Efficient Approaches for Prediction of Brain Tumor using Machine Learning Techniques Alzubi Jafar A.1, Kumar Ambeshwar2, Alzubi Omar. A.3, Manikandan R.4 1Associate Professor, School of Engineering, Al-Balqa Applied University-JORDAN 2Teaching Assistant, School of Computing, SASTRA Deemed University, India 3Assistant Professor, Prince Abdullah Bin Ghazi College of Information Technology. Al-Balqa Applied University-Jordan 4Assistant Professor, School of Computing, SASTRA Deemed University, India Online published on 8 March, 2019. Abstract Tumor is one of the most prevalentdiseases in the brain. It is the reason why the diagnosis and treatment of the brain tumor have critical importance. MRI (Magnetic resonance imaging) is a scientific maneuver used to produce a computerized image of internal body tissue. MRI image is a diagnostic approach is used for detection ofa brain tumor and classifies it as atype malignant and benign. There aredifferent processes to detect the brain tumor. Image processing, feature extraction, and many algorithms havebeen implemented for the detection of brain tumor, but a convincing and accurate technique to detect thetumor'sprecise position and to diagnose in minimal time is in great need. The execution and complexity involved in the medical image segmentation process are enhanced by applying feature extraction techniques and mathematical models. Furthermore, to improve the accuracy and performance of detection of brain tumor using existing Artificial Neural Network and Naïve Bayes Classifier, In this article we propose a novel technique using mathematical analysis to predict and enhance the better models. The proposed method has been implementedfor detecting the type of brain tumor and its specific location in the brain. The proposed system will be used to diagnose the patient's brain tumor with goodaccuracy success rate. Top Keywords Magnetic Resonance Image, Braintumor, Malignant, Benign, Feature extraction, Preprocessing, Artificial Neural Network, Naïve Bayes Algorithm, Canny Edge Detection. Top |