Deep Learning Technique for Congenital Heart Disease Detection Using Stacking-Based CNN-LSTM Models From Fetal Echocardiogram: A Pilot Study

<p dir="ltr">Congenital heart defects (CHDs) are a leading cause of death in infants under 1 year of age. Prenatal intervention can reduce the risk of postnatal serious CHD patients, but current diagnosis is based on qualitative criteria, which can lead to variability in diagnosis be...

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Bibliographic Details
Main Author:	Tawsifur Rahman (14150523) (author)
Other Authors:	Mahmoud Khatib A. A. Al-Ruweidi (12535527) (author), Md. Shaheenur Islam Sumon (17983810) (author), Reema Yousef Kamal (17983813) (author), Muhammad E. H. Chowdhury (14150526) (author), Huseyin C. Yalcin (6695099) (author)
Published:	2023
Subjects:	Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Heart Echocardiography Videos Diseases Ultrasonic imaging Deep learning Convolutional neural networks Cardiovascular diseases Fetal heart rate Machine learning Congenital heart defects (CHDs) fetal echocardiogram CNN-LSTM stacking machine learning hypoplastic left heart syndrome (HLHS)
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Deep Learning Technique for Congenital Heart Disease Detection Using Stacking-Based CNN-LSTM Models From Fetal Echocardiogram: A Pilot Study

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