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

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Department of Computer Science, Lahore Garrison University, Lahore 54000, Pakistan
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Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore 54000, Pakistan
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Department of Computer Science, Umm Al-Qura University, Makkah 23500, Saudi Arabia
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Department of Information Sciences, Division of Science and Technology, University of Education Township, Lahore 54700, Pakistan
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Author to whom correspondence should be addressed.
Brain Sci. 2020, 10(2), 118; https://doi.org/10.3390/brainsci10020118
Received: 27 December 2019 / Revised: 7 February 2020 / Accepted: 13 February 2020 / Published: 22 February 2020
(This article belongs to the Collection Collection on Clinical Neuroscience)
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. View Full-Text
Keywords: deep learning; brain tumor; computer vision; bioinformatics; segmentation; medical images; review deep learning; brain tumor; computer vision; bioinformatics; segmentation; medical images; review
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Nadeem, M.W.; Ghamdi, M.A.A.; Hussain, M.; Khan, M.A.; Khan, K.M.; Almotiri, S.H.; Butt, S.A. Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges. Brain Sci. 2020, 10, 118.

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