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Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges
Muhammad Waqas Nadeem1,2, Mohammed A Al Ghamdi3, Muzammil Hussain2
1Department of Computer Science, Lahore Garrison University, Lahore 54000, Pakistan.
Brain Sciences
|February 27, 2020
Summary
Deep learning (DL) significantly enhances brain tumor analysis, improving segmentation, classification, and prediction. This review maps the research landscape and discusses future challenges in applying DL to neuro-oncology.
Area of Science:
- Artificial Intelligence
- Medical Imaging
- Bioinformatics
Background:
- Deep Learning (DL) algorithms utilize multi-layered processing for data abstraction.
- DL applications are rapidly expanding across various scientific domains, particularly in healthcare.
- DL has revolutionized medical image processing, analysis, and bioinformatics, enhancing diagnostic capabilities.
Purpose of the Study:
- To review key Deep Learning concepts relevant to brain tumor analysis.
- To summarize scientific contributions in the field of DL for brain tumor research.
- To map the research landscape and analyze emerging trends in DL for neuro-oncology.
Main Methods:
- Comprehensive literature review of scientific contributions.
- Summarization of major Deep Learning techniques applied to brain tumor analysis.
- Taxonomic mapping of the research landscape.
Main Results:
- Identification of DL's impact on brain tumor segmentation, classification, and prediction.
- Analysis of the current state and key aspects of DL in brain tumor research.
- Discussion of limitations and open challenges in the field.
Conclusions:
- Deep Learning offers transformative potential in brain tumor analysis.
- Further research is needed to address current limitations and explore future directions.
- DL is a critical emerging area for advancing neuro-oncology diagnostics and treatment planning.
