| Sumario: | Brain tumors are the outcome of unusual growths and uncontrolled cells splitting in the brain. If the diagnosis of the brain tumor should be done early with high accuracy, else the treatment will be much difficult or sometimes it may lead to death. Certain sorts of brain tumor like Meningioma, Glioma, and Pituitary tumors are more usual than the others. Magnetic Resonance Imaging (MRI) is a form of medical imaging that uses magnetic fields to produce images, which is widely used for identification and treatment of brain tumors in clinical practice. The photographs of Magnetic Resonance are drawn from three different perspectives. Sagittal, axial, and coronal views are the three types. The most complicated aspect of detecting a brain tumor is segmenting it. The pattern of Brain tumor is detected by using Deep Learning techniques to overcome the human error or mistakes in manual segmentation. In this project, we can use a range of image processing techniques, such as grayscale conversion and attribute extraction using Grey level Co-occurrence. By using Neural Network Algorithm like Convolution Neural Network (CNN), the ultimate classification process concludes whether the person is diseased or not. Experimental result shows that the proposed CNN can be outperforms than the existing machine learning algorithms.
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