Ieee transactions on antennas and propagation

Breast cancer is one of the most frequent malignancies among women worldwide. Methods for screening and diagnosis allow health care professionals to provide personalized treatments that improve the outcome and survival. Scientists and physicians are working side-by-side to develop evidence-based guidelines and equipment to detect cancer earlier. However, the lack of comprehensive interdisciplinary information and understanding between biomedical, medical, and technology professionals makes innovation of new screening and diagnosis tools difficult. This critical review gathers, for the first time, information concerning normal breast and cancer biology, established and emerging methods for screening and diagnosis, staging and grading, molecular and genetic biomarkers. Our purpose is to address key interdisciplinary information about these methods for physicians and scientists. Only the multidisciplinary interaction and communication between scientists, health care professionals, technical experts and patients will lead to the development of better detection tools and methods for an improved screening and early diagnosis.

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Breast cancer, screening and diagnostic tools: All you need to know.

Breast cancer plays a significant role in affecting female mortality. Researchers are actively seeking to develop early detection methods of breast cancer. Several technologies contributed to the reduction in mortality rate from this disease, but early detection contributes most to preventing disease spread, breast amputation and death. Thermography is a promising technology for early diagnosis where thermal cameras employed are of high resolution and sensitivity. The combination of Artificial Intelligence (AI) with thermal images is an effective tool to detect early stage breast cancer and is foreseen to provide impressive predictability levels. This paper reviews systematically the related works employing thermography with AI highlighting their contributions and drawbacks and proposing open issues for research. Several different types of Artificial Neural Networks (ANNs) and deep learning models were used in the literature to process thermographic images of breast cancer, such as Radial Basis Function Network (RBFN), K-Nearest Neighbors (KNN), Probability Neural Network (PNN), Support Vector Machine (SVM), ResNet50, SeResNet50, V Net, Bayes Net, Convolutional Neural Networks (CNN), Convolutional and DeConvolutional Neural Networks (C-DCNN), VGG-16, Hybrid (ResNet-50 and V-Net), ResNet101, DenseNet and InceptionV3. Previous studies were found limited to varying the numbers of thermal images used mostly from DMR-IR database. In addition, analysis of the literature indicate that several factors do affect the performance of the Neural Network used, such as Database, optimization method, Network model and extracted features. However, due to small sample size used, most of the studies achieved a classification accuracy of 80% to 100%.

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A Systematic Review of Breast Cancer Detection Using Thermography and Neural Networks

Breast cancer is not preventable To reduce the death rate and improve the survival chances of breast cancer patients, early and accurate detection is the only panacea Delay in diagnosis of this disease causes 60% of deaths Thermal imaging is a low-risk modality for early breast cancer decision making without injecting any form of energy into the human body Thermography as a screening tool was first introduced and well accepted in 1956 However, a study in 1977 found that it lagged behind other screening tools and is subjective Soon after, its use was discontinued This review discusses various screening tools used to detect breast cancer with a focus on thermography along with their advantages and shortcomings With the maturation of thermography equipment and technological advances, this technique is emerging and has become the refocus of many biomedical researchers across the globe in the past decade This study dispenses an exhaustive review of the work done related to interpretation of breast thermal variations and confers the discipline, frameworks, and methodologies used by different authors to diagnose breast cancer Different performance metrics like accuracy, specificity, and sensitivity have also been examined This paper outlines the most pressing research gaps for future work to improvise the accuracy of results for diagnosis of breast abnormalities using image processing tools, mathematical modelling and artificial intelligence However, supplementary research is needed to affirm the potential of this technology for predicting breast cancer risk effectively Altogether, our findings inform that it is a promising research problem and a potential solution for early detection of breast cancer in younger women

Thermal Imaging - An Emerging Modality for Breast Cancer Detection: A Comprehensive Review

This article proposes gain-enhanced circular patch antenna and array with introduction of multiple shorting pins. The antenna consists of a circular patch and several shorting pins. Equivalent inductance is brought to the patch after introducing shorting pins. Thus, resonance of the patch shifts at higher frequency. Compared to a conventional circular patch without any pins, radiation area of the proposed patch is enlarged given the same resonance frequency, hence leads to gain enhancement. The number of pins introduced to a circular patch can be set freely due to its symmetrical feature. Moreover, surface current is restricted within a small area around the center of the patch after introducing multiple pins, leading to more concentrated radiation. An equivalent circuit model is built for the proposed antenna. Layout and number optimization rules for introducing shorting pins are formulated. With these rules, gain of a circular patch can be maximized for the applications under certain size restrictions. A prototype operating at 5.8 GHz is designed and fabricated. Furthermore, a 2*2 array based on the gain-enhanced circular patch elements is implemented. In the measurements, high gain, high efficiency, and narrowed half-power beamwidth of the patch antenna and the array are observed.

High-Gain Circular Patch Antenna and Array With Introduction of Multiple Shorting Pins

The performance of IEEE 802.11 with different network densities and protocol configurations is of interest, particularly in distributed coordination function (DCF) mode. A mathematical model for single hop network IEEE 802.11 protocol was introduced by Bianchi [1] to analytically derive the saturated throughput. The ultimate goal is to enhance the capacity of Ad-hoc network closer to the analytical values of this model. As an attempt, the Receiver Based Capacity Enhancement Algorithm using Cross-Layer Design Approach (RCECLD) is proposed which dynamically adapts the data rate. It uses Signal-to-Noise Ratio (SNR) values calculated by Physical layer and exported to Medium Access Control (MAC) layer via the cross-layer interface to estimate the prevailing channel state. In RCECLD the receiver decides the transmission data rate by calculating the SNR value of received RTS (Readyto-Send), which is in turn an estimate of the prevailing channel state, and piggybacking it through CTS (Clear-to-Send) to the transmitter. Accordingly, transmitter transmits the data frame with adopted data rate. The capacity of the Ad-hoc network is enhanced with RCECLD. It is investigated through an extensive set of single hop and multi-hop simulations. The results indicate that the enhancement is very close to analytical values for smaller network size and it is about 2.5 times more than Auto-Rate Fallback (ARF) [2], inspite of fading and mobility effects in case of single hop, whereas in case of multi-hop with a chain of nodes it is almost doubled .

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Performance Investigation of Capacity Enhancement Algorithm for IEEE 802.11 Wireless Ad-hoc Networks

A Hexagonal Microstrip Antenna (HMSA) is an approximation to a Circular MSA (CMSA), which is not symmetrical over two principal planes, unlike a CMSA. Consequently, two types of feed positions are feasible for each X and Y -axes. When a feeding technique like a quarter-wave transformer or inset fed is used to match the edge impedance, the HMSA structure becomes asymmetrical over the feed axis ( E -plane),  which leads to increase in the cross-polar level in the H -plane. In this paper, to reduce the cross-polarisation level of HMSA along H -plane, a method of loading shorting posts into the patch have been used. To match the edge impedance of the HMSA on each X and Y -axes with the 50- Ohm Microstrip (MS)-line-fed, initially a single shorting post has been used. Later on, a pair of shorting post is used along both the axes of HMSA for operation at around 2.4 GHz. The current distribution of the shorted HMSA has been changed due to the loading of shorting posts into the patch,  which in-turns help to match the edge input impedance. Therefore, without changing the patch geometry, a wide extent of impedance matching can be realised. A detailed investigation on the characteristics of the shorted HMSA with modified fundamental mode frequencies has been presented. The co-polarization to cross-polarization ratios (CTCR) in H -plane of the HMSA with a pair of shorting posts has been reduced substantially with more than 20 dB at the broadsight direction in comparison with that of an HMSA with a single shorting post or with an inset. The simulated results of the HMSA with direct 50 Ohm-MS-line-feed are in good agreement with measured ones.

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A 50Ω Microstrip Line Fed Shorted Hexagonal Microstrip Antennas with Reduced Cross-Polarization

The prime objective of an Optical Character Recognition (OCR) is to convert printed, handwritten words or characters into electronic text. The main aim of this paper is to detect and recognize the labels or part number that are engraved on the materials. Also, for further processing one can store the recognized data into the database with time and other related information as per the end user's requirements. This implementation is being conducted by using YOLOv4 for detection and recognition, Darknet is being used as the backbone in proposed paper. It has been found that the mean average precesion (mAP) for this proposed implementation is around 68.43%.

Character Recognition on Forged Entities using AI

Microarray technology presents a challenge due to the large dimensionality of the data, which can be difficult to interpret. To address this challenge, the article proposes a feature extraction-based cancer classification technique coupled with artificial bee colony optimization (ABC) algorithm. The ABC-support vector machine (SVM) method is used to classify the lung cancer datasets and compared them with existing techniques in terms of precision, recall, F-measure, and accuracy. The proposed ABC-SVM has the advantage of dealing with complex nonlinear data, providing good flexibility. Simulation analysis was conducted with 30% of the data reserved for testing the proposed method. The results indicate that the proposed attribute classification technique, which uses fewer genes, performs better than other modalities. The classifiers, such as naïve Bayes, multi-class SVM, and linear discriminant analysis, were also compared and the proposed method outperformed these classifiers and state-of-the-art techniques. Overall, this study demonstrates the potential of using intelligent algorithms and feature extraction techniques to improve the accuracy of cancer diagnosis using microarray gene expression data. 

SVM‐ABC based cancer microarray (gene expression) hybrid method for data classification

In this paper, a technique using a single shorting post has been used to match the peripheral impedance of the circular microstrip antenna (CMSA) with that of the 50 Ω-microstrip (MS)-line feed. The shorting posts perturb the current distribution on the patch, altering the input impedance at the periphery. Thus, by selecting shorting post positions, a wide range of impedance has been adjusted without changing the patch geometry. A systematic analysis of the performance of the CMSA at a lower order mode and fundamental mode frequencies with a loading of a single shorting post has been presented. Simulated results of the directly 50 Ω-MS-line-fed CMSA are experimentally validated with good agreement.

R. N. Awale

Papers

Performance Investigation of Capacity Enhancement Algorithm for IEEE 802.11 Wireless Ad-hoc Networks

A 50Ω Microstrip Line Fed Shorted Hexagonal Microstrip Antennas with Reduced Cross-Polarization

Character Recognition on Forged Entities using AI

SVM‐ABC based cancer microarray (gene expression) hybrid method for data classification

Analysis of Circular Microstrip Antenna with Single Shorting Post for 50 Ω Microstrip-Line Feed