SPIKING NEURAL NETWORKS FOR BREAST CANCER CLASSIFICATION IN A DIELECTRICALLY HETEROGENEOUS BREAST

The considerable overlap in the dielectric properties of benign and malignant tissue at microwave frequencies means that breast tumour classiflcation using traditional UWB Radar imaging algorithms could be very problematic. Several studies have examined the possibility of using the Radar Target Signature (RTS) of a tumour to classify the tumour as either benign or malignant, since the RTS has been shown to be in∞uenced by the size, shape and surface texture of tumours. The main weakness of existing studies is that they mainly consider tumours in a 3D dielectrically homogenous or 2D heterogeneous breast model. In this paper, the efiects of dielectric heterogeneity on a novel Spiking Neural Network (SNN) classifler are examined in terms of both sensitivity and speciflcity, using a 3D dielectrically heterogeneous breast model. The performance of the SNN classifler is compared to an existing LDA classifler. The efiect of combining con∞icting classiflcation readings in a multi-antenna system is also considered. Finally and importantly, misclassifled tumours are analysed and suggestions for future work are discussed.