Journal
MULTIMEDIA TOOLS AND APPLICATIONS
Volume 81, Issue 7, Pages 10189-10220Publisher
SPRINGER
DOI: 10.1007/s11042-022-12162-1
Keywords
Gliomas; Threshold based segmentation; Hybrid approach; Machine learning; Deep learning
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One of the common approaches in medical research is to detect and track the growth of brain tumors from MRI images. However, manual detection is challenging due to the changes in brain structure caused by tumors. Therefore, using hybrid computerized approaches to detect brain tumors from MRI images can improve diagnostic accuracy.
One of the most common approaches in medical research is to detect a brain tumor and its growth from an MRI of the brain. Therefore, the process of scanning brain images from the internal structure of the human brain provides information about the growth of brain tumors. The manual detection of brain tumor from the MRI is a challenging task in the medical research field because the tumor also causes high changes in internal and external structure of the brain. For that purpose, it is proposed to review the detection of brain tumor from MRI images by using hybrid computerized approaches. Therefore, brain tumor growth performance and analysis are described to generalize symptoms and guide diagnosis towards a treatment plan. Several approaches for the segmentation process of MRI are discussed from existing papers, the detection of brain tumors can be concluded.
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