ABSTRACT
Neurological disorders are disabilities of the growth and development of the brain or central nervous system. Cancer-related neurological disorders are frequent among brain tumor survivors, and they can arise months or years after therapy. Furthermore, the location and characteristics of a brain tumor, in addition to radio-chemotherapy treatment, can alter neurocognitive behaviour of the individual. As a result, precise brain tumor prediction and progression is crucial for mitigating their negative impacts on neurodevelopment. This paper provides a systematic analysis of recent methods for segmenting brain tumors from brain Magnetic Resonance Imaging via multi-modalities. It covers the success of state-of-the-art approaches as well as their quantitative analysis. With the recent contributions of numerous researchers in the field of deep learning, different methods of image segmentation are briefly stated. Furthermore, we have addressed the most common problems in brain tumor segmentation and offered potential solutions.
