Optimized Multilayer Perceptron with Dynamic Learning Rate to Classify Breast Microwave Tomography Image

Most recently developed Computer Aided Diagnosis (CAD) systems and their related research is based on medical images that are usually obtained through conventional imaging techniques such as Magnetic Resonance Imaging (MRI), x-ray mammography, and ultrasound. With the development of a new imaging technology called Microwave Tomography Imaging (MTI), it has become inevitable to develop a CAD system that can show promising performance using new format of data. The platform can have a flexibility on its input by adopting Artificial Neural Network (ANN) as a classifier. Among the various phases of CAD system, we have focused on optimizing the classification phase that directly affects its performance. In this paper, we present the optimized Multilayer Perceptron (MLP) binary classifier, which can be plugged into the CAD system, that uses Dynamic Learning Rate (DLR) for alleviating local minima problem. The proposed classifier has an optimized size of neural network so that it will not fall into indeterminate equation problem by having reasonable amount of weights between each perceptron. Also, the proposed model will dynamically assign a learning rate onto each training points in the way that model earmarks a higher learning rate onto each training points belonging into minority class in order to escape from local minima which is a typical jeopardy of MLP. In experiment, we evaluate performance of our model with following measures; precision, recall, specificity, accuracy, and Matthews Correlation Coefficient (MCC) and compare them to that of work by Samaneh et al. The results show that our model outperforms existing model not only for the performance such as recall, specificity, accuracy, and precision, but also for the quality, and thus it empowers physicians to make better decision on breast cancer screening in early stage, as it also alleviates the cost burden from the patients

Similarity Measurement of Breast Cancer Mammographic Images Using Combination of Mesh Distance Fourier Transform and Global Features

Similarity measurement in breast cancer is an important aspect of determining the vulnerability of detected masses based on the previous cases. It is used to retrieve the most similar image for a given mammographic query image from a collection of previously archived images. By analyzing these results, doctors and radiologists can more accurately diagnose early-stage breast cancer and determine the best treatment. The direct result is better prognoses for breast cancer patients. Similarity measurement in images has always been a challenging task in the field of pattern recognition. A widely-adopted strategy in Content-Based Image Retrieval (CBIR) is comparison of local shape-based features of images. Contours summarize the orientations and sizes images, allowing for heuristic approach in measuring similarity between images. Similarly, global features of an image have the ability to generalize the entire object with a single vector which is also an important aspect of CBIR. The main objective of this paper is to enhance the similarity measurement between query images and database images so that the best match is chosen from the database for a particular query image, thus decreasing the chance of false positives. In this paper, a method has been proposed which compares both local and global features of images to determine their similarity. Three image filters are applied to make this comparison. First, we filter using the mesh distance Fourier descriptor (MDFD), which is based on the calculation of local features of the mammographic image. After this filter is applied, we retrieve the five most similar images from the database. Two additional filters are applied to the resulting image set to determine the best match. Experiments show that this proposed method overcomes shortcomings of existing methods, increasing accuracy of matches from 68% to 88%

Enhanced Breast Cancer Classification with Automatic Thresholding Using Support Vector Machine and Harris Corner Detection

Image classification and extracting the characteristics of a tumor are the powerful tools in medical science. In case of breast cancer medical treatment, the breast cancer classification methods can be used to classify input images as benign and malignant classes for better diagnoses and earlier detection with breast tumors. However, classification process can be challenging because of the existence of noise in the images, and complicated structures of the image. Manual classification of the images is timeconsuming, and need to be done only by medical experts. Hence using an automated medical image classification tool is useful and necessary. In addition, having a better training data set directly affect the quality of classification process. In this paper, a method is proposed based on supervised learning and automatic thresholding for both generating better training data set, and more accurate classification of the mammogram images into benign/malignant classes. The procedure consists of pre-processing, removing noise, elimination of unwanted objects, features extraction, and classification. A Support Vector Machine (SVM) is used as the supervised model in two phases which are testing and training. Intensity value, auto-correlation matrix value of detected corners, and, energy, are three extracted features used to train the SVM. Experimental results show this method classify images with more accuracy and less execution time compared to the existing method

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Biomedical Sensing and Imaging

This book mainly deals with recent advances in biomedical sensing and imaging. More recently, wearable/smart biosensors and devices, which facilitate diagnostics in a non-clinical setting, have become a hot topic. Combined with machine learning and artificial intelligence, they could revolutionize the biomedical diagnostic field. The aim of this book is to provide a research forum in biomedical sensing and imaging and extend the scientific frontier of this very important and significant biomedical endeavor

Intelligent Microwaves-Based Modalities for Breast Cancer Detection

Breast cancer is considered to be one of the major causes of mortality in women worldwide. Detection of breast tumors in their early stage is the key factor for possible successful treatment and can significantly reduce mortality rates. In recent years, microwaves have emerged as a potential technique for breast cancer detection one that avoids the discomfort, risks and costs associated with x-rays and excessive cost and availability of MRI. Microwave technique is simpler to use, much less expensive to generate, and is non-ionizing. The microwave detection used in earlier works relied on the sharp contrast in the electrical properties between tumors and healthy tissue. In such methods, the breast was scanned by microwaves of various frequencies and the reflection recorded. An image depicting the electrical properties of the breast was then developed. The challenge, however, is that female breasts contain a complex network of fat and fibrous tissues, the electrical properties of which can very well resemble those of cancerous or benign tumors. Also, the electrical properties of the breast vary with frequency, requiring the earlier techniques to employ complex receptors. Motivated by these drawbacks, this thesis addresses the development of an inexpensive, non-ionizing and highly sensitive microwave technique for detecting early-stage breast tumors. In the first part of this dissertation, anatomically-realistic numerical breast phantom models are constructed using computer simulation technology (CST). The phantoms are anatomically realistic three dimensional (3D) numerical models that are realistic in both structural and dielectric properties. In the second part of the thesis, first a single electric probe and then a magnetic probe are individually combined with classification algorithms to help in detecting the presence of breast tumors. A key feature of our proposed detection concept is the almost simultaneous sensing of both a woman breasts, since right and left healthy breasts are morphologically and materially identical except amongst very small percentage of women. The two tests then can be compared to reveal any tissues property discrepancies. The concept employs a near-field resonant probe with an ultra-narrow frequency response. The resonant probe is highly sensitive to any changes in the electromagnetic properties of breast tissues, such that the presence of a tumor can be gauged by determining the changes in the magnitude and phase response of the sensor's reflection coefficient. Once the probe response is recorded for both breasts, Principle Component Analysis (PCA) method is employed to emphasize any difference in probe responses. For validation of the concept, tumors embedded in realistic breast phantoms were simulated. To provide additional confidence in the detection modality introduced here, experimental results of three different sizes of metallic spheres, mimicking tumors, inserted inside chicken and beef meat were detected, first by using an electric probe and then using a magnetic probe, operating at 200 and 528 MHz respectively. The results obtained from the numerical tests and experiments strongly suggest that the detection modality presented here might lead to inexpensive and portable modality for early and regular breast tumor detection. A novel modality proposed in the third part of the thesis significantly enlarges the sensitivity area beyond that of a single probe. This modality, based on a sensor we developed, relies on a 4-element identical antenna array fed with a single port. The use of this senor array improves the sensitivity area as compared to a single sensor, resulting in better detection of tumors located deeply inside breast tissues. Two different sensors are developed in this part,a dipole sensor and a loop sensor. The dipole sensor comprises a 4-element identical dipole antenna array fed with a single port. Numerical simulations have been conducted using a numerical breast model with and without tumor cells placed in the near-field of the sensor. The sensor is capable of detecting a breast tumor inserted at four different locations and of various sizes. Experimental validation was conducted using chicken meat and metallic and dielectric spheres that resemble healthy and tumourous breast tissues. The simulation and experimental results show that the array sensor has a high sensitivity for detecting various sizes of breast tumor inserted at different locations. The developed loop sensor comprises a 4-element identical loop antenna array fed with a single port. Numerical simulations have been conducted using a numerical breast model with and without tumor cells placed in the near-field of the sensor. The sensor is capable of detecting various sizes of tumor inserted at five different locations. Experimental validation was conducted using a glass box filled with vegetable oil and metallic spheres that resemble healthy and tumourous breast tissues, respectively. The simulation and experimental results show that the array sensor has a high sensitivity for detecting a metallic sphere placed at five different locations inside a dielectric medium as well as for detecting variable sizes of metallic sphere. In the fourth part of this thesis, a near-field metasurface sensor is introduced whereby a near-field array sensor operating in the microwave regime is used statically to identify the presence of a breast tumor. In a departure from conventional near-field sensors, the sensor is a metasurface comprising an array of 8$\times$8 electrically-small resonating elements. The elements of the metasurface are designed to respond to both electric and magnetic fields. This capability enables the metasurface to emphasize seemingly small changes in the composition of the electric and magnetic fields in its environment, thus leading to higher overall sensor sensitivity. Furthermore, unlike previous near-field probes, the overall metasurface sensor is not electrically small, which means that it provides a larger sensing surface while maintaining the effectiveness of near-field probes in the sense of detecting material changes in the near proximity of the sensor. Numerical and experimental tests were used to validate the proposed detection methodology. This was achieved by testing the metasurface with a breast phantom having tumor placed at single location at three different stand off distances and with a breast phantom having tumors placed at different locations. Measurements were carried out on a realistic phantom that mimic a real female breast in terms of electric properties. The results showed high sensitivity of the metasurface which can indicate the existence of an anomaly that resembles a tumor inside a breast phantom having inhomogeneous material composition. The advantage of the proposed metasurface sensor array as compared to previously introduced sensors is that the proposed array sensor is fed by a single-feed point. Unlike multiple-feed points sensors, this single feeding port sensor array significantly reduces the computational cost and complexity caused by processing the data from multiple feeds. The thesis then discusses the idea of using machine learning approaches to improve the performance of the proposed microwave detection system. The machine learning methods proposed discriminated between normal and abnormal breast phantoms in different sizes and classes of breasts, then also significantly improved the accuracy, sensitivity and specificity of the proposed detection system. As future work, the last part introduces several ideas for solving challenges in various aspects of the proposed sensors and the classification logarithms introduced in the developed system. The first idea is introduced to improve the sensitivity of the metasurface sensor by using multiple polarization sensors. The metasurface sensor, presented in chapter seven has one diploe in the middle of the loop, which will be extended to have two cross dipoles for vertical and horizontal polarization excitations. The second idea is to improve the sensitivity area of the proposed system by using multiple metasurface sensors that cover the whole breast and therefore eliminate the use of mechanical motors to move the sensor all over a breast. The third idea is to develop a portable detection system and integrate of the standalone VNA and the sensor into one miniaturized unit. The VNA circuitry will be positioned at the back of the sensor and will be connected with a laptop

Biosensors for Diagnosis and Monitoring

Biosensor technologies have received a great amount of interest in recent decades, and this has especially been the case in recent years due to the health alert caused by the COVID-19 pandemic. The sensor platform market has grown in recent decades, and the COVID-19 outbreak has led to an increase in the demand for home diagnostics and point-of-care systems. With the evolution of biosensor technology towards portable platforms with a lower cost on-site analysis and a rapid selective and sensitive response, a larger market has opened up for this technology. The evolution of biosensor systems has the opportunity to change classic analysis towards real-time and in situ detection systems, with platforms such as point-of-care and wearables as well as implantable sensors to decentralize chemical and biological analysis, thus reducing industrial and medical costs. This book is dedicated to all the research related to biosensor technologies. Reviews, perspective articles, and research articles in different biosensing areas such as wearable sensors, point-of-care platforms, and pathogen detection for biomedical applications as well as environmental monitoring will introduce the reader to these relevant topics. This book is aimed at scientists and professionals working in the field of biosensors and also provides essential knowledge for students who want to enter the field

High-performance wireless interface for implant-to-air communications

Nous élaborons une interface cerveau-machine (ICM) entièrement sans fil afin de fournir un système de liaison directe entre le cerveau et les périphériques externes, permettant l’enregistrement et la stimulation du cerveau pour une utilisation permanente. Au cours de cette thèse, nous explorons la modélisation de canal, les antennes implantées et portables en tant que propagateurs appropriés pour cette application, la conception du nouveau système d’un émetteur-récepteur UWB implantable, la conception niveau système du circuit et sa mise en oeuvre par un procédé CMOS TSMC 0.18 um. En plus, en collaboration avec Université McGill, nous avons conçu un réseau de seize antennes pour une détection du cancer du sein à l’aide d’hyperfréquences. Notre première contribution calcule la caractérisation de canal de liaison sans fil UWB d’implant à l’air, l’absorption spécifique moyennée (ASAR), et les lignes directrices de la FCC sur la densité spectrale de puissance UWB transmis. La connaissance du comportement du canal est nécessaire pour déterminer la puissance maximale permise à 1) respecter les lignes directrices ANSI pour éviter des dommages aux tissus et 2) respecter les lignes directrices de la FCC sur les transmissions non autorisées. Nous avons recours à un modèle réaliste du canal biologique afin de concevoir les antennes pour l’émetteur implanté et le récepteur externe. Le placement des antennes est examiné avec deux scénarios contrastés ayant des contraintés de puissance. La performance du système au sein des tissus biologiques est examinée par l’intermédiaire des simulations et des expériences. Notre deuxième contribution est dédiée à la conception des antennes simples et à double polarisation pour les systèmes d’enregistrement neural sans fil à bande ultra-large en utilisant un modèle multicouches inhomogène de la tête humaine. Les antennes fabriquées à partir de matériaux flexibles sont plus facilement adaptées à l’implantation ; nous étudions des matériaux à la fois flexibles et rigides et examinons des compromis de performance. Les antennes proposées sont conçues pour fonctionner dans une plage de fréquence de 2-11 GHz (ayant S11-dessous de -10 dB) couvrant à la fois la bande 2.45 GHz (ISM) et la bande UWB 3.1-10.6 GHz. Des mesures confirment les résultats de simulation et montrent que les antennes flexibles ont peu de dégradation des performances en raison des effets de flexion (en termes de correspondance d’impédance). Finalement, une comparaison est réalisée entre quatre antennes implantables, couvrant la gamme 2-11 GHz : 1) une rigide, à la polarisation simple, 2) une rigide, à double polarisation, 3) une flexible, à simple polarisation et 4) une flexible, à double polarisation. Dans tous les cas une antenne rigide est utilisée à l’extérieur du corps, avec une polarisation appropriée. Plusieurs avantages ont été confirmés pour les antennes à la polarisation double : 1) une taille plus petite, 2) la sensibilité plus faible aux désalignements angulaires, et 3) une plus grande fidélité. Notre troisième contribution fournit la conception niveau système de l’architecture de communication sans fil pour les systèmes implantés qui stimulent simultanément les neurones et enregistrent les réponses de neurones. Cette architecture prend en charge un grand nombre d’électrodes (> 500), fournissant 100 Mb/s pour des signaux de stimulation de liaison descendante, et Gb/s pour les enregistrements de neurones de liaison montante. Nous proposons une architecture d’émetteur-récepteur qui partage une antenne ultra large bande, un émetteur-récepteur simplifié, travaillant en duplex intégral sur les deux bandes, et un nouveau formeur d’impulsions pour la liaison montante du Gb/s soutenant plusieurs formats de modulation. Nous présentons une démonstration expérimentale d’ex vivo de l’architecture en utilisant des composants discrets pour la réalisation les taux Gb/s en liaison montante. Une bonne performance de taux d’erreur de bit sur un canal biologique à 0,5, 1 et 2 Gb/s des débits de données pour la télémétrie de liaison montante (UWB) et 100 Mb/s pour la télémétrie en liaison descendante (bande 2.45 GHz) est atteinte. Notre quatrième contribution présente la conception au niveau du circuit d’un dispositif d’émission en duplex total qui est présentée dans notre troisième contribution. Ce dispositif d’émission en duplex total soutient les applications d’interfaçage neural multimodal et en haute densité (les canaux de stimulant et d’enregistrement) avec des débits de données asymétriques. L’émetteur (TX) et le récepteur (RX) partagent une seule antenne pour réduire la taille de l’implant. Le TX utilise impulse radio ultra-wide band (IR-UWB) basé sur une approche alliant des bords, et le RX utilise un nouveau 2.4 GHz récepteur on-off keying (OOK).Une bonne isolation (> 20 dB) entre le trajet TX et RX est mis en oeuvre 1) par mise en forme des impulsions transmises pour tomber dans le spectre UWB non réglementé (3.1-7 GHz), et 2) par un filtrage espace-efficace du spectre de liaison descendante OOK dans un amplificateur à faible bruit RX. L’émetteur UWB 3.1-7 GHz peut utiliser soit OOK soit la modulation numérique binaire à déplacement de phase (BPSK). Le FDT proposé offre une double bande avec un taux de données de liaison montante de 500 Mbps TX et un taux de données de liaison descendante de 100 Mb/s RX, et il est entièrement en conformité avec les standards TSMC 0.18 um CMOS dans un volume total de 0,8 mm2. Ainsi, la mesure de consommation d’énergie totale en mode full duplex est de 10,4 mW (5 mW à 100 Mb/s pour RX, et de 5,4 mW à 500 Mb/s ou 10,8 PJ / bits pour TX). Notre cinquième contribution est une collaboration avec l’Université McGill dans laquelle nous concevons des antennes simples et à double polarisation pour les systèmes de détection du cancer du sein à l’aide d’hyperfréquences sans fil en utilisant un modèle multi-couche et inhomogène du sein humain. Les antennes fabriquées à partir de matériaux flexibles sont plus facilement adaptées à des applications portables. Les antennes flexibles miniaturisées monopôles et spirales sur un 50 um Kapton polyimide sont conçus, en utilisant high frequency structure simulator (HFSS), à être en contact avec des tissus biologiques du sein. Les antennes proposées sont conçues pour fonctionner dans une gamme de fréquences de 2 à 4 GHz. Les mesures montrent que les antennes flexibles ont une bonne adaptation d’impédance dans les différentes positions sur le sein. De Plus, deux antennes à bande ultralarge flexibles 4 × 4 (simple et à double polarisation), dans un format similaire à celui d’un soutien-gorge, ont été développés pour un système de détection du cancer du sein basé sur le radar.We are working on a fully wireless brain-machine-interface to provide a communication link between the brain and external devices, enabling recording and stimulating the brain for permanent usage. In this thesis we explore channel modeling, implanted and wearable antennas as suitable propagators for this application, system level design of an implantable UWB transceiver, and circuit level design and implementing it by TSMC 0.18 um CMOS process. Also, in a collaboration project with McGill University, we designed a flexible sixteen antenna array for microwave breast cancer detection. Our first contribution calculates channel characteristics of implant-to-air UWB wireless link, average specific absorption rate (ASAR), and FCC guidelines on transmitted UWB power spectral density. Knowledge of channel behavior is required to determine the maximum allowable power to 1) respect ANSI guidelines for avoiding tissue damage and 2) respect FCC guidelines on unlicensed transmissions. We utilize a realistic model of the biological channel to inform the design of antennas for the implanted transmitter and the external receiver. Antennas placement is examined under two scenarios having contrasting power constraints. Performance of the system within the biological tissues is examined via simulations and experiments. Our second contribution deals with designing single and dual-polarization antennas for wireless ultra-wideband neural recording systems using an inhomogeneous multi-layer model of the human head. Antennas made from flexible materials are more easily adapted to implantation; we investigate both flexible and rigid materials and examine performance trade-offs. The proposed antennas are designed to operate in a frequency range of 2–11 GHz (having S11 below -10 dB) covering both the 2.45 GHz (ISM) band and the 3.1–10.6 GHz UWB band. Measurements confirm simulation results showing flexible antennas have little performance degradation due to bending effects (in terms of impedance matching). Finally, a comparison is made of four implantable antennas covering the 2-11 GHz range: 1) rigid, single polarization, 2) rigid, dual polarization, 3) flexible, single polarization and 4) flexible, dual polarization. In all cases a rigid antenna is used outside the body, with an appropriate polarization. Several advantages were confirmed for dual polarization antennas: 1) smaller size, 2) lower sensitivity to angular misalignments, and 3) higher fidelity. Our third contribution provides system level design of wireless communication architecture for implanted systems that simultaneously stimulate neurons and record neural responses. This architecture supports large numbers of electrodes (> 500), providing 100 Mb/s for the downlink of stimulation signals, and Gb/s for the uplink neural recordings. We propose a transceiver architecture that shares one ultra-wideband antenna, a streamlined transceiver working at full-duplex on both bands, and a novel pulse shaper for the Gb/s uplink supporting several modulation formats. We present an ex-vivo experimental demonstration of the architecture using discrete components achieving Gb/s uplink rates. Good bit error rate performance over a biological channel at 0.5, 1, and 2 Gbps data rates for uplink telemetry (UWB) and 100 Mbps for downlink telemetry (2.45 GHz band) is achieved. Our fourth contribution presents circuit level design of the novel full-duplex transceiver (FDT) which is presented in our third contribution. This full-duplex transceiver supports high-density and multimodal neural interfacing applications (high-channel count stimulating and recording) with asymmetric data rates. The transmitter (TX) and receiver (RX) share a single antenna to reduce implant size. The TX uses impulse radio ultra-wide band (IR-UWB) based on an edge combining approach, and the RX uses a novel 2.4-GHz on-off keying (OOK) receiver. Proper isolation (> 20 dB) between the TX and RX path is implemented 1) by shaping the transmitted pulses to fall within the unregulated UWB spectrum (3.1-7 GHz), and 2) by spaceefficient filtering (avoiding a circulator or diplexer) of the downlink OOK spectrum in the RX low-noise amplifier. The UWB 3.1-7 GHz transmitter can use either OOK or binary phase shift keying (BPSK) modulation schemes. The proposed FDT provides dual band 500-Mbps TX uplink data rate and 100 Mbps RX downlink data rate, and it is fully integrated into standard TSMC 0.18 um CMOS within a total size of 0.8 mm2. The total measured power consumption is 10.4 mW in full duplex mode (5 mW at 100 Mbps for RX, and 5.4 mW at 500 Mbps or 10.8 pJ/bit for TX). Our fifth contribution is a collaboration project with McGill University which we design single and dual-polarization antennas for wireless ultra-wideband breast cancer detection systems using an inhomogeneous multi-layer model of the human breast. Antennas made from flexible materials are more easily adapted to wearable applications. Miniaturized flexible monopole and spiral antennas on a 50 um Kapton polyimide are designed, using a high frequency structure simulator (HFSS), to be in contact with biological breast tissues. The proposed antennas are designed to operate in a frequency range of 2–4 GHz (with reflection coefficient (S11) below -10 dB). Measurements show that the flexible antennas have good impedance matching while in different positions with different curvature around the breast. Furthermore, two flexible conformal 4×4 ultra-wideband antenna arrays (single and dual polarization), in a format similar to that of a bra, were developed for a radar-based breast cancer detection system