A Breast Cancer Detection Approach Based on Radar Data Processing using Artificial Neural Network

A new breast cancer detection approach is proposed as an accurate, non-invasive Yes-No diagnostic tool without the need for breast imaging. The approach is based on the processing of ultra wideband (UWB) mono-static radar signals backscattered around heterogeneous two-dimensional (2D) and three-dimensional (3D) breast models. Suitable data are extracted and input in an artificial neural network (ANN) able to detect the presence or absence of the tumor for each single radar trace. Then, a diagnostic criterion is applied, considering the collective ANN outputs. The best results were obtained for tumors positioned outside the fibro-glandular tissues. Using 2D breast models and an ideal skin artifact removal technique, tumors were detected with 80% accuracy for 2000 testing data values. When a realistic model-based skin artifact removal technique was applied, 74% accuracy was obtained. Using a realistic 3D breast model, this technique correctly detected tumors with diameters as small as 2 mm located at different distances from the chest. Moreover, for the analyzed cases, the application of the diagnostic criterion showed an accuracy of 100%. The ANN processing technique applied to radar systems realizes a simple, fast, and highly accurate breast cancer diagnostic criterion with low computational burden.