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20 April 2023 Auxiliary diagnosis method for congenital heart disease based on multi-modality and double-branch dense residual network
Yang Guo, Hongbo Yang, Tao Guo, Weilian Wang
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Proceedings Volume 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022); 126020Y (2023) https://doi.org/10.1117/12.2668093
Event: International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 2022, Changchun, China
Abstract
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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Yang Guo, Hongbo Yang, Tao Guo, and Weilian Wang "Auxiliary diagnosis method for congenital heart disease based on multi-modality and double-branch dense residual network", Proc. SPIE 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 126020Y (20 April 2023); https://doi.org/10.1117/12.2668093
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KEYWORDS
Heart
Feature extraction
Neural networks
Data modeling
Signal processing
Matrices
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