Brain tumor is an abnormal growth of cells in the brain . Magnetic resonance imaging ( MRI ) is an advanced diagnostic tools that enable us to visualize anatomical details more clearly so superior in detecting abnormalities in the soft tissues of the brain . On this study , a data classification system built Magnetic resonance imaging ( MRI ) of the brain by using energy coefficients and neural network . On the results of brain MRI performed three stages of the process , namely feature extraction , feature reduction , and classification . Results in the form of feature extraction vector detail coefficients horizontal , vertical , diagonal and vector approximation of wavelet decomposition of each level . Feature is the result of the reduction of energy in the form of vector approximation coefficients of each wavelet decomposition level . In the process of neural network classification method is used to classify the types of normal brain disease , Alzheimer's disease , glioma and carcinoma . Percentage success rate of recognition obtained brain MRI features by 95 % by using 10 energy coefficient , learning rate of 0.4, the activation function is a function logsig the hidden layer and the output layer and the process stops at the epoch to 515 .
An MRI brain classification system is performed REF using energy coefficients and neural network.
Classification of magnetic resonance (MR) brain images using energy coefficient and neural network
Applied mathematical sciences
Computer Science