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An automatic tool to assist the interpretation of single photon emission computed tomography (SPECT) and positron emission tomography (PET) images for the diagnosis of the Alzheimer's disease (AD) is demonstrated. The main problem to be handled is the so-called small size sample, which consists of having a small number of available images compared to the large number of features. This problem is faced by intensively reducing the dimension of the feature space by means of principal component analysis (PCA). Our approach is based on Bayesian classifiers, which uses a posteriori information to determine in which class the subject belongs, yielding 88.6 and 98.3% accuracy values for SPECT and PET images, respectively. These results mean an improvement over the accuracy values reached by other existing techniques.