1Asst. Prof, Dept. of ECE, Sri Venkateswara College of Engineering and Technology, Chittoor, A.P.
2Professor, Dept. of ECE, Mother Theresa Institute of Engineering and Technology, Palamaner, Andhra Pradesh, India
Online published on 1 November, 2018.
The classification of brain images divided into two types normal or abnormal based on the abnormality detection. In this method, the Magnetic Resonance Imaging (MRI) brain classification done by three phases such as preprocessing, feature extraction and classification. The preprocessing step is used for removing noise in the image for the feature extraction process. In this method, Genetic Algorithm (GA) and K-Nearest Neighbour (KNN) classifier is used for the classification of MRI brain image. The GA has better accuracy for the classification results. Then KNN classifier use confusion matrix computed and shows the higher accuracy than the support vector machine classifier. The performance of this method is calculated by the GA and KNN, whether the image is normal or abnormal.
Classification, Magnetic Resonance Imaging (MRI), Genetic Algorithm (GA), K-Nearest Neighbour (KNN)
