CWC-MP-MC Image-based breast tumor classification using an optimized Vision Transformer (ViT)
Biomedical Signal Processing and Control, ISSN: 1746-8094, Vol: 100, Page: 106941
2025
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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Article Description
This study introduces a novel approach to improve breast tumor classification by integrating advanced image processing techniques and a state-of-the-art Vision Transformer (ViT) model. Our methodology involves transforming B-mode ultrasound images into Cross-Correlated Weighted Contourlet Multi-Parametric Multi-channel (CWC-MP-MC) images. This process includes applying Nakagami, Normal Inverse Gaussian (NIG), and Rician Inverse Gaussian (RiIG) statistical modeling to generate three distinct channels representing different statistical properties of the ultrasound data. These ultrasound data undergo a multi-resolution transform domain like contourlet transform and are weighted by cross-correlation to produce the CWC-MP-MC image. This composite image encapsulates comprehensive information about breast tissue characteristics, offering a robust representation for tumor classification. For classification, we utilize a optimized Vision Transformer (ViT) architecture specially designed to be lightweight, fine-tuned, and suitable for operating in a low-configuration GPU environment. Our experiments, conducted on three publicly available datasets (Mendeley, UDIAT, and BUSI), demonstrate that our proposed methodology achieves accuracy, sensitivity, specificity, NPV, PPV, and F1 scores exceeding 98% when employing CWC-MP-MC images.
Bibliographic Details
Elsevier BV
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