The human brain, which is made up of a white mass of cells, is the center of nervous system. A brain tumor is a collection of abnormally growing cells found in many parts of the brain, such as glial cells, neurons, lymphatic tissues, blood vessels, pituitary glands, and other parts of the brain, which leads to cancer. There are two forms of brain cancer. The first is benign, which is not cancerous and poses no threat; the second is malignant, which is a cancerous tumor that grows unnaturally, rapidly reproducing cells and eventually kills the individual if not identified. Manually detecting and identifying the tumor is more difficult. Program division method (PDM along with MRI (magnetic resonance imaging) can be used to discover and diagnose tumors. A powerful segmentation mechanism is required to provide precise results. In order to discover a patient's brain tumor, we look at their data, such as MRI images of their brain. The key concern is the segmentation, detection, and extraction of contaminated tumor areas from MR images, but this is a difficult and time-consuming task conducted by radiologists or clinical experts, and their accuracy is solely dependent on their experience. As a result, the employment of computer-assisted technology becomes increasingly important in order to overcome these constraints. This study uses a convolutional neural network to detect the type of brain tumor from MRI scans as input.