Identification of Brain Tumors Using SVM Classifier

Abstract

A vital non-invasive method that is frequently employed in the medical sector for the examination, diagnosis, and management of anomalies in brain tissue is magnetic resonance imaging, or MRI. Effective treatment of brain cancers depends on early identification, which also greatly enhances patient outcomes. However, it is a difficult and time-consuming task to accurately detect and segment tumors from MRI slices. The suggested system uses cutting-edge methods to automate the tumor classification and segmentation procedure in order to overcome this difficulty. The technique finds aberrant tissue in MRI slices by using a Support Vector Machine (SVM) classifier for tumor localization. The tumor's borders are then drawn using segmentation techniques, which enables an accurate tumor size measurement. In order to train an artificial neural network (ANN) to identify the type of tumor present, these segmented regions undergo additional processing to extract important properties.