  Early detection and localization of myocardial infarction (MI) can reduce the
severity of cardiac damage through timely treatment interventions. In recent
years, deep learning techniques have shown promise for detecting MI in
echocardiographic images. However, there has been no examination of how
segmentation accuracy affects MI classification performance and the potential
benefits of using ensemble learning approaches. Our study investigates this
relationship and introduces a robust method that combines features from
multiple segmentation models to improve MI classification performance by
leveraging ensemble learning. Our method combines myocardial segment
displacement features from multiple segmentation models, which are then input
into a typical classifier to estimate the risk of MI. We validated the proposed
approach on two datasets: the public HMC-QU dataset (109 echocardiograms) for
training and validation, and an E-Hospital dataset (60 echocardiograms) from a
local clinical site in Vietnam for independent testing. Model performance was
evaluated based on accuracy, sensitivity, and specificity. The proposed
approach demonstrated excellent performance in detecting MI. The results showed
that the proposed approach outperformed the state-of-the-art feature-based
method. Further research is necessary to determine its potential use in
clinical settings as a tool to assist cardiologists and technicians with
objective assessments and reduce dependence on operator subjectivity. Our
research codes are available on GitHub at
https://github.com/vinuni-vishc/mi-detection-echo.
