Ultrawideband, Stable Normal and Cancer Skin Tissue Phantoms for Millimeter-Wave Skin Cancer Imaging

Biosensor with Microchannel for Broadband Dielectric Characterization of Nanoliter Cell Suspensions up to 110 GHz

Cell dielectric property measurement holds significant potential for application in cell detection and diagnosis due to its label-free and noninvasive nature. In this study, we developed a biosensor designed to measure the permittivity of liquid samples, particularly cell suspensions at the nanoliter scale, utilizing microwave and millimeter wave coplanar waveguides in conjunction with a microchannel. This biosensor facilitates the measurement of scattering parameters within a frequency domain ranging from 1 GHz to 110 GHz. The obtained scattering parameters are then converted into dielectric constants using specific algorithms. A cell capture structure within the microchannel ensures that cell suspensions remain stable within the measurement zone. The feasibility of this biosensor was confirmed by comparison with a commercial Keysight probe. We measured the dielectric constants of three different cell suspensions (HepG2, A549, MCF-7) using our biosensor. We also counted the number of cells captured in multiple measurements for each cell type and compared the corresponding changes in permittivity. The results indicated that the real part of the permittivity of HepG2 cells is 0.2-0.8 lower than that of the other two cell types. The difference between A549 and MCF-7 was relatively minor, only 0.2-0.4. The fluctuations in the dielectric spectrum caused by changes in cell numbers during measurements were smaller than the differences observed between different cell types. Thus, the sensor is suitable for measuring cell suspensions and can be utilized for label-free, noninvasive studies in identifying biological cell suspensions.

Noninvasive Glucose Sensing in Dielectrically Equivalent Multilayer Skin Phantoms

The interstitial fluid of the skin contains glucose levels comparable to those of blood. Noninvasive glucose sensing by microwaves has great potential to relieve diabetics from the burden of daily blood sampling, but improving the selectivity of this method remains a challenge. This study reports a dielectrically equivalent multilayer skin phantom and provides insight into the criteria for noninvasive glucose sensing by conducting dielectric analysis. The skin phantom was a hydrogel composed of gelatin, glucose, sodium chloride, and water covered by paraffin-impregnated paper. Investigations conducted on a wide range of component concentrations revealed characteristic relative permittivity and dielectric loss determined by the amount of electrolyte and solution that was independent of the amount of glucose. Since the microwave response due to glucose tends to be buried in noise, we developed a flowchart that first identifies the amounts of electrolytes and proteins, which are the major components other than glucose, and then quantifies the remaining glucose content. This noninvasive glucose sensing method would not be limited to the medical healthcare field; it could potentially be used in food manufacturing processes, livestock farming, and plant cultivation management.

Metamaterial based AMC backed archimedean spiral antenna for in-vitro microwave hyperthermia of skin cancer

This research article presents a study that uses microwave frequencies (ISM band) for treatment of skin cancer by heating the malignant cells on skin with a Microwave Hyperthermia (MWHT) applicator. The proposed MWHT applicator has been designed as an Archimedean Spiral Microstrip Patch Antenna (AMSPA) of dimensions 38 × 38 × 1.64 mm3 backed with a Meshed-shaped AMC (48 × 48 × 3.27mm3) reflector, placed at an optimized distance of 12 mm from AMSPA. The proposed AMSPA is designed as a single spiral resonator and fabricated on FR-4 substrate, excited using a feed network. The proposed AMSPA shows a resonance at 2.5 GHz with an impedance BW of 260 MHz (2.37-2.63 GHz) and peak gain of 3.20 dB with a bidirectional radiation pattern. An AMC is placed at its backside that can be exploited as a phase-compensation surface to attain an in-phase profile for directive emission and improve the BW upto 470 MHz, peak gain to 6.8 dB and also enhance the front-to-back ratio of the radiating antenna with radiation efficiency of 80%. The simulated environment for hyperthermia analysis is set up using penne's Bio-Heat equations to deliver microwave energy to the bio-mimic, that leads to a rise in temperature over the designed bio-mimic in CST MWS in the range of 41-45°C. The validation of MWHT radiation properties and temperature rise inside the malignancy of phantom is carried out by fabricating the bio-mimic using gelatine, vegetable oils and glycerol. This set up enhances the penetration-depth of EM waves inside the tri-layered phantom up-to 29.5 mm with Effective Field Surface of 36 × 36 mm2 and SAR of 8 W/Kg.

Deep learning for tumor margin identification in electromagnetic imaging

In this work, a novel method for tumor margin identification in electromagnetic imaging is proposed to optimize the tumor removal surgery. This capability will enable the visualization of the border of the cancerous tissue for the surgeon prior or during the excision surgery. To this end, the border between the normal and tumor parts needs to be identified. Therefore, the images need to be segmented into tumor and normal areas. We propose a deep learning technique which divides the electromagnetic images into two regions: tumor and normal, with high accuracy. We formulate deep learning from a perspective relevant to electromagnetic image reconstruction. A recurrent auto-encoder network architecture (termed here DeepTMI) is presented. The effectiveness of the algorithm is demonstrated by segmenting the reconstructed images of an experimental tissue-mimicking phantom. The structure similarity measure (SSIM) and mean-square-error (MSE) average of normalized reconstructed results by the DeepTMI method are about 0.94 and 0.04 respectively, while that average obtained from the conventional backpropagation (BP) method can hardly overcome 0.35 and 0.41 respectively.

Detection and diagnosis of melanoma skin cancers in dermoscopic images using pipelined internal module architecture (PIMA) method

Detection and diagnosis of melanoma skin cancer is important to save the life of humans. The main objective of this article is to perform both detection and diagnosis of the skin cancers in dermoscopy images. Both skin cancer detection and diagnosis system uses deep learning architectures for the effective performance improvement as the main objective. The detection process involves by identifying the cancer affected skin dermoscopy images and the diagnosis process involves by estimating the severity levels of the segmented cancer regions in skin images. This article proposes parallel CNN architecture for the classification of skin images into either melanoma or healthy. Initially, color map histogram equalization (CMHE) method is proposed in this article to enhance the source skin images and then thick and thin edges are detected from the enhanced skin image using the Fuzzy system. The gray-level co-occurrence matrix (GLCM) and Law's texture features are extracted from the edge detected images and these features are optimized using genetic algorithm (GA) approach. Further, the optimized features are classified by the developed pipelined internal module architecture (PIMA) of deep learning structure. The cancer regions in the classified melanoma skin images are segmented using mathematical morphological process and these segmented cancer regions are diagnosed into either mild or severe using the proposed PIMA structure. The proposed PIMA-based skin cancer classification system is applied and tested on ISIC and HAM 10000 skin image datasets. RESEARCH HIGHLIGHTS: The melanoma skin cancer is detected and classified using dermoscopy images. The skin dermoscopy images are enhanced using color map histogram equalization. GLCM and Law's texture features are extracted from the enhanced skin images. To propose pipelined internal module architecture (PIMA) for the classification of skin images.

Novel Sensing Technique for Stem Cells Differentiation Using Dielectric Spectroscopy of Their Proteins

Dielectric spectroscopy (DS) is the primary technique to observe the dielectric properties of biomaterials. DS extracts complex permittivity spectra from measured frequency responses such as the scattering parameters or impedances of materials over the frequency band of interest. In this study, an open-ended coaxial probe and vector network analyzer were used to characterize the complex permittivity spectra of protein suspensions of human mesenchymal stem cells (hMSCs) and human osteogenic sarcoma (Saos-2) cells in distilled water at frequencies ranging from 10 MHz to 43.5 GHz. The complex permittivity spectra of the protein suspensions of hMSCs and Saos-2 cells revealed two major dielectric dispersions, β and γ, offering three distinctive features for detecting the differentiation of stem cells: the distinctive values in the real and imaginary parts of the complex permittivity spectra as well as the relaxation frequency in the β-dispersion. The protein suspensions were analyzed using a single-shell model, and a dielectrophoresis (DEP) study was performed to determine the relationship between DS and DEP. In immunohistochemistry, antigen-antibody reactions and staining are required to identify the cell type; in contrast, DS eliminates the use of biological processes, while also providing numerical values of the dielectric permittivity of the material-under-test to detect differences. This study suggests that the application of DS can be expanded to detect stem cell differentiation.

THz Sensing of Human Skin: A Review of Skin Modeling Approaches

The non-ionizing and non-invasive nature of THz radiation, combined with its high sensitivity to water, has made THz imaging and spectroscopy highly attractive for in vivo biomedical applications for many years. Among them, the skin is primarily investigated due to the short penetration depth of THz waves caused by the high attenuation by water in biological samples. However, a complete model of skin describing the THz-skin interaction is still needed. This is also fundamental to reveal the optical properties of the skin from the measured THz spectrum. It is crucial that the correct model is used, not just to ensure compatibility between different works, but more importantly to ensure the reliability of the data and conclusions. Therefore, in this review, we summarize the models applied to skin used in the THz regime, and we compare their adaptability, accuracy, and limitations. We show that most of the models attempt to extract the hydration profile inside the skin while there is also the anisotropic model that displays skin structural changes in the stratum corneum.

HOSMI-LBP-BASED FEATURE EXTRACTION FOR MELANOMA DETECTION USING HYBRID DEEP LEARNING MODELS

“Melanoma is a serious form of skin cancer that begins in cells known as melanocytes and more dangerous due to its spreading ability to other organs more rapidly if it is not treated at an early stage”. This paper aims to propose a Melanoma detection methodology that includes four major phases: “(i) pre-processing (ii) segmentation (iii) the proposed feature extraction and (iv) classification”. Initially, pre-processing is performed, where the input image is subjected to processing like resizing and edge smoothening. Subsequently, segmentation is carried out by the Otsu thresholding process. In the feature extraction phase, the proposed Higher-Order Standardized Moment Induced-Local Binary Patterns (HOSMI-LBP)-based features are extracted. These features are then subjected to a classification process for classifying the disease. For this, it is planned to use a hybrid classification framework, where the Convolutional Neural Network (CNN) and the Neural Network (NN) are deployed. Two-phase of classification gets processed: the extracted features are subjected to NN; the input image is directly classified using an optimized CNN framework. Finally, the classified outputs from NN and optimized CNN are averaged and the final output is considered as detected output. Particularly, the weight and initial rate of CNN is optimized using the proposed algorithm known as the Sea Lion Integrated Grey Wolf Algorithm (SLI-GWO) method that hybrid the concepts of both Sea Lion Optimization (SLnO) and Grey Wolf Optimization (GWO) algorithm. At last, the proposed work performance is computed with traditional systems in terms of various measures.

A novel melanoma detection model: adapted K-means clustering-based segmentation process

Objectives

The main intention of this paper is to propose a new Improved K-means clustering algorithm, by optimally tuning the centroids.

Methods

This paper introduces a new melanoma detection model that includes three major phase’s viz. segmentation, feature extraction and detection. For segmentation, this paper introduces a new Improved K-means clustering algorithm, where the initial centroids are optimally tuned by a new algorithm termed Lion Algorithm with New Mating Process (LANM), which is an improved version of standard LA. Moreover, the optimal selection is based on the consideration of multi-objective including intensity diverse centroid, spatial map, and frequency of occurrence, respectively. The subsequent phase is feature extraction, where the proposed Local Vector Pattern (LVP) and Grey-Level Co-Occurrence Matrix (GLCM)-based features are extracted. Further, these extracted features are fed as input to Deep Convolution Neural Network (DCNN) for melanoma detection.

Results

Finally, the performance of the proposed model is evaluated over other conventional models by determining both the positive as well as negative measures. From the analysis, it is observed that for the normal skin image, the accuracy of the presented work is 0.86379, which is 47.83% and 0.245% better than the traditional works like Conventional K-means and PA-MSA, respectively.

Conclusions

From the overall analysis it can be observed that the proposed model is more robust in melanoma prediction, when compared over the state-of-art models.

A Comparative Study of Skin Phantoms for Microwave Applications

In the here-reported study, we investigated the dielectric properties of three different skin-mimicking materials used in the reported controlled experiments for development and testing of microwave-range medical devices. Each of the phantom materials under consideration is tested in two forms: a thicker, larger block and a 2-mm sheet. The measured properties are compared to a reference of human skin tissue measurements from literature. Depending on the frequency range of the medical device in development, some phantoms are more suitable to represent human skin than others. We observe that all phantoms still show lower dielectric properties than the human skin reference, but are suitable representations of skin at microwave applications if used as 2-mm thin layers.

Millimeter-Wave Substrate Integrated Waveguide Probe for Skin Cancer Detection

This article presents an efficient and low-cost near-field probe, designed for early-stage skin cancer detection. Thanks to a tapered section, the device can achieve a sharp concentration of electric field at its tip. Moreover, the adoption of substrate integrated waveguide (SIW) technology ensures an easy and cheap fabrication process. The probe is realized on a high dielectric constant substrate (Rogers RO3210) that provides a good impedance matching with the skin, thus allowing to use the device in direct contact with it. This feature is essential to ensure that the proposed system can be adopted as a practical and effective tool for a fast scanning of many suspected skin regions. The probe is designed to operate at around 40 GHz in order to achieve the penetration depth required to detect small cancer lumps in the skin, while preventing the fields from interacting with the underlying biological tissues. Furthermore, the concept of detection depth is defined with the goal of introducing a metric that is more suitable than the penetration depth to express the notion of the maximum distance from the skin surface at which a tumor can be detected. Thanks to a differential imaging algorithm, the probe is capable of working on every different skin types and body region. The proposed device has a lateral sensitivity and detection depth of 0.2 and 0.55 mm respectively. The probe was designed and tested through simulations in CST Microwave Studio, as well as fabricated and validated through measurements on an artificial human skin phantom.

Emerging medical applications based on non-ionizing electromagnetic fields from 0 Hz to 10 THz

The potential for using non-ionizing electromagnetic fields (EMF; at frequencies from 0 Hz up to the THz range) for medical purposes has been of interest since many decades. A number of established and familiar methods are in use all over the world. This review, however, provides an overview of applications that already play some clinical role or are in earlier stages of development. The covered methods include modalities used for bone healing, cancer treatment, neurological conditions, and diathermy. In addition, certain other potential clinical areas are touched upon. Most of the reviewed technologies deal with therapy, whereas just a few diagnostic approaches are mentioned. None of the discussed methods are having such a strong impact in their field of use that they would be expected to replace conventional methods. Partly this is due to a knowledge base that lacks mechanistic explanations for EMF effects at low-intensity levels, which often are used in the applications. Thus, the possible optimal use of EMF approaches is restricted. Other reasons for the limited impact include a scarcity of well-performed randomized clinical trials that convincingly show the efficacy of the methods and that standardized user protocols are mostly lacking. Presently, it seems that some EMF-based methods can have a niche role in treatment and diagnostics of certain conditions, mostly as a complement to or in combination with other, more established, methods. Further development and a stronger impact of these technologies need a better understanding of the interaction mechanisms between EMF and biological systems at lower intensity levels. The importance of the different physical parameters of the EMF exposure needs also further investigations.