The effects of dielectric values, breast and tumor size on the detection of breast tumor

Although breast cancer is the second main cause of female deaths after lung cancer, early diagnosis plays a crucial role to diminish the death rate. Many techniques have been improved to detect the cancerous cells. At different microwave frequencies, the malignant cells indicate different electrical characteristics as compared to the normal cells. According to these frequencies, the breast tissue is more permeable than other tissues such as the brain and muscle. Due to this property of the breast tissue, microwaves can be used for the detection of breast cancer. In this study, the breast prototype was modelled using the CST STUDIO SUITE electromagnetic simulation software with respect to different breast size, tumor size and dielectric values tested at a range of the 0-3.0 GHz frequency. The objective of this paper is to investigate the effects of each factor and the interactions of factors on detecting cancer cells using the factorial analysis. The results indicate that the factors such as fat and skin permittivity, tumor and breast sizes are more effective in the detection of breast tumor. Although the effect of fibro permittivity is not significant alone, there are considerable interaction effects of a large breast size and small tumor size through low-to-high values of fibro permittivity. Furthermore, the combinations of a breast radius smaller than almost 8.5 cm with a high level tumor radius and breast radius larger than 8.5 cm with a low level tumor radius are desirable for lessening the return loss value

Biomedical Sensing - A Sensor Fusion Approach for Improved Medical Detection and Monitoring

Enhanced technological advancement in computation, communication, and sensing has dramatically changed the dynamics of modern medicine. Advancing preventive medicine is paramount to a sustainable improvement in the quality of life and life expectancy. On-body sensors provide continuous measurements for healthy and ailing individuals leading to faster recovery and more timely detection of illnesses. Novel sensor designs and sensor fusion for preventive monitoring can provide extensible benefits, including a better understanding of ailment progression, treatment optimization, and patient feedback through data analytics and visualization. However, existing research does not thoroughly investigate sensor fusion approaches in biomedical sensing, as well as spot sensing, which can provide better information through more accurate detection of specific tissues instead of secondary measurements. This article presents the development of an ex-vivo sensor fusion system to track a person\u27s muscular condition. The embedded system provides a significant benefit by notifying users of particular muscle events in real-time

Compact Wideband Microstrip Patch Antenna Design for Breast Cancer Detection

The current breast cancer detection techniques are mostly invasive and suffer from high cost, high false rate and inefficacy in early detection. These limitations can be subdued by development of non-invasive microwave detection system whose performance is predominantly dependent on the antenna used in the system. The designing of a compact wideband antenna and matching its impedance with breast phantom is a challenging task. In this paper, we have designed a compact antenna matched with the breast phantom operating in wideband frequency from 1 to 6 GHz capable to detect the dielectric (or impedance) contrast of the benign and malignant tissue. The impedance of the antenna is matched to a cubically shaped breast phantom and a very small tumor (volume=1 cm3). The antenna is tuned to the possible range of electrical properties of breast phantom and tumour (permittivity ranging from 10 to 20 and conductivity from 1.5 to 2.5 S/m). The return loss (S11), E-field distribution and specific absorption rate (SAR) are simulated. The operating band of antenna placed near the phantom without tumor was found to be (1.11-5.47)GHz and with tumor inside phantom is (1.29-5.50)GHz. Results also show that the SAR of the antenna is within the safety limit

Medical Internet-of-Things Based Breast Cancer Diagnosis Using Hyperparameter-Optimized Neural Networks

In today’s healthcare setting, the accurate and timely diagnosis of breast cancer is critical for recovery and treatment in the early stages. In recent years, the Internet of Things (IoT) has experienced a transformation that allows the analysis of real-time and historical data using artificial intelligence (AI) and machine learning (ML) approaches. Medical IoT combines medical devices and AI applications with healthcare infrastructure to support medical diagnostics. The current state-of-the-art approach fails to diagnose breast cancer in its initial period, resulting in the death of most women. As a result, medical professionals and researchers are faced with a tremendous problem in early breast cancer detection. We propose a medical IoT-based diagnostic system that competently identifies malignant and benign people in an IoT environment to resolve the difficulty of identifying early-stage breast cancer. The artificial neural network (ANN) and convolutional neural network (CNN) with hyperparameter optimization are used for malignant vs. benign classification, while the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) were utilized as baseline classifiers for comparison. Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models. We employ a particle swarm optimization (PSO) feature selection approach to select more satisfactory features from the breast cancer dataset to enhance the classification performance using MLP and SVM, while grid-based search was used to find the best combination of the hyperparameters of the CNN and ANN models. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used to test the proposed approach. The proposed model got a classification accuracy of 98.5% using CNN, and 99.2% using ANN.publishedVersio

An Active Microwave Sensor for Near Field Imaging

Near field imaging using microwaves in medical applications is of great current interest for its capability and accuracy in identifying features of interest, in comparison with other known screening tools. This paper documents microwave imaging experiments on breast cancer detection, using active antenna tuning to obtain matching over a wide bandwidth. A simple phantom consisting of a plastic container with a low dielectric material emulating fatty tissue and a high dielectric constant object emulating a tumor is scanned between 4 to 8 GHz with a UWB microstrip antenna. Measurements indicate that this prototype microwave sensor is a good candidate for such imaging applications

Optical spectroscopy-based imaging techniques for the diagnosis of breast cancer: A novel approach

There have been substantial advancements in optical spectroscopy-based imaging techniques in recent years. These developments can potentially herald a transformational change in the diagnostic pathway for diseases such as cancer. In this paper, we review the clinical and engineering aspects of novel optical spectroscopy-based imaging tools. We provide a comprehensive analysis of optical and non-optical spectroscopy-based breast cancer diagnosis techniques vis-à-vis the current standard techniques such as X-Ray mammography, ultrasonography, and tissue biopsy. The recent advancements in optical spectroscopy-based imaging systems such as Transillumination Imaging (TI) and the various types of Diffuse Optical Imaging (DOI) systems (parallel-plate, bed-based, and handheld) are examined. The engineering aspects, including mechanical, electronics, optics, automatic interpretation using artificial intelligence (AI), and ergonomics are discussed. The abilities of these technologies for measuring several cancer biomarkers such as hemoglobin, water, lipid, collagen, oxygen saturation (SO2), and tissue oxygenation index (TOI) are investigated. This article critically assesses the diagnostic ability and practical deployment of these new technologies to differentiate between the normal and cancerous tissue

Microwave Devices for Wearable Sensors and IoT

The Internet of Things (IoT) paradigm is currently highly demanded in multiple scenarios and in particular plays an important role in solving medical-related challenges. RF and microwave technologies, coupled with wireless energy transfer, are interesting candidates because of their inherent contactless spectrometric capabilities and for the wireless transmission of sensing data. This article reviews some recent achievements in the field of wearable sensors, highlighting the benefits that these solutions introduce in operative contexts, such as indoor localization and microwave sensing. Wireless power transfer is an essential requirement to be fulfilled to allow these sensors to be not only wearable but also compact and lightweight while avoiding bulky batteries. Flexible materials and 3D printing polymers, as well as daily garments, are widely exploited within the presented solutions, allowing comfort and wearability without renouncing the robustness and reliability of the built-in wearable sensor

Microwave Imaging Sensor Using Low Profile Modified Stacked Type Planar Inverted F Antenna

Microwave imaging is the technique to identify hidden objects from structures using electromagnetic waves that can be applied in medical diagnosis. The change of dielectric property can be detected using microwave antenna sensor, which can lead to localization of abnormality in the human body. This paper presents a stacked type modified Planar Inverted F Antenna (PIFA) as microwave imaging sensor. Design and performance analysis of the sensor antenna along with computational and experimental analysis to identify concealed object has been investigated in this study. The dimension of the modified PIFA radiating patch is 40 × 20 × 10 mm3. The reflector walls used, are 45 mm in length and 0.2-mm-thick inexpensive copper sheet is considered for the simulation and fabrication which addresses the problems of high expenses in conventional patch antenna. The proposed antenna sensor operates at 1.55–1.68 GHz where the maximum realized gain is 4.5 dB with consistent unidirectional radiation characteristics. The proposed sensor antenna is used to identify tumor in a computational human tissue phantom based on reflection and transmission coefficient. Finally, an experiment has been performed to verify the antenna’s potentiality of detecting abnormality in realistic breast phantom

Functionalised microwave sensors for real-time monitoring of copper and zinc concentration in mining-impacted water

Microwave spectroscopy has been identified as a novel and inexpensive method for the monitoring of water pollutants. Integrating microwave sensors with developed coatings is a novel strategy to make the sensing system more specific for a target contaminant. This study describes the determination of copper and zinc concentration in water in both lab-prepared and acquired mine-water samples from two abandoned mining areas in Wales, UK. Uncoated sensors immersed in samples spiked with 1.25 mg/L concentrations of copper and zinc, using the standard addition method, were able to quantify the concentration at 0.44 GHz with a strong linear correlation (R2=0.99) for the reflection coefficient magnitude (|S11|). Functionalised microwave sensors with l-cysteine, chitosan and bismuth-zinc-cobalt oxide based coatings have shown improvement in the sensing performance. Specifically, the linear correlation at 0.91-1.00 GHz between |S11| and a polluted sample spiked with Cu showed a higher R2 (=0.98), sensitivity (1.65ΔdB/mg/L) and quality factor (135) compared with uncoated sensors (R2=0.88, sensitivity of 0.82 ΔdB/mg/L and Q-factor 30.7). A Lorentzian-peak fitting function was applied for performing advanced multiple peak analysis and identifying the changes in the resonant frequency peaks which are related to the change in metal ion content. This novel sensor platform offers the possibility of in situ monitoring of toxic metal concentrations in mining-impacted water and multiple peak features, such as area, full-width half maximum, centre, and height of the peaks have the possibility to offer higher specificity for similar toxic metals, as between copper and zinc ions

Microwave sensors for in situ monitoring of trace metals in polluted water

Thousands of pollutants are threatening our water supply, putting at risk human and environmental health. Between them, trace metals are of significant concern, due to their high toxicity at low concentrations. Abandoned mining areas are globally one of the major sources of toxic metals. Nowadays, no method can guarantee an immediate response for quantifying these pollutants. In this work, a novel technique based on microwave spectroscopy and planar sensors for in situ real-time monitoring of water quality is described. The sensors were developed to directly probe water samples, and in situ trial measurements were performed in freshwater in four polluted mining areas in the UK. Planar microwave sensors were able to detect the water pollution level with an immediate response specifically depicted at three resonant peaks in the GHz range. To the authors' best knowledge, this is the first time that planar microwave sensors were tested in situ, demonstrating the ability to use this method for classifying more and less polluted water using a multiple-peak approach