Analysis of pathology in patients from heterogeneous datasets using machine learning techniques provide valuable information for identifying patients with carotid artery atherosclerosis disease. We propose and evaluate a method to automatically identify these patients based only on MR brain imaging findings in a dataset also containing multiple sclerosis patients and healthy control subjects. The features extracted using convolutional networks were discriminative, showing high accuracy rates (>96%) to distinguish between the three classes: atherosclerosis patients, multiple sclerosis patients or healthy controls. The method may help specialists in the diagnosis (specially in critical cases), and evaluation of disease activity.
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