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
DOI: 10.2174/1573405618666220404162241
Abstract: Noise in computed tomography (CT) images may occur due to low radiation dose. 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 corona virus 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: The COVID-19 attacks people who have 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: In this paper, a new denoising technique for such low dose Covid-19 CT images has been introduced using a convolution neural network (CNN) and the method noise-based thresholding. The major concern of the methodology for reducing the risk associated with radiation while diagnosing.
Results: The results are evaluated visually and also by using standard performance metrics. From comparative analysis, it was observed that proposed works gives better outcomes.
Conclusions: 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 terms of noise suppression and clinical edge preservation.
Keywords: COVID-19, CT Images, Image processing, deep learning, CNN, DWT