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

Magnetic Resonance Imaging (MRI) has commonly been used to detect and diagnose brain disease and monitor treatment as non-invasive imaging technology. MRI produces three-dimensional images that help neurologists to identify anomalies from brain images precisely. However, this is a time-consuming and labor-intensive process. The improvement in machine learning and efficient computation provides a computer-aid solution to analyze MRI images and identify the abnormality quickly and accurately. Image segmentation has become a hot and research-oriented area in the medical image analysis community. The computer-aid system for brain abnormalities identification provides the possibility for quickly classifying the disease for early treatment. This article presents a review of the research papers (from 1998 to 2020) on brain tumors segmentation from MRI images. We examined the core segmentation algorithms of each research paper in detail. This article provides readers with a complete overview of the topic and new dimensions of how numerous machine learning and image segmentation approaches are applied to identify brain tumors. By comparing the state-of-the-art and new cutting-edge methods, the deep learning methods are more effective for the segmentation of the tumor from MRI images of the brain.

Automated summary

Magnetic Resonance Imaging (MRI) is commonly used in brain disease detection and diagnosis, providing three-dimensional images for precise anomaly identification. However, the process is time-consuming. Machine learning and efficient computation offer a computer-aided solution for quick and accurate abnormality identification. Brain tumor segmentation from MRI images is a hot research topic, and deep learning methods are found to be more effective in this regard.