Localization and Classification of Brain Tumor Using Multi-layer Perceptron

A brain tumor is a group of structures created through the gradual accumulation of unusual cells. It occurs when cells in the brain develop abnormally. It has recently become a leading cause of death for many people. Because brain tumors are among the most dangerous cancers, prompt detection and treatment are required to preserve life. Because of the growth of tumor cells, detecting these cells is a difficult task. Therefore, it is essential to contrast MRI findings with brain tumor therapy. Abnormal brain areas are very challenging to visualize using straightforward imaging methods. Ensemble approaches have been the most significant development in information mining and machine intelligence over the past ten years. They combine several models into one, typically more realistic than the sum of its components. This chapter offers a framework for identifying and categorizing different tumor kinds. Based on the examination of sizable datasets, this study presents a strategy for identifying various tumor forms of the brain. The precise structure of the brain may be seen in MRI pictures and thus taken into consideration for analysis without the need for surgery. The MRI scan reveals the brain’s structure, which aids in further processing and tumor diagnosis. The field of artificial neural networks, frequently referred to as multi-layer perceptrons, is the most practical variant of neural networks used for brain tumor analysis. The study uses 212 samples of brain MR images for classifying brain tumors using the multi-layer perceptron model. The classification accuracy achieved by the model for classifying brain tumors is 98%.