Auxiliary diagnosis method for congenital heart disease based on multi-modality and double-branch dense residual network

Le résumé fourni par la source

Heart sound is an important basis for analyzing heart health. The feature extraction and classification model of heart sound signal are optimized, and an auxiliary diagnosis algorithm for congenital heart disease based on multi-modality and dense residual network is proposed. This method does not need to segment heart sounds, but only needs to extract double features in the time domain, which simplifies the process of preprocessing and feature extraction. Double features are learned using a parallel double-branch convolutional recurrent neural network with dense residual connections. The proposed classification algorithm was trained, validated, and tested on a total of 4050 clinical congenital heart disease heart sound samples, and obtained a classification accuracy of 97.60%, which is expected to be used for clinical auxiliary diagnosis and screening of congenital heart disease.

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