Robust Alzheimer's disease diagnosis using radial basis classifier

Alzheimer's disease (AD) is the most common cause of dementia in aged people and affects more than 30 million individuals worldwide. The particular evolution of AD patients and their increasing dependence on the close affective environment provokes an important social repercussion, as the cognitive functions of the patient gradually disappear and his individual essence blurs. This project presents a novel computer-aided diagnosis (CAD) technique for the early diagnosis of the Alzheimer's disease (AD) based on Radial basis Function Neural Network (RBFNN) with bounds of confidence. The CAD tool is designed for the study and classification of functional brain images. For this purpose, two different brain image databases are selected: a single photon emission computed tomography (SPECT) database and positron emission tomography (PET) images, both of them containing data for both Alzheimer's disease (AD) patients and healthy controls as a reference. These databases are analyzed by applying the Fisher discriminant ratio (FDR) and multiresoultion wavelet filter for feature selection and extraction of the most relevant features. The resulting multiresoultion wavelet -transformed sets of data, which contain a reduced number of features, are classifier by means of a RBFNN-based classifier with bounds of confidence for decision.