Cancer Diagnosis in Breast Using Biological and Electromagnetic Properties of Breast Tissues on Antenna Performance

Ali, Nagia; Uyguroğlu, Rasime; Allam, A. M. M. A.

doi:10.1109/mms.2018.8612104

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…The method is verified by comparing the measured return loss of the designed antenna implanted on a normal female breast; where average values are adopted, with different breast model simulation results. These results include a box model composed of planar breast layers [17] and a hemispherical model resembling the real breast shape associated with neighboring parts and lymph nodes. The simulation results show that both models are in good agreement with the measured results of a female human breast.…”

Section: Validation Of the Methodology And Resultsmentioning

confidence: 99%

“…Figure 1 depicts the printed antenna [17] which consists of different bricks of copper annealed and implemented on FR4 substrate with relative dielectric constant of 4.3, thickness of 1.6 mm and loss tangent of 0.025. The dimensions of the antenna are 5 × 4 cm 2 and is fed with a microstrip line of 50 Ω.…”

Section: Antenna Designmentioning

confidence: 99%

“…Mass density of the skin, fat and glandular tissues in the models have been taken as 972 kg/m 3 , 890 kg/m 3 and 900 kg/m 3 respectively [18]. The average SAR is depicted in figure 13 for the old box model [17] and figure 14 for new breast hemispherical model. The SAR values detected were 0.145 and 0.155 w/kg respectively; which are significantly lower than the safety parameters [18,19].…”

Section: Jinst 15 P09016mentioning

confidence: 99%

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Stage II cancer diagnosis using printed antenna implemented on hemispherical model for human breast

et al. 2020

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In this paper electromagnetic properties of a female human breast are used for cancer diagnosis. Deviation of the malignant cells' properties from those of healthy cells' directly affect the performance of an antenna placed on the breast. Return loss, gain and directivity of an antenna designed on an FR4 substrate were used as parameters for studying the effects of normal and cancerous tissue. A hemispherical shaped breast model is presented covering the breast, skin, fat and part of the connected lymph nodes. Tumors in breast and lymph nodes with different sizes and numbers were used to illustrate the second stage of cancer diagnosis. The antenna was designed and fabricated, and return loss measurements were taken using a network analyzer. Separate measurements were taken in free space and when placed on a healthy human breast. The return loss was simulated using CST Microwave Studio for antenna implemented on normal breast models and it is in good agreement with those of the measured results. The presented work has shown to be a preliminary model way for breast cancer diagnosis thereby reducing the need for invasive surgical operations.

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Section: Validation Of the Methodology And Resultsmentioning

confidence: 99%

Section: Antenna Designmentioning

confidence: 99%

Section: Jinst 15 P09016mentioning

confidence: 99%

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Stage II cancer diagnosis using printed antenna implemented on hemispherical model for human breast

et al. 2020

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“…Antennas placed within or on top of bodily organs can be used to detect cancer. Additionally, while researching and using an implanted antenna for the detection of human cancer, SAR presents a safety risk [28][29][30][31][32][33][34][35][36][37][38][39][40]. After fabrication, the antennas are measured.…”

mentioning

confidence: 99%

Metamaterial Structure Effect on Printed Antenna for LTE/WIFI /Cancer Diagnosis

Fatma Taher,

A. M. M. A. Allam,

Ahmed F. Miligy

et al. 2024

ARASET

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In this paper, we will look at the impact of METAMORAL ULC on the performance of LTE / Wi-Fi printed antennas. We will look at two different antenna versions. One version will have the METAMORAL ground layer and will be the traditional antenna. The other version will have a METAMORAL load attached to the altered antenna. The metamaterial ground layer in the antenna will support the unit cell with the MTM structure, and we will look at how the MTM structure affects the performance of the antenna. We will use Roger 5880 with a substrate thickness (1.575 mm) and Dielectric constant (2.2) to test both antenna versions. The antenna’s overall dimensions are 60×49×1.575mm and the loss tangent is 0,0009. Once we have the ideal inductor/capacitor values, we will be able to create equivalent circuits for our planned circuit as well as the conventional circuit. We will then simulate the circuit in CST microwave studio. The path wave ADS simulator runs the equivalent circuit. The antenna is manufactured and measured by Network Analyser Rode Frequency ranges covered by the antenna are: 1.68 GHz 2.51 GHz 3.56 4.63 GHz 4.1 GHz 5.1 GHz Standard applications 2.58 dB/2.45 dB simulated gain 2.22 dB/5.19 dB observed gain Excellent agreement between measured and quoted value from both simulators Particular rate of absorption simulated On a sample Breast Phantom 1g / 10g SAR value must fall between 10g EU limit and 1g US limit Is suitable for use in Cancer diagnosis and detection?

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Detection of breast cancer by deep belief network with improved activation function

Archana

2024

Adaptive Control & Signal

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SummaryBreast cancer is the most prevalent kind of tumor to occur in females and the primary cause of death for women. Early detection is perhaps the most successful strategy to minimize breast cancer mortality. Early diagnosis necessitates a consistent and efficient diagnostics method that allows doctors to differentiate benign from malignant breast cancers without a surgical sample. The goal of this endeavor is to develop a sophisticated breast cancer diagnosis method. The primary goal of the paper is to reduce the death rate among women by promoting early detection of breast cancer. First, pre‐processing techniques such as median filtering and contrast limiting adaptive histogram equalization are used to the obtained raw images. By doing this, the machine‐learning model's computational complexity is decreased and the image quality is enhanced. K‐means clustering is used to segregate the pre‐processed image. Additionally, features including the enhanced local vector pattern, grey‐level co‐occurrence matrix and local vector patterns are produced in the course of the feature extraction stage. Finally, an optimized deep belief network (DBN) is carrying out the classification process. To boosts the classification accuracy, activation function of DBN (tanh, softmax, ReLu) as well as its weight function is optimized by the proposed grey wolf updated whale optimization algorithm The accuracy of the greywolf updated whale optimization algorithm+DBN is above 93% for datasets 1 and 2 when compared to extant models. Finally, calculation of the performance validates the proposed model's performance.

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Cancer Diagnosis in Breast Using Biological and Electromagnetic Properties of Breast Tissues on Antenna Performance

Cited by 5 publications

References 4 publications

Stage II cancer diagnosis using printed antenna implemented on hemispherical model for human breast

Stage II cancer diagnosis using printed antenna implemented on hemispherical model for human breast

Metamaterial Structure Effect on Printed Antenna for LTE/WIFI /Cancer Diagnosis

Detection of breast cancer by deep belief network with improved activation function

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