Image Segmentation for MR Brain Tumor Detection Using Machine Learning: A Review.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Neurology

Background:

• Magnetic Resonance Imaging (MRI) is a crucial non-invasive tool for diagnosing brain diseases and monitoring treatment.
• Manual analysis of MRI scans for brain anomalies is time-consuming and labor-intensive.
• Automated analysis using machine learning offers a faster and more accurate approach to identifying abnormalities.

Approach:

• This article reviews research papers from 1998 to 2020 focusing on brain tumor segmentation from MRI images.
• Core segmentation algorithms from selected studies are examined in detail.
• Various machine learning and image segmentation techniques applied to brain tumor identification are explored.

Key Points:

• Image segmentation is a critical area in medical image analysis for computer-aided diagnosis.
• Machine learning algorithms enhance the speed and accuracy of identifying brain abnormalities.
• Deep learning methods have emerged as highly effective for segmenting brain tumors in MRI scans.

Conclusions:

• The review provides a comprehensive overview of brain tumor segmentation techniques using MRI.
• It highlights the evolution and application of machine learning in medical image analysis.
• Deep learning demonstrates superior performance for brain tumor segmentation compared to traditional methods.