Segmenting and Classifiying the Brain Tumor from MRI Medical Images Based on Machine Learning Algorithms: A Review
Omar Sedqi Kareem *
Shekhan Technical Institute, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.
Ahmed Khorsheed AL-Sulaifanie
College of Engineering, University of Duhok, Duhok, Kurdistan Region, Iraq.
Dathar Abas Hasan
Shekhan Technical Institute, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.
Dindar Mikaeel Ahmed
Duhok Technical Institute, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.
*Author to whom correspondence should be addressed.
Abstract
A brain tumor is a problem that threatens life and impedes the normal working of the human body. The brain tumor needs to be identified early for the proper diagnosis and effective treatment planning. Tumor segmentation from an MRI brain image is one of the most focused areas of the medical community, provided that MRI is non-invasive imaging. Brain tumor segmentation involves distinguishing abnormal brain tissue from normal brain tissue. This paper presents a systematic literature review of brain tumor segmentation strategies and the classification of abnormalities and normality in MRI images based on various deep learning techniques, interbreeding. It requires presentation and quantitative analysis, from standard segmentation and classification methods to the best class strategies.
Keywords: Brain tumor, classification, MRI, deep learning, segmentation