Brain tumor detection and classification: A framework of marker-based watershed algorithm and multilevel priority...

Area of Science:

• Medical Imaging
• Artificial Intelligence in Healthcare

Background:

• Brain tumor identification from MRI is crucial for diagnosis and treatment.
• Computerized techniques can aid clinicians in managing brain cancer.

Purpose of the Study:

• To develop an automated system for brain tumor extraction and classification from MRI scans.
• To improve the precision and accuracy of brain tumor detection.

Main Methods:

• The system employs a five-step process: contrast enhancement, segmentation, feature extraction, feature selection, and classification.
• Key techniques include gamma contrast stretching, marker-based watershed segmentation, chi-square feature selection, and support vector machine classification.

Main Results:

• The proposed system demonstrated superior performance compared to existing methods.
• High-ranked features (70%) were selected using a chi-square approach, followed by serial feature concatenation.

Conclusions:

• The developed automated system effectively extracts and classifies brain tumors from MRI data.
• The system offers enhanced precision and accuracy, outperforming current methodologies in brain tumor identification.