Breast cancer detection via wavelet energy and feed-forward neural network trained by genetic algorithm

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.