A Systematic Mapping Study on Brain Tumor Segmentation Based on Machine Learning Algorithms
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Abstract
A brain tumor is a type of cancer that develops from abnormal cells in the brain or forms a mass within the brain. It is characterized by the growth of these abnormal cells in brain tissue, which can be challenging to treat and significantly affect a patient's cognitive abilities. Magnetic resonance imaging (MRI) is commonly used to evaluate brain tumors. This paper presents a survey of recent work on brain tumour segmentation, aiming to identify trends and requirements for future research endeavours. A systematic study determined the most significant research and categorized the findings. Approximately 100 papers were reviewed, all utilizing convolutional neural networks (CNNs) for 70% of their methodologies. This approach represents the majority of studies. Additionally, 15% of the papers employed fuzzy methods, 6% used discrete wavelet transforms, and 9% utilized the U-Net architecture for brain tumor segmentation. Although further research is required within MDWE's (Multi-Domain Workflow Engineering) scope, various composition mechanisms have been thoroughly recommended in the existing literature. Furthermore, there is a consistent suggestion for the need for solution proposals, validation studies, and philosophical papers based on prior analyses. The findings indicate that brain tumor segmentation is an active, emerging, and expansive field of research. However, particular areas still require further exploration. This is an improved area for publication.
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