  Alzheimers disease (AD) is a severe neurological brain disorder. It is not
curable, but earlier detection can help improve symptoms in a great deal. The
machine learning based approaches are popular and well motivated models for
medical image processing tasks such as computer-aided diagnosis. These
techniques can improve the process for accurate diagnosis of AD. In this paper,
we investigate the performance of these techniques for AD detection and
classification using brain MRI and PET images from the OASIS database. The
proposed system takes advantage of the artificial neural network and support
vector machines as classifiers, and principal component analysis as a feature
extraction technique. The results indicate that the combined scheme achieves
good accuracy and offers a significant advantage over the other approaches.
