Abstract
AbstractMyocardial infarction is one of the most common cardiovascular diseases. Clinical information and Delayed Enhancement cardiac MRI (DE-MRI) are crucial to diagnose the myocardial infarction. However, some discrepancies can occur between clinical characteristics and DE-MRI when the disease is diagnosed. In order to deal in an efficient way with the correlation between these data and to be able to automatically classify patients suffering from myocardial infarction, this paper proposes a mixed classification model that takes both the clinical characteristics and DE-MRI into account. In the mixed model, a 3D Convolutional Neural Network (CNN) encodes the MRI as the surface of infarction then the surface is fed with Random Forest and other clinical characteristics to make the final decision.KeywordsClassificationMRIMyocardial infarctionCNNRandom Forest
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