Computer vision-based breast self-examination stroke position and palpation pressure level classification using artificial neural networks and wavelet transforms
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, ISSN: 1557-170X, Vol: 2012, Page: 6259-6262
2012
- 8Citations
- 1Usage
- 32Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
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.
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.
Metrics Details
- Citations8
- Citation Indexes8
- CrossRef4
- Usage1
- Abstract Views1
- Captures32
- Readers32
- 32
Conference Paper Description
This paper focuses on breast self-examination (BSE) stroke position and palpation level classification for the development of a computer vision-based BSE training and guidance system. In this study, image frames are extracted from a BSE video and processed considering the color information, shape, and texture by wavelet transform and first order color moment. The new approach using artificial neural network and wavelet transform can identify BSE stroke positions and palpation levels, i.e. light, medium, and deep, at 97.8 % and 87.5 % accuracy respectively. © 2012 IEEE.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84870827031&origin=inward; http://dx.doi.org/10.1109/embc.2012.6347425; http://www.ncbi.nlm.nih.gov/pubmed/23367360; http://ieeexplore.ieee.org/document/6347425/; http://xplorestaging.ieee.org/ielx5/6320834/6345844/06347425.pdf?arnumber=6347425; https://animorepository.dlsu.edu.ph/faculty_research/563; https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1562&context=faculty_research
Institute of Electrical and Electronics Engineers (IEEE)
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know