Comparison of supervised learning algorithms for RF-based breast cancer detection

Three-dimensional imaging based on radio frequency that exploits the contrast in dielectric properties of tissues may be used as a low-cost, non-invasive and nonionizing methodology for breast cancer detection. This paper demonstrates the use of various supervised machine learning algorithms in classification of breast tissues into less-dense fatty and dense fibroglandular or malignant classes from the measured scattered electric field data obtained through antennas placed around the breast tissue. A comparison on the performance of these algorithms are also presented. Such a classification step may be followed by a quantitative non-linear optimization scheme to obtain a more precise reconstruction of the tissue profile.