A Probabilistic Information Fusion Approach to MR-based Automated Diagnosis of Dementia

In this work, we present a probabilistic information fusion approach for the diagnosis of dementia from cross-sectional magnetic resonance (MR) images. The approach relies on first mapping the outputs of a support vector classifier (SVM) trained on image features to probabilities and then on combining these probabilities with the class-conditional distributions of neuropsychiatric test scores, such as the mini-mental state examination (MMSE). The SVM classifier is trained and tested on 121 subjects drawn from the Open Access Series of Imaging Studies (OASIS) database. Two independent sets of MMSE related statistics are estimated from data, one from the training set in OASIS and the other from the Alzheimer´s Disease Neuroimaging Initiative (ADNI) database. The probabilistic fusion of image-based SVM decisions with no visual MMSE information exhibits very steep receiver operating characteristic curves on the test set; giving, at the equal error rate operating point, 92% accuracy.