Integration of a Deep Convolutional Neural Network With Adaptive Channel Weight Technique for Automated Identification of Standard Fetal Biometry Planes-Reference-Cited by-同舟云学术

Integration of a Deep Convolutional Neural Network With Adaptive Channel Weight Technique for Automated Identification of Standard Fetal Biometry Planes

Published:2024 Issue: Volume:73 Page:1-11
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Krishna Thunakala Bala¹^ORCID,Kokil Priyanka¹^ORCID

Affiliation:

1. Department of Electronics and Communication Engineering, Advanced Signal and Image Processing (ASIP) Laboratory, Indian Institute of Information Technology, Design and Manufacturing Kancheepuram, Chennai, India

Funder

Department of Science and Technology (DST) through the Fund for Improvement of S&T Infrastructure (FIST), Government of India

Science and Engineering Research Board

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10452318.pdf?arnumber=10452318

Reference52 articles.

1. FPUS23: An Ultrasound Fetus Phantom Dataset With Deep Neural Network Evaluations for Fetus Orientations, Fetal Planes, and Anatomical Features

2. ISUOG Practice Guidelines (updated): performance of the routine mid‐trimester fetal ultrasound scan

3. Ultrasound Standard Plane Detection Using a Composite Neural Network Framework

4. Automatic Fetal Ultrasound Standard Plane Recognition Based on Deep Learning and IIoT

5. A review on deep-learning algorithms for fetal ultrasound-image analysis

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Lightweight Deep Learning Model for Melanoma Classification in Dermoscopy Images for Smart Healthcare;2024 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET);2024-03-21