A NEW FRAMEWORK FOR MRI BRAIN TUMOR DETECTION AND SEGMENTATION

Abstract

A brain tumor is an abnormal mass of cells growing within the brain. Tumors can be classified into two types: primary tumors, which originate from brain cells, and secondary tumors, which result from cancer cells spreading to the brain from other organs such as the lungs or breast. Magnetic Resonance Imaging (MRI) is widely used for brain tumor detection due to its high resolution and superior image quality. In this study, we propose a novel data mining framework for brain tumor detection using MRI scans. The proposed framework consists of seven key stages: Preprocessing, Enhancement, Segmentation, Feature Extraction, Feature Selection, Classification, and Performance Analysis. In the Preprocessing stage, unwanted artifacts and non-relevant portions of the MRI are removed. During Enhancement, noise in the images is reduced using various filtering techniques. The Segmentation stage focuses on isolating tumor regions from the brain MRI. Feature Extraction and Feature Selection are employed to identify and extract relevant tumor-related pixels. The Classification stage uses data mining and machine learning techniques to categorize or detect anomalies in the brain. Finally, Performance Analysis is conducted to evaluate the effectiveness of the proposed approach. This framework aims to improve the accuracy and efficiency of brain tumor detection through advanced data mining and image processing techniques.