Automatic classification system for the diagnosis of Alzheimer disease using component-based SVM aggregations

Abstract

The early detection of subjects with probable Alzheimer Type Dementia (ATD) is crucial for effective appliance of treatment strategies. Functional brain imaging including SPECT (Single-Photon Emission Computed Tomography) and PET (Positron Emission Tomography) are commonly used to guide the clinician's diagnosis. Nowadays, no automatic tool has been developed to aid the experts to diagnose the ATD. Instead, conventional evaluation of these scans often relies on subjective, time consuming and prone to error steps. This paper shows a fully automatic computer-aided diagnosis (CAD) system for improving the accuracy in the early diagnosis of the ATD. The proposed approach is based on the majority voting cast by an ensemble of Support Vector Machine (SVM) classifiers, trained on a component-based feature extraction technique which searches the most discriminant regions over the brain volume. © 2009 Springer Berlin Heidelberg.