Distinction of breast tissues based on segmented integral area of frequency-resistance curves

Abstract

Breast cancer seriously endangers the health of women, which makes intra-operative assessment of cancer focus have vital significance. The information of bioelectrical impedance has unique ability to distinguish cancerous and normal tissue, and can provide basis for intra-operative assessment of cancer focus. In order to achieve accurate measurement, a measurement system is established composed of the impedance analyzer and probe with optimized electrode. Segmented integral area is regarded as characteristic parameter to reflect the over all trend. To utilize the advantages of different frequency-resistance curves, BP neural network is finally selected and good-training neural networks are integrated to make the final decision. The result indicates that the characteristic parameter selected can reflect differences of tissues and the integrated BP neural network has better performance than single neural network. © 2013 Springer-Verlag.