AI Article Synopsis
- - The work focuses on
- three key innovations
- : smart data collection, an optimized training algorithm, and a
- Bayesian approach combined with split learning
- to ensure the privacy of patent data.
- - By utilizing consumer electronics like
- wearable devices
- and the Internet of Things (IoT) to capture THz images, the researchers implement an
- EM algorithm for training
- using a new split learning method aimed at improving imaging depth and tissue contrast for early breast cancer detection.
- - Their
- hybrid algorithm achieves an impressive accuracy of 97.5%
- over 100 training epochs, outperforming older models that required more training epochs (e.g., 165) for less accuracy.
Article Abstract
The proposed work contains three major contribution, such as smart data collection, optimized training algorithm and integrating Bayesian approach with split learning to make privacy of the patent data. By integrating consumer electronics device such as wearable devices, and the Internet of Things (IoT) taking THz image, perform EM algorithm as training, used newly proposed slit learning method the technology promises enhanced imaging depth and improved tissue contrast, thereby enabling early and accurate disease detection the breast cancer disease. In our hybrid algorithm, the breast cancer model achieves an accuracy of 97.5 percent over 100 epochs, surpassing the less accurate old models which required a higher number of epochs, such as 165.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11074303 | PMC |
| http://dx.doi.org/10.1038/s41598-024-58741-0 | DOI Listing |
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