Neural networks approach for mammography diagnosis using wavelets
features
- URL: http://arxiv.org/abs/2003.03000v1
- Date: Fri, 6 Mar 2020 02:10:47 GMT
- Title: Neural networks approach for mammography diagnosis using wavelets
features
- Authors: Essam A. Rashed and and Mohamed G. Awad
- Abstract summary: The diagnosis processes are done by transforming the data of the images into a feature vector using wavelets multilevel decomposition.
The suggested model consists of artificial neural networks designed for classifying mammograms according to tumor type and risk level.
- Score: 1.3750624267664155
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A supervised diagnosis system for digital mammogram is developed. The
diagnosis processes are done by transforming the data of the images into a
feature vector using wavelets multilevel decomposition. This vector is used as
the feature tailored toward separating different mammogram classes. The
suggested model consists of artificial neural networks designed for classifying
mammograms according to tumor type and risk level. Results are enhanced from
our previous study by extracting feature vectors using multilevel
decompositions instead of one level of decomposition. Radiologist-labeled
images were used to evaluate the diagnosis system. Results are very promising
and show possible guide for future work.
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