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Published on: August 28, 2018
FCM-DNN: diagnosing coronary artery disease by deep accuracy fuzzy C-means clustering model
Javad Hassannataj Joloudari1, Hamid Saadatfar1, Mohammad GhasemiGol1
1Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.
Insights
Artificial intelligence, specifically the FCM-DNN model, accurately diagnoses coronary artery disease (CAD) using cardiac magnetic resonance imaging. This AI approach offers a promising alternative to traditional angiography for improved patient outcomes.
Area of Science:
- Cardiovascular imaging and diagnostics
- Artificial intelligence in medicine
- Machine learning for disease detection
Background:
- Cardiovascular disease, particularly coronary artery disease (CAD), poses significant mortality risks in middle-aged and older populations.
- Traditional diagnostic methods like angiography have limitations, including dangerous side effects and high costs.
- Artificial intelligence (AI) presents a valuable opportunity for developing advanced disease diagnostic tools.
Purpose of the Study:
- To develop and evaluate AI-based methods for diagnosing CAD using cardiac magnetic resonance imaging (CMRI) data.
- To compare the performance of Neural Network (NN), Deep Neural Network (DNN), and a hybrid Fuzzy C-Means combined with Deep Neural Network (FCM-DNN) model.
- To investigate the effectiveness of an unsupervised clustering approach (FCM) integrated with DNN for improved diagnostic accuracy.
Main Methods:
- Development of NN and DNN models using a labeled CMRI dataset for CAD diagnosis.
- Implementation of an FCM-DNN model by first clustering an unlabeled CMRI dataset using Fuzzy C-Means (FCM) and then training a DNN on the clustered data.
- Evaluation of models using 10-fold cross-validation on the CMRI dataset.
Main Results:
- The proposed FCM-DNN model achieved a superior accuracy of 99.91% in diagnosing CAD, identifying 5 clusters for healthy and 5 for diseased subjects.
- The DNN model reached an accuracy of 99.63%, while the NN model achieved 92.18%.
- The FCM-DNN approach demonstrated improved training and increased accuracy compared to standard NN and DNN models.
Conclusions:
- The FCM-DNN model shows exceptional performance for CAD diagnosis on CMRI datasets, outperforming traditional NN and DNN methods.
- This AI-driven approach offers a highly accurate and potentially cost-effective alternative to invasive diagnostic procedures.
- The study highlights the potential of integrating unsupervised clustering with deep learning for enhanced medical image analysis and disease diagnosis.
Abstract:
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.
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