Investigation of Classifiers for Early-Stage Breast Cancer Based on Radar Target Signatures

Conceição, Raquel C.; O’Halloran, Martin; Jones, Edward; Glavin, Martin

doi:10.2528/pier10051904

Cited by 31 publications

(65 citation statements)

References 41 publications

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“…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

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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.

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Section: Combining Breast and Tumour Modelsmentioning

confidence: 99%

Section: Combining Breast and Tumour Modelsmentioning

confidence: 99%

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Numerical Modelling for Ultra Wideband Radar Breast Cancer Detection and Classification

ConceiÃ§Ã£o¹,

O’Halloran²,

Glavin³

et al. 2011

PIER B

Self Cite

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show abstract

“…Principal Component Analysis (PCA) is applied to the difference signals to reduce the dimensionality of the data [16]. 50 components are chosen as this offered the best trade-off between computational load and performance [11].…”

Section: Feature Extraction and Classifier Optimizationmentioning

confidence: 99%

Breast Cancer Detection Based on Differential Ultrawideband Microwave Radar

Byrne¹,

O’Halloran²,

Glavin³

et al. 2011

PIER M

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Abstract-Ultrawideband (UWB) microwave imaging is a promising emerging method for the detection of breast cancer. Fibroglandular tissue has been shown to significantly limit the effectiveness of UWB imaging algorithms, particularly in the case of premenopausal women who may present with more dense breast tissue. Rather than trying to create an image of the breast, this study proposes to compare the UWB backscattered signals from successive scans of a dielectrically heterogeneous breast, to identify the presence of cancerous tissue. The temporal changes between signals are processed using Support Vector Machines to determine if a cancerous growth has occurred during the time between scans. Detection rates are compared to the results from a previous study by the authors, where UWB backscatter signals from a single scan were processed for cancer detection.

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“…Delay and sum procedure (and their different variants [6]), time-reversal algorithm [7] and the distributional approach [8] are a few of focusing algorithms based on linear inversion. Of course, as simplified models are employed, only "qualitative" information can be gathered and, hence, a further classification step is generally needed [9]. However, problems of reliability and computational burden are avoided.…”

Section: Introductionmentioning

confidence: 99%

RF breast cancer detection employing a noncharacterized vivaldi antenna and a MUSIC-inspired algorithm

Ruvio¹,

Solimene²,

D'Alterio³

et al. 2012

Int J RF and Microwave Comp Aid Eng

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Investigation of Classifiers for Early-Stage Breast Cancer Based on Radar Target Signatures

Abstract: Abstract-Ultra Wideband (UWB) radar has been extensively investigated as a means of detecting early-stage breast cancer.

Cited by 31 publications

References 41 publications

Numerical Modelling for Ultra Wideband Radar Breast Cancer Detection and Classification

Numerical Modelling for Ultra Wideband Radar Breast Cancer Detection and Classification

Breast Cancer Detection Based on Differential Ultrawideband Microwave Radar

RF breast cancer detection employing a noncharacterized vivaldi antenna and a MUSIC-inspired algorithm

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