Enhancing Low-Dose CT Image Quality Through Deep Learning: A DoG-Sharpened U-Net Approach With Attention Mechanism-Reference-Cited by-同舟云学术

Enhancing Low-Dose CT Image Quality Through Deep Learning: A DoG-Sharpened U-Net Approach With Attention Mechanism

Published:2024-01-28 Issue: Volume:116 Page:1037-1041
ISSN:
Container-title:2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)
language:
Short-container-title:

Author:

Zubair Muhammad¹,Rais Helmi B Md¹,Ullah Fasee¹,Yousafzai Arsalaan Khan²,Hassan Farrukh³

Affiliation:

1. Universiti Teknologi PETRONAS,Department of Computer and Information Sciences,Seri Iskandar,Malaysia,32610

2. Universiti Teknologi PETRONAS,Department of Civil and Enviromental Engineering,Seri Iskandar,Malaysia,32610

3. Sunway University, Bandar University Sunway,Department of Computing and Information Systems, School of Engineering and Technology,Selangor Darul Ehsan,Malaysia,47500

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10459226/10459347/10459568.pdf?arnumber=10459568

Reference23 articles.

1. Denoising Medical Images Using Machine Learning, Deep Learning Approaches: A Survey

2. Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET

3. Creating Artificial Images for Radiology Applications Using Generative Adversarial Networks (GANs) – A Systematic Review

4. The use of artificial intelligence in computed tomography image reconstruction - A literature review

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. Enabling Predication of the Deep Learning Algorithms for Low-Dose CT Scan Image Denoising Models: A Systematic Literature Review;IEEE Access;2024