ArticleScientific reports2022
RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed.
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- Gene expression profiling and predictive modeling of cancer biomarkers using machine learning and IoT-Enabled biosensors.Scientific reports · 2025Article
- Improving Coronary Artery Disease Diagnosis in Cardiac MRI with Self-Supervised Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Uncover This Tech Term: Random Forest.Korean journal of radiology · 2025Article
- AI Applied to Cardiac Magnetic Resonance for Precision Medicine in Coronary Artery Disease: A Systematic Review.Journal of cardiovascular development and disease · 2025Review
- AI-Enhanced Electrochemical Sensing Systems: A Paradigm Shift for Intelligent Food Safety Monitoring.Biosensors · 2025Review
- Deep Convolutional Generative Adversarial Network for Improved Cardiac Image Classification in Heart Disease Diagnosis.Journal of imaging informatics in medicine · 2025Article
- Methodological Review of Classification Trees for Risk Stratification: An Application Example in the Obesity Paradox.Nutrients · 2025Review
- Artificial Intelligence in Coronary Artery Interventions: Preprocedural Planning and Procedural Assistance.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Establishing and internally validating a predictive model for coronary heart disease incorporating triglyceride-glucose index, monocyte-to-high-density lipoprotein cholesterol ratio and conventional risk factors in patients experiencing chest pain and referred for invasive coronary angiography.Lipids in health and disease · 2025Article
- A systematic review of machine learning in heart disease prediction.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- Federated learning-based multimodal approach for early detection and personalized care in cardiac disease.Frontiers in physiology · 2025Article
- Predicting cardiovascular disease risk using retinal optical coherence tomography imaging.Frontiers in artificial intelligence · 2025Article
- Improving performance in colorectal cancer histology decomposition using deep and ensemble machine learning.Heliyon · 2024Article
- Ensemble model for grape leaf disease detection using CNN feature extractors and random forest classifier.Heliyon · 2024Article
- Advanced Image Analytics for Mobile Robot-Based Condition Monitoring in Hazardous Environments: A Comprehensive Thermal Defect Processing Framework.Sensors (Basel, Switzerland) · 2024Article
- Explaining decisions of a light-weight deep neural network for real-time coronary artery disease classification in magnetic resonance imaging.Journal of real-time image processing · 2024Article
- Metabolism-related biomarkers, molecular classification, and immune infiltration in diabetic ulcers with validation.International wound journal · 2023Article
- Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives.Healthcare (Basel, Switzerland) · 2023Review
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Authors and funding
16 authors.
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Abstract
Coronary artery disease (CAD) is a prevalent disease with high morbidity and mortality rates. Invasive coronary angiography is the reference standard for diagnosing CAD but is costly and associated with risks. Noninvasive imaging like cardiac magnetic resonance (CMR) facilitates CAD assessment and can serve as a gatekeeper to downstream invasive testing. Machine learning methods are increasingly applied for automated interpretation of imaging and other clinical results for medical diagnosis. In this study, we proposed a novel CAD detection method based on CMR images by utilizing the feature extraction ability of deep neural networks and combining the features with the aid of a random forest for the very first time. It is necessary to convert image data to numeric features so that they can be used in the nodes of the decision trees. To this end, the predictions of multiple stand-alone convolutional neural networks (CNNs) were considered as input features for the decision trees. The capability of CNNs in representing image data renders our method a generic classification approach applicable to any image dataset. We named our method RF-CNN-F, which stands for Random Forest with CNN Features. We conducted experiments on a large CMR dataset that we have collected and made publicly accessible. Our method achieved excellent accuracy (99.18%) using Adam optimizer compared to a stand-alone CNN trained using fivefold cross validation (93.92%) tested on the same dataset.
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