Review of Automated Computerized Methods for Brain Tumor Segmentation and Classification

Area of Science:

• Medical Imaging
• Machine Learning
• Computational Biology

Background:

• Brain tumor detection is challenging due to complex structures and variations.
• Early detection is crucial for patient survival rates.
• Computerized diagnostic methods are gaining importance.

Purpose of the Study:

• To review existing work on brain tumor detection using medical imaging and machine learning.
• To discuss image processing techniques for tumor enhancement, segmentation, and feature extraction.
• To analyze mathematical modeling, classification, performance metrics, datasets, and future directions.

Main Methods:

• Survey of existing literature on brain tumor diagnosis.
• Discussion of image processing techniques including preprocessing, segmentation, and feature extraction.
• Analysis of mathematical models, classification algorithms, and performance evaluation metrics.

Main Results:

• Identified key challenges in brain tumor segmentation and classification.
• Highlighted the importance of preprocessing techniques for accurate analysis.
• Provided an overview of various computerized diagnosis methods and relevant datasets.

Conclusions:

• Machine learning and medical imaging offer significant potential for early brain tumor detection.
• Further research is needed to refine segmentation, classification, and diagnostic accuracy.
• Standardized datasets and advanced image processing are crucial for future advancements.