Brain Tumor Classification using a Support Vector Machine

A person's life may be protected if a brain tumor is recognized early and treated effectively. The exact diagnosis of malignancies in MRI layers becomes a meticulous effort to perform, and as a consequence, the proposed method is capable of precisely classifying the tumor. Magnetic resonance imaging (MRI) is one of the most often used methods for analyzing brain tumor pictures. There are several image classification methodologies and algorithms. The purpose of machine learning and classification algorithms is to learn automatically from training and then make accurate conclusions. This study looked at the efficacy of tumor classification algorithms for categorizing MR brain image properties.During the classification process, the statistical features of the incoming images were evaluated, and the data was carefully split into multiple categories. These data were tested using SVM (support vector machines) and Logistic Regression machine learning algorithms. With a 96 percent accuracy rate, the SVM (support vector machines) technique was demonstrated to be better than other algorithms.