CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering

Danial Sharifrazi; Roohallah Alizadehsani; Javad Hassannataj Joloudari; Shahab S. Band; Sadiq Hussain; Zahra Alizadeh Sani; Fereshteh Hasanzadeh; Afshin Shoeibi; Abdollah Dehzangi; Mehdi Sookhak; Hamid Alinejad-Rokny; Danial Sharifrazi; Roohallah Alizadehsani; Javad Hassannataj Joloudari; Shahab S. Band; Sadiq Hussain; Zahra Alizadeh Sani; Fereshteh Hasanzadeh; Afshin Shoeibi; Abdollah Dehzangi; Mehdi Sookhak; Hamid Alinejad-Rokny

doi:10.3934/mbe.2022110

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 3: 2381-2402. doi: 10.3934/mbe.2022110

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Research article Special Issues

CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering

1.
Department of Computer Engineering, School of Technical and Engineering, Shiraz Branch, Islamic Azad University, Shiraz, IR
2.
Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC 3216, AU
3.
Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, IR
4.
Future Technology Research Center, College of Future, National Yunlin University of Science and Technology 123 University Road, Section 3, Douliou, Yunlin 64002, TW
5.
System Administrator, Dibrugarh University, Assam 786004, IN
6.
Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
7.
Omid hospital, Iran University of Medical Sciences, Tehran, IR
8.
FPGA Laboratory, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, IR
9.
Department of Computer Science, Rutgers University, Camden, NJ 08102, USA
10.
Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA
11.
Department of Computer Science, Texas A & M University at Corpus Christi, Corpus Christi, TX 78412, USA
12.
BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, AU
13.
Health Data Analytics Program, AI-enabled Processes (AIP) Research Centre, Macquarie University, Sydney 2109, AU

Academic Editor: Yang Kuang

Received: 28 August 2021 Revised: 07 November 2021 Accepted: 16 November 2021 Published: 04 January 2022

Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age. Cardiac MRI (CMR) has been considered a noninvasive and golden standard diagnostic tool for suspected myocarditis and plays an indispensable role in diagnosing various cardiac diseases. However, the performance of CMR depends heavily on the clinical presentation and features such as chest pain, arrhythmia, and heart failure. Besides, other imaging factors like artifacts, technical errors, pulse sequence, acquisition parameters, contrast agent dose, and more importantly qualitatively visual interpretation can affect the result of the diagnosis. This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose Myocarditis. In this study, we used 47 subjects with a total number of 98,898 images to diagnose myocarditis disease. Our results demonstrate that the proposed method achieves an accuracy of 97.41% based on 10 fold-cross validation technique with 4 clusters for diagnosis of Myocarditis. To the best of our knowledge, this research is the first to use deep learning algorithms for the diagnosis of myocarditis.
- myocarditis,
- diagnosis,
- convolutional neural network,
- biomedical machine learning,
- cardiac MRI,
- prediction
Citation: Danial Sharifrazi, Roohallah Alizadehsani, Javad Hassannataj Joloudari, Shahab S. Band, Sadiq Hussain, Zahra Alizadeh Sani, Fereshteh Hasanzadeh, Afshin Shoeibi, Abdollah Dehzangi, Mehdi Sookhak, Hamid Alinejad-Rokny. CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering[J]. Mathematical Biosciences and Engineering, 2022, 19(3): 2381-2402. doi: 10.3934/mbe.2022110

Related Papers:

Abstract

Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age. Cardiac MRI (CMR) has been considered a noninvasive and golden standard diagnostic tool for suspected myocarditis and plays an indispensable role in diagnosing various cardiac diseases. However, the performance of CMR depends heavily on the clinical presentation and features such as chest pain, arrhythmia, and heart failure. Besides, other imaging factors like artifacts, technical errors, pulse sequence, acquisition parameters, contrast agent dose, and more importantly qualitatively visual interpretation can affect the result of the diagnosis. This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose Myocarditis. In this study, we used 47 subjects with a total number of 98,898 images to diagnose myocarditis disease. Our results demonstrate that the proposed method achieves an accuracy of 97.41% based on 10 fold-cross validation technique with 4 clusters for diagnosis of Myocarditis. To the best of our knowledge, this research is the first to use deep learning algorithms for the diagnosis of myocarditis.

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