Identification of Congenital Heart Defects in Ultrasound Images using U-Net Segmentation-Reference-Cited by-同舟云学术

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Identification of Congenital Heart Defects in Ultrasound Images using U-Net Segmentation

Published:2023-12-29 Issue: Volume: Page:1-6
ISSN:
Container-title:2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI)
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Author:

Pandey Saroj Kumar¹,Diwan Ritesh²,Rose Anthony³,Singh Kamred Udham⁴,Singh Teekam⁵,Somasekar J.⁶

Affiliation:

1. GLA University,Department of Computer Engineering & Apllications,Mathura,UP,India

2. Department Of Ele. & Tele. Raipur Institute Of Technology,Raipur

3. Institute of Management & Entrepreneurship Development, Bharati Vidyapeeth (Deemed to be University),Pune

4. School of Computing, Graphic Era Hill University,Dehradun,India

5. Graphic Era Deemed to be University,Department of Computer Science and Engineering,Dehradun

6. Jain (Deemed-to-be University),Professor of CSE Department,Bangalore

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10488914/10488923/10489015.pdf?arnumber=10489015

Reference30 articles.

1. Segmentation atrioventricular septal defect by using convolutional neural networks based on U-NET architecture

2. Automated image segmentation for cardiac septal defects based on contour region with convolutional neural networks: A preliminary study

3. Deep Learning-Based Computer-Aided Fetal Echocardiography: Application to Heart Standard View Segmentation for Congenital Heart Defects Detection

4. Convolutional neural network for semantic segmentation of fetal echocardiography based on four-chamber view

5. Image Segmentation of the Ventricular Septum in Fetal Cardiac Ultrasound Videos Based on Deep Learning Using Time-Series Information

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