Magnetic Resonance Imaging (MRI) is a critical tool for diagnosing and managing cardiovascular diseases due to its non-invasive, high-resolution capabilities and its ability to provide detailed insights into cardiac structure and function. This review examines recent advancements in MRI-based image classification, emphasizing the integration of machine learning techniques such as deep learning and automated segmentation. These innovations have significantly improved diagnostic accuracy, efficiency, and reproducibility for complex conditions, including pericardial disease, ischemic heart disease, aortic disorders, and cardiomyopathies. By enhancing the detection of subtle abnormalities and supporting more informed clinical decision-making, machine learning is poised to further transform cardiovascular diagnostics. The review also discusses the broader implications of these advancements and explores future research directions and clinical applications in cardiac imaging.
A Review of Cardiovascular Diseases Using MRI Image Classification Techniques
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