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

Manoj Diwakar, Prabhishek Singh, Girija Rani Karetla, Preeti Narooka, Arvind Yadav, Rajesh Kumar Maurya, Reena Gupta, José Luis Arias-Gonzáles, Mukund Pratap Singh, Dasharathraj K. Shetty, Rahul Paul, Nithesh Naik

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Article number3375
JournalElectronics (Switzerland)
Issue number20
Publication statusPublished - 10-2022

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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