FCM-DNN: diagnosing coronary artery disease by deep accuracy Fuzzy C-Means clustering model. (arXiv:2202.04645v1 [eess.IV])

Cardiovascular disease is one of the most challenging diseases in middle-aged
and older people, which causes high mortality. Coronary artery disease (CAD) is
known as a common cardiovascular disease. A standard clinical tool for
diagnosing CAD is angiography. The main challenges are dangerous side effects
and high angiography costs. Today, the development of artificial
intelligence-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 with
deep neural network (FCM-DNN) are developed for diagnosing CAD on a cardiac
magnetic resonance imaging (CMRI) dataset. The original dataset is used in two
different approaches. First, the labeled dataset is applied to the NN and DNN
to create the NN and DNN models. Second, the labels are removed, and the
unlabeled dataset is clustered via the FCM method, and then, the clustered
dataset is fed to the DNN to create the FCM-DNN model. By utilizing the second
clustering and modeling, the training process is improved, and consequently,
the accuracy is increased. As a result, the proposed FCM-DNN model achieves the
best performance with a 99.91% accuracy specifying 10 clusters, i.e., 5
clusters for healthy subjects and 5 clusters for sick subjects, through the
10-fold cross-validation technique compared to the NN and DNN models reaching
the accuracies of 92.18% and 99.63%, respectively. To the best of our
knowledge, no study has been conducted for CAD diagnosis on the CMRI dataset
using artificial intelligence methods. The results confirm that the proposed
FCM-DNN model can be helpful for scientific and research centers.

Source: https://arxiv.org/abs/2202.04645


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