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The diagnosis of brain tumours in a timely and efficient manner will assist in making better treatment decisions and will increase the patients' survival rate. Magnetic resonance imaging (MRI) segmentation plays a critical role in detecting brain malignancies. Because of the huge number of MRI images generated during cancer detection, manual image segmentation will be a time-consuming task. For medical analysis and interpretation, precise and automated classification of brain MRI images is critical. A hybrid methodology for brain tumour classification is suggested, based on kernel fuzzy c-means and Convolutional Neural Networks (CNN). Pre-processing and segmentation are applied to the captured image. Then there's feature extraction, which is followed by classification. The proposed methodology is intended to distinguish between normal and cancerous brain (benign or malign).