Detection of Brain Tumor using KNN and LLOYED Clustering

Abstract

Today world the brain tumor is life threatening and the main reason for the death. The growth of abnormal cells in brain leads to brain tumor. Brain tumor is categorized into malignant tumor and benign tumor. Malignant is cancerous whereas Benign tumor is non-cancerous. Diagnosing at earlier stage can save the person. It is actually a great challenge to find the brain tumor and classifying its type. Detection of Brain Tumor and the correct analysis of the Tumor structure is difficult task. To overcome the drawbacks of exiting brain tumor detection methods the proposed system is presented using KNN & LLOYED clustering. Undoubtedly, this saves the time as well as it gives more accurate results as in comparison to manual detection. The proposed method is a novel approach for detection Tumor along with the ability to calculate the area (%age) occupied by the Tumor in the overall brain cells. Firstly, Tumor regions from an MR image are segmented using an OSTU Algorithm. KNN& LLOYED are used for detecting as well as distinguishing Tumor affected tissues from the not affected tissues. Total twelve features are extracted like correlation, contrast, energy, homogeneity etc. by performing "wavelet transform on the converted gray scale image". For feature extraction DB5 wavelet transform is used.

Automated Identification and Classification of Brain Tumors Using Hybrid Machine Learning Models and MRI Imaging

“…Experts like radiologists meticulously examine images from CT scans, MRI scans, and positron emission tomography scans, forming the basis for subsequent treatment recommendations. This taxing process often spans several hours, underscoring the need for automation to expedite the detection process [5].…”