Evolutionary Bits\'n\'Spikes

Abstract-The considerable overlap in the dielectric properties of benign and malignant tissue at microwave frequencies means that breast tumour classification 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 influenced 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 effects of dielectric heterogeneity on a novel Spiking Neural Network (SNN) classifier are examined in terms of both sensitivity and specificity, using a 3D dielectrically heterogeneous breast model. The performance of the SNN classifier is compared to an existing LDA classifier. The effect of combining conflicting classification readings in a multi-antenna system is also considered. Finally and importantly, misclassified tumours are analysed and suggestions for future work are discussed.

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Spiking Neural Networks for Breast Cancer Classification Using Radar Target Signatures

McGinley¹,

O’Halloran²,

Conceição³

et al. 2010

PIER C

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Recent studies have shown that the dielectric properties of normal breast tissue vary considerably. This dielectric heterogeneity may mean that the identification of tumours using Ultra Wideband Radar imaging alone may be quite difficult. Significantly, since the dielectric properties of benign tissue were shown to overlap with those of malignant, breast tumour classification using traditional UWB Radar imaging algorithms could be very problematic. Rather than simply examining the dielectric properties of scatterers within the breast, other features of scatterers must be used for classification. Radar Target Signatures have been previously used to classify tumours due to the significant difference in size, shape and surface texture between benign and malignant tumours. This paper investigates Spiking Neural Networks (SNNs) applied as a novel tumour classification method. This paper will describe the creation of 3D tumour models, the generation of representative backscatter, the application of a feature extraction method and the use of SNNs to classify tumours as either benign or malignant. The performance of the SNN classifier is shown to outperform existing UWB Radar classification algorithms.

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Evolutionary Bits\'n\'Spikes

Cited by 6 publications

References 22 publications

From Wheels to Wings with Evolutionary Spiking Circuits

From Wheels to Wings with Evolutionary Spiking Circuits

Spiking Neural Networks for Breast Cancer Classification in a Dielectrically Heterogeneous Breast

Spiking Neural Networks for Breast Cancer Classification Using Radar Target Signatures

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