Özet:

Abstract The automatic support intelligent system can find brain tumors by using soft computing techniques and machine learning algorithms. This technological development has made it simpler to diagnose and cure brain cancer. Finding a brain tumor is challenging because brain tumor cells are evasive. This study suggests a strategy for analysing samples that can identify brain tumor cells in their early stages by using a fuzzy clustering algorithm and a neural network system to train and categorize samples. Three stages of brain tumors may be identified using an artificial neural network. One of the most challenging issues in medical imaging is brain tumor segmentation. Medical professionals must do this operation by hand due to the huge range of tumor kinds and the close similarity between tumor and normal tissue. Despite the present interest in automated medical image segmentation, much work remains. This research will concentrate on segmenting brain images using MRI (MRI). Finding a way to distinguish between normal pixels and those that aren't is what we're trying to achieve here. One of the most popular methods for this is SVM classification. The brain serves as the body's main processing center. If a tumor is not treated and is not discovered in time, it might be fatal. MRI is better than other imaging modalities at determining the grade and size of the tumor. MRI radiation is not harmful. Currently, there is no automatic method for figuring out the tumor's grade. As this study showed, brain tumors may be segmented and categorized using an MRI scan. It might serve as a guide for clinicians as they create therapy or surgical strategies. In order to categorize tumors as benign or malignant, an SVM is required (SVM).

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