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

• Published in: Mathematical biosciences and engineering : MBE Vol. 19; no. 3; pp. 2381 - 2402
• Main Authors: Sharifrazi, Danial, Alizadehsani, Roohallah, Joloudari, Javad Hassannataj, Band, Shahab S., Hussain, Sadiq, Sani, Zahra Alizadeh, Hasanzadeh, Fereshteh, Shoeibi, Afshin, Dehzangi, Abdollah, Sookhak, Mehdi, Alinejad-Rokny, Hamid
• Format: Journal Article
• Language: English
• Published: United States AIMS Press 01.01.2022
• Subjects: Adult; Algorithms; biomedical machine learning; cardiac mri; Cluster Analysis; convolutional neural network; diagnosis; Humans; Magnetic Resonance Imaging; myocarditis; Myocarditis - diagnostic imaging; Neural Networks, Computer; prediction; prediction; diagnosis; cardiac MRI; convolutional neural network; myocarditis; biomedical machine learning
• ISSN: 1551-0018; 1551-0018
• DOI: 10.3934/mbe.2022110

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.