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THE OPEN BIOINFORMATICS JOURNAL


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BENTHAM REVIEW CORRESPONDENCE - DECLINE CONFIRMATION


BMS-TOBIOIJ-2024-18

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Article Title:
Role and Impact of Method Noise on CT Image Denoising
Abstract:
Background: The main emphasis of this study is the medical Computed Tomography
(CT) imaging denoising technique, which is particularly useful in interpreting
patient illness information for medical diagnosis. CT imaging is indispensable
for diagnosing this illness. However, image clarity is affected by noise and
other artefacts. The primary goal is to improve the accuracy of denoising
algorithms ,which allows for early disease prediction and increase the accuracy
of a patient’s diagnostic outcome. Objective: The major objective was to examine
and assess the effectiveness of applying a method noise-based Low-dose CT (LDCT)
image denoising using Convolutional-neural-network (CNN) in diagnostic imaging.
This method aims to suppress noise, improve image quality, and effectively
minimize radiation. This, in turn, improving the accuracy of denoising
algorithm, enabling it to accurately predict the disease diagnosis. Method
noise, or residual noise, plays a major role in denoising CT images accurately
while preserving noise and other artefacts generated during the denoising
process. Method noise encompasses the omitted structural and other minute
artefacts during the denoising process as well as the preservation of fine
details. Applying the denoising technique to the method noise through CNN
enhances the final image quality and clarity, thereby enhancing the diagnostic
accuracy. Methods: The study includes a systematic, method noise-based approach
to determine the performance of denoising algorithm in diagnosing various
diseases from medical CT images often encountered with Gaussian noise. It
entails selecting a pervasive data set, applying a method noise approach using
CNN, and comparing it with quantitative measures such as PSNR, SNR and SSIM to
assess diagnostic interpretation, thereby improving accuracy in identifying the
efficacy of the method noise approach in CT medical imaging.

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