Echocardiographic Image Classification Using Deep Learning
Resumo
Left ventricular ejection fraction is a physiological measure obtained by evaluating the cardiac phases of systole or diastole. This parameter represents the contractile capacity of the cardiac ventricular chambers, which several methods can measure, echocardiography being the most cost-effective. The correct ejection fraction assessment is critical for diagnosing and treating most cardiovascular diseases. Although using deep learning to estimate the ejection fraction significantly improves the method’s accuracy, there are still difficulties with its extensive application for several reasons. This paper proposes a deep learning pipeline for classifying echocardiographic images in systole or diastole, comparing its performance to the state-of-the-art. The proposed pipeline features a set of pre-processing methods suitable to echocardiographic images and a convolutional neural network tuned for the considered classification task. We also introduce a novel dataset of echocardiographic images without excessive pre-selection of images, thus presenting real-life conditions. We performed several experiments to assess the performance of our approach, through which it was possible to obtain an accuracy of 97.69% and a cross-entropy loss of 0.1883. Our convolutional neural network was able to classify systolic and diastolic images with accuracy similar to the benchmark in the literature. The proposed pipelines present pre-processing methods suitable for echocardiographic images, a convolutional neural network adjusted for the considered classification. However, our network is simpler than the reference, and the dataset is closer to real-life conditions, avoiding excessive pre-selection of images.
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