Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sahoo, Ashok Kumar; Rajavel, Rajkumar Title A name given to the resource Enhanced Wavelet Block Shrinkage Technique For Mammogram Denoising Using K-Means Clustering And Neural Networks Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Journal of Basic and Applied Sciences;Volume;14;Issue;4;pp.648-660 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.14419/8psp9843" target="_blank" rel="noreferrer noopener">https://doi.org/10.14419/8psp9843</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105014807521?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105014807521?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sahoo A.K., School of Computer Science and Engineering, Christ University, Bangalore, India; Rajavel R., Department of AI, ML and Data Science, Christ University, Bangalore, India Description An account of the resource The proposed research study introduces a novel approach for denoising digital mammograms by improving the existing wavelet block shrinkage filtering method with K-Means clustering and a convolutional neural network. This approach involves decomposing both the original and noisy mammograms into frequency subbands using 2D discrete wavelet transformation. The resulting subbands are then grouped into multiple clusters based on similar features of the wavelet coefficients, employing K-Means clustering. This represents an improvement over the traditional block shrinkage method, which uses fixed-size blocks. These clusters from the original and noisy mammograms are paired to train a convolutional neural network, which serves as an optimal shrinkage function. This neural network-based thresholding mechanism replaces traditional hard and soft thresholding methods that rely on a universal threshold. Test results demonstrate that the proposed enhanced wavelet block shrinkage mechanism achieves a 20% improvement in peak signal-to-noise ratio and a 5% increase in structural similarity index score compared to traditional wavelet block shrinkage. Authors. Subject The topic of the resource Breast Cancer; Convolutional Neural Network; Digital Mammogram Denoising; Enhanced Wavelet Block Shrink; K-Means Clustering; Wavelet Shrinkage Publisher An entity responsible for making the resource available Science Publishing Corporation Inc. Relation A related resource ISSN: 22275053; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online