Spiking Neural Networks for Breast Cancer Classification Using Radar Target Signatures

McGinley, Brian; O’Halloran, Martin; Conceição, Raquel C.; Morgan, Frank; Glavin, Martin; Jones, Edward

doi:10.2528/pierc10100202

Cited by 16 publications

(15 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…UWB tumour classification was examined by Chen et al [44][45][46][47][48] and Teo et al [49] using tumours located in 2D breast models, while studies by Davis et al [30] and Conceição [51][52][53][54][55], McGinley et al [56], O'Halloran et al [57] and Alshehri et al [64] considered tumours in 3D breast models. The latter will not be further discussed in this study since discrimination between benign and malignant tumours is only assessed in terms of dielectric differences between the two types of tumours and does not address resulting tumour signatures due to different shapes, which is the scope of this paper.…”

Section: Combining Breast and Tumour Modelsmentioning

confidence: 99%

“…In studies by Conceição et al [51][52][53]55] and McGinley et al [56] a similar approach to that of Davis et al [30] is used. The main differences are the dimensions of the TF and SF regions, as illustrated in Figure 13.…”

Section: Combining Breast and Tumour Modelsmentioning

confidence: 99%

“…Two methods have principally been used for tumour modelling in 2D and in 3D studies: polygonal approximation using an elliptical baseline [44][45][46][47][48][49] and Gaussian Random Spheres [30,35,[50][51][52][53][54][55][56][57]. The basis for these methods is described in the following subsections.…”

Section: Tumour Modellingmentioning

confidence: 99%

See 2 more Smart Citations

Numerical Modelling for Ultra Wideband Radar Breast Cancer Detection and Classification

ConceiÃ§Ã£o¹,

O’Halloran²,

Glavin³

et al. 2011

PIER B

Self Cite

View full text Add to dashboard Cite

Abstract-Microwave Imaging is one of the most promising emerging imaging technologies for breast cancer detection, and exploits the dielectric contrast between normal and malignant breast tissue at microwave frequencies. The development of many UWB Radar imaging approaches requires the use of accurate numerical models for the propagation and scattering of microwave signals within the breast. The Finite-Difference Time-Domain (FDTD) method is the most commonly used numerical modelling technique used to model the propagation of Electromagnetic (EM) waves in biological tissue. However, it is critical that an FDTD model accurately represents the dielectric properties of the constituent tissues, including tumour tissues, and the highly correlated distribution of these tissues within the breast. This paper presents a comprehensive review of the latest findings regarding dielectric properties of normal and cancerous breast tissue, and the heterogeneity of normal breast tissue. Furthermore, existing FDTD models of the breast described in the literature are examined.

show abstract

Section: Combining Breast and Tumour Modelsmentioning

confidence: 99%

Section: Combining Breast and Tumour Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Numerical Modelling for Ultra Wideband Radar Breast Cancer Detection and Classification

ConceiÃ§Ã£o¹,

O’Halloran²,

Glavin³

et al. 2011

PIER B

Self Cite

View full text Add to dashboard Cite

show abstract

“…The authors analyzed the possible breast cancer risks using odds-ratio and risk-ratio analysis. McGinley et al [18] applied Spiking Neural Networks algorithm as a novel tumor classification method in classifying tumors as either benign or malignant cancer. The performance of the technique was rated to outperform the existing UWB Radar imaging algorithm.…”

Section: Introductionmentioning

confidence: 99%

Prediction of breast cancer survivability using ensemble algorithms

Adegoke

Chen

Banissi

et al. 2017

2017 International Conference on Smart Systems and Technologies (SST)

View full text Add to dashboard Cite

-In this paper, several ensemble cancer survivability predictive models are presented and tested based on three variants of AdaBoost algorithm. In the models we used Random Forest, Radial Basis Function Network and Neural Network algorithms as base learners while AdaBoostM1, Real AdaBoost and MultiBoostAB were used as ensemble techniques and ten other classifiers as standalone models. There has been major research in ensemble modelling in statistics, medicine, technology and artificial intelligence in the last three decades. This might be because of the effectiveness and reliability of the technique in medical diagnosis and incident predictions compare with the standalone classifiers.We used Wisconsin breast cancer dataset in training and testing the models. The performances of the ensemble and standalone models were evaluated using Accuracy, RMSE and confusion matrix predictive parameters. The result shows that despite the complexity of the ensemble models and the required training time, the models did not outperform most of the standalone classifiers.

show abstract

“…These boundaries reflect UWB signals due to difference in dielectric properties between these various constituent tissues at microwave frequencies. The wide frequency-spectrum of the UWB signal means they are relatively robust to interference, while also allowing for very fine spatial resolution, making the technology ideal breast cancer detection and classification [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17], heart and respiration monitoring [18][19][20][21]. Furthermore, UWB Radar uses low-power non-ionising radiation and is therefore a safe method for imaging and continuous monitoring.…”

Section: Introductionmentioning

confidence: 99%

The Effects of Compression on Ultra Wideband Radar Signals

McGinley¹,

O’Halloran²,

Conceição³

et al. 2011

PIER

Self Cite

View full text Add to dashboard Cite

Abstract-Over the past ten years, Ultra Wideband (UWB) Radar has been widely investigated as a biomedical imaging modality, used to detect early-stage breast cancer and to continuously monitor vital signs using both wearable and contactless devices. The advantages of the technology in terms of low-power requirements and non-ionising radiation are well recognised, with the technology being applied to a range of non-invasive medical applications, from respiration to heart monitoring. Across all these applications, there is a strong necessity to efficiently manage the large quantities of UWB data which will be captured. For wearable devices in particular, the efficient compression of UWB data allows the monitoring system to conserve limited resources such as memory and battery capacity, by reducing data storage and in some cases transmission requirements. In contrast to lossless compression techniques, lossy compression algorithms can achieve higher compression ratios and consequently greater power savings, at the expense of a marginal degradation of the reconstructed signal. This paper compares the lossy JPEG2000 and Set Partitioning In Hierarchical Trees (SPIHT) algorithms for UWB signal compression. This study examines the effects of lossy signal compression on an UWB breast cancer classification algorithm. This particular application was chosen because the classification algorithm relies heavily on shape and surface texture detail embedded in the Radar Target Signature (RTS) of the tumour, and therefore will provide both a robust and easily quantifiable test platform for the compression algorithms. The

show abstract

Spiking Neural Networks for Breast Cancer Classification Using Radar Target Signatures

Cited by 16 publications

References 51 publications

Numerical Modelling for Ultra Wideband Radar Breast Cancer Detection and Classification

Numerical Modelling for Ultra Wideband Radar Breast Cancer Detection and Classification

Prediction of breast cancer survivability using ensemble algorithms

The Effects of Compression on Ultra Wideband Radar Signals

Contact Info

Product

Resources

About