Self-Supervised Representation Learning for Diagnosis of Cardiac Abnormalities on Echocardiograms

Abstract

Self-supervised representation is trendy in developing new gadget-mastering techniques to enhance diagnostic accuracy for diagnosing modern cardiac abnormalities. In this paper, we speak about the applicability and capacity of present-day self-supervised illustration to gain modern knowledge for analyzing cardiac abnormalities on echocardiograms. We talk about the impact of modern-day supervised and unsupervised gaining knowledge state modern techniques on feature extraction from echocardiogram facts. We also speak about the unsupervised mastering techniques for characteristic extraction, including a self-supervised representation trendy model for directly detecting cutting-edge cardiac abnormalities. The proposed model combines recurrent neural networks with a car-encoder to extract useful excessive-level functions from echocardiogram information and classify the abnormality. We exhibit the accuracy modern our proposed model with the experimental results on two echocardiography datasets. Our proposed version finished promising outcomes and outperformed existing processes. The results imply our proposed version's capacity to enhance the generalization of trendy cardiac abnormality analysis and reduce the education time…