A novel CT image de-noising and fusion based deep learning network to screen for disease (COVID-19)

• Published in: Scientific reports Vol. 13; no. 1; p. 6601
• Main Authors: Khan, Sajid Ullah, Ullah, Imdad, Ullah, Najeeb, Shah, Sajid, Affendi, Mohammed El, Lee, Bumshik
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 23.04.2023; Nature Publishing Group UK; Nature Portfolio
• Subjects: Computed tomography; Coronaviruses; COVID-19; COVID-19 - diagnostic imaging; COVID-19 Testing; Deep Learning; Humans; Image processing; Laboratory tests; Noise reduction; Pandemics; Reverse transcription; SARS-CoV-2; Severe acute respiratory syndrome coronavirus 2; Tomography, X-Ray Computed - methods
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-33614-0

A COVID-19, caused by SARS-CoV-2, has been declared a global pandemic by WHO. It first appeared in China at the end of 2019 and quickly spread throughout the world. During the third layer, it became more critical. COVID-19 spread is extremely difficult to control, and a huge number of suspected cases must be screened for a cure as soon as possible. COVID-19 laboratory testing takes time and can result in significant false negatives. To combat COVID-19, reliable, accurate and fast methods are urgently needed. The commonly used Reverse Transcription Polymerase Chain Reaction has a low sensitivity of approximately 60% to 70%, and sometimes even produces negative results. Computer Tomography (CT) has been observed to be a subtle approach to detecting COVID-19, and it may be the best screening method. The scanned image's quality, which is impacted by motion-induced Poisson or Impulse noise, is vital. In order to improve the quality of the acquired image for post segmentation, a novel Impulse and Poisson noise reduction method employing boundary division max/min intensities elimination along with an adaptive window size mechanism is proposed. In the second phase, a number of CNN techniques are explored for detecting COVID-19 from CT images and an Assessment Fusion Based model is proposed to predict the result. The AFM combines the results for cutting-edge CNN architectures and generates a final prediction based on choices. The empirical results demonstrate that our proposed method performs extensively and is extremely useful in actual diagnostic situations.