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RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights
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...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9262570/ https://www.ncbi.nlm.nih.gov/pubmed/35833074 http://dx.doi.org/10.1155/2022/8733632 |
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author | 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. Acharya, U. Rajendra |
author_facet | 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. Acharya, U. Rajendra |
author_sort | Moravvej, Seyed Vahid |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9262570 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-92625702022-07-12 RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights 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. Acharya, U. Rajendra Contrast Media Mol Imaging Research Article 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. Hindawi 2022-06-30 /pmc/articles/PMC9262570/ /pubmed/35833074 http://dx.doi.org/10.1155/2022/8733632 Text en Copyright © 2022 Seyed Vahid Moravvej et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research 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. Acharya, U. Rajendra RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights |
title | RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights |
title_full | RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights |
title_fullStr | RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights |
title_full_unstemmed | RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights |
title_short | RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights |
title_sort | rlmd-pa: a reinforcement learning-based myocarditis diagnosis combined with a population-based algorithm for pretraining weights |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9262570/ https://www.ncbi.nlm.nih.gov/pubmed/35833074 http://dx.doi.org/10.1155/2022/8733632 |
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