Detection of Brain Tumor Using Unsupervised Enhanced K-Means, PCA and Supervised SVM Machine Learning Algorithms

The brain tumor is an abnormal cell growth in the human body. To know which type of brain tumor it is and where is the exact location of it. We are using the MR image is a tomographic imaging technique. MRI is based on Nuclear Magnetic Resonance signals. A brain tumor is of two types 1. Benignant 2. malignant. Benignant belongs to I and II grade; this type of tumor is not active cells and have a low-grade tumor. It has a uniform structure. Malignant belongs to III and IV grades, this type of tumor are active cells and have a high grade. It has a non-uniformity structure. The initial phase Input MR image is transformed into a binary image by the Otsu threshold technique. The second step k-means segmentation process is used on binary images. Third step Discrete Wavelet Transform is used on segmented image for extracting the image and it reduces the large dimensionality by using PCA. It identifies the tumor by using Support Vector Machine classification it gives the final output of a brain tumor that normal or abnormal. The proposed paper experimented on the detection of brain tumors using classification algorithms dataset about BraTS dataset and compared with existing methodologies, and it is then proved that superior to existed.

Keywords:

Brain tumor

MRI

K-means

Segmentation

SVM

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