Abstract
Medical picture segmentation refers to the process of categorizing the different parts of an image into anatomical components or defining the changes in structure and intensity in relation to the underlying functional process. The primary objective of this research is to analyze atherosclerotic lesions in living organisms using Magnetic Resonance Imaging (MRI). This research primarily aims to utilize computer technology to aid in the diagnosis and assessment of illness, with a particular emphasis on finding vulnerable plaques. MRI is a commonly used technique that helps to detect and forecast brain cancers in different neurological conditions and circumstances. Standard MRI sequences are frequently employed to distinguish various forms of brain tumors through visual examination of their characteristics and analysis of the contrast texture of the soft tissue. The brain tumor detection and classification system are developed using Discrete Wavelet Transform (DWT) and Support Vector Machines (SVMs). An efficient automated method for identifying a potential Region of Interest (ROI) where the existence of a tumor is certain. This study assesses the efficacy of the brain tumor extraction technique using symmetry analysis by calculating the mean and median values of the picture, specifically focusing on precision and sensitivity. Hence, the suggested technique is appropriate for rapid initial tumor extraction rather than precise segmentation.