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Current Medical Imaging


ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Research Article

Low-dose COVID-19 CT Image Denoising Using CNN and its Method Noise Thresholding

Author(s): Manoj Diwakar, Neeraj Kumar Pandey, Ravinder Singh, Dilip Sisodia, Chandrakala Arya, Prabhishek Singh* and Chinmay Chakraborty

Volume 19, Issue 2, 2023

Published on: 16 September, 2022

Page: [182 - 193] Pages: 12

DOI: 10.2174/1573405618666220404162241

Price: $65


Noise in computed tomography (CT) images may occur due to low radiation doses. Hence, the main aim of this paper is to reduce the noise from low-dose CT images so that the risk of high radiation dose can be reduced.

Background: The novel coronavirus outbreak has ushered in different new areas of research in medical instrumentation and technology. Medical diagnostics and imaging are one of the ways in which the area and level of infection can be detected.

Objective: COVID-19 attacks people with less immunity, so infants, kids, and pregnant women are more vulnerable to the infection. So, they need to undergo CT scanning to find the infection level. But the high radiation diagnostic is also fatal for them, so the intensity of radiation needs to be reduced significantly, which may generate the noise in the CT images.

Method: This paper introduces a new denoising technique for low-dose Covid-19 CT images using a convolution neural network (CNN) and noise-based thresholding method. The major concern of the methodology for reducing the risk associated with radiation while diagnosing.

Results: The results are evaluated visually and using standard performance metrics. From comparative analysis, it was observed that proposed works give better outcomes.

Conclusion: The proposed low-dose COVID-19 CT image denoising model is therefore concluded to have a better potential to be effective in various pragmatic medical image processing applications in noise suppression and clinical edge preservation.

Keywords: COVID-19, CT Images, Image processing, deep learning, CNN, DWT.

Graphical Abstract
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