Breast Cancer Detection System Research Articles

Extraction of hottest blood vessels from breast thermograms using state-of-the-art image segmentation methods

ABSTRACT A major concern for women’s health in today’s age is breast cancer. Thermography is an upcoming technology that is painless, private and relatively cheap to screen breast health. The presence of asymmetric hot blood vessel patterns in the breast thermogram portrays an abnormality. Proper extraction of these hotspots from the breast can help build a reliable breast cancer detection system and play a critical role in knowing the extent of spread of the cancer. In this work, segmentation of mammary thermograms is performed to extract the hottest blood vessel patterns using five state-of-the-art image segmentation methods. The proposed work is tested on the benchmark breast thermogram public dataset available at the Visual Lab. The most vascularised areas of each breast are extracted, and their areas are matched with the patches in the ground truth images. Based on metrics like DICE similarity coefficient and Jaccard index, it is concluded that particle swarm optimisation (PSO) algorithm and multi-seed region-growing technique provide the best segmentation results that are closer to the ground truth images. This indicates that infrared imaging is a promising tool that can act as a catalyst in predicting breast anomalies.

Hybrid Inception Recurrent Residual Convolutional Neural Network (HIRResCNN) with Harmony Search Optimization (HSO) for Early Breast Cancer Detection System

For women, most common cause of death is Breast tumour and in worldwide, it is the second leading reason for cancer deaths. Due the requirement of breast cancer’s early detection and false diagnosis impact on patients, made researchers to investigate Deep Learning (DL) techniques for mammograms. There are four stages in this proposed HIRResCNN framework, namely, Pre-processing, reduction of dimensionality, segmentation and classification. From images, noises are removed using two filtering algorithms called Median and mean filtering in pre-processing stage. Then canny edge detector is used for detecting edges. Gaussian filtering is used in canny edge detector to smoothen the images. In the next dimensionality reduction stage, attributes are correlated using Principal Component Analysis (PCA) inclusive of related features. So, this huge dataset is minimized and only few variables are used for expressing it. In order to detect the breast cancer accurately, foreground and background subtraction is done in the third stage called segmentation stage. At last, for detecting and classifying breast cancer, a Hybrid Inception Recurrent Residual Convolutional Neural Network (HIRResCNN) is introduced, which integrates Harmony Search Optimization (HSO) to tune bias and weight parameters and classification accuracy is enhanced using HIRResCNN-HSO model. Strength of Recurrent Convolutional Neural Network (RCNN), Residual Network (ResNet) and Inception Network (Inception-v4), are combined in a powerful Deep Convolutional Neural Network (DCNN) model called HIRResCNN. using Mammographic Image Analysis Society (MIAS) dataset, various experiments are conduced and results are compared with other available techniques. Around 92.6% accuracy rate is produced using this proposed HIRResCNN classifier in finding breast cancer.

Segmentation of pectoral muscle from digital mammograms with depth-first search algorithm towards breast density classification

Digital mammography acts as a unique screening technology to protect the lives of females against breast cancer for the past few decades. Mammographic breast density is a well-known biomarker and plays a substantial role in breast cancer prediction and treatments. Breast density is calculated based on the opacity of fibro-glandular tissue reflected on digital mammograms concerning the whole area of the breast. The opacity of pectoral muscle and fibro-glandular tissue is similar to each other; hence, the small presence of the pectoral muscle in the breast area can hamper the accuracy of breast density classification. Successful removal of pectoral muscle is challenging due to changes in shape, size, and texture of pectoral muscle in every MLO and LMO views of mammogram. In this article, the depth-first search (DFS) algorithm is proposed to remove artifacts and pectoral muscle from digital mammograms. In the proposed algorithm, image enhancement is performed to improve the pixel quality of the input image. The whole breast as a single connected component is identified from the background region to remove the artifacts and tags. The depth-first search method with and without the heuristic approach is used to delineate the pectoral muscle, and then final suppression is performed on it. This algorithm is tested on 2675 images of the DDSM dataset, which is further divided into four density classes as per BIRADs classification. Segmentation results are calculated individually on each BIRADs density class of the DDSM dataset. Results are validated subjectively by the expert's Radiologist's ground truth with segmentation accuracy and objectively by the Jaccard coefficient and a dice similarity coefficient. This algorithm is found robust on each density class and provides overall segmentation accuracy of 86.18%, a mean value of Jaccard index, and a Dice similarity coefficient of 0.9315 and 0.9548, respectively. The experimental results show that the proposed algorithms applied for pectoral muscle removal follow the ground truth marked by an expert radiologist. The proposed algorithm can be part of the pre-processing unit of breast density measurement and breast cancer detection system used during clinical practice.

Wideband-Narrowband Switchable Tapered Slot Antenna for Breast Cancer Diagnosis and Treatment

In this paper, a wideband-narrowband switchable tapered slot antenna (TSA) with a compact meander line resonator for an integrated microwave imaging and hyperthermia system was proposed. A compact meander line resonator, which exhibited band-pass characteristics and provided narrowband characteristics by using one PIN diode, was fabricated beneath the tapered slot of the wideband TSA to minimize the degradation of the wideband characteristics. Moreover, the electromagnetic energy was transferred to the meander line resonator with a coupling effect to ensure effective frequency switching. By adapting a PIN diode on the meander line resonator, frequency switching could be achieved. In this way, the proposed antenna could operate in a real-time frequency switching mode between the ultra-wideband (UWB; 3.1~10 GHz), which is used for microwave imaging, and the 2.45 GHz band (industrial, scientific, and medical, ISM band), which is used for microwave hyperthermia. Frequency and time-domain results proved the applicability of the proposed antenna to an integrated breast cancer detection and treatment system.

Automated early breast cancer detection and classification system

Early detection of breast cancer is clinically important to reduce the mortality rate. In this study, a new computer-aided detection (CAD) and classification system is introduced to classify two types of mammogram tumors (i.e., mass and calcification) as either benign or malignant. In this CAD system, the tumor-like regions (TLRs) are identified using the automated optimal Otsu thresholding method. Afterward, deep convolutional neural networks (CNNs) process the extracted TLRs to extract relevant mammogram features, investigating AlexNet and ResNet-50 architectures. The normalized extracted CNN features are further input to a support vector machine classifier to decode the classes of mammogram structures (i.e., Benign Calcification, Benign Mass, Malignant Calcification, and Malignant Mass nodules). The experimental results are tested on 2800 mammogram images from the Curated Breast Imaging Subset of Digital Database of Screening Mammography, a publicly available dataset. The accuracy of the proposed CAD system, to classify the ROI into one of the four classes, achieves 0.91 using AlexNet and 0.84 using ResNet-50 models, using fivefold cross-validation. Comparison results with the related methods confirm the advantages of the proposed CAD system.

Deep convolutional neural network and emotional learning based breast cancer detection using digital mammography

Breast cancer is one of the deadly diseases among women. However, the chances of death are highly reduced if it gets diagnosed and treated at its early stage. Mammography is one of the reliable methods used by the radiologist to detect breast cancer at its initial stage. Therefore, an automatic and secure breast cancer detection system that accurately detects abnormalities not only increases the radiologist's diagnostic confidence but also provides more objective evidence. In this work, an automatic Diverse Features based Breast Cancer Detection (DFeBCD) system is proposed to classify a mammogram as normal or abnormal. Four sets of distinct feature types are used. Among them, features based on taxonomic indexes, statistical measures and local binary patterns are static. The proposed DFeBCD dynamically extracts the fourth set of features from mammogram images using a highway-network based deep convolution neural network (CNN). Two classifiers, Support Vector Machine (SVM) and Emotional Learning inspired Ensemble Classifier (ELiEC), are trained on these distinct features using a standard IRMA mammogram dataset. The reliability of the system performance is ensured by applying 5-folds cross-validation. Through experiments, we have observed that the performance of the DFeBCD system on dynamically generated features through highway network-based CNN is better than that of all the three individual sets of ad-hoc features. Furthermore, the hybridization of all four types of features improves the system's performance by nearly 2–3%. The performance of both the classifiers is comparable using the individual sets of ad-hoc features. However, the ELiEC classifier's performance is better than SVM using both hybrid and dynamic features.

Ultra‐wideband on‐body elliptical monopole antenna

This study presents an ultra-wideband, elliptical slot, planar monopole antenna for early breast cancer microwave imaging. The on-body antenna's operation is optimised by direct contact with the patient's skin. With a compact size of 9 × 7 mm, the antenna covers a wide bandwidth from 16 to 24 GHz for reflection coefficients lower than –10 dB. Besides, it also features an electrode for electrical impedance tomography applications. Verification on a volunteer's breast gives an excellent agreement with the simulation for the defined bandwidth. Furthermore, as the first stage of the system's characterisation, pork fat is also used to demonstrate the possibility to enhance the transmission between the antennas within the high loss environment. Those results propose the feasibility of implementing a high-frequency radar system for breast cancer detection.

Experimental Validation on Tissue-Mimicking Phantoms of Millimeter-Wave Imaging for Breast Cancer Detection

Breast cancer is one of the leading causes of cancer death among women; to decrease the death rate for this disease, early detection plays a key role. Recently, microwave imaging systems have been proposed as an alternative to the current techniques, but they suffer from poor resolution due to the low frequencies involved. In this paper, for the first time, an innovative millimeter-wave imaging system for early-stage breast cancer detection is proposed and experimentally verified on different breast phantoms. This has the potential to achieve superior resolution for breasts with a high volumetric percentage of adipose tissue, and the merit to overcome the common misconception that millimeter-waves cannot achieve useful penetration depths for biological applications. Three phantoms were prepared according to the dielectric properties of human breast ex vivo tissues in the frequency range [0.5–50] GHz. Two cylindrical inclusions made by water and gelatin or agar, mimicking dielectric properties of neoplastic tissues, were embedded in the phantom at different depths up to 3 cm. Two double ridge waveguides, with mono-modal frequency band equal to [18–40] GHz, were used to synthetize a linear array of 24 elements in 28 positions, acquiring signals with a Vector Network Analyzer. The images were reconstructed by applying the Delay and Sum algorithm to calibrated data. The feasibility of a new imaging system with a central working frequency of about 30 GHz is experimentally demonstrated for the first time, and a target detection capability up to 3 cm within the phantom is shown. The presented results pave the way for a possible use of millimeter-waves to image non-superficial neoplasms in the breast.

Decision Support System for Breast Cancer Detection Using Biomarker Indicators.

Breast cancer is the second most common type of cancer among women in the USA, and it is very common to appear in its invasive form. Detecting its presence in the early stages can potentially aid in the mortality rate depletion since at that point large tumours are highly unlikely to have developed. Technological advances of the last decades have provided advanced tools that employ machine learning for early detection. Common techniques include tumour imaging using special equipment that in most cases is not widely accessible. In order to overcome this limitation, new techniques that employ blood-based biomarkers are being explored. In the current work machine learning algorithms are exploited for the development of a decision support system for breast cancer using easily obtainable user information, age, body mass index, glucose and resistin. The explored algorithms include Logistic Regression, Naive Bayes, Support Vector Machine and Gradient Boosting Classification, all of which are used for the classification of new patients based on a dataset that includes information from previous breast cancer incidents. The results depict that the optimal algorithm based on the current methodology and implementation is the Gradient Boosting Classification which exhibits the highest prediction scores. In order to ensure wide accessibility, a mobile application is developed. The user can easily provide the required information for the prediction to the application and obtain the results rapidly.

A novel UWB compact elliptical-patch antenna for early detection of breast cancer in women with high mammographic density

Microwave imaging is one of the emerging technologies for early detection of breast cancer among women having dense mammographic densities. One of the critical and valuable components of an accurate, effective and compact, involving minimum risk - microwave imaging system for early breast cancer detection is an ultra wideband (UWB) antenna. A novel, compact elliptical UWB microwave antenna is presented in this research article that might be suitable for early breast cancer detection. The simulation of antenna structure is carried out using HFSS13 FEM-based EM software. The simulation results yield better UWB response. The antenna structure provides a wide practical fractional bandwidth of more than 156%. A significant performance factor of the proposed antenna is its ability to provide sufficient gain level for short distance communication. Thus, the proposed antenna is a strong candidate for design and development of microwave imaging system for early detection of breast cancer among women with dense mammographic densities.

High Frequency Breast Imaging: Experimental Analysis of Tissue Phantoms

This article presents the experimental proof of concept of high–frequency microwave breast imaging system operating from 16 to 20 GHz. At those frequencies the wavelengths are in the order of millimeter range which support better resolution in localizing cancerous tumors compared to the conventional microwave imaging systems. A new antenna is developed with a compact size of 7 × 7 mm2, which relies on an ultra-wideband, planar bowtie-like antenna structure. Practical results show that a bandwidth of 23.45 GHz is achieved (16 – 40 GHz) for a reflection loss higher than 10 dB. Furthermore, artificial breast tissue models based on glycerol–oil mixture with agar powder are developed. Dielectric properties of the tissue are stable over a wide frequency range up to 20 GHz at room temperature. The typical high microwave power losses within biological tissue at the operating frequency range are addressed by contacting the antennas directly to the tissue and slightly compressing the phantom without any coupling medium. Hence, the compressed breast geometry has a flat surface with well–defined boundaries, which simplifies tumor localization algorithms. For the purpose of demonstrating the current stage of research, a one–dimensional scanning system is used to localize the tumor’s positions inside the simulated, heterogeneous breast model of 36 mm thickness. Using the root mean square deviation algorithm, we can accurately detect positions of the tumors, which have a cross-sectional area down to 4 × 4 mm2. The preliminary results show the feasibility of achieving a high resolution with a compact microwave system for early-stage breast cancer detection.

Design of THz High-Resistivity Silicon-Based Microstrip Antenna for Breast Cancer Imaging

Cancer is the second deadliest disease globally, with approximately 18 million cases and 9.6 million deaths in 2018. There were 2.088 million breast cancer cases and 627 thousand deaths. This cancer is the second most prevalent after lung cancer, resulting in the fifth most cancer deaths worldwide. A critically important technique for the detection of primary-stage tumors or cancer is imaging. Early detection of cancer is necessary to minimize the number of fatalities associated with breast cancer. Several techniques have been applied to breast tissue imaging such as computed tomography scans (CT Scans), transrectal ultrasound (TRUS), magnetic resonance imaging (MRI), positron emission tomography (PET), and single-photon emission computed tomography (SPECT), but these techniques have well-known limitations. THz imaging is also one of the techniques for early detection. THz imaging approaches address key deficiencies in alternative techniques and are typically non-ionizing, non-contact, non-destructive, and have a high spatial resolution. An appropriate detector is a crucial aspect of the development of this promising technique. In this report, a THz silicon-based antenna with a frequency centered at 0.312 THz is proposed for application to breast cancer imaging. A silicon substrate has a high permittivity and is useful for device miniaturization. Inset-fed and array techniques are essential for focusing the beamwidth in the desired direction. The results show that the proposed design can be used to develop a directional antenna with reliable performance. This study will contribute to the development of a THz imaging system for early breast cancer detection.

The Performance Comparison of a Dual-Ridge Horn Antenna and a Planar Monopole Antenna in the Microwave Breast Cancer Detection

Detection of the breast cancer tumors at an early stage is very crucial to be successful in the treatment. Microwave measurement systems have gained much attention for this aim over last decades. The main principle of these systems is based on the significant difference in the dielectric properties of the malignant tumor and normal breast tissue in the microwave frequencies. In this paper, firstly several breast cancer detection techniques are mentioned. Then the advantages of the using microwaves in the detection systems are given. After that, some simulation and experimental studies of the radar-based ultra-wideband microwave measurement system are presented to detect tumor. The main purposes of these measurements are comparing the performance of a previously designed planar monopole antenna (PMA) with a dual-ridge horn (DRH) antenna and demonstrating a simple microwave breast cancer detection system. In the system, a planar breast phantom which is consisted of low dielectric constant material to represent the healthy tissue and high dielectric constant material to represent the tumor is used. Firstly, the measurements are made without tumor in the phantom. Then, the tumor-mimicking object is located to the phantom. In the measurements, both the PMA and DRH antennas are used respectively. These antennas are ultra-wideband and directional. They have narrow beamwidth and stable directional pattern at the interval of 3-10 GHz. According to the return loss results, the reflected energy increases when the antenna gets close to the tumor. Therefore, it can be said that the scattering parameters give important information about the tumor. According to the obtained results in this study, it can be said that the performance of the compact-sized PMA is better than the DRH antenna having larger size.

A 9.6-mW/Ch 10-MHz Wide-Bandwidth Electrical Impedance Tomography IC With Accurate Phase Compensation for Early Breast Cancer Detection

An eight-channel 10-MHz wide-bandwidth electrical impedance tomography (EIT) IC is proposed for early breast cancer detection system. To increase the resolution of EIT images, the proposed IC has three key features: 1) wide-bandwidth instrumentation amplifier (WB-IA) to detect large impedance changes in cancer cells at a high frequency; 2) dual-mode driver (DM-driver) to obviate the complex switching network to reduce the noise and the system form factor; and 3) phase compensation loop (PCL) to efficiently correct the phase error for accurate images without artifacts. The proposed EIT IC occupies 16 mm2 in the 65-nm CMOS technology and consumes 9.6 mW for each channel. Thanks to the key features, the proposed breast cancer detection system with the dedicated EIT IC can operate up to 10 MHz with a small phase error of 4.32° and a state-of-the-art impedance resolution of 1.6 $\text{m}\Omega /\surd $ Hz eventually can detect a small-size target object of 0.5 cm and verify with the phantom experiments.

Custom optical imaging system for ex-vivo breast cancer detection based on spectral signature

Background and purposeBreast cancer is a popular well-known tumor in women globally and the subsequent driving reason for malignancy death. The purpose of the present study is to develop Low cost, commercial, and affordable system that discriminates malignant from normal breast tissues by exploiting the unique properties of Hyperspectral (HS) Imaging. Materials and methodsThe difference in the optical properties of the investigated breast tissues gives various reactions to light transmission, absorption, and especially the reflection over the spectral range. A custom optical imaging system (COIS) was designed to assess variable responses to monochromatic LEDs (415, 565, 660 nm) to highlight the differences in the reflectance properties of malignant/normal tissue. Statistical analysis was computed for determining the ideal wavelength to differentiate between normal and malignant regions. The experiment was repeated using the same LEDs, and low-cost CCD camera to examine the capability of such a system to discriminate between normal and malignant tissue. ResultsSpectral images obtained by Hyperspectral camera, have been analyzed to reveal the difference of reflectance malignant and normal breast tissue. Superficial spectral reflection image with blue LED (415 nm) showed high variance (10.11). However, a more-depth reflection image with red LED (660 nm) showed low variance (4.44). So the optimum contrast image was produced by combining the three spectral information images from blue, green, and red LED. The COIS using a commercial CCD camera was in agreement with the HS camera. ConclusionsThe novel COIS of the commercial Low-cost CCD Camera is reliable and can be used with endoscopy technique as an assistant tool for surgical doctor to make decision and assess the resection edges in real time during surgery.

Meme Kanseri Tespiti için Destek Vektör Makinası ile Alexnet Kullanarak Transfer Öğrenimi

Breast cancer is one of the leading causes of women death worldwide currently. Developing a computer-aided diagnosis system for breast cancer detection became an interesting problem for many researchers in recent years. Researchers focused on deep learning techniques for classification problems, including Convolutional Neural Networks (CNNs), which achieved great success. CNN is a particular type of deep, feedforward network that has gained attention from the research community and achieved great successes, especially in biomedical image processing. In this paper, transfer learning and deep feature extraction methods are used which adapt a pre-trained CNN model to classify breast cancer histopathological images from the publically available (BreakHis dataset). The data set includes both benign and malignant images with four different magnification factors. A patch strategy method proposed based on the extraction of image patches for training the CNN and the combination of these patches for final classification. AlexNet model is considered in this work with patch strategy, and pre-trained AlexNet is used for further fine-tuning. The obtained features are then classified by using support vector machines (SVM). The evaluation results show that the pre-trained Alexnet with SVM classification and patch strategy yields the best accuracy. Accuracy between 92% and 96% was achieved using five-fold cross-validation technique for different magnification factors.

Early detection of breast cancer using mammography images and software engineering process

The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.

Classification of Benign and Malignant Breast Masses on Mammograms for Large Datasets using Core Vector Machines.

Breast cancer is one of the most leading causes of cancer deaths among women. Early detection of cancer increases the survival rate of the affected women. Machine learning approaches that are used for classification of breast cancer usually takes a lot of processing time during the training process. This paper attempts to propose a Machine Learning approach for breast cancer detection in mammograms, which does not depend on the number of training samples. The paper aims to develop a core vector machine-based diagnosis system for breast cancer detection using the date from MIAS. The main motivation behind using this system is to reduce the computational and memory requirement for large training data and to improve the classification accuracy. The proposed method has four stages: 1) Pre-processing is done to extract the breast region using global thresholding and enhancement using histogram equalization; 2) identification of potential mass using Otsu thresholding; 3) feature extraction using Laws Texture energy measures; and 4) mass detection is done using Core vector machine (CVM) classifier. Comparative analysis was done with different existing algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Fuzzy Support Vector Machines (FSVM). The results illustrate that the proposed Core Vector Machine (CVM) classifier produced a promising result in terms of sensitivity (96.9%), misclassification rate (0.0443) and accuracy (95.89%). The time taken for training process is 0.0443, which is less when compared with other machine learning algorithms. Performance analysis shows that CVM classifier is superior to other classifiers like ANN, SVM and FSVM. The computational time of the CVM classifier during the training process was also analysed and found to be better than other discussed algorithms. The results achieved show that CVM classifier is the best algorithm for breast mass detection in mammograms.

A 7.21-bit ENOB 100MS/s 1.51mW Inverter based Pipelined ADC with Digital Calibration for Breast Cancer Detection System

A 7.21-bit ENOB 100MS/s 1.51mW Inverter based Pipelined ADC with Digital Calibration for Breast Cancer Detection System

Review of Microwaves Techniques for Breast Cancer Detection.

Conventional breast cancer detection techniques including X-ray mammography, magnetic resonance imaging, and ultrasound scanning suffer from shortcomings such as excessive cost, harmful radiation, and inconveniences to the patients. These challenges motivated researchers to investigate alternative methods including the use of microwaves. This article focuses on reviewing the background of microwave techniques for breast tumour detection. In particular, this study reviews the recent advancements in active microwave imaging, namely microwave tomography and radar-based techniques. The main objective of this paper is to provide researchers and physicians with an overview of the principles, techniques, and fundamental challenges associated with microwave imaging for breast cancer detection. Furthermore, this study aims to shed light on the fact that until today, there are very few commercially available and cost-effective microwave-based systems for breast cancer imaging or detection. This conclusion is not intended to imply the inefficacy of microwaves for breast cancer detection, but rather to encourage a healthy debate on why a commercially available system has yet to be made available despite almost 30 years of intensive research.