Classification of Brain Tumor Using Support Vector Machine (SVM)

The intelligent classification method proposed in this study report can distinguish between abnormal and normal MRI brain images. Medical scans, such as CT, MRI, and ECG images, are crucial for accurately diagnosing human diseases. The traditional method of manually analysing Tumors through visual inspection by radiologists or physicians may result in incorrect classification when examining multiple MRIs. Classification is necessary when examining a significant number of MRI scans. To minimize the occurrence of human errors and meet the requirements for image classification, An automated categorization system. is suggested. Brain Tumors are among the main reasons for death for individuals. If the tumor is detected early, there is a potential for survival. Through the utilization of magnetic resonance imaging (MRI) examine the brain and identify Tumors. Categorization methods, Proposals, such as Support Vector Machines (SVM), are used to provide support. and utilized to classify brain images. The accuracy value for the study is 92%. In this research symmetry, texture, and grayscale characteristics will be used to extract features from MRI pictures. This paper's primary goal is to accomplish Improved precision and reduced inaccuracy in categorizing MRI brain Tumors using SVM.