Comparison of methods for classification of Alzheimer's disease, frontotemporal dementia and asymptomatic controls

Single photon emission computed tomography (SPECT) and positron emission tomography (PET) are commonly used for the study of neurodegenerative diseases such as Alzheimer's disease (AD) and frontotemporal dementia (FTD). Many methods have been proposed to identify different types of dementia based on PET and SPECT images. However, an extensive evaluation and comparison of different methods for feature extraction and classification of such image data has not been performed yet. In this work, two commonly used feature extraction methods, principal component analysis (PCA) and partial least squares analysis (PLS), were used for dimensionality reduction, and three classification methods comprising multiple discriminant analysis (MDA), elastic-net logistic regression (ENLR) and support-vector machine (SVM) were used for classification of SPECT image data of asymptomatic controls (CTR), AD and FTD participants. Hence, six image classification procedures were evaluated and compared. The results indicate that PCA-based procedures have more robust and reliable performance than PLS-based procedures, and PCA-ENLR has the best estimated predictive accuracy among all three PCA-based procedures.