Abstract:

An abnormal tissue mass that grows and proliferates uncontrollably and cannot be controlled by the mechanisms that control normal cells in the brain is called an intracranial tumour or brain tumour. First, brain tumours begin to show symptoms such as headaches, seizures, feeling sick all the time (nausea), being sick (vomiting), and lethargy. Generally, diagnosing a brain tumour begins with magnetic resonance imaging (MRI). When an MRI shows that there is a tumour in the brain, the most common way to determine the type of brain tumour is to look at the results of a biopsy or tissue sample taken after surgery. Early diagnosis is of great importance in treating these tumours. With the developing artificial intelligence technologies and methods in recent years, early detection and analysis of such tumours has become easier. Segmentation of images is performed during the detection of tumours with computer-aided algorithms. The output of an effective medical segmentation algorithm guides the classifier more efficiently in identifying the stages of such abnormalities. In this study, how medical segmentation methods, including artificial intelligence technologies, can be used to detect tumours in MRI images was examined and various models were analysed. The highest accuracy rate (84,4%) was obtained with the Linear-SVM (Support Vector Machine) algorithm using the Chi Square test in feature extraction. The Random Forest algorithm also gave a very successful result with an accuracy rate of 81,5%. When the results of the study were evaluated, it was seen that Random Forest and SVM algorithms showed the best performance.

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