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Cardiac Fibrosis Automated Diagnosis Based on FibrosisNet Network Using CMR Ischemic Cardiomyopathy
Mohamed Bekheet1,2, Mohammed Sallah3, Norah S Alghamdi4
1Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura 35516, Egypt.
Insights
Early detection of heart muscle fibrosis is crucial for improving outcomes in ischemic heart disease. A new deep learning model, FibrosisNet, accurately identifies and classifies cardiac fibrosis using magnetic resonance imaging, enhancing diagnostic capabilities.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Ischemic heart disease is a leading cause of mortality, with early identification improving treatment efficacy and survival rates.
- Heart muscle fibrosis, a key factor in ischemic heart disease, impairs cardiac function and is associated with adverse cardiovascular events.
- Cardiac magnetic resonance (CMR) imaging is vital for detecting fibrosis, a significant risk factor for ischemic heart disease.
Purpose of the Study:
- To introduce a novel deep learning (DL) network, FibrosisNet, for the accurate detection and classification of heart muscle fibrosis.
- To evaluate the performance of FibrosisNet in diagnosing cardiac fibrosis using magnetic resonance imaging (MRI) data.
- To compare FibrosisNet's efficacy against existing state-of-the-art methods and advanced convolutional neural network (CNN) approaches.
Main Methods:
- Development of FibrosisNet, a deep network incorporating 17 series layers for fibrosis detection.
- Training and evaluation of the FibrosisNet classification system for optimal performance.
- Application of deep transfer learning models on established CNN architectures for fibrosis detection.
Main Results:
- FibrosisNet achieved high performance metrics: 96.05% accuracy, 97.56% sensitivity, and 96.54% F1-Score.
- The study demonstrated FibrosisNet's superior performance compared to current state-of-the-art methods.
- Experimental results confirmed the effectiveness of FibrosisNet in detecting and classifying cardiac fibrosis.
Conclusions:
- FibrosisNet represents a significant advancement in the early and accurate diagnosis of heart muscle fibrosis.
- The proposed DL model offers substantial medical benefits by improving therapeutic outcomes and patient survival rates in ischemic heart disease.
- FibrosisNet shows promise as a valuable tool in clinical practice for managing patients with cardiac fibrosis.
Abstract:
Ischemic heart condition is one of the most prevalent causes of death that can be treated more effectively and lead to fewer fatalities if identified early. Heart muscle fibrosis affects the diastolic and systolic function of the heart and is linked to unfavorable cardiovascular outcomes. Cardiac magnetic resonance (CMR) scarring, a risk factor for ischemic heart disease, may be accurately identified by magnetic resonance imaging (MRI) to recognize fibrosis. In the past few decades, numerous methods based on MRI have been employed to identify and categorize cardiac fibrosis. Because they increase the therapeutic advantages and the likelihood that patients will survive, developing these approaches is essential and has significant medical benefits. A brand-new method that uses MRI has been suggested to help with diagnosing. Advances in deep learning (DL) networks contribute to the early and accurate diagnosis of heart muscle fibrosis. This study introduces a new deep network known as FibrosisNet, which detects and classifies fibrosis if it is present. It includes some of 17 various series layers to achieve the fibrosis detection target. The introduced classification system is trained and evaluated for the best performance results. In addition, deep transfer-learning models are applied to the different famous convolution neural networks to find fibrosis detection architectures. The FibrosisNet architecture achieves an accuracy of 96.05%, a sensitivity of 97.56%, and an F1-Score of 96.54%. The experimental results show that FibrosisNet has numerous benefits and produces higher results than current state-of-the-art methods and other advanced CNN approaches.
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