Permittivity estimation for breast cancer detection using particle swarm optimization algorithm

In this paper, particle swarm optimization (PSO) algorithm is used to estimate the permittivities of the tissue layers at microwave frequency band. According to the literature, microwave radiometry (MWR) is potentially a promising cancer detection technique. In addition, breast cancer is an appropriate candidate of MWR due to the breast's exclusive physiology. Several algorithms have been evaluated for analyzing the measurement data and solving the inverse scattering problem in MWR, and different levels of accuracy have been reported. In this paper, the potential of PSO in solving this problem is demonstrated at 1-2.25 GHz. Two distinct algorithms are developed for the two considered scenarios. In the first scenario, we assume no a priori knowledge of the tissue under the test, whereas, in the second scenario, a priori knowledge is assumed. It is noteworthy that, there are only a few research articles studying PSO for permittivity estimation. However, since these studies underestimate the loss encountered by the test samples, the methods are not valid for body tissue case. Here, measurement-based loss coefficients, reported in the existing literature, are included in the calculations. It is shown that the algorithm converges relatively fast, and, distinguishes between different tissues with an acceptable accuracy.