Computer-assisted brain tumor type discrimination using magnetic resonance imaging features.

Area of Science:

• Neurology
• Medical Imaging
• Computer Science

Background:

• Medical imaging, particularly Magnetic Resonance Imaging (MRI), is crucial for brain tumor identification, segmentation, and classification.
• Digital image processing techniques are increasingly applied to MRI for extracting, analyzing, and categorizing brain tumors.
• Existing research primarily focuses on binary classification (benign vs. malignant), with limited work on multiclass classification that tags tumors with major and minor categories.

Purpose of the Study:

• To provide a comprehensive review of recent research on multiclass brain tumor classification using MRI.
• To categorize existing multiclass classification studies into two major groups (XX and YY) with further sub-grouping.
• To extract and compare common parameters from reviewed studies to identify their strengths and weaknesses.

Main Methods:

• Systematic literature review of recent research on brain tumor multiclass classification using MRI.
• Categorization of reviewed studies into two primary groups (XX and YY) and three sub-groups each.
• Extraction and comparative analysis of common parameters across selected studies.

Main Results:

• Identified and categorized recent multiclass brain tumor classification studies based on MRI.
• Compared various methodologies, highlighting the merits and demerits of different approaches.
• Revealed challenges in precise classification due to image ambiguities and overlapping tumor characteristics.

Conclusions:

• Multiclass classification of brain tumors from MRI is complex but essential for accurate diagnosis.
• A comparative analysis of existing methods provides insights into current research trends and limitations.
• Recommendations are provided for researchers and professionals to advance brain tumor classification techniques.