means of recognition, prediction, and diagnosis effectively in numerous areas of healthcare such as pathology, brain tumor, lung cancer, abdomen, cardiac, and retina. Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification, prediction, evaluation.). A review conducted by summarizing a large number of scientific contributions to the field (i.e., deep learning in brain tumor analysis) is presented in this study. A coherent taxonomy of research landscape from the literature has also been mapped, and the major aspects of this emerging field have been discussed and analyzed. A critical discussion section to show the limitations of deep learning techniques has been included at the end to elaborate open research challenges and directions for future work in this emergent area.
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spelling pubmed-70714152020-03-19 Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges Nadeem, Muhammad Waqas Ghamdi, Mohammed A. Al Hussain, Muzammil Khan, Muhammad Adnan Khan, Khalid Masood Almotiri, Sultan H. Butt, Suhail Ashfaq Brain Sci Review Deep Learning (DL) algorithms enabled computational models consist of multiple processing layers that represent data with multiple levels of abstraction. In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics. Consequently, deep learning has dramatically changed and improved the means of recognition, prediction, and diagnosis effectively in numerous areas of healthcare such as pathology, brain tumor, lung cancer, abdomen, cardiac, and retina. Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification, prediction, evaluation.). A review conducted by summarizing a large number of scientific contributions to the field (i.e., deep learning in brain tumor analysis) is presented in this study. A coherent taxonomy of research landscape from the literature has also been mapped, and the major aspects of this emerging field have been discussed and analyzed. A critical discussion section to show the limitations of deep learning techniques has been included at the end to elaborate open research challenges and directions for future work in this emergent area. MDPI 2020-02-22 /pmc/articles/PMC7071415/ /pubmed/32098333 http://dx.doi.org/10.3390/brainsci10020118 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Nadeem, Muhammad Waqas
Ghamdi, Mohammed A. Al
Hussain, Muzammil
Khan, Muhammad Adnan
Khan, Khalid Masood
Almotiri, Sultan H.
Butt, Suhail Ashfaq
Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges
title Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges
title_full Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges
title_fullStr Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges
title_full_unstemmed Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges
title_short Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges
title_sort brain tumor analysis empowered with deep learning: a review, taxonomy, and future challenges
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7071415/
https://www.ncbi.nlm.nih.gov/pubmed/32098333
http://dx.doi.org/10.3390/brainsci10020118
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