Diagnosis of Fetal Congenital Heart Disease Based on Deep Learning

Abstract

Fetal congenital heart disease is the most common birth defect. In early diagnosis, the use of echocardiography is an important means of diagnosis, but due to the unique structure of the fetal heart, there are still many challenges in the early screening process. Hence, this study proposes a diagnosis model called ConvNeXt based on Attention Mechanism and Transfer Learning (ConvNeXt-AMTL) for congenital heart disease, which utilizes a large-kernel convolutional neural network to extract features from fetal echocardiography, and using attention mechanisms to focus and optimize key features. At the same time, in order to alleviate the problem that image data samples are too few to train the model well, this study proposes to use transfer learning to train the model. Numerous experiments have shown that the proposed model can efficiently diagnose fetal congenital heart disease, achieving an accuracy of 98.8% on the test set, effectively promoting prenatal screening of fetal congenital heart disease.