Data Mining Approach for Detection and Classification of Brain Tumor

Abstract

Tumor is a mass or cells inside the brain that grows abnormally in one’s brain. Brain tumor is of two types primary and secondary. Primary tumors are hailed from brain cells and secondary tumors take place from cancer cells spread to one’s brain from other organs like lungs or breast. The Magnetic Resonance Imaging (MRI) is widely used because it gives high resolution and better-quality images. The main problem with the images is the inhomogeneity, unsharp boundaries and irregular noise which affects the results. Inhomogeneity means presence of some irrelevant information that must be removed. Unsharp boundaries are the most common problem in the images, they give blurry effect on the images that is why the information is not clear. To overcome these problems, we use the bilateral filter with the other techniques for the effective detection and segmentation. The proposed framework presents the detection and classification of the brain tumor. Bilateral filter is used to remove noise and preserves details. Bilateral filter is the best to preserve edges, sharpens the boundaries and takes care about the details of the image. By doing segmentation and classification we get the tumor detected.