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

doi:10.3934/MBE.2022110

DRO

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

Sharifrazi, D, Alizadehsani, Roohallah, Joloudari, JH, Band, SS, Hussain, S, Sani, ZA, Hasanzadeh, F, Shoeibi, A, Dehzangi, A, Sookhak, M and Alinejad-Rokny, H 2022, CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering, Mathematical Biosciences and Engineering, vol. 19, no. 3, pp. 2381-2402, doi: 10.3934/MBE.2022110.

Title	CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering
Author(s)	Sharifrazi, D Alizadehsani, Roohallah Joloudari, JH Band, SS Hussain, S Sani, ZA Hasanzadeh, F Shoeibi, A Dehzangi, A Sookhak, M Alinejad-Rokny, H
Journal name	Mathematical Biosciences and Engineering
Volume number	19
Issue number	3
Start page	2381
End page	2402
Total pages	22
Publisher	American Institute of Mathematical Sciences
Place of publication	Springfield, Mo
Publication date	2022-01-04
ISSN	1547-1063 1551-0018
Keyword(s)	biomedical machine learning cardiac MRI CLASSIFICATION convolutional neural network CORONARY-ARTERY-DISEASE DEEP diagnosis FEATURES IDENTIFICATION IMAGES Life Sciences & Biomedicine Mathematical & Computational Biology myocarditis prediction Science & Technology SYSTEM UPDATE biomedical machine learning cardiac MRI convolutional neural network diagnosis myocarditis prediction
Summary	<abstract><p>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.</p></abstract>
Language	eng
DOI	10.3934/MBE.2022110
Field of Research	0102 Applied Mathematics 0903 Biomedical Engineering 0904 Chemical Engineering
HERDC Research category	C1 Refereed article in a scholarly journal
Free to Read?	Yes
Persistent URL	http://hdl.handle.net/10536/DRO/DU:30162317

Document type:	Journal Article
Collections:	Institute for Intelligent Systems Research and Innovation (IISRI) Open Access Collection

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Created:	Tue, 08 Feb 2022, 07:13:20 EST