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Cardiovascular disease is one of the most challenging diseases in middle-agedand older people, which causes high mortality. Coronary artery disease (CAD) isknown as a common cardiovascular disease. A standard clinical tool fordiagnosing CAD is angiography. The main challenges are dangerous side effectsand high angiography costs. Today, the development of artificialintelligence-based methods is a valuable achievement for diagnosing disease.Hence, in this paper, artificial intelligence methods such as neural network(NN), deep neural network (DNN), and Fuzzy C-Means clustering combined withdeep neural network (FCM-DNN) are developed for diagnosing CAD on a cardiacmagnetic resonance imaging (CMRI) dataset. The original dataset is used in twodifferent approaches. First, the labeled dataset is applied to the NN and DNNto create the NN and DNN models. Second, the labels are removed, and theunlabeled dataset is clustered via the FCM method, and then, the clustereddataset is fed to the DNN to create the FCM-DNN model. By utilizing the secondclustering and modeling, the training process is improved, and consequently,the accuracy is increased. As a result, the proposed FCM-DNN model achieves thebest performance with a 99.91% accuracy specifying 10 clusters, i.e., 5clusters for healthy subjects and 5 clusters for sick subjects, through the10-fold cross-validation technique compared to the NN and DNN models reachingthe accuracies of 92.18% and 99.63%, respectively. To the best of ourknowledge, no study has been conducted for CAD diagnosis on the CMRI datasetusing artificial intelligence methods. The results confirm that the proposedFCM-DNN model can be helpful for scientific and research centers.

FCM-DNN: diagnosing coronary artery disease by deep accuracy Fuzzy C-Means clustering model