Microwave Imaging and Sensing Techniques for Breast Cancer Detection

Wang, Lulu

doi:10.3390/mi14071462

Cited by 34 publications

(6 citation statements)

References 196 publications

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“…The proposed sensor is compared to other sensors in literature reported for Breast cancer detection as shown in Table IV. Several prototypes are reported either on flexible or rigid substrates in [1,4]. Among these [2][3][4][5]7] are monopole flexible antenna-based sensors.…”

Section: Discussionmentioning

confidence: 99%

“…Several prototypes are reported either on flexible or rigid substrates in [1,4]. Among these [2][3][4][5]7] are monopole flexible antenna-based sensors. Vivaldi antennas are reported as well in [6][7][8], however its field mechanism will not suit wearables.…”

Section: Discussionmentioning

confidence: 99%

“…Many biomedical applications require features like disease monitoring, health monitoring with minimal invasiveness, skin friendliness, and strong biocompatibility. Advances using Microwaves in monitoring chronic diseases have been reported in [4][5][6]. Among these diseases is the identification and diagnosis of breast cancer [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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An Early Breast Cancer Detection by Using Wearable Flexible Sensors and Artificial Intelligent

Elsheakh,

Fahmy,

Farouk

et al. 2024

IEEE Access

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Wearable devices are currently of great importance in developing health management and care applications and devices especially for early breast cancer detection (BCD). This is supported by the novel innovations in materials and techniques to construct highly accurate and comfortable biosensors for wearables. In this paper, the proposed flexible sensor is integrated with a Bra to realize a wearable breast cancer detection system. The proposed antenna-based sensor is composed of a CPW monopole antenna with an overall compact size of 24 × 45 mm 2 on flexible PCB Roger substrate with thickness 0.17 mm. The proposed sensors have enough bandwidth from 1.5 to 8 GHz at -6 dB reflection coefficient and conformal structure for biological structures and biocompatibility. The specific absorption rate (SAR) has been calculated and measured for the proposed sensor with a value of 0.75 W/kg at 0 dBm to ensure safety level. For testing, real shaped rubber phantoms from medical school enables the dynamic combination of breast and tumor to create test scenarios for breast cancer detection. 2×2 antenna-based sensors elements are placed around the breast phantom to gather data on scattering parameters for tumor characterization. Several simulation and measurement scenarios are presented to validate detection, optimum number of sensors to be used as well as training data for developed detection algorithms. Artificial intelligence techniques are used among which the CAT-Boost technique for scanning collected data and identifying the undesired tumor component inside the breast.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Early Breast Cancer Detection by Using Wearable Flexible Sensors and Artificial Intelligent

Elsheakh,

Fahmy,

Farouk

et al. 2024

IEEE Access

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“…Microwave sensing relies on the use of electromagnetic waves within the microwave frequency range, spanning from several hundreds of MHz to multiple GHz [6,35,36]. By processing the waves scattered by breast tissues, internal structural images of the breast can be constructed.…”

Section: Microwave Sensingmentioning

confidence: 99%

Microwave Breast Sensing via Deep Learning for Tumor Spatial Localization by Probability Maps

Borghouts,

Ambrosanio,

Franceschini

et al. 2023

Bioengineering

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Background: microwave imaging (MWI) has emerged as a promising modality for breast cancer screening, offering cost-effective, rapid, safe and comfortable exams. However, the practical application of MWI for tumor detection and localization is hampered by its inherent low resolution and low detection capability. Methods: this study aims to generate an accurate tumor probability map directly from the scattering matrix. This direct conversion makes the probability map independent of specific image formation techniques and thus potentially complementary to any image formation technique. An approach based on a convolutional neural network (CNN) is used to convert the scattering matrix into a tumor probability map. The proposed deep learning model is trained using a large realistic numerical dataset of two-dimensional (2D) breast slices. The performance of the model is assessed through visual inspection and quantitative measures to assess the predictive quality at various levels of detail. Results: the results demonstrate a remarkably high accuracy (0.9995) in classifying profiles as healthy or diseased, and exhibit the model’s ability to accurately locate the core of a single tumor (within 0.9 cm for most cases). Conclusion: overall, this research demonstrates that an approach based on neural networks (NN) for direct conversion from scattering matrices to tumor probability maps holds promise in advancing state-of-the-art tumor detection algorithms in the MWI domain.

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“…Meanwhile, the resolution increases with the frequency. Therefore, the choice of a suitable frequency band provides a trade-off between the penetration depth and the resolution, which is an open issue in microwave imaging [18]. On the basis of ensuring high penetration depth, the microwave image post-processing based on an image resolution enhancement algorithm can better deal with the trade-off between the penetration depth and the resolution.…”

Section: Introductionmentioning

confidence: 99%

Accurate blood glucose level monitoring using microwave imaging

Wang,

Xiao,

Pang

et al. 2024

Meas. Sci. Technol.

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Painless, and non-invasive detection techniques are needed to replace finger-pick blood for diabetic. In this paper, a first-of-its-kind, noninvasive, and continuous blood glucose level (BGL) detection method based on microwave imaging is introduced. This method avoids the complex task of frequency choice for the design of electromagnetic sensor. A radar-based microwave imaging technology combined with improved very-deep super resolution (VDSR-BL) method is introduced to obtain high-resolution (HR) microwave images. After image super-resolution reconstruction by VDSR-BL, the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) of HR images reach 35.4461 dB and 0.9761, respectively. Then an ensemble learning strategy based on support vector regression (SVR) and random forest algorithms is proposed to identify HR microwave images for BGL estimation. The developed detection system has been verified on the medium under tests (MUT) with different glucose solution. The final detection results obtain the root mean squared error (RMSE) of 0.1394 mg/mL and the mean absolute relative difference (MARD) of 8.02%, which show well accuracy with clinical acceptance. Meanwhile, we also conducted human trials. The high correlation coefficient (R) of 0.9254 was achieved between the results of microwave imaging and invasive BGL. Together, these results show that microwave imaging offers a promising new approach for noninvasive BGL monitoring.

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Microwave Imaging and Sensing Techniques for Breast Cancer Detection

Cited by 34 publications

References 196 publications

An Early Breast Cancer Detection by Using Wearable Flexible Sensors and Artificial Intelligent

An Early Breast Cancer Detection by Using Wearable Flexible Sensors and Artificial Intelligent

Microwave Breast Sensing via Deep Learning for Tumor Spatial Localization by Probability Maps

Accurate blood glucose level monitoring using microwave imaging

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