Low-Dose COVID-19 CT Image Denoising Using Batch Normalization and Convolution Neural Network

Diwakar M.; Singh P.; Karetla G.R.; Narooka P.; Yadav A.; Maurya R.K.; Gupta R.; Arias-Gonzáles J.L.; Singh M.P.; Shetty D.K.; Paul R.; Naik N. · Electronics (Switzerland) (2022) · DOI: 10.3390/electronics11203375

Resumen

Computed tomography (CT) is used in medical applications to produce digital medical imaging of the human body and is acquired by the reconstruction process, where X-rays are the key component of CT imaging. The present coronavirus outbreak has spawned new medical device and technology research fields. COVID-19 most severely affects people with poor immunity; children and pregnant women are more susceptible. A CT scan will be required to assess the infection’s severity. As a result, to reduce the radiation levels significantly there is a need to minimize the CT scan noise. The quality of CT images may degrade in the form of noisy images due to low radiation levels. Hence, this study proposes a novel denoising methodology for COVID-19 CT images with a low dose, where a convolution neural network (CNN) and batch normalization were utilized for denoising. From different output metrics such as peak signal-to-noise ratio (PSNR) and image quality index (IQI), the accuracy of the resulting CT images was checked and evaluated, where IQI obtained the best results in terms of 99% accuracy. The findings were also compared with the outcomes of related recent research in the domain. After a detailed review of the findings, it was noted that the proposed algorithm in the present study performed better in comparision to the existing literature.

Instituciones: Pontificia Universidad Católica del Perú