Research Article
Breast cancer detection via wavelet energy and feed-forward neural network trained by genetic algorithm
@ARTICLE{10.4108/airo.3506, author={Jiaji Wang}, title={Breast cancer detection via wavelet energy and feed-forward neural network trained by genetic algorithm}, journal={EAI Endorsed Transactions on AI and Robotics}, volume={2}, number={1}, publisher={EAI}, journal_a={AIRO}, year={2023}, month={8}, keywords={wavelet energy, breast cancer, genetic algorithm}, doi={10.4108/airo.3506} }- Jiaji Wang
Year: 2023
Breast cancer detection via wavelet energy and feed-forward neural network trained by genetic algorithm
AIRO
EAI
DOI: 10.4108/airo.3506
Abstract
Enhancing the precision of breast cancer detection is the primary objective of this investigation, given its status as the most prevalent cancer among women worldwide. Timely identification of breast cancer can significantly improve the likelihood of successful diagnosis. To achieve this, we propose a innovative way that combines wavelet energy and a feedforward neural network. Our method employs the genetic algorithm and undergoes 20 iterations of 10-fold cross-validation for robustness. Via utilizing wavelet energy as a feature extractor and a feedforward neural network as the classifier, our method outperforms three alternative algorithms.
Copyright © 2023 J-J. Wang, licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
