RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights
Article
Moravvej, Seyed Vahid, Alizadehsani, Roohallah, Khanam, Sadia, Sobhaninia, Zahra, Shoeibi, Afshin, Khozeimeh, Fahime, Sani, Zahra Alizadeh, Tan, Ru-San, Khosravi, Abbas, Nahavandi, Saeid, Kadri, Nahrizul Adib, Azizan, Muhammad Mokhzaini, Arunkumar, N. and Acharya, U. Rajendra. 2022. "RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights." Contrast Media and Molecular Imaging. 2022. https://doi.org/10.1155/2022/8733632
Article Title | RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights |
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ERA Journal ID | 41651 |
Article Category | Article |
Authors | Moravvej, Seyed Vahid, Alizadehsani, Roohallah, Khanam, Sadia, Sobhaninia, Zahra, Shoeibi, Afshin, Khozeimeh, Fahime, Sani, Zahra Alizadeh, Tan, Ru-San, Khosravi, Abbas, Nahavandi, Saeid, Kadri, Nahrizul Adib, Azizan, Muhammad Mokhzaini, Arunkumar, N. and Acharya, U. Rajendra |
Journal Title | Contrast Media and Molecular Imaging |
Journal Citation | 2022 |
Article Number | 8733632 |
Number of Pages | 15 |
Year | 2022 |
Publisher | Hindawi Publishing Corporation |
Place of Publication | United Kingdom |
ISSN | 1555-4309 |
1555-4317 | |
Digital Object Identifier (DOI) | https://doi.org/10.1155/2022/8733632 |
Web Address (URL) | https://www.hindawi.com/journals/cmmi/2022/8733632/ |
Abstract | Myocarditis is heart muscle inflammation that is becoming more prevalent these days, especially with the prevalence of COVID-19. Noninvasive imaging cardiac magnetic resonance (CMR) can be used to diagnose myocarditis, but the interpretation is time-consuming and requires expert physicians. Computer-aided diagnostic systems can facilitate the automatic screening of CMR images for triage. This paper presents an automatic model for myocarditis classification based on a deep reinforcement learning approach called as reinforcement learning-based myocarditis diagnosis combined with population-based algorithm (RLMD-PA) that we evaluated using the Z-Alizadeh Sani myocarditis dataset of CMR images prospectively acquired at Omid Hospital, Tehran. This model addresses the imbalanced classification problem inherent to the CMR dataset and formulates the classification problem as a sequential decision-making process. The policy of architecture is based on convolutional neural network (CNN). To implement this model, we first apply the artificial bee colony (ABC) algorithm to obtain initial values for RLMD-PA weights. Next, the agent receives a sample at each step and classifies it. For each classification act, the agent gets a reward from the environment in which the reward of the minority class is greater than the reward of the majority class. Eventually, the agent finds an optimal policy under the guidance of a particular reward function and a helpful learning environment. Experimental results based on standard performance metrics show that RLMD-PA has achieved high accuracy for myocarditis classification, indicating that the proposed model is suitable for myocarditis diagnosis. |
Keywords | COVID-19; Myocarditis ; Molecular Devices |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Byline Affiliations | Isfahan University of Technology, Iran |
University of Kashan, Iran | |
Deakin University | |
Dhaka Dental College, Bangladesh | |
K. N. Toosi University of Technology, Iran | |
Iran University of Medical Sciences, Iran | |
National Heart Centre, Singapore | |
Duke-NUS Medical School, Singapore | |
Harvard University, United States | |
University of Malaya, Malaysia | |
Islamic Science University of Malaysia, Malaysia | |
Rathinam College of Engineering, India | |
Ngee Ann Polytechnic, Singapore | |
Asia University, Taiwan | |
Singapore University of Social Sciences (SUSS), Singapore |
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https://research.usq.edu.au/item/z1vy3/rlmd-pa-a-reinforcement-learning-based-myocarditis-diagnosis-combined-with-a-population-based-algorithm-for-pretraining-weights
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