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Cloud based Medical Image De-Noising using Deep Convolution Neural Network

by Bhavesh Suneja, Ashish Negi, Rishav Bhardwaj
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 20
Year of Publication: 2022
Authors: Bhavesh Suneja, Ashish Negi, Rishav Bhardwaj
10.5120/ijca2022922224

Bhavesh Suneja, Ashish Negi, Rishav Bhardwaj . Cloud based Medical Image De-Noising using Deep Convolution Neural Network. International Journal of Computer Applications. 184, 20 ( Jul 2022), 37-42. DOI=10.5120/ijca2022922224

@article{ 10.5120/ijca2022922224,
author = { Bhavesh Suneja, Ashish Negi, Rishav Bhardwaj },
title = { Cloud based Medical Image De-Noising using Deep Convolution Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2022 },
volume = { 184 },
number = { 20 },
month = { Jul },
year = { 2022 },
issn = { 0975-8887 },
pages = { 37-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number20/32436-2022922224/ },
doi = { 10.5120/ijca2022922224 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:22:00.032493+05:30
%A Bhavesh Suneja
%A Ashish Negi
%A Rishav Bhardwaj
%T Cloud based Medical Image De-Noising using Deep Convolution Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 20
%P 37-42
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

It is difficult to remove noise from images because of the many sources of noise. Among the many sources of noise in imaging, Gaussian, impulse, salt, pepper, and speckle are the most complex. Image processing for medical purposes has no other major aim, such as beautifying an image or generating art, whereas conventional image processing has primary goals such as improving an image's aesthetics. This may include enhancing the picture itself, as well as the extraction of information either manually or automatically, depending on the needs of the work. Deep Convolutional Neural Networks (DnCNN) are the kinds of deep neural networks that do visual processing of images. An old but still relevant area of image processing research is denoising images. This subject has seen a surge with the advent of Deep Convolutional Neural Networks thanks to the several advantages. The first advantage is that, It saves time and affords, Denoising networks that have been pre-trained are very well tuned. There are no noticeable artifacts after denoising and It generates excellent denoising results. In proposed work, The implementation process has been divided into four parts. Working with cloud-based medical images in the initial phase. The previously trained network will be loaded in the second step. In the third stage, the denoised picture is obtained by sending the noisy image to the network and then performing. Afterwards, in the last stage, the resulting image is denoised image. The result is compared with various existing denoising methods. The outcome result is better in the terms of PSNR and SSIM.

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Index Terms

Computer Science
Information Sciences

Keywords

Deep Convolutional Neural Networks Cloud medical image Denoising Image processing Deep Learning

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