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Microwave Breast Tumor Localization Using Wavelet Feature Extraction and Genetic Algorithm‐neural Network by Min Lu; Xia Xiao; Guancong Liu; Hong Lu is a Medicine article available to read on EtoBox.

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## Abstract ## Purpose Ultra‐Wide Band (UWB) microwave breast cancer detection is a promising new technology for routine physical examination and home monitoring. The existing microwave imaging algorithms for breast tumor detection are complex and the effect is still not ideal, due to the heterogeneity of breast tissue, skin reflection, and fibroglandular tissue reflection in backscatter signals. This study aims to develop a machine learning method to accurately locate breast tumor. ## Methods A microwave‐based breast tumor localization method is proposed by time‐frequency feature extraction and neural network technology. First, the received microwave array signals are converted into representative and compact features by 4‐level Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA). Then, the Genetic Algorithm‐Neural Network (GA‐NN) is developed to tune hyper‐parameters of the neural network adaptively. The neural network embedded in the GA‐NN algorithm is a four‐layer architecture and 10‐fold cross‐validation is performed. Through the trained neural network, the tumor localization performance is evaluated on four datasets that are created by FDTD simulation meth

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Author

Min Lu; Xia Xiao; Guancong Liu; Hong Lu

Publisher

Wiley

Published

2021

Language

EN

Field

Medicine (Health Sciences)