| Autor: |
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 |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Electronics; Volume 11; Issue 20; Pages: 3375 |
| ISSN: |
2079-9292 |
| DOI: |
10.3390/electronics11203375 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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