Towards objective heart disease classification: A deep neural network approach for comprehensive diagnosis

Heart disease is a major issue in modern society owing to its severity. However, to date, it heavily relies on human judgment, necessitating the need for technology that can aid in objective and rapid human diagnosis. Several studies attempted data-driven approaches to classify heart disease, but they are limited to specific diseases and may not apply to the real medical field. To address these challenges, we propose a suite of deep learning-based classifiers, including a CNN and a state-of-the-art ViT enhanced with an auxiliary UNet feature extractor. To classify the eight types of heart disease, we utilize multi-view echocardiogram images consisting of numbers that reflect the proportion of actual cardiac patients. The experimental results reveal that vanilla ViT is not suitable for the echocardiogram dataset (accuracy of 0.6451). However, the performance can be improved using the UNet auxiliary feature extraction network (achieving an accuracy of 0.8121 for EfficientUNet+ViT). Among the comparison models, our CNN achieved the highest performance with an accuracy of 0.8829 and minimal computational cost, demonstrating its efficacy for direct disease classification without the need for ViT.