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Objective: This paper presents an Alzheimer's disease (AD) detection methodbased on learning structural similarity between Magnetic Resonance Images(MRIs) and representing this similarity as a graph. Methods: We construct thesimilarity 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 clusteringalgorithms to construct the best similarity graph to capture the similaritybetween the same class images using the cosine distance as a similaritymeasure. We utilize the similarity graph to present (sample) the training datato a convolutional neural network (CNN). We use the similarity graph as aregularizer in the loss function of a CNN model to minimize the distancebetween the input images and their k-nearest neighbours in the similarity graphwhile minimizing the categorical cross-entropy loss between the training imagepredictions and the actual image class labels. Results: We conduct extensiveexperiments with several pre-trained CNN models and compare the results toother recent methods. Conclusion: Our method achieves superior performance onthe testing dataset (accuracy = 0.986, area under receiver operatingcharacteristics curve = 0.998, F1 measure = 0.987). Significance: Theclassification results show an improvement in the prediction accuracy comparedto the other methods. We release all the code used in our experiments toencourage reproducible research in this area

Detection of Alzheimer's Disease Using Graph-Regularized Convolutional  Neural Network Based on Structural Similarity Learning of Brain Magnetic  Resonance Images