Morphogenesis Method to Detect Brain Tumor using Machine Learning Technique and Noise Filtering

Tumor is a type of cancer that has become a major health issue these days. Teams related to health sector are focusing on strategies and therapies to help it heal. A brain tumor is a unique cell proliferation in central nervous system that is not usually visible in computed tomography. Magnetic Resonance Imaging (MRI) is the only means for displaying a sharp view of the brain region that has been harmed. The diagnostic imaging method has a crucial role to play in sickness detection mostly in brain region. The brain MRI picture is investigated in this research, and a strategy is intended for a clearer view of the tumor-affected site. The major steps of the described technique are MRI aberrant brain images is given as input, anisotropic filtering is used for the removal of noise present in the image, and support vector machine for segmenting the image, feature extraction to sort out infected part from the normal part. The foundation of this strategy is obtaining unblemished MRI portraits of the brain. Locating the tumor affected area in the MRI image is done by finding the frequencies of the pixels present in the noise removed images. The experimental results showed 93 percent of accuracy when Genetic Algorithm and SVM are used for segmentation and classification in the experiments.