  Objective: This paper presents an Alzheimer's disease (AD) detection method
based on learning structural similarity between Magnetic Resonance Images
(MRIs) and representing this similarity as a graph. Methods: We construct the
similarity graph using embedded features of the input image (i.e., Non-Demented
(ND), Very Mild Demented (VMD), Mild Demented (MD), and Moderated Demented
(MDTD)). We experiment and compare different dimension-reduction and clustering
algorithms to construct the best similarity graph to capture the similarity
between the same class images using the cosine distance as a similarity
measure. We utilize the similarity graph to present (sample) the training data
to a convolutional neural network (CNN). We use the similarity graph as a
regularizer in the loss function of a CNN model to minimize the distance
between the input images and their k-nearest neighbours in the similarity graph
while minimizing the categorical cross-entropy loss between the training image
predictions and the actual image class labels. Results: We conduct extensive
experiments with several pre-trained CNN models and compare the results to
other recent methods. Conclusion: Our method achieves superior performance on
the testing dataset (accuracy = 0.986, area under receiver operating
characteristics curve = 0.998, F1 measure = 0.987). Significance: The
classification results show an improvement in the prediction accuracy compared
to the other methods. We release all the code used in our experiments to
encourage reproducible research in this area
