A Comprehensive Review of Brain Tumor Detection and Segmentation Techniques

Abstract

Brain tumors are particularly dangerous type of tumor, and if this is not treated in time it maybe prove to be deadly and may also spread across other body parts. Brain tumor is the swelling or growth of unwanted tissues in the brain that results from the unregulated and disordered division of cells. The presence of these tissues resulting abnormal behavior and lot of other complications. The detection of brain tumor is done by using different techniques out of which through magnetic resonance images (MRIs). The scanning process is a time-consuming manual task that needs the involvement of medical professionals. Automating the task of detection of the brain tumor while also grading the severity accurately can help in managing the patients’ disease effectively. As tumor tissue of different patients is different, automating such processes is often a challenging task. Researchers have incorporated image segmentation for extraction of suspicious regions from MRI, using image processing and AI-based techniques. Radiomic analysis also plays a big role in feature extraction processes. In this paper, we present a comprehensive review of existing approaches for brain tumour detection, covering deep neural models, radiomic analysis and segmentation-based methods for brain tumor classification and segmentation, along with a discussion on prevalent issues, challenges, and future directions of research.