An intelligent ensemble EfficientNet prediction system for interpretations of cardiac magnetic resonance images in heart failure severity diagnosis

Abstract

Ensemble models as part of federated learning leverage the ability of individual models to learn unique patterns from the training dataset to make more efficient predictions than single predicting systems. This study aggregates the output of four best-performing EfficientNet models to arrive at the final heart failure severity prediction through federated learning. The seven variants of EfficientNet models (B0-B7) learn the features from the cardiac magnetic resonance images that are most relevant to heart failure severity. Further, the performance of every model variant has been analysed with three different optimizers i.e. Adam, SGD, and RMSprop. It has been observed that the developed ensemble prediction system provides an improved overall testing accuracy of 0.95. It is also worthy to note that the ensemble prediction has yielded significant improvement in the prediction of individual classes which is evident from sensitivity measure of 0.95, 0.88, 1.00, 0.93, and 0.98 for hyperdynamic, mild, moderate, normal and severe classes respectively. It is obvious from these results that the proposed ensemble EfficientNet prediction system would assist the radiologist in better interpretation of cardiac magnetic resonance images. This in turn would benefit the cardiologist in understanding the HF progress and planning effective therapeutic intervention.