A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm
Abstract Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly treated. It is triggered by viral infections and presents symptoms such as chest pain and heart dysfunction. Early detection is crucial for successful treatment, and cardiac magnetic resonan...
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Wiley
2024-12-01
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| Series: | CAAI Transactions on Intelligence Technology |
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| Online Access: | https://doi.org/10.1049/cit2.12289 |
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| author | Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz |
| author_facet | Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz |
| author_sort | Jing Yang |
| collection | DOAJ |
| description | Abstract Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly treated. It is triggered by viral infections and presents symptoms such as chest pain and heart dysfunction. Early detection is crucial for successful treatment, and cardiac magnetic resonance imaging (CMR) is a valuable tool for identifying this condition. However, the detection of myocarditis using CMR images can be challenging due to low contrast, variable noise, and the presence of multiple high CMR slices per patient. To overcome these challenges, the approach proposed incorporates advanced techniques such as convolutional neural networks (CNNs), an improved differential evolution (DE) algorithm for pre‐training, and a reinforcement learning (RL)‐based model for training. Developing this method presented a significant challenge due to the imbalanced classification of the Z‐Alizadeh Sani myocarditis dataset from Omid Hospital in Tehran. To address this, the training process is framed as a sequential decision‐making process, where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority class. Additionally, the authors suggest an enhanced DE algorithm to initiate the backpropagation (BP) process, overcoming the initialisation sensitivity issue of gradient‐based methods like back‐propagation during the training phase. The effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance metrics. Overall, this method shows promise in expediting the triage of CMR images for automatic screening, facilitating early detection and successful treatment of myocarditis. |
| format | Article |
| id | doaj-art-9c15b2668c3a450f8eeef15a42bcfd15 |
| institution | Kabale University |
| issn | 2468-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | CAAI Transactions on Intelligence Technology |
| spelling | doaj-art-9c15b2668c3a450f8eeef15a42bcfd152025-01-13T14:05:51ZengWileyCAAI Transactions on Intelligence Technology2468-23222024-12-01961347136010.1049/cit2.12289A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithmJing Yang0Touseef Sadiq1Jiale Xiong2Muhammad Awais3Uzair Aslam Bhatti4Roohallah Alizadehsani5Juan Manuel Gorriz6Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur MalaysiaCentre for Artificial Intelligence Research (CAIR), Department of Information and Communication Technology University of Agder Grimstad NorwayDepartment of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur MalaysiaDepartment of Creative Technologies Air University Islamabad PakistanSchool of Information and Communication Engineering Hainan University Haikou Hainan ChinaInstitute for Intelligent Systems Research and Innovation (IISRI) Deakin University Waurn Ponds Victoria AustraliaData Science and Computational Intelligence Institute University of Granada Granada SpainAbstract Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly treated. It is triggered by viral infections and presents symptoms such as chest pain and heart dysfunction. Early detection is crucial for successful treatment, and cardiac magnetic resonance imaging (CMR) is a valuable tool for identifying this condition. However, the detection of myocarditis using CMR images can be challenging due to low contrast, variable noise, and the presence of multiple high CMR slices per patient. To overcome these challenges, the approach proposed incorporates advanced techniques such as convolutional neural networks (CNNs), an improved differential evolution (DE) algorithm for pre‐training, and a reinforcement learning (RL)‐based model for training. Developing this method presented a significant challenge due to the imbalanced classification of the Z‐Alizadeh Sani myocarditis dataset from Omid Hospital in Tehran. To address this, the training process is framed as a sequential decision‐making process, where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority class. Additionally, the authors suggest an enhanced DE algorithm to initiate the backpropagation (BP) process, overcoming the initialisation sensitivity issue of gradient‐based methods like back‐propagation during the training phase. The effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance metrics. Overall, this method shows promise in expediting the triage of CMR images for automatic screening, facilitating early detection and successful treatment of myocarditis.https://doi.org/10.1049/cit2.12289classificationdifferential evolutionmyocarditisreinforcement learning |
| spellingShingle | Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm CAAI Transactions on Intelligence Technology classification differential evolution myocarditis reinforcement learning |
| title | A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm |
| title_full | A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm |
| title_fullStr | A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm |
| title_full_unstemmed | A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm |
| title_short | A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm |
| title_sort | novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm |
| topic | classification differential evolution myocarditis reinforcement learning |
| url | https://doi.org/10.1049/cit2.12289 |
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