A Review on a Deep Learning Perspective in Brain Cancer Classification.

Area of Science:

• Neuroscience
• Oncology
• Medical Imaging
• Artificial Intelligence

Background:

• The World Health Organization reported the highest mortality rates from brain/central nervous system (CNS) cancer in Asia.
• Early cancer detection is vital for improving patient survival rates.
• Current diagnostic methods for brain cancer are invasive, costly, and time-consuming.

Purpose of the Study:

• To review the pathophysiology and imaging modalities of brain cancer.
• To summarize automated computer-assisted methods for brain cancer characterization using machine learning and deep learning.
• To identify current challenges and future directions in engineering approaches for brain cancer analysis.

Main Methods:

• Review of existing literature on brain cancer pathophysiology and imaging techniques (MRI, CT).
• Exploration of machine learning and deep learning paradigms for automated brain cancer characterization.
• Analysis of current engineering methods and their limitations.

Main Results:

• Imaging modalities like MRI and CT offer safer, faster tumor detection.
• Machine learning and deep learning show promise for non-invasive brain cancer characterization and grading.
• The paper highlights the potential of AI in differentiating brain cancers from other neurological disorders.

Conclusions:

• Non-invasive, cost-effective tools are needed for brain cancer characterization and grade estimation.
• Machine learning and deep learning offer a promising avenue for advancing brain cancer diagnosis and analysis.
• Further research is needed to address current issues and develop future engineering paradigms for brain cancer management.