Brain Disorders Identification by Machine Learning Classifiers

Abstract

Medical imaging like MRI and CT scan images are crucial for accurately diagnosing human brain disease. The traditional method for tumour analysis relies on the radiologist or physician visually inspecting the specimen, which can result in some incorrect classifications when a large number of MRI pictures need to be processed. An automated intelligent classification system is suggested that requires picture categorization in order to reduce human mistake rates. One of the illnesses that kills the majority of individuals worldwide is the brain tumour. If the tumour is accurately anticipated at an early stage, the likelihood that someone would survive can be increased. The human brain is studied using the magnetic resonance imaging (MRI) method to identify illnesses. In this project, Support Vector Machines (SVM)-based classification approaches are suggested and implemented to classify brain images; DWT will extract features from MRI images. The primary goal of this research is to provide a superior result, which is higher accuracy and reduced error rates for SVM-based MRI brain tumour prediction.