RETRACTED ARTICLE: Non-sample fuzzy based convolutional neural network model for noise artifact in biomedical images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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| Format: | Article |
| Language: | English |
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Springer
2024-01-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-024-05634-6 |
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| author | Haewon Byeon Ruchi Kshatri Patel Deepak A. Vidhate Sherzod Kiyosov Saima Ahmed Rahin Ismail Keshta T. R. Vijaya Lakshmi |
| author_facet | Haewon Byeon Ruchi Kshatri Patel Deepak A. Vidhate Sherzod Kiyosov Saima Ahmed Rahin Ismail Keshta T. R. Vijaya Lakshmi |
| author_sort | Haewon Byeon |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a617a5573abd42abb331c98ab837dda3 |
| institution | OA Journals |
| issn | 3004-9261 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-a617a5573abd42abb331c98ab837dda32025-08-20T02:31:38ZengSpringerDiscover Applied Sciences3004-92612024-01-016111410.1007/s42452-024-05634-6RETRACTED ARTICLE: Non-sample fuzzy based convolutional neural network model for noise artifact in biomedical imagesHaewon Byeon0Ruchi Kshatri Patel1Deepak A. Vidhate2Sherzod Kiyosov3Saima Ahmed Rahin4Ismail Keshta5T. R. Vijaya Lakshmi6Department of Digital Anti-Aging Healthcare, Inje UniversityShri Ram Institute of TechnologyDepartment of Information Technology, Dr Vithalrao Vikhe Patil College of EngineeringThe Department of Tax and Taxation, Tashkent Institute of FinanceUnited International UniversityComputer Science and Information Systems Department, College of Applied Sciences, AlMaarefa UniversityMahatma Gandhi Institute of Technology GandipetAbstract 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.https://doi.org/10.1007/s42452-024-05634-6CNNBiomedical ImagePeak Signal-To-Noise ratioStructural similarityArtifact Problem |
| spellingShingle | Haewon Byeon Ruchi Kshatri Patel Deepak A. Vidhate Sherzod Kiyosov Saima Ahmed Rahin Ismail Keshta T. R. Vijaya Lakshmi RETRACTED ARTICLE: Non-sample fuzzy based convolutional neural network model for noise artifact in biomedical images Discover Applied Sciences CNN Biomedical Image Peak Signal-To-Noise ratio Structural similarity Artifact Problem |
| title | RETRACTED ARTICLE: Non-sample fuzzy based convolutional neural network model for noise artifact in biomedical images |
| title_full | RETRACTED ARTICLE: Non-sample fuzzy based convolutional neural network model for noise artifact in biomedical images |
| title_fullStr | RETRACTED ARTICLE: Non-sample fuzzy based convolutional neural network model for noise artifact in biomedical images |
| title_full_unstemmed | RETRACTED ARTICLE: Non-sample fuzzy based convolutional neural network model for noise artifact in biomedical images |
| title_short | RETRACTED ARTICLE: Non-sample fuzzy based convolutional neural network model for noise artifact in biomedical images |
| title_sort | retracted article non sample fuzzy based convolutional neural network model for noise artifact in biomedical images |
| topic | CNN Biomedical Image Peak Signal-To-Noise ratio Structural similarity Artifact Problem |
| url | https://doi.org/10.1007/s42452-024-05634-6 |
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