ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration
Article
Kasmaee, Adele Mirzaee Moghaddam, Ataei, Alireza, Moravvej, Seyed Vahid, Alizadehsani, Roohallah, Gorriz, Juan M, Zhang, Yu-Dong, Tan, Ru-San and Acharya, U Rajendra A. 2024. "ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration." Physiological Measurement. 45 (5). https://doi.org/10.1088/1361-6579/ad46e2
| Article Title | ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration |
|---|---|
| ERA Journal ID | 14630 |
| Article Category | Article |
| Authors | Kasmaee, Adele Mirzaee Moghaddam, Ataei, Alireza, Moravvej, Seyed Vahid, Alizadehsani, Roohallah, Gorriz, Juan M, Zhang, Yu-Dong, Tan, Ru-San and Acharya, U Rajendra A |
| Journal Title | Physiological Measurement |
| Journal Citation | 45 (5) |
| Article Number | 055011 |
| Number of Pages | 20 |
| Year | 2024 |
| Publisher | IOP Publishing |
| ISSN | 0967-3334 |
| 1361-6579 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1088/1361-6579/ad46e2 |
| Abstract | Objective. Myocarditis poses a significant health risk, often precipitated by viral infections like coronavirus disease, and can lead to fatal cardiac complications. As a less invasive alternative to the standard diagnostic practice of endomyocardial biopsy, which is highly invasive and thus limited to severe cases, cardiac magnetic resonance (CMR) imaging offers a promising solution for detecting myocardial abnormalities. Approach. This study introduces a deep model called ELRL-MD that combines ensemble learning and reinforcement learning (RL) for effective myocarditis diagnosis from CMR images. The model begins with pre-training via the artificial bee colony (ABC) algorithm to enhance the starting point for learning. An array of convolutional neural networks (CNNs) then works in concert to extract and integrate features from CMR images for accurate diagnosis. Leveraging the Z-Alizadeh Sani myocarditis CMR dataset, the model employs RL to navigate the dataset's imbalance by conceptualizing diagnosis as a decision-making process. Main results. ELRL-DM demonstrates remarkable efficacy, surpassing other deep learning, conventional machine learning, and transfer learning models, achieving an F-measure of 88.2% and a geometric mean of 90.6%. Extensive experimentation helped pinpoint the optimal reward function settings and the perfect count of CNNs. Significance. The study addresses the primary technical challenge of inherent data imbalance in CMR imaging datasets and the risk of models converging on local optima due to suboptimal initial weight settings. Further analysis, leaving out ABC and RL components, confirmed their contributions to the model's overall performance, underscoring the effectiveness of addressing these critical technical challenges. |
| Keywords | myocarditis; deep learning |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 420311. Health systems |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | University of Guilan, Iran |
| Persian Gulf University, Iran | |
| Isfahan University of Technology, Iran | |
| Deakin University | |
| University of Granada, Spain | |
| University of Leicester, United Kingdom | |
| Duke-NUS Medical School, Singapore | |
| School of Mathematics, Physics and Computing |
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https://research.usq.edu.au/item/z865x/elrl-md-a-deep-learning-approach-for-myocarditis-diagnosis-using-cardiac-magnetic-resonance-images-with-ensemble-and-reinforcement-learning-integration
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