The Magnetic Resonance Imaging (MRI) based classification process for the classification of dementia is presented in this work. The classifier's performance may be enhanced by means of improving the extracted features that are inputted into its classifier. These MRI images are all duly
segmented by making use of the wavelet. For choosing a subset that has optimal features, it may become inflexible and all issues relating to the feature selection will be shown as the NonDeterministic Polynomial (NP)-hard. The work further deals with techniques of optimization that are used
in the case of feature selection for picking an optimal feature set. The Principal Component Analysis (PCA) will find an application of a large scale in signal processing. The noise estimation and the source separation are all possible. For this, the Radial Basis Function (RBF) and its classifier
have been optimized to this structure by making use of the Genetic Algorithm (GA)-Artificial Immune System (AIS) algorithm. Such an optimized classifier of the RBF will classify a feature set that is provided by the GA, the AIS and the GA-AIS algorithm of feature selection. A classifier will
be evaluated on the basis of its performance metrics. All classifiers will be evaluated keeping the accuracy, specificity, and sensitivity in making use of an optimized set of features. The results of the experiment have clearly demonstrated the feature selection and its effectiveness to improve
the accuracy of the classification of all the images.
Dynamic Threshold Selection for Sequential Learning in Radial Basis Function Networks
