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Reseach Article

Real-Time Medical Video Denoising with Deep Learning: Application to Angiography

by Praneeth Sadda, Taha Qarni
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 13
Year of Publication: 2018
Authors: Praneeth Sadda, Taha Qarni
10.5120/ijais2018451755

Praneeth Sadda, Taha Qarni . Real-Time Medical Video Denoising with Deep Learning: Application to Angiography. International Journal of Applied Information Systems. 12, 13 ( May 2018), 22-28. DOI=10.5120/ijais2018451755

@article{ 10.5120/ijais2018451755,
author = { Praneeth Sadda, Taha Qarni },
title = { Real-Time Medical Video Denoising with Deep Learning: Application to Angiography },
journal = { International Journal of Applied Information Systems },
issue_date = { May 2018 },
volume = { 12 },
number = { 13 },
month = { May },
year = { 2018 },
issn = { 2249-0868 },
pages = { 22-28 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number13/1031-2018451755/ },
doi = { 10.5120/ijais2018451755 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:09:11.147705+05:30
%A Praneeth Sadda
%A Taha Qarni
%T Real-Time Medical Video Denoising with Deep Learning: Application to Angiography
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 13
%P 22-28
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper describes the design, training, and evaluation of a deep neural network for removing noise from medical fluoroscopy videos. The method described in this work, unlike the current standard techniques for video denoising, is able to deliver a result quickly enough to be used in real-time scenarios. Furthermore, this method is able to produce results of a similar quality to the existing industry-standard denoising techniques.

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Index Terms

Computer Science
Information Sciences

Keywords

Angiography deep learning denoising fluoroscopy machine learning neural network real-time