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February Edition 2019

International Journal of Applied Information Systems solicits high quality original research papers for the January 2019 Edition of the journal. The last date of research paper submission is January 15, 2019.

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

Praneeth Sadda, Taha Qarni in Image Processing

International Journal of Applied Information Systems
Year of Publication: 2018
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors:Praneeth Sadda, Taha Qarni
10.5120/ijais2018451755
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  1. Praneeth Sadda and Taha Qarni. Real-Time Medical Video Denoising with Deep Learning: Application to Angiography. International Journal of Applied Information Systems 12(13):22-28, May 2018. URL, DOI BibTeX

    @article{10.5120/ijais2018451755,
    	author = "Praneeth Sadda and 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",
    	url = "http://www.ijais.org/archives/volume12/number13/1031-2018451755",
    	doi = "10.5120/ijais2018451755",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, 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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Keywords

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