RETRACTED ARTICLE: Non-sample fuzzy based convolutional neural network model for noise artifact in biomedical images

RETRACTED ARTICLE: Non-sample fuzzy based convolutional neural network model for noise artifact in biomedical images

Abstract The use of a light-weight deep learning Convolutional Neural Network (CNN) augmented with the power of Fuzzy Non-Sample Shearlet Transformation (FNSST) has successfully solved the problem of reducing noise and artifacts in Low-Dose Computed Tomography (LDCT) pictures. Both the Normal-Dose C...

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Bibliographic Details
Main Authors: Haewon Byeon, Ruchi Kshatri Patel, Deepak A. Vidhate, Sherzod Kiyosov, Saima Ahmed Rahin, Ismail Keshta, T. R. Vijaya Lakshmi
Format: Article
Language:English
Published: Springer 2024-01-01
Series:Discover Applied Sciences
Subjects:
CNN
Biomedical Image
Peak Signal-To-Noise ratio
Structural similarity
Artifact Problem
Online Access:https://doi.org/10.1007/s42452-024-05634-6
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Summary:Abstract The use of a light-weight deep learning Convolutional Neural Network (CNN) augmented with the power of Fuzzy Non-Sample Shearlet Transformation (FNSST) has successfully solved the problem of reducing noise and artifacts in Low-Dose Computed Tomography (LDCT) pictures. Both the Normal-Dose Computed Tomography (NDCT) and the Low-Dose Computed Tomography (LDCT) images from the dataset are subjected to the FNSST decomposition procedure during the training phase, producing high-frequency sub-images that act as input for the CNN. The CNN creates a meaningful connection between the high-frequency sub-images from LDCT and their corresponding residual sub-images during the training operation. The CNN is given the capacity to distinguish between LDCT high-frequency sub-images and expected high-frequency sub-images, which frequently have varying levels of noise or artifacts, especially in a fuzzy setting. The FNSST-CNN then successfully distinguishes LDCT high-frequency sub-images from the expected high-frequency sub-images during the testing phase, thereby reducing noise and artifacts. When compared to other approaches like KSVD, BM3D, and conventional image domain CNNs, the performance of FNSST-CNN is impressive as shown by better peak signal-to-noise ratios, stronger structural similarity, and a closer likeness to NDCT pictures.
ISSN:3004-9261

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