SUPERVISED LEARNING (CLASSIFICATION) ALGORITHMS FOR BRAIN TUMOUR DETECTION

Several medical diagnostic applications use Automated defect detection. Automated tumour diagnosis in magnetic resonance imaging (MRI) is extremely important since it provides details about aberrant tissues that are required for treatment planning. Human inspection is the standard procedure for flaw detection in magnetic resonance brain pictures. For vast amounts of data, this strategy is impractical. Therefore, automated tumour identification techniques are created in order to free up radiologist time. Due to the intricacy and variety of malignancies, MRI brain tumour detection is a challenging task . In this study, machine learning techniques are used to find tumours in brain MRIs. The proposed study is broken down into three sections: applying preprocessing techniques to brain MRI images, extracting texture features using a Gray Level Co-occurrence Matrix (GLCM), and classifying the results using a machine learning method.