Application of ensemble method for classification of cardiac medical images
DOI:
https://doi.org/10.59681/2175-4411.v17.2025.1207Keywords:
machine learning, cardiomyopathies, magnetic resonance imaging, cineAbstract
Heart diseases are responsible for approximately 17 million deaths worldwide, according to the World Health Organization. This scenario has led to an increased demand for preventive medical care, resulting in a higher number of cardiac magnetic resonance imaging (MRI) exams. Identifying cardiomyopathies within this growing volume of exams poses a significant challenge for medical teams. To support this process, this study proposes a supervised machine learning methodology for the recognition of cardiomyopathies. The method takes into account different slices of the heart and the specific characteristics of the cardiac cycle, addressing limitations found in previous approaches. During the experiments, an accuracy of 80.00% and a precision of 82.26% were achieved in the best test case, which considers the structures of the epicardium and endocardium during the diastolic phase of the cardiac cycle. The results highlight the potential of the proposed approach in supporting medical diagnosis, especially in contexts of high exam demand.
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