Analysis and Design of SVM Based Brain Tumor Classification and Detection Technique

Abstract

Brain cancers can be detected using the Automatic Support Intelligent System, which utilises both a neural network and a fuzzy logic system. Both the diagnosis and treatment of brain cancers are made easier because to this technology. Finding a tumour in the brain is difficult due to the elusive nature of brain tumour cells. There remains a considerable challenge in automated medical image segmentation, which has attracted attention from researchers in recent years. Research in this area will centre around segmentation of MRI brain images (MRI). A classification problem is what we're approaching here, and we're looking for ways to distinguish between regular pixels and those that aren't. Support Vector Machine (SVM) classification is one of the most often used methods for this purpose. In the experiment, a dataset of gliomas of varied forms, sizes, and intensities will be employed. The brain serves as the central processing unit for the body. It is possible for a tumour to cause mortality if it is not discovered early enough. Magnetic Resonance Imaging (MRI) is superior to other imaging modalities when it comes to determining the tumor's size and determining its grade. MRI does not produce any harmful radiation. For the time being, there is no automated method for determining the grade of the tumour. This study demonstrates how MRI data can be used to segment and classify brain tumours. It's a helpful tool for clinicians to use when putting together therapy or surgery plans. Classifying tumours as benign or malignant requires the use of a support vector machine (SVM).